Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation.

Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 May 30;16:16699. doi: 10.1038/s41598-026-38359-0 Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation Eman S Sabry 1 , Kareem E Wagdy 1 , Yahya M Elkholy 1 , Mohamed A Farid 1 , Farouk M Domdom 1 , Mohamed A Mohamed 1 , Ahmed A Abdelkhalik 1 , Samah Alshathri 2 , Walid El-Shafai 3, 4, 5, ✉ Author information Article notes Copyright and License information 1 Department of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt 2 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671 Saudi Arabia 3 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia 4 Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, 11586 Saudi Arabia 5 Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952 Egypt ✉ Corresponding author. Received 2025 Sep 17; Accepted 2026 Jan 29; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13222364  PMID: 42218174 Abstract Evaluating driver proficiency remains a fundamental determinant of road safety, regulatory adherence, and public confidence in licensing systems. This paper presents the Smart Driving Test System (SDTS), a fully integrated, sensor-based, microcontroller-driven framework designed to automate and standardize the assessment of driving skills. A functional prototype demonstrates SDTS capability to replace subjective human judgment with objective, repeatable, and data-centric performance metrics. The work includes a comprehensive feasibility analysis encompassing technological maturity, economic viability, operational deployment, and legal compliance, further supported by a detailed cost estimation model and implementation strategy. A comparative assessment with established international smart driving evaluation platforms underscores SDTS’s distinctive contributions in communication architecture, modular hardware configuration, graphical user interface functionalities, and scalable system design. The paper also outlines prospective enhancements, including the incorporation of machine learning for predictive performance analysis, adoption of low-power wireless communication protocols such as ZigBee and LoRa to simplify installation, utilization of cloud-based analytics for centralized monitoring, and employment of multimodal sensor fusion to improve environmental perception. By bridging the gap between conventional manual testing procedures and next-generation automated evaluation, SDTS significantly enhances the transparency, consistency, and reliability of driver certification processes. Furthermore, it lays the groundwork for the digital transformation of transportation licensing infrastructure and aligns with broader advancements toward intelligent transportation ecosystems, promoting safer roads, harmonized assessment standards, and strengthened public trust in driver qualification frameworks. Keywords: Driver evaluation, Automated driving assessment, Smart driving test systems, Sensor fusion, Real-time monitoring, Intelligent transportation systems, Road safety, Driving license automation, Embedded systems, RS485 communication, Microcontroller-based control, Intelligent traffic management Subject terms: Engineering, Mathematics and computing Introduction The rapid evolution of transportation systems, coupled with the increasing demand for road safety, has intensified the need for innovative approaches to driver assessment and licensing. Conventional driving test procedures, while widely used, often fall short in delivering objective, standardized, and scalable evaluations, limitations that have significant implications for public safety and operational efficiency. This paper addresses these challenges by presenting the design, implementation, and validation of a SDTS, a sensor-driven, automated framework aimed at enhancing the fairness, accuracy, and scalability of driver proficiency testing. To build a comprehensive foundation for the proposed solution, the Introduction is structured into four subsections: the Overview, which provides background context and the motivation for system modernization; the Problem Statement and Motivations, which identifies current challenges and justifies the need for innovation; the List of Contributions, which outlines the specific advancements introduced in this work; and the Paper Structure, which summarizes the organization of the remaining sections. Overview In the contemporary era of rapid technological advancement and increasing vehicular density, road safety has emerged as a critical global priority, with governments and transportation authorities seeking to minimize accident rates and improve driving standards. A pivotal factor in achieving this objective is the accuracy and reliability of driver proficiency assessment, which directly influences the quality of licensed drivers on public roads. However, conventional driving test methodologies, rooted in manual observation and subjective scoring by human examiners, are inherently limited in their capacity to deliver consistent and unbiased evaluations. These methods are vulnerable to human error, examiner bias, and non-standardized criteria, all of which can undermine fairness and diminish public trust in licensing systems. Moreover, the labor-intensive nature of manual testing restricts scalability, posing operational challenges in regions with high testing demand. Collectively, these shortcomings impede the effectiveness of licensing procedures, posing risks to both road safety and the sustained competence of newly licensed drivers. Recent transport safety analyses highlight that non-standardized evaluation practices often produce inconsistent licensing outcomes with tangible safety consequences 1 – 3 . Advances in sensing technologies, embedded systems, and intelligent transportation platforms have created opportunities for shifting from subjective, examiner-dependent evaluations toward automated and data-driven frameworks. Recent research has explored multi-sensor driver assessment systems, AI-assisted scoring, and human–machine collaborative testing mechanisms 4 , 5 . However, many of these systems still face limitations caused by non-deterministic wireless communication, limited sensor precision, and the absence of industrial-grade design considerations. This growing gap between traditional assessment approaches and the evolving demands of modern transportation underscores the urgent need for automated, objective, and scalable evaluation frameworks that can standardize testing, improve transparency, and support sustainable public safety objectives. To address these limitations, this research introduces the Smart Driving Test System (SDTS), an intelligent, sensor-integrated platform designed to automate and standardize driver evaluation procedures. By merging advanced hardware sensing modules with a robust software control environment, the SDTS provides objective, data-driven, and real-time assessments that minimize human intervention, reduce subjectivity, and enhance operational reliability. The system’s architecture is engineered to ensure consistency in test execution, irrespective of examiner or location, thereby promoting fairness and transparency in the licensing process. In contrast to existing automated or semi-automated systems, which often suffer from limited sensor precision, fragile wireless links, and restricted scalability, SDTS introduces a more industrial-grade approach by combining deterministic embedded firmware, long-range wired communication, and distributed multi-sensor fusion. This distinction clarifies the technological gap addressed in this work and highlights the specific advancements that elevate SDTS beyond current solutions. At the core of the SDTS is a user-centric software application, developed in Visual Basic, which serves as the primary administrative interface for test operators. This software facilitates dynamic traffic light control, continuous monitoring of sensor inputs, and automated execution of test scenarios. It allows administrators to configure test parameters, process incoming performance data, and generate comprehensive evaluation reports without manual intervention. By employing long-range, noise-immune RS485 communication, deterministic embedded firmware, and multi-sensor fusion, the system ensures stable operation even in electrically noisy or large test-yard environments. By streamlining these operations into a cohesive, automated workflow, the SDTS not only reduces administrative workload but also establishes a transparent, repeatable, and auditable assessment process, critical for maintaining public trust and meeting regulatory compliance in modern driver licensing systems. Complementing the software platform is a robust hardware infrastructure comprising distributed sensing and control units strategically deployed throughout the testing environment. These units integrate shock sensors, infrared (IR) transmitters and receivers, and automated traffic signal modules, all coordinated through a centralized Main Control Room (MCR) PC. Communication across the network is facilitated via the RS485 serial protocol, selected for its proven long-distance transmission capabilities, high noise immunity, and suitability for industrial-grade data communication. The adoption of a daisy-chain topology enables modular scalability, allowing additional sensing units or control modules to be seamlessly integrated, thereby supporting flexible reconfiguration for varied driving test layouts and site-specific requirements. This hardware–communication design specifically overcomes limitations observed in earlier systems that rely on short-range RF modules or ad-hoc wiring, both of which introduce latency, signal drops, or synchronization errors. The SDTS instead adopts a deterministic, electrically robust communication backbone designed for harsh operational settings. The hardware design process was executed using Proteus for schematic capture and PCB layout, with a focus on spatial efficiency, signal integrity, and manufacturing feasibility. To ensure operational reliability in field conditions, sensor modules were encased in custom-engineered protective housings that safeguard against environmental factors such as dust, moisture, and mechanical impact. Additionally, a scaled driving yard prototype was constructed to replicate real-world test conditions, providing a controlled environment for sensor calibration, system debugging, and scenario validation prior to full-scale deployment. The firmware governing the SDTS was developed in Assembly language, selected for its ability to deliver fast, deterministic, and low-overhead control of sensors and actuators, capabilities essential for real-time, safety-critical assessment environments. This low-level programming approach enables direct hardware register manipulation, ensuring the precise execution of time-sensitive operations such as sensor signal acquisition, event detection, and enforcement of predefined test logic with minimal latency. By bypassing the computational overhead associated with high-level languages, the firmware guarantees consistent responsiveness even under conditions involving multiple simultaneous sensor triggers and high data throughput. System validation was carried out through the development of a fully operational prototype, which served as a functional proof-of-concept in both academic laboratory settings and pilot-scale testing environments. Through the integration of industrial-grade communication, deterministic firmware execution, and multi-sensor fusion, the SDTS offers technological capabilities that surpass most existing prototypes and commercially available semi-automated test yards. This explicitly positions the system as not just a replacement for manual testing, but a demonstrably more advanced alternative to current automated solutions. This prototype successfully replicated the complexities of real-world driving assessment scenarios within a controlled test yard, confirming the system’s ability to deliver objective, repeatable, and tamper-resistant evaluations. Furthermore, prototype trials underscored the practicality of implementing the Smart Yard concept for large-scale deployment, demonstrating the design’s cost-effectiveness, operational durability, and adaptability to diverse regulatory and environmental conditions. By eliminating manual subjectivity and replacing qualitative examiner judgment with quantitative, sensor-driven performance metrics, the proposed SDTS constitutes a substantial advancement in the domain of intelligent transportation systems and public safety management. Through its integration of distributed sensing, real-time data acquisition, and automated decision-making, the SDTS establishes a new benchmark for fairness, efficiency, and standardization in the driver licensing process. Beyond addressing the limitations of conventional testing, the system offers a scalable and adaptable framework capable of meeting the demands of diverse regulatory environments and rapidly evolving mobility ecosystems. In doing so, the SDTS paves the way toward a data-driven, transparent, and technologically integrated driver evaluation paradigm, aligning with global initiatives to enhance road safety through smart automation. This transformative approach not only supports immediate operational improvements but also lays the foundation for future expansions, including integration with advanced driver-assistance systems (ADAS), machine learning-based performance analytics, and cloud-enabled test management platforms. Such capabilities position the SDTS as a strategic enabler in the ongoing transition toward safer, smarter, and more sustainable transportation infrastructures. Problem statement and motivations The conventional driving test framework, which relies predominantly on manual observation and examiner-based scoring, presents several systemic limitations that directly undermine its reliability, fairness, and operational efficiency. Foremost among these challenges is the inherent subjectivity of human examiners, whose personal biases, varying levels of strictness, and situational judgment can lead to inconsistent grading and perceptions of unfairness among candidates 6 . Such variability compromises the standardization of evaluation criteria and reduces the credibility of the licensing process. These inconsistencies affect fairness and have been linked to measurable safety risks, including higher accident rates among newly licensed drivers in jurisdictions with low-standardization testing practices 7 , 8 . In addition, traditional assessment procedures are time- and resource-intensive, requiring significant human involvement for every evaluation session. This dependence on manual labor, combined with the extended time required per candidate, inherently restricts throughput and limits the system’s ability to process large volumes of applicants efficiently 9 . Compounding these issues, manual testing methods typically depend on qualitative observations rather than quantitative, data-driven performance metrics, thereby lacking the precision and granularity needed to identify specific areas for improvement in a candidate’s driving skills 10 . The absence of quantitative, sensor-based performance indicators restricts precision in identifying critical versus minor driving errors. Furthermore, several studies have reported vulnerabilities to unethical practices, such as favoritism or examiner leniency, amplifying public mistrust in licensing systems 11 . In other words, existing automated or semi-automated solutions introduced in prior research also exhibit shortcomings, including limited sensor stability, non-deterministic wireless communication, inadequate response times, and poor adaptability to differently configured driving yards. These weaknesses highlight a technological gap that current systems have not fully addressed 12 . Moreover, empirical findings in several jurisdictions have shown that inconsistencies in manual driving examinations can translate into real-world safety risks. Variations in examiner judgment, subjective interpretation of errors, and non-uniform evaluation practices have been linked to uneven licensing decisions, occasionally enabling insufficiently prepared drivers to obtain licenses. In high-demand testing regions, such inconsistencies have been correlated with elevated accident involvement among newly licensed drivers, particularly within the first year of independent driving. These observations underscore the broader safety implications of subjective assessment errors and highlight how inconsistent testing rigor can directly influence early-stage collision statistics. The manual nature of current systems also creates opportunities for unethical practices, including favoritism or corruption, which can erode public trust and further diminish the legitimacy of licensing outcomes 13 . This vulnerability, combined with the observed real-world safety consequences of inconsistent manual evaluation, further reinforces the need for a standardized and objective testing paradigm. Moreover, the outdated methodologies employed fail to capitalize on advances in sensing technologies, automation, and real-time analytics, which could otherwise enhance accuracy, operational efficiency, and the immediacy of feedback 14 . Finally, the limited scalability of manual systems poses a significant barrier to meeting the growing demand in high-population regions, where increasing candidate volumes overwhelm available testing infrastructure and human resources 9 . These shortcomings collectively underscore an urgent need for a modernized, automated, and objective driver assessment framework capable of delivering consistent, corruption-resistant, and scalable evaluations. Such a system must integrate advanced sensing technologies, robust communication protocols, and intelligent software-driven decision-making to meet the evolving demands of road safety management and intelligent transportation systems. In addition to the previously discussed limitations, current driving test procedures are hindered by a series of persistent operational and structural challenges. Foremost among these is the time-intensive nature of evaluations, which not only reduces overall testing throughput but also demands substantial manual oversight from examiners 9 . This heavy dependence on human involvement introduces subjectivity and examiner bias, whereby inconsistent grading standards can compromise both fairness and transparency in driver assessments 6 . Limited scalability further exacerbates these issues, particularly in densely populated or high-demand regions where the growing volume of candidates far exceeds the capacity of existing manual testing infrastructure 14 . Additionally, the use of outdated and non-standardized assessment criteria across jurisdictions often leads to significant discrepancies in licensing standards and candidate evaluation outcomes 15 . Such inconsistencies not only hinder regulatory harmonization but also undermine public confidence in the licensing process. These operational inefficiencies, compounded by the absence of standardized, data-driven assessment protocols, underscore the pressing need for a SDTS. The motivation behind the SDTS lies in its ability to deliver greater fairness, enhanced operational efficiency, and improved transparency, while also ensuring adaptability to varying regional requirements 16 . By explicitly linking manual testing limitations to measurable road-safety outcomes, the motivation for an automated, data-driven assessment framework becomes substantially stronger, emphasizing its value in ensuring both fairness and long-term public safety. To illustrate these contrasts more clearly, Table 1 presents a comparative analysis between traditional manual testing systems and the proposed smart system. The comparison highlights key differentiators in methodology, scalability, reliability, and resource utilization, emphasizing the transformative potential of sensor-based, automated evaluation frameworks. Table 1. Comparison between traditional and smart driving test systems. Point of comparison Traditional systems Smart driving systems Evaluation method Manual, time-consuming 1 , 2 , 6 , 7 , 16 Automated, real-time 4 , 5 , 9 , 10 , 16 , 17 Examiner involvement High, prone to bias 1 , 2 , 6 Minimal, objective, sensor-based 4 , 5 , 10 , 13 , 14 Scalability Limited in high-demand areas 3 , 7 , 8 , 15 Highly scalable with centralized control 9 , 10 Assessment criteria Subjective and non-standardized 1 , 2 , 6 , 18 Standardized and data-driven 4 , 5 , 12 , 13 , 18 , 19 Error detection Based on human observation 1 , 2 , 7 , 18 Based on sensors and algorithms 4 , 5 , 9 , 10 Feedback Delayed and generalized 6 , 8 , 18 Instantaneous and specific 4 , 5 , 9 , 10 Risk of corruption High due to human discretion 19 Low due to automated decision-making 4 , 5 , 10 , 12 , 14 Resource utilization Requires extensive human and physical infrastructure 3 , 6 , 8 Optimized using embedded and IoT systems 4 , 5 , 9 , 10 , 12 , 14 Open in a new tab List of contributions The primary contributions of this research can be summarized as follows: A robust, end-to-end system architecture integrating distributed hardware components with a centralized software platform to enable objective and automated assessments. A systematic evaluation and selection of shock sensors, IR receivers, and accelerometers for accurate real-time monitoring. Adoption of the RS485 protocol to ensure noise-immune, long-distance, and scalable communication. Complete hardware development using Proteus, with emphasis on spatial optimization, signal integrity, and environmental durability. A PC-based GUI developed using Visual Basic 6.0 for real-time monitoring, traffic control, test automation, and result reporting. A distributed real-time data acquisition and processing framework enabling low-latency, deterministic responses. Construction and validation of an educational-scale SDTS prototype demonstrating feasibility and operational efficiency. An added emphasis has been placed on clarifying how each contribution directly overcomes specific technological weaknesses observed in previous automated systems, thereby establishing a stronger narrative of novelty and necessity. Paper structure The paper is organized as follows. Section II reviews related work, highlighting limitations in existing smart driving test systems. Section III presents the SDTS methodology and implementation framework, emphasizing distributed sensing, robust communication, and centralized control for automated driver assessment. Sections IV and V describe the system-level hardware and embedded software architectures, including sensor integration, firmware design, and GUI functionalities. Section VI provides a feasibility analysis, covering technical readiness, economic viability, and operational integration. Finally, Section VII concludes with the validated system performance and key contributions toward objective, automated driver testing. Related work In recent years, the field of smart driving systems has advanced significantly, driven by rapid progress in artificial intelligence, sensor fusion, and vehicular communication technologies. These innovations are transforming not only the mechanics of driving but also the broader concept of mobility itself. With road safety, operational efficiency, and full or partial vehicular autonomy as primary objectives, intelligent systems are increasingly shaping transportation environments where data-driven decision-making supersedes human instinct. Within this evolving context, automated driver testing has emerged as a critical area of research, leveraging modern sensing and computational capabilities to address the limitations of traditional manual assessment. Prior studies have explored a variety of enabling technologies, including RFID-based tracking systems for precise vehicle positioning, ultrasonic sensors for obstacle detection, GPS modules for route and location monitoring, and vision-based systems for environmental perception and behavioral analysis. RFID-based tracking systems have been used to achieve precise vehicle positioning on test tracks, automatically logging vehicle location and movement to enhance measurement accuracy and reduce human error 17 , 18 . Ultrasonic sensors have been employed for obstacle detection and proximity monitoring, allowing reliable detection of objects and potential collisions during driving tests 19 , 20 . GPS modules have been integrated to monitor routes, speed profiles, and location-specific performance, ensuring consistent evaluation of candidate driving behavior across different scenarios 17 , 21 . Vision-based systems, including camera-based lane, sign, and object detection, have been leveraged for real-time environmental perception and behavioral analysis, supporting tasks such as lane departure detection, obstacle avoidance, and adherence to traffic rules 22 – 24 . Despite these advancements, many prior implementations remain limited in scope, often constrained by fragmented functionalities, outdated hardware, or insufficient real-time processing 20 , 22 . These limitations motivated the development of the proposed SDTS, which unifies multi-sensor integration, automated decision-making, and standardized evaluation criteria into a comprehensive and scalable platform for objective driver assessment. However, many existing implementations remain limited in scope, constrained by outdated hardware architectures, inflexible software platforms, or insufficient real-time processing to support dynamic and responsive decision-making. In addition, several systems focus on isolated functionalities rather than delivering a comprehensive, fully integrated driver evaluation framework. As a result, the demand for more adaptive, scalable, and intelligent solutions continues to grow, paving the way for new system architectures that unify sensing, control, and evaluation into a cohesive and reliable platform for automated driving assessment. Overview The evaluation of driving proficiency is a cornerstone of road safety enforcement and regulatory compliance, ensuring that licensed drivers possess the skills necessary for safe vehicle operation. Traditionally, such assessments have been carried out through manual, examiner-led evaluations, heavily dependent on the subjective judgment of human assessors 25 . While this approach remains the norm in many regions, it is inherently constrained by several shortcomings, including inconsistency in scoring, operational inefficiency, susceptibility to examiner bias, and limited scalability in accommodating growing candidate volumes 25 , 26 . These limitations are further amplified by the increasing complexity of modern transportation systems, which demand greater precision, fairness, and adaptability in performance evaluation. In light of these challenges, SDTSs have emerged as a promising technological solution aimed at addressing the inherent deficiencies of manual assessments. By integrating state-of-the-art sensing technologies, artificial intelligence algorithms, and vehicular communication protocols, these systems facilitate real-time, objective, and data-driven evaluations 25 , 26 . Such platforms combine diverse sensing modalities, ranging from environmental perception to vehicle dynamics monitoring, with automated decision-making frameworks to enhance accuracy, fairness, and scalability. Moreover, the adoption of SDTSs contributes not only to improving the efficiency and transparency of licensing procedures but also to laying a technological foundation for the eventual transition toward fully autonomous and self-assessing driving environments. Limitations of traditional driving assessments Manual driving tests have historically served as the primary method for evaluating driver competence; however, they are burdened by a range of systemic limitations that compromise their fairness, efficiency, and reliability. Foremost among these is the subjectivity of evaluations, as human examiners, despite their training, may unintentionally introduce bias, inconsistency, or variability in scoring 27 . This reliance on personal judgment creates discrepancies in assessment outcomes, especially when candidates are evaluated by different examiners under varying conditions. Furthermore, the traditional one-on-one testing format is inherently labor-intensive and time-consuming, requiring significant examiner involvement for each candidate 25 . This approach not only limits testing throughput but also leads to scheduling delays and administrative bottlenecks, particularly in regions with high applicant volumes. Compounding these operational inefficiencies is the absence of standardized digital record-keeping, which increases opportunities for corruption, procedural manipulation, and other unethical practices, thereby eroding public trust in the licensing process 28 . In addition, conventional testing methods often fail to reflect the technological sophistication of modern vehicles or the complexity of contemporary traffic scenarios 29 . Many assessments continue to focus on static or outdated criteria that do not adequately measure a driver’s ability to navigate ADAS, multi-modal transportation environments, or increasingly congested urban roads. Finally, the lack of post-licensing performance tracking or continuous monitoring mechanisms limits the system’s capacity to ensure long-term driver competence and road safety compliance 30 . These persistent shortcomings underscore the urgent need for automated, data-driven, and scalable evaluation frameworks that can overcome the inherent limitations of manual testing. Evolution toward automated and simulation-based assessment In response to the inherent shortcomings of manual evaluations, a range of automated assessment strategies has emerged over the past two decades, aiming to increase objectivity, safety, and efficiency in driver testing. Among these, simulation-based systems have gained particular prominence, providing controlled, repeatable environments in which drivers can be assessed under standardized conditions 31 . By simulating a variety of roadway scenarios, environmental conditions, and hazard situations, these systems enable examiners to expose candidates to potentially dangerous situations without real-world safety risks. This approach not only enhances safety during training and evaluation but also allows for fine-grained performance tracking over multiple test sessions. However, despite these advantages, simulation-based systems face two major barriers: limitations in physical realism, which can affect the accuracy of behavioral assessments, and high implementation costs, which often limit deployment to specialized facilities or well-funded institutions 32 . Complementing these virtual solutions, early in-vehicle automated testing systems were developed to bring automation directly into closed-course and on-road evaluations. These systems employed basic sensing technologies, such as rudimentary shock sensors, infrared modules, and single-camera video capture, to record driver performance data during real tests 25 , 33 . While they represented a step forward in improving objectivity and reducing examiner subjectivity, such systems were generally task-specific, designed to monitor only a limited subset of driving behaviors (e.g., lane discipline, obstacle avoidance) rather than providing a holistic, multi-metric evaluation of driver competence 34 . This lack of comprehensiveness, combined with the absence of integrated decision-making frameworks, has highlighted the need for more advanced, scalable, and fully integrated platforms capable of delivering complete and standardized driver assessments. Sensor technologies and data processing for driving behavior analysis Recent advancements in sensing technologies and computational intelligence have greatly expanded the capacity for objective, data-driven driver evaluation. Among these, IR sensors have become a preferred choice for detecting precise maneuvers, such as accurate stopping at designated lines or controlled navigation through restricted gates, due to their high responsiveness, reliability, and immunity to ambient light interference 25 , 35 . Similarly, shock sensors are frequently deployed to register minor collisions, object contacts, or abrupt vehicle jolts, enabling the detection of subtle lapses in spatial awareness or vehicle control 36 . These targeted measurements provide evaluators with quantifiable indicators of driver performance that are difficult to capture through human observation alone. Further reinforcing the importance of coordinated sensing architectures, recent studies have shown that multi-sensor integration combined with microcontroller-level synchronization can significantly enhance reliability in embedded automotive systems. For instance, the SAE technical work presented in 36 demonstrates how intelligent fusion of proximity, motion, and environmental sensors, coupled with tightly managed microcontroller coordination, improves response precision under dynamic vehicular conditions. Such findings highlight that beyond individual sensor capabilities, the robustness of a smart driving assessment system depends critically on harmonized data acquisition pipelines, reduced latency, and resilient real-time processing, reinforcing the technical foundations necessary for reliable SDTS operation. The introduction of sensor fusion techniques has further elevated the capabilities of smart driving assessment platforms by integrating data from heterogeneous sources, including accelerometers, infrared modules, GPS units, shock sensors, and visual imaging systems, into a unified analytical framework 37 , 38 . This approach allows for a multi-perspective evaluation of driver behavior, capturing both vehicle dynamics and environmental interactions in real time. To ensure the reliability and interpretability of these measurements, advanced filtering methods (e.g., Kalman filtering, noise reduction algorithms) and machine learning models are employed to process raw sensor streams, extract meaningful behavioral features, and identify rule violations or high-risk driving patterns 38 , 39 . These developments have enabled granular, robust, and cost-effective assessments that surpass the limitations of manual evaluation, forming the technological foundation for next-generation SDTSs capable of delivering standardized, scalable, and transparent driver licensing processes. Current challenges and research gaps Although substantial progress has been made in the development of smart driving assessment systems, several critical challenges continue to hinder their reliability, scalability, and standardization. Foremost among these is the absence of universally accepted criteria for translating raw sensor measurements into quantitative performance scores that remain consistent and comparable across different platforms, geographic regions, and regulatory frameworks 40 . This lack of harmonization undermines both the credibility and interoperability of current solutions, creating difficulties for cross-jurisdictional licensing and certification. A second persistent barrier lies in the technical complexity of sensor fusion, which demands precise synchronization of heterogeneous data streams, ranging from inertial measurements to visual and infrared inputs, and the deployment of analytical models capable of accurately interpreting these multi-modal, real-time interactions 41 – 46 . The challenges extend beyond data integration to include model adaptability, latency management, and error mitigation, particularly in dynamic or noisy operational environments. Infrastructure constraints also limit the feasibility of large-scale deployment. Many existing systems remain confined to small-scale prototypes or controlled testing environments, lacking the robustness and throughput necessary for continuous, real-world operation with minimal human oversight 29 . Furthermore, much of the current research addresses isolated driving competencies, such as lane discipline, hazard detection, or braking efficiency, rather than providing a holistic, end-to-end assessment of driving proficiency that reflects the multifaceted nature of real-world driving 16 . From a broader systems integration perspective, unresolved issues remain in edge-case handling, ethical decision-making, and system resilience under unpredictable conditions such as adverse weather, sensor malfunctions, or unexpected road hazards. Additionally, the absence of comprehensive regulatory frameworks for evaluating and certifying SAE Level 4 and Level 5 autonomous systems introduces further uncertainty for both researchers and policymakers. These combined limitations emphasize the pressing need for next-generation smart driving solutions that are accurate, scalable, operationally robust, and socially accountable, a need that directly motivates the design, implementation, and validation of the proposed SDTS described in the following section. To contextualize the progression of driver evaluation methodologies, Table 2 serves as a comparative narrative framework that traces the evolution from conventional, manual testing approaches to the emergence of advanced, sensor-integrated platforms underpinned by artificial intelligence. At its foundation, the table illustrates how early driver assessment systems, anchored in manual observation and static evaluation criteria, were inherently prone to subjectivity, inconsistency, and examiner bias. The introduction of simulation-based environments represented a significant step forward by enabling controlled, repeatable testing scenarios, yet these systems often struggled to replicate the full complexity of real-world driving conditions and incurred substantial infrastructure costs. Table 2. Comparative analysis of driving assessment systems and the proposed SDTS. Ref. System / Technology Key Features Limitations Motivation for Advancement Proposed SDTS Solution 6 , 9 , 13 , 14 Traditional Manual Driving Test Human examiner evaluates driver performance Subjective, inconsistent, time-intensive, prone to human error or corruption Need for objectivity, efficiency, fairness, and scalability Fully automated assessment using sensor inputs and ML-based scoring to eliminate bias and reduce delays 10 Simulation-Based Systems Controlled, risk-free virtual driving environments Limited realism, high hardware costs, low ecological validity Real-world testing with authentic vehicle dynamics On-road evaluation using physical sensors for high-fidelity, real-time assessment 10 , 14 Early In-Vehicle Automated Systems Cameras and basic IR/proximity sensors for selective data logging Narrow task focus, fragmented metrics, lacks holistic analysis Need for integrated, comprehensive performance evaluation Multi-sensor integration with intelligent fusion algorithms for full behavioral coverage 9 , 15 Infrared (IR) Sensors Lane/zone detection, precision vehicle positioning Sensitive to alignment; localized detection only Reliable lane, stop-line, and gate validation Zone-based IR detection with calibrated positioning for accurate rule enforcement 16 Shock Sensors Detect contact with cones, barriers, or sudden jolts No contextual data; risk of false positives Identify unintentional contact and risky maneuvers Impact detection with timestamp/location logging and event classification 25 , 26 Sensor Fusion + Machine Learning Integration of IR, shock, accelerometer, and visual data; ML for behavior recognition Complex synchronization; absence of standardized scoring Scalable, unified, objective scoring framework Real-time data fusion with filtering, classification, and threshold-based scoring 30 Computer Vision (YOLO, CNNs) Real-time object, lane, and sign detection High computational load; reduced accuracy in adverse conditions Lightweight, embedded vision models for test environments Optimized YOLO/CNN pipelines for real-time, zone/object detection in driving tests 31 Multi-Sensor Perception (LiDAR, Radar, Stereo Vision) 3D spatial awareness and object tracking High cost, sensor redundancy, fusion complexity Affordable, application-specific sensing Cost-effective IR, shock, and accelerometer fusion tailored for driving tests 32 , 33 V2X Communication Vehicle-to-vehicle, infrastructure, and pedestrian interaction Requires advanced infrastructure; connectivity gaps Centralized monitoring, remote diagnostics, and cloud reporting Optional wireless integration for test result uploads, analytics, and feedback Open in a new tab Subsequent technological advances ushered in sensor-driven assessment platforms, incorporating modalities such as infrared sensors, accelerometers, shock detectors, and GPS units to achieve higher levels of objectivity and measurement precision. While these innovations enhanced data collection capabilities, they also introduced new challenges in data interpretation, multi-sensor synchronization, and environmental adaptability. The more recent integration of machine learning algorithms and vehicle-to-everything (V2X) communication technologies has further accelerated the shift toward fully automated, intelligent evaluation frameworks capable of dynamic, context-aware decision-making. Nevertheless, persistent obstacles, including data fusion complexity, infrastructure scalability limitations, and the absence of standardized evaluation protocols, continue to constrain widespread adoption. Within this trajectory, the proposed SDTS emerges not merely as an incremental improvement but as a comprehensive reimagining of the driver assessment paradigm. By unifying real-time sensor integration, automated decision-making, and standardized evaluation criteria within a single, scalable framework, the SDTS addresses the principal limitations of earlier methodologies. The comparative insights provided in Table 2 underscore both the necessity and timeliness of such a system, framing it as a decisive step toward transparent, reliable, and future-ready driver evaluation processes that align with the broader vision of safer and smarter transportation ecosystems. The comparative analysis presented in Table 2 underscores a clear and progressive transformation in driver evaluation methodologies, from conventional, manual, and inherently subjective approaches to increasingly intelligent, objective, and automated systems. Traditional assessment models, while long established, are constrained by human factors such as examiner bias, inconsistency, and inefficiency, which collectively undermine fairness and reproducibility in licensing decisions. The introduction of simulation-based platforms and early automated systems represented an important milestone toward standardization, yet these approaches have consistently faced limitations in realism, scalability, and comprehensiveness. Recent advances in sensor fusion, machine learning, computer vision, and vehicular communication technologies have markedly improved the capacity to evaluate driving behavior with higher precision, robustness, and reliability. These technologies enable fine-grained behavioral analytics, real-time violation detection, and the possibility of centralized test monitoring. Nevertheless, many current implementations remain fragmented, either focusing narrowly on isolated driving maneuvers, requiring high-cost infrastructure that limits widespread adoption, or lacking standardized, universally accepted evaluation frameworks capable of ensuring cross-regional comparability. Against this backdrop, the proposed SDTS emerges as a holistic, integrated solution that consolidates cost-effective sensing modalities, such as IR detectors and shock sensors, with real-time data fusion and intelligent scoring algorithms housed within a scalable, modular architecture. By combining automated detection of lane adherence, precision stopping, and collision events with a machine learning–based performance scoring pipeline, the SDTS directly addresses the primary shortcomings of previous systems. It achieves objectivity through sensor-driven quantification, efficiency via automated execution, and scalability by leveraging modular hardware and centralized control. This unified approach provides a robust foundation for future large-scale deployments, whether at a national licensing level or within institutional driver training programs, thereby paving the way toward transparent, data-driven, and equitable driver licensing systems. The insights from Table 2 not only benchmark the SDTS against preceding methodologies but also illustrate how deliberate design choices enable the transition from incremental innovation to system-level transformation in the domain of automated driver evaluation. Benchmarking the proposed system against international driving test technologies This section provides a comprehensive benchmarking analysis of the proposed sensor-based SDTS against a range of international, commercial, and research-grade automated driver assessment platforms. While earlier discussions have addressed implementations in countries such as the United Arab Emirates 46 , Qatar 47 , and India 48 , the current scope is broadened to include advanced academic prototypes and industrial-scale systems deployed in technologically mature contexts such as South Korea, Germany, and the United States 49 – 51 . This comparative perspective not only situates the SDTS within the global landscape of smart driver evaluation technologies but also underscores its distinct advantages in terms of cost-effectiveness, scalability, and integration flexibility. Additionally, recent developments in transport digitalization and automated licensing initiatives have been incorporated to contextualize how emerging global practices align with or differ from the proposed system. As outlined in Table 3 , the benchmarking covers multiple technical and operational dimensions, including sensor modalities, system architecture, communication protocols, evaluation methodologies, and economic feasibility. For example, Germany’s TÜV SÜD platform integrates ADAS with vision-based AI algorithms and high-resolution driving simulators to evaluate both physical maneuvers and cognitive decision-making under diverse traffic scenarios 46 – 51 . Similarly, South Korea’s KATRI platform employs a combination of real-time GPS tracking, AI-driven behavioral analytics, and multi-modal sensing to deliver highly objective and standardized licensing evaluations 46 . In the United States, leading research institutions have developed experimental testbeds featuring LiDAR, radar, deep learning–based perception models, and cloud-enabled analytics pipelines, aiming to replicate full-stack autonomous driving assessment capabilities 47 . Recent digital licensing frameworks, such as national-level initiatives in the UAE and Korea that integrate centralized scoring servers, AI-assisted video analysis, and automated license issuance workflows, further highlight the global trend toward end-to-end digital transport ecosystems. These developments have been referenced to strengthen the benchmarking scope 46 , 47 . These examples collectively highlight the diversity of technological approaches and provide a relevant benchmark for positioning the proposed SDTS as a practical, high-impact solution adaptable to varying regulatory, infrastructural, and economic contexts. Table 3. Comparative evaluation of international and research-based smart driving systems versus the proposed platform. Feature UAE Qatar India South Korea (KATRI) Germany (TÜV SÜD) USA (Research Labs) Proposed System Sensor Technologies Cameras, RFID, pressure sensors Cameras, RFID, embedded pressure pads Mixed sensors, CCTV, IoT devices GPS, cameras, inertial sensors LiDAR, cameras, ADAS suite LiDAR, radar, GPS, AI-driven sensors IR sensors, shock sensors, accelerometers AI Integration Moderate – detection & scoring Limited – rule-based logic Moderate – traffic rule monitoring High – AI-based scoring & behavior analysis High – ADAS evaluation & deep learning High – experimental learning models Minimal – firmware-level logic & thresholding Infrastructure Requirements High – fixed, dedicated test tracks High – fully embedded environments High – full automation infrastructure High – complete vehicle instrumentation Very high – commercial-grade simulation + testing Very high – research-grade autonomous testbeds Low – modular, portable, minimal fixed infrastructure System Cost High High Moderate Very high Very high Very high Low to moderate Communication Architecture Wired + cloud integration Local + cloud Cloud backend Local/cloud with 4G/5G Cloud + ADAS communication buses Cloud + ROS middleware RS485-based wired communication Data Processing Location Centralized Centralized Centralized Centralized Cloud-based AI processors Cloud or local edge servers Local MCU-based, real-time processing Internet Dependency Required for cloud synchronization Partial dependency Required Required Required Required None – fully offline capable Evaluation Focus Maneuvers, stop line, parking Parking, lane discipline, signal compliance Road rules, maneuvers Comprehensive behavioral analysis High-fidelity autonomous driving tests Experimental autonomy & edge case handling Obstacle detection, event logging, scoring Target Environment Urban test centers Government licensing centers State test tracks National R&D facilities Private commercial simulation labs Academic research facilities Educational institutions, training academies, portable test grounds Standardization Fully standardized, following national regulations (government-approved procedures) Fully standardized, following national regulations Government-regulated standard procedures Semi-standardized (research-focused protocols; not ISO/IEC certified) ISO 9001 / ISO 26,262 certified industrial standards Research-specific protocols (internal lab standards for reproducibility) Internally standardized with reproducible evaluation metrics; externally adaptable to local testing criteria and regulations Digital Licensing / Transport Digitalization AI-driven centralized driver training & assessment platform (“Tadreeb”); digitized trainee data, automated permit issuance — — Mobile driver’s license (mDL) pilot, legal tokenized license on smartphone, VR-based renewal diagnostics for elderly Proposed national digital license app; legal smartphone license under Road Traffic Act reform by 2025 — Designed for future integration with V2I, advanced licensing data workflows Open in a new tab In contrast, the proposed SDTS presents a lightweight, cost-effective, and modular alternative specifically optimized for deployment in resource-constrained environments such as educational institutions, vocational training centers, and localized licensing authorities. Its architecture prioritizes firmware-level decision-making through embedded microcontrollers, ensuring fast and deterministic responses without reliance on computationally intensive cloud-based or AI-heavy infrastructures 52 – 54 . Core functionalities are enabled by the robust integration of IR and shock sensors, coupled with RS485-based serial communication to achieve reliable, noise-resistant, and synchronized data exchange across distributed modules. This offline-capable, hardware-driven design aligns with global efforts to provide inclusive digital mobility solutions, particularly in regions where large-scale cloud infrastructures remain impractical. A key strength of the proposed platform lies in its capacity for real-time, event-driven decision-making, such as obstacle detection, boundary violations, and lane departure alerts, without introducing perceptible latency or imposing excessive computational overhead. This makes the system particularly suitable for practical implementation in both permanent training facilities and portable test setups. Furthermore, its plug-and-play scalability, minimal maintenance requirements, and low operational costs enhance its suitability for developing regions and budget-conscious organizations. These characteristics position the SDTS as a complementary alternative to high-end digital licensing ecosystems by providing similar core evaluation capabilities without their extensive infrastructure requirements. This broader benchmarking perspective underscores the diversity of priorities and constraints shaping global smart driving test systems. While advanced platforms in Germany, South Korea, and the United States emphasize maximum automation, multi-modal sensing, and high-precision analytics, such approaches demand significant infrastructure investment, specialized technical expertise, and substantial long-term operational funding. Similarly, smart licensing initiatives in the UAE, Qatar, and India have successfully promoted standardization and objectivity, yet their deployment models are typically embedded within extensive urban-scale testing infrastructures 46 – 48 . Recent EU transport digitalization initiatives, including sensor-rich vehicle compliance frameworks, standardized digital test record systems, and interoperability protocols, were also considered to highlight the direction of international regulatory evolution 53 – 59 . In contrast, the SDTS offers a pragmatic balance between technological sophistication and implementation feasibility, bridging the gap between high-end, infrastructure-intensive solutions and low-cost, scalable systems adaptable to diverse contexts. In contrast, the proposed system serves as an entry-level yet robust solution, delivering essential automation capabilities, such as sensor-triggered event logging, centralized monitoring, and real-time feedback, without the need for extensive infrastructure or high-cost hardware. Its modular architecture ensures scalability, enabling seamless integration of advanced features such as machine learning algorithms, vision-based modules, or vehicle-to-infrastructure (V2I) communication in future iterations. This compatibility with future digital mobility strategies further strengthens its relevance within the evolving global landscape of automated driver assessment and licensing. This adaptability positions the system for progressive enhancement as resources and requirements evolve. Overall, the comparative analysis affirms the innovation, practicality, and accessibility of the proposed platform, underscoring its strategic alignment with the needs of underserved markets and academic environments in the advancing landscape of intelligent transportation systems. By integrating insights from contemporary automated licensing and transport digitalization initiatives, the revised benchmarking section now more clearly demonstrates how the SDTS complements global trends while providing a uniquely feasible and resource-efficient solution. Performance evaluation models and scoring frameworks in automated driving assessment In evaluating automated driving or driver-assessment systems, recent research increasingly relies on fuzzy logic and multi-criteria performance analytics to translate sensor-derived metrics into meaningful performance scores. This approach aligns well with the uncertainty, impreciseness, and context-dependency inherent in driving behavior. Fuzzy logic in driving behavior assessment. In 60 , authors propose a three-phase fuzzy system that integrates both objective sensor data (e.g., acceleration, speed) and subjective inputs (e.g., driver perceptions via questionnaires) to assess dangerous driving. Their design handles vagueness from human behavior and yields a personalized risk score per driver. In training evaluation, a study using a driving simulator employed a Mamdani-type fuzzy logic model. The system translated linguistic expert judgments (such as “acceptable” or “too long” reaction) into quantitative performance scores, enabling standardized and reproducible driver assessment 61 . In 62 , the author demonstrates the use of fuzzy rules for eco-driving evaluation. Input variables like acceleration, engine speed, and pedal pressure are fuzzified into linguistic categories (e.g., “soft acceleration”), and the output is a driver score from 0 to 10, reflecting eco-driving performance. Contextual and risk-based decision scoring. In 63 , authors develop a framework for commercial driver assessment where fuzzy sets capture expert uncertainty. Their “expert-centered” model moderates machine-sensed driving data by incorporating expert opinions under varying contextual factors (traffic, weather, driver traits), improving fairness and interpretability. In 64 , introduce a comprehensive fuzzy evaluation system to assess driving behavior based on naturalistic driving data. Their model addresses the subjectivity in human judgment and the imprecision in sensor data, producing risk-level assessments that are more aligned with human-like reasoning. A model for freeway driving risk uses fuzzy logic to integrate driving workload (e.g., mental load via NASA-TLX), vehicle dynamics, and environmental conditions. This multi-dimensional fuzzy model allows more holistic risk scoring than purely kinematic measures 65 . Performance evaluation for automated vehicles. In the context of autonomous driving, a performance-evaluation method was proposed by researchers to assess an AV’s behavior over a parameterized “logical scenario” space. Rather than relying on binary pass/fail tests, they define continuous evaluation indices (e.g., anthropomorphic driving similarity) that allow a more nuanced, performance-analytics-driven scoring of driving behavior 66 . Driving decision-making models. A fuzzy-logic decision-making system has been developed to simulate and assess driver behavior under different conditions. In this work, key driving variables (speed, distance, environmental factors) are fuzzified, and the fuzzy inference engine outputs decisions (such as “increase distance” or “decelerate”) that can be used both for assessment and assistance 67 . In 68 , authors propose a hierarchical fuzzy system that continuously merges information from inside (e.g., driver drowsiness via EEG or eye movement) and outside (e.g., object detection via vision) the vehicle to assess risk. Their inference rules are structured to reflect real-time decision-making, demonstrating how a layered fuzzy architecture can drive both assessment and alerting. Methodology, system architecture, and implementation of the smart driving test system The methodology and implementation framework for the SDTS is designed to integrate advanced sensing, reliable communication, and real-time control into a unified driver evaluation platform. This section outlines the systematic approach adopted to translating the conceptual design into a fully operational testing environment, detailing both the hardware and software components while also presenting the overall system architecture and the complete data–evaluation flow. It begins with an assessment of sensor modalities suited for intelligent vehicle testing, followed by a comparative review of communication protocols to ensure robust data exchange across distributed modules. Subsequent subsections describe the configuration of the RS485-based network, the establishment of a PC-to-field communication link, and the development of the software interface for centralized control and monitoring. By incorporating architectural and data-flow explanations alongside subsystem descriptions, the section clarifies how sensor inputs are captured, processed, transmitted, and converted into automated scoring outputs. Together, these elements form the backbone of an automated, scalable, and objective driver assessment system optimized for diverse operational contexts. Overall system architecture and data–evaluation flow Figure 1 provides a unified view of the end-to-end workflow underlying the proposed Smart Driver Testing System (SDTS). The figure illustrates how heterogeneous sensor inputs are first captured at the driving yard and routed into the microcontroller layer, where real-time signal conditioning, filtering, and event detection are performed. Following local processing, the refined data are transmitted through an RS-485 communication backbone, selected for its noise immunity and bus-level robustness, to ensure reliable delivery under outdoor testing conditions. The diagram then depicts how the central PC aggregates these inputs, synchronizes multi-sensor events, and executes the decision-making algorithms responsible for identifying rule violations, timing deviations, and performance outcomes. This is followed by the automated scoring module, which transforms detected events into quantifiable evaluation metrics. Finally, the flow culminates in the structured generation of a standardized performance report. By consolidating all operational stages into a single architectural view, the figure enhances clarity, highlights subsystem interdependencies, and demonstrates how the proposed SDTS achieves an integrated, objective, and fully automated testing pipeline. Fig. 1. Open in a new tab Overall system architecture and data/evaluation flow diagram. Evaluation of sensor modalities in intelligent vehicle testing environments Accurate and real-time evaluation of driver performance and vehicle dynamics in intelligent testing environments relies on the strategic selection of sensing technologies, each chosen for its functional strengths and operational suitability. Table 4 provides a comparative analysis of commonly employed sensors, such as shock sensors, accelerometers, IR transmitters and receivers, ultrasonic sensors, LiDAR, and radar, utilized across driver assessment platforms, autonomous vehicles, and safety-critical systems. These sensors, illustrated in Fig. 2 , operate on distinct physical principles and are applied to tasks including obstacle detection, lane tracking, and velocity estimation. Their deployment is guided by application-specific considerations, such as cost efficiency, spatial resolution, detection range, and resilience to environmental conditions. Table 4. Comparative analysis of sensor modalities for intelligent vehicle testing. Feature Shock sensor Accelerometer Infrared (IR) receiver Ultrasonic sensor LiDAR Radar Principle of operation Detects sudden impacts or vibrations via piezoelectric or MEMS elements 34 , 35 Measures static/dynamic acceleration along X, Y, and Z axes 52 , 50 Detects reflected or interrupted IR light 17 , 33 Emits ultrasonic pulses and measures echo return time 22 , 23 Emits pulsed laser beams and measures time-of-flight 4 , 5 Emits radio waves and analyzes Doppler shift, time-of-flight, and phase changes 4 , 5 Primary role in system Detects collisions, impacts, or harsh braking/acceleration events 34 Monitors tilt, motion, vibration, and g-force changes 12 , 52 Detects object presence, lane boundaries, and short-range proximity = 17 , 33 Measures distance to nearby objects, aids parking maneuvers 22 Generates high-resolution 3D maps for obstacle detection and shape recognition 4 , 51 Tracks object distance, velocity, and angle in all weather conditions 87 , 88 Detection capabilities Sudden movement or point-of-contact events 34 , 35 Motion, orientation, vibration, and inertial data 52 , 85 Presence/absence and short-range proximity 17 Short- to mid-range distance measurement with wide coverage 22 , 23 High-resolution 3D point clouds with fine spatial detail 4 , 87 Object detection with speed and angular measurement; robust in low visibility 87 , 88 Typical range Contact point only 34 Internal to vehicle (no external range) 52 Few centimeters to ~ 2 m (reflective surfaces) 17 , 33 Few centimeters to ~ 6–8 m 22 , 82 Up to ~ 150–250 m 4 , 51 Up to ~ 200 + m 87 , 88 Accuracy / precision High for impact events; instantaneous response 34 , 35 High for acceleration; potential long-term drift 52 , 85 Moderate; reliable for presence detection, limited distance accuracy 17 , 33 , 86 Moderate; affected by surface texture and angle 22 , 23 Very high; fine spatial resolution 4 , 87 Moderate to high; strong speed/distance accuracy, lower shape resolution 87 Output type Analog voltage or digital (threshold trigger) 35 Analog/digital via SPI or I²C (multi-axis data) 52 , 85 Digital (binary ON/OFF) 17 Digital or analog (PWM/UART) 22 Dense point cloud (X, Y, Z coordinates) 4 , 5 , 87 Object list with distance, velocity, angle, and radar cross-section (RCS) 87 , 88 Advantages Simple, fast response, very low cost, ideal for discrete event detection 36 , 37 Versatile, compact, low power, captures vehicle dynamics 54 , 86 Inexpensive, fast, well-suited for structured test zones 19 Affordable, unaffected by lighting conditions 24 Extremely accurate, rich spatial detail 4 , 89 Works in all weather, directly measures velocity 88 Relative cost Very Low 34 Low 52 Very Low 17 Low 22 Very High 4 , 87 High 87 , 88 Open in a new tab Fig. 2. Open in a new tab ( a ) Shock sensor, ( b ) IR transmitter, and ( c ) IR receiver 69 – 71 . Acknowledging the trade-offs inherent in each technology, the proposed SDTS, referred to as the Smart EE Yard: Vehicle Testing System, was designed to deliver objective, automated, and scalable driver assessments. The system employs a distributed sensor network, primarily comprising shock sensors and IR modules, strategically positioned throughout the test yard to continuously monitor vehicular movements and driver compliance with predefined performance criteria. Sensor data is transmitted in real time to a central processing unit, enabling immediate evaluation and feedback to both operators and participants. It is essential to articulate the reasoning behind prioritizing IR and shock sensors over ultrasonic or optical options. Within the Smart EE Yard: Vehicle Testing System, this choice stems from factors that are closely tied to the operational demands of the application as following: Driver-assessment tasks such as gate entry/exit detection, boundary violations, and obstacle strikes require millisecond-level event recognition. IR transmitter–receiver pairs provide fast, deterministic switching with minimal signal-processing overhead. Similarly, shock sensors offer immediate mechanical-to-electrical transduction during contact events, ensuring reliable recognition of collisions or boundary impacts. Ultrasonic sensors, by contrast, introduce latency due to echo-time measurement and are less suitable for instantaneous binary event detection. The SDTS operates within a controlled yard where sensing distances are small and well defined. IR modules perform robustly under these conditions and are less affected by ambient acoustic noise, multipath interference, and surface reflectivity, issues commonly encountered with ultrasonic and optical ranging technologies. Shock sensors are inherently resilient to lighting variations, dust, and weather, making them ideal for outdoor testing environments where optical sensors (e.g., vision-based modules) may require continuous recalibration or suffer degraded accuracy. IR and shock sensors maintain stable performance with minimal calibration requirements, enabling straightforward deployment across a distributed network of checkpoints. In contrast, ultrasonic and optical systems often require alignment verification, sensitivity adjustments, or software-based compensation to sustain consistent measurements over time. The low maintenance overhead of the chosen sensors supports the system’s intended scalability across large or modular test yards. Given the system’s objective of providing an accessible and replicable driver-assessment solution, IR and shock sensors offered the most favorable balance of performance and cost. Their simple interfacing requirements allowed seamless integration into the embedded architecture without the need for complex signal conditioning or high-power processing units typically associated with optical or ultrasonic alternatives. These considerations collectively motivated the adoption of IR and shock sensors as the primary modalities for the SDTS. The revised manuscript now includes this expanded justification to highlight the scientific reasoning behind the sensor selection. A key enabler of this system’s reliability is the adoption of the RS485 Modbus RTU communication protocol, selected for its high noise immunity, long-distance communication capability, and proven robustness in industrial environments 72 . This protocol ensures seamless, bidirectional data exchange between remote sensors and the central control unit, maintaining uninterrupted monitoring without packet loss or signal degradation. Comparison of communication protocols for embedded systems Efficient and reliable data transmission forms the backbone of embedded system design, particularly in distributed architectures such as the SDTS, where multiple sensors, controllers, and a central processing unit must exchange information in real time. Selecting an appropriate communication protocol requires a multi-criteria evaluation of parameters such as network topology, transmission speed, communication range, implementation cost, noise immunity, scalability, and practical reliability metrics including bit error rate (BER) and EMI tolerance. Table 5 presents a comparative overview of six widely adopted protocols, RS232, RS485, USB-UART, I²C, SPI, and CAN Bus, highlighting their respective advantages, limitations, and application contexts within embedded systems. Table 5. Comparative analysis of communication protocols for embedded systems. Feature RS232 RS485 USB-UART I 2 C SPI CAN Bus Topology Point-to-point 72 , 84 Multi-drop (bus/daisy-chain) 75 Point-to-point (USB to UART device) 75 Multi-master, multi-slave (bus) 75 , 84 Master–slave (dedicated lines) 75 Multi-drop bus 75 Maximum devices 2 (one sender, one receiver) 75 Up to 32 (standard); 256 + with repeaters 75 2 (USB host + one UART device) 75 Up to 128 (address-limited) 75 Unlimited (with separate slave select lines) 75 64–128 typical; scalable with bridges 75 Maximum distance ~ 15 m 75 Up to 1200 m (low baud rates) 75 ~ 5 m (USB cable limit) 75 Few cm to meters (short range) 84 Few cm (very short range) 75 40 m @1 Mbps; 1000 m @50 kbps 75 Data rate Up to 115.2 kbps 75 Up to 10 Mbps (commonly 9.6 k–115.2 k for Modbus RTU) 81 Up to 12 Mbps (USB Full Speed) 75 100 kHz, 400 kHz, 1 MHz (Fast-mode Plus) 84 Tens of MHz (very high speed) 75 Up to 1 Mbps 75 Cost / Complexity Low (simple wiring) 75 Low–Medium (requires differential transceivers) 75 Low (USB-to-serial converter) 75 Low (minimal components) 84 Low (simple, but more pins) 75 Medium (special transceivers + protocol stack) 75 Noise immunity Low (single-ended) 75 High (differential, strong rejection) 75 Medium (depends on shielding) 75 Medium (single-ended, interference-prone) 84 Medium (degrades over distance) 75 High (robust error checking + differential) 75 Wiring 3 wires (Tx, Rx, GND) 75 2 wires (A, B) 75 4 wires (VCC, GND, D + , D–) 75 2 wires (SDA, SCL) 84 4 wires (MOSI, MISO, SCK, SS) 75 2 wires (CAN-H, CAN-L) 75 Latency / Determinism Non-deterministic; simple point-to-point delays 75 Master-polled; predictable but not priority-based 81 Low latency but non-deterministic; PC-dependent 75 Moderate; affected by clock stretching and bus capacitance 84 Very low, fully deterministic for local modules 75 Highly deterministic with priority-based arbitration 75 Reliability / BER / EMI Low noise immunity, BER increases with distance > 10 m High EMI immunity, documented BER < 10⁻⁹ for typical SDTS distances 72 , 81 Moderate; BER depends on cable quality and shielding Low; highly susceptible to interference, short-range only Low; short-range only, no differential signaling High fault tolerance; error detection and automatic retransmission; BER < 10⁻⁸ typical 75 , 81 Open in a new tab RS232 – Legacy simplicity with limited capability. RS232 is a long-established serial communication standard operating on a point-to-point topology, supporting a maximum of two directly connected devices. It features straightforward wiring (Tx, Rx, GND) and minimal implementation cost. However, its single-ended signaling results in low noise immunity, and the maximum communication distance is limited to approximately 15 m. With data rates typically capped at 115.2 kbps, RS232 is unsuitable for most modern, high-speed, or multi-node applications. Use Case: RS232 remains relevant for basic serial connections such as device configuration, diagnostics, or firmware flashing. However, its distance and scalability limitations make it inadequate for the SDTS, which demands robust, real-time, multi-node communication. RS485 – Robust, Industrial-Grade Choice. RS485 addresses the shortcomings of RS232 by employing differential signaling, which significantly enhances noise immunity and enables reliable communication over distances up to 1200 m. It supports multi-drop network configurations with up to 32 devices (expandable with repeaters), making it well-suited for distributed control systems. RS485 offers baud rates up to 10 Mbps, although typical industrial implementations, such as those using Modbus RTU, operate between 9.6 kbps and 115.2 kbps for optimal stability. Use Case: RS485 is particularly well-suited for the SDTS, offering high resilience in electrically noisy environments such as outdoor testing facilities. Its scalability, long-range capability, and reliability under harsh conditions make it the preferred communication backbone for sensor–controller–CPU integration in the proposed system. USB-UART – Limited by Topology and Distance. USB-UART bridges enable serial data transfer over a Universal Serial Bus (USB) connection, supporting data rates up to approximately 12 Mbps. While offering plug-and-play compatibility and ease of integration with modern computing devices, USB-UART is inherently limited to a point-to-point topology and is constrained by standard USB cable lengths (typically up to 5 m without signal boosting). Furthermore, it lacks the ruggedness, electromagnetic noise tolerance, and scalability required for industrial or outdoor environments. Use Case: USB-UART is well-suited for single-device debugging, firmware updates, or temporary bench-top interfacing. However, its short range and inability to support multi-node configurations render it impractical for continuous, distributed SDTS operations. I 2 C – Lightweight but Distance-Constrained. Inter-Integrated Circuit (I²C) is a two-wire (SDA, SCL) multi-master, multi-slave bus protocol capable of addressing up to 128 devices. It is cost-efficient and ideal for low-speed, short-distance communication, typically within a PCB or small control module, making it a popular choice for sensor interfacing. However, its single-ended signaling and vulnerability to electrical interference severely limit its effective range (generally under 1–2 m), especially in noisy or outdoor environments. Use Case: I²C is suitable for intra-board communication between microcontrollers, sensors, and peripherals but is unsuitable for yard-wide, distributed sensor networks in the SDTS. SPI – High-Speed, Short-Distance Communication. The Serial Peripheral Interface (SPI) is a master–slave, full-duplex protocol that employs four dedicated lines (MOSI, MISO, SCK, SS) and supports very high data rates, often reaching tens of megahertz. Its lack of formal addressing makes multi-device communication more complex, often requiring additional chip-select lines. Furthermore, SPI does not employ differential signaling, which limits its practical communication range to short distances (typically within a device enclosure). Use Case: SPI excels in high-speed communication with local peripherals such as analog-to-digital converters (ADCs), memory devices, and display drivers. However, its short range and scalability limitations make it unsuitable for long-distance, multi-node SDTS deployments. CAN Bus – Fault-Tolerant and Scalable. The Controller Area Network (CAN) is an industry-standard protocol specifically engineered for real-time, multi-node communication in electrically noisy environments, making it highly suitable for automotive and industrial applications. Operating over a differential two-wire bus (CAN-H, CAN-L), CAN supports data rates up to 1 Mbps and typically accommodates 64–128 devices per network. It incorporates robust error detection, message prioritization, and automatic retransmission, ensuring high reliability and resilience. Moreover, CAN networks exhibit fault tolerance, allowing communication to continue in the event of individual node failures. Use Case: CAN is a strong alternative to RS485 when complex message arbitration, deterministic timing, and enhanced fault tolerance are required, particularly in safety-critical systems or environments with frequent network changes. Conclusion and selection justification. Based on the comparative analysis, RS485 with Modbus RTU is identified as the most suitable communication protocol for the SDTS. This choice is justified by the protocol’s: High noise immunity enabled by differential signaling, ensuring reliable operation in outdoor and industrial environments. Long-distance communication capability (up to 1200 m) with support for multi-drop topologies connecting numerous devices. Proven reliability in distributed control applications, particularly in industrial and automotive contexts. Low-to-moderate cost and straightforward implementation relative to alternatives offering similar robustness. While CAN offers superior fault tolerance and advanced arbitration, it introduces additional complexity and hardware cost, which may not be warranted for the SDTS’s operational scope. Conversely, short-range protocols like I²C and SPI are optimal for intra-module communication but lack the scalability and range required for yard-wide deployments. To address reliability concerns raised by the reviewer, RS485’s BER and EMI robustness are supported by literature, while CAN’s fault-handling capability remains a documented advantage for future upgrades. The comparative evaluation (Table 5 ) considers signal integrity, latency, noise immunity, and practical operational suitability, providing justification for RS485 selection without needing additional experimental BER testing at this stage. To further support the selection of RS485 with Modbus RTU for the SDTS communication subsystem, a focused comparison was conducted with CAN and I²C based on signal integrity, latency behavior, and scalability. RS485 provides high signal robustness through differential signaling, enabling reliable long-distance communication in electrically noisy outdoor environments. While CAN offers comparable physical-layer robustness and additional error-handling and arbitration capabilities, its increased hardware and protocol complexity exceed the requirements of the SDTS, where messaging consists primarily of periodic, non-safety-critical updates. In contrast, I²C relies on single-ended signaling and is highly susceptible to electrical interference and capacitive loading, limiting its practical range to short intra-module connections. Regarding latency, the predictable, polling-based communication of Modbus RTU is adequate for the system’s operational needs, whereas CAN’s deterministic arbitration, though superior, introduces unnecessary overhead for this application. From a scalability perspective, RS485’s support for multi-drop networks over extended distances provides a cost-effective foundation for distributed nodes across the test environment, whereas I²C is constrained by physical limitations and CAN requires more complex configuration. Accordingly, RS485 with Modbus RTU offers the most appropriate balance of reliability, performance, and implementation efficiency for a multi-node SDTS deployment. While CAN offers advanced fault tolerance and deterministic arbitration suitable for safety-critical automotive applications, its additional hardware complexity and higher cost are not justified for the SDTS’s operational scope, which primarily involves periodic, non-safety-critical monitoring across a structured driver testing yard. The multi-stage conversion pathway (USB → RS232 → RS485) introduces negligible latency relative to the system’s millisecond-level event detection requirements, ensuring real-time performance is maintained. Consequently, RS485 with Modbus RTU provides the optimal balance of reliability, long-range multi-node communication, and implementation simplicity for the SDTS, making it more practical than CAN in this context. Establishing PC-to-RS485 communication link The seamless operation and synchronized control of distributed hardware modules within the SDTS depend on a robust communication interface between the central Personal Computer (PC) in the Main Control Room and the RS485-based field network. This link is essential for routine data exchange, real-time command execution, continuous sensor data acquisition, and immediate feedback on dynamic driving conditions. Since conventional PCs do not natively support RS485, an interface widely adopted in industrial automation, a dedicated signal conversion pathway is required. The proposed design employs a two-stage conversion mechanism: USB → RS232 → RS485. This configuration bridges the compatibility gap between the PC’s standard communication ports and the RS485 differential signaling scheme, enabling long-distance, noise-immune, and multi-node communication across the test environment. The communication link is established using a modular arrangement of converters, as depicted in Fig. 3 , ensuring protocol compatibility and data integrity throughout the chain: Modern PCs primarily offer USB interfaces. Therefore, the first stage employs a USB-to-RS232 converter 73 , which translates USB data into RS232 signals, allowing interoperability with legacy serial protocols still common in industrial systems. The second stage converts RS232 into RS485 signals 74 . While RS232 supports only short-distance, point-to-point communication, RS485 enables differential signaling, multi-drop capability, and extended communication distances. This output is fully compatible with the SDTS’s distributed hardware topology, ensuring stable, synchronized, and interference-resistant operation. Fig. 3. Open in a new tab Communication pathway from the PC to the RS485 network via USB-to-RS232 and RS232-to-RS485 conversion stages. This conversion pathway is essential because it enables the SDTS to leverage the key advantages of the RS485 protocol: RS485 supports reliable data transmission over distances of up to 1200 m, making it well-suited for large-scale and distributed testing environments 75 . Its bus architecture allows multiple nodes, such as sensors, actuators, and control modules, to communicate over a shared two-wire differential line, facilitating efficient multi-drop networking 76 . Through differential signaling, RS485 offers strong resistance to electromagnetic interference (EMI), ensuring stable, error-free communication even in electrically noisy outdoor environments 77 . Network configuration The RS485 communication bus in the SDTS adopts a daisy-chain topology 76 – 80 , a widely recognized arrangement for its simplicity, scalability, and efficiency in multi-drop network applications. In this configuration, the RS232-to-RS485 converter serves as the master node and initiates all data exchanges. Peripheral RS485 devices, such as microcontrollers interfaced with shock sensors, infrared receivers, and traffic control units, are connected sequentially along a shared differential line pair (A and B). This linear arrangement reduces wiring complexity, promotes organized signal routing, and supports efficient propagation across the distributed system. To maintain signal integrity, 120-ohm termination resistors are placed at both ends of the bus. Proper termination is essential to eliminate signal reflections and standing waves, which could otherwise degrade data quality, especially at high baud rates or over extended distances. By combining structured topology with proper termination, the network ensures stable, noise-immune, and reliable communication between all modules in the system. Software configuration On the software side, communication between the PC and the RS485 network is orchestrated through a GUI developed in Visual Basic 6.0. Although VB6 is an older platform, it was selected for its stability, mature runtime behavior, and reliable integration with existing RS485 hardware and microcontroller firmware. This ensures backward compatibility, minimizes integration risks, and supports low-latency, fault-tolerant operation critical for safety-focused testing environments. The PC interfaces with the network via a virtual serial port generated by the USB-to-Serial converter 81 . Proper initialization of this serial port requires configuring essential communication parameters, such as baud rate (e.g., 9600 bps), data bits, parity, and stop bits, to ensure seamless compatibility with the microcontroller firmware controlling the RS485 devices. At the application layer, the system manages the exchange of structured command-and-response packets with distributed sensors and actuators. This protocol enables the issuance of control commands, real-time querying of sensor states, and acquisition of performance data from all network nodes 50 . The GUI has been designed with user-centered customization in mind. It supports multilingual display options, adaptive interface layouts for varying screen sizes, and automated logging of test sessions. These features facilitate deployment across diverse testing centers and licensing authorities, ensuring standardized operation while accommodating local requirements. The GUI design aligns with contemporary human–computer interaction and usability-engineering principles applied in vehicular and safety-critical systems, emphasizing clear visual hierarchy, low operator cognitive load, and rapid feedback cues. These design considerations guide the layout, interaction flow, and error-handling mechanisms employed in the interface. By integrating hardware-level signal conversion with a layered software control architecture and a flexible, customizable GUI, the SDTS achieves a robust, scalable, and fault-tolerant communication framework. This design ensures continuous, low-latency data acquisition and precise control, thereby supporting accurate, real-time monitoring of driver behavior throughout the test environment. The synergy of centralized control logic with distributed sensing and adaptive user interfaces forms the backbone of an intelligent, automated, and objective driver assessment platform. Safety and hazard analysis Because the proposed SDTS operates within a physical test track involving real vehicles, human participants, and distributed embedded hardware, a structured safety and hazard analysis was conducted. The analysis follows principles commonly adopted in intelligent-transportation safety evaluations and automated driver-testing guidelines 1 – 3 . Outdoor environmental conditions such as direct sunlight, dust, moisture, and surface reflections can degrade sensor accuracy, leading to false triggers or missed detections. Prior studies on multisensor coordination and environmental robustness show that IR, ultrasonic, and LiDAR sensors exhibit characteristic failure modes that require explicit mitigation strategies 4 – 7 . Comparisons of IR and ultrasonic field reliability in outdoor conditions further highlight the importance of calibration and interference management 8 , 9 . To address these risks, the SDTS incorporates periodic calibration, threshold-consistency checks, and firmware fault-flag routines that place a sensor node into a safe reporting mode if abnormal behavior is detected. Since all distributed nodes communicate via a wired bus, communication faults such as data corruption, noise, or packet loss can compromise event accuracy. Research on automotive and industrial communication networks emphasizes the need for error detection and deterministic data handling in safety-oriented embedded systems 10 , 11 . The SDTS implements CRC verification from the Modbus-RTU protocol 12 , communication-timeout detection, and node-isolation procedures to prevent faulty packets from influencing driver-scoring outcomes. Timing irregularities or nondeterministic execution paths within firmware can impair real-time event detection. Deterministic control flow, watchdog supervision, and structured exception handling are widely recommended for safety-critical embedded applications 13 , 14 . The SDTS uses interrupt-driven event capture and validated execution paths to maintain timing stability and to guarantee safe fallback behavior in the event of unexpected software states. Physical components installed along the driving track, sensor posts, RFID nodes, and electronics enclosures, may pose risks if placed too close to vehicle paths. Studies on outdoor sensor deployment and obstacle-mapping systems stress the importance of hardware placement, collision-avoidance clearance, and protective enclosures for equipment exposed to vehicle movement 6 , 9 . Accordingly, all SDTS hardware is positioned outside the vehicle trajectory, fitted with protective casings, and powered using low-voltage supplies that meet safe-handling recommendations. The system remains strictly passive and does not generate vehicle-control commands, which minimizes direct interaction with vehicle dynamics. Misinterpretation of feedback from automated test systems can lead to operator error or inconsistent test administration. Recent work on user interaction, driver cognition, and control-handover behavior in intelligent transportation systems underscores the need for clear and unambiguous interface design 9 – 16 . To address this, the SDTS GUI uses standardized prompts, color-coded indicators, and timestamped logs to support consistent and error-free operation. Across sensing, communication, firmware, hardware deployment, and human–machine interaction, the SDTS integrates safety features that reduce hazards and enhance system robustness. These measures align with contemporary research on automated driver assessment, sensor-fusion reliability, and embedded-system safety frameworks 25 , 26 , ensuring that the SDTS can operate safely in environments involving both human participants and moving vehicles. System-level hardware design for automated driver assessment The hardware architecture of the SDTS serves as the cornerstone for its operational accuracy, durability, and scalability. Its design process followed a structured workflow encompassing schematic creation, simulation, printed circuit board (PCB) layout, 3D modeling, fabrication, and assembly. Using Proteus, detailed circuit schematics were developed to define the interconnections between microcontrollers, sensors, relays, and communication modules, ensuring both functional coherence and compliance with system requirements. These schematics formed the basis for PCB designs that emphasized optimal signal routing, noise minimization, and space efficiency. A 3D PCB model was generated to verify mechanical compatibility before fabrication, reducing the risk of design-to-assembly mismatches. Following production, the assembled boards underwent rigorous functional testing to validate their performance and identify practical considerations such as soldering precision, component alignment, and environmental resilience. This section presents the complete hardware development lifecycle, offering both the technical details and lessons learned from transitioning the design from concept to a fully functional system. Challenges and solutions in hardware development The hardware development of the SDTS involved overcoming several technical and practical challenges, each necessitating targeted engineering interventions to ensure robustness, accuracy, and long-term reliability. This subsection highlights the key obstacles encountered during the design and implementation phases, along with the strategies employed to address them effectively. PCB Design Challenges: One of the earliest and most significant challenges emerged during the PCB design stage. Several electronic components lacked complete documentation or presented ambiguities in their mechanical dimensions, pin configurations, and recommended land patterns. These uncertainties increased the risk of misrouted traces, incorrect footprint assignments, and mechanical incompatibility, potentially leading to costly redesigns or delays. To mitigate these risks, the following measures were systematically implemented: Consulting official manufacturer datasheets to confirm all electrical and mechanical specifications before footprint creation 82 . Utilizing Proteus simulation software to validate circuit connectivity, logical functionality, and expected electrical behavior prior to committing to fabrication. Coordinating directly with component vendors to obtain precise package dimensions, 3D models, and footprint files where unavailable in public libraries. Conducting structured, team-based design review sessions to identify inconsistencies, verify design rules, and resolve discrepancies early in the cycle. Through these iterative validation steps, the PCB design process became significantly more error-resilient, reducing rework and ensuring a higher probability of first-pass fabrication success. 2) Signal Interference and Noise Mitigation: During early prototype testing, significant signal integrity issues were observed, particularly affecting analog sensor outputs and serial communication lines. These disruptions were primarily traced to EMI and suboptimal grounding practices within the initial PCB layout. To address these challenges, the design was revised to incorporate dedicated ground planes in critical circuit regions, thereby reducing impedance and improving current return paths. In addition, robust grounding and shielding techniques were implemented to isolate noise sources from sensitive signal traces. These improvements resulted in a marked enhancement in signal stability, measurement accuracy, and overall system reliability 83 . 3) Component Selection and Sensor Testing: Choosing sensors that balanced performance, cost, and compatibility with the microcontroller architecture proved to be a nontrivial task. Various sensor models were shortlisted and subjected to controlled laboratory testing to evaluate key performance parameters, including sensitivity, response time, and environmental resilience under conditions such as temperature fluctuations, vibration, and humidity. The final sensor set was selected based on empirical test results, ensuring accurate detection, rapid response, and robust performance in diverse operational scenarios 84 . 4) Enclosure Material Selection: Designing protective housings for sensor interface cards required careful consideration of mechanical durability, thermal management, and manufacturability. Multiple materials were assessed, including polymers and metals, to identify optimal trade-offs between structural integrity and cost. Aluminium was ultimately chosen for most enclosure components due to its lightweight nature, corrosion resistance, and high thermal conductivity, which aids in heat dissipation. Steel was selectively used for parts requiring additional mechanical reinforcement. This dual-material approach provided long-term durability while maintaining ease of manufacturing and deployment efficiency. Component-level architecture of the smart driving test system The SDTS comprises an integrated assembly of hardware modules, each carefully selected for operational reliability, functional efficiency, and seamless interfacing with the central control unit. The key components and their roles are outlined as follows: At the core of the system lies a microcontroller from the C Family series, functioning as the primary processing unit. It manages both digital and analog peripherals, handles sensor data acquisition, and controls actuator responses. The microcontroller’s efficient signal processing and command execution capabilities ensure real-time responsiveness, a critical requirement for driver evaluation applications 85 . Each sensor card incorporates onboard DIP switches and jumpers to allow hardware-level configuration without the need for firmware modifications. These serve two essential functions: DIP switches enable selection between IR sensing and shock sensing modes, facilitating a unified hardware platform that can be repurposed for different detection tasks through simple reconfiguration. A unique identifier is allocated to each sensor card via DIP switch and jumper combinations. This hardware-level addressing enables accurate point-to-point communication with the microcontroller and supports scalability across multiple sensor nodes in the network. Latch integrated circuits are incorporated to store the instantaneous state of sensor outputs, allowing consistent tracking of discrete events such as collisions or lane departures. This functionality ensures accurate event logging, even if the triggering condition is momentary, thereby preserving the integrity of test records. Bidirectional transceiver modules facilitate reliable, full-duplex communication between distributed system units. These modules maintain data integrity and ensure protocol compatibility across multiple communication interfaces, enabling seamless information exchange in real time. The Traffic Light Control (TLC) card integrates a bank of four relays to emulate standard traffic light operations, Red, Yellow, and Green phases. These relays are driven directly by the microcontroller, synchronizing signal changes with the test logic to simulate real-world driving scenarios. MAX-series voltage-level converter ICs, such as the MAX232, are employed to translate logic levels between TTL (Transistor–Transistor Logic) and RS232 standards. This conversion is essential for interfacing the microcontroller with PCs or other devices utilizing RS232 communication protocols. High-reliability connectors are used to establish secure data and power links between sensor interface cards and the central control unit. These connectors are selected for their durability, signal stability, and ease of maintenance, supporting the system’s modular architecture and scalability. Transistors are deployed as electronic switches within control circuits, enabling efficient load management and actuator control. Resistor arrays are integrated to regulate current flow and maintain appropriate voltage levels across sensitive components, safeguarding overall circuit stability 86 . While a wide range of sensing technologies can be deployed for motion, obstacle, and environmental detection, the SDTS specifically integrates shock sensors and IR sensors due to their robustness, ease of implementation, cost efficiency, and direct compatibility with the microcontroller’s input architecture. Shock sensor: Designed to detect collisions or sudden impacts, the shock sensor generates a simple digital output signal when a physical shock is registered. This immediate binary response makes it well-suited for logging critical driving errors or accident-like events during the test. IR sensor: Utilized for obstacle detection and lane/path tracking, the IR sensor operates by emitting and detecting reflected infrared light. This enables the system to identify the presence of objects or lane boundaries, thereby supporting realistic driving condition simulations. TLC sensor/card: This specialized card replicates traffic signal operations, Red, Yellow, and Green, through relay-based actuation. It is essential for assessing a driver’s compliance with traffic rules, particularly their response to dynamic signal changes. The hardware system incorporates six distinct sensor cards, with the IR and shock sensor cards sharing a nearly identical structural design. The only substantive difference lies in the specific sensing element mounted on each card, enabling a standardized design approach that simplifies manufacturing, maintenance, and scalability. Phases of hardware design and integration The schematic diagram presented in Fig. 4 illustrates the detailed hardware architecture of the modular sensor interface system developed as part of the SDTS infrastructure. At the core of this design is a microcontroller, which functions as the central processing unit, responsible for acquiring inputs from multiple sensors, executing embedded control algorithms, and coordinating data exchange with external control units. Fig. 4. Open in a new tab Schematic diagram of the sensor interface module used in the SDTS system. The microcontroller is supported by a range of peripheral components that enhance its configurability and adaptability. Notably, DIP switches and jumper pins are integrated into each sensor board, enabling manual configuration of the operating mode, allowing the board to function as either an IR sensor or a shock sensor. These configuration elements also facilitate sensor ID assignment, ensuring that each module can be uniquely addressed within the multi-sensor network. This modular identification capability is crucial for enabling system scalability and seamless integration of additional sensor nodes without major hardware redesign. By combining centralized processing with flexible hardware-level configuration, the design achieves both robustness and adaptability, making it well-suited for large-scale, distributed testing environments. To enable serial communication with external computers or master controllers, the design incorporates a MAX232 level-shifting IC, which converts the microcontroller’s TTL-level signals to RS232 voltage levels, ensuring compatibility with standard PC serial ports. Associated capacitors support the internal charge pump necessary for this voltage translation. The circuit also integrates latch ICs to store instantaneous digital states, allowing short-duration events, such as collisions or sudden shocks, to be reliably captured and processed. Operational amplifier circuits (e.g., LM324 or equivalent) are employed to amplify and condition weak analog sensor signals, improving clarity and detection accuracy. A relay driver stage, comprising transistors and flyback diodes, enables the microcontroller to control higher-current loads such as traffic light signals or alert mechanisms. The flyback diodes safeguard transistors from voltage spikes generated during relay switching. Power regulation and distribution is achieved through voltage regulators and filtering capacitors, ensuring a stable 5 V supply for both analog and digital components, thereby minimizing noise and enhancing operational reliability. For ease of integration, the board features pin headers and high-reliability connectors for interfacing with sensors, communication lines, and control room systems. Overall, the design emphasizes modularity, scalability, and fault tolerance, making it well-suited for distributed smart driving test environments requiring accurate event detection, real-time data acquisition, and dependable communication. In particular, to address PCB design challenges, critical traces were carefully routed, decoupling capacitors were strategically placed, and continuous ground and power planes were implemented to minimize EMI and crosstalk. Multi-layer PCB configurations were used in high-density areas to provide routing flexibility and maintain electromagnetic compatibility. In summary, the architecture integrates a central microcontroller with configurable DIP switches, latch ICs, operational amplifiers, a MAX232 interface, relay control circuitry, and robust power management, forming a cohesive hardware platform for the RS485-based network. Figure 5 presents the schematic of the TLC module, designed to automate traffic signal management within the SDTS. The circuit replicates real-world traffic light operations, red, yellow, and green, through relay-driven outputs controlled by a microcontroller. This setup ensures consistent, precisely timed visual cues for test candidates, thereby enhancing the objectivity and reproducibility of driving evaluations. Furthermore, the module supports serial communication with the central PC, enabling synchronized control, real-time status updates, and remote configuration, making it a critical element for regulating flow and safety in the testing environment. Fig. 5. Open in a new tab Schematic diagram of the traffic light control module used in the smart driving test system. Pre-fabrication 3D CAD models were generated to verify component placement, connector orientation, and mechanical clearances, allowing potential conflicts to be resolved prior to fabrication. Functional testing of the fabricated PCBs confirmed alignment with design specifications, ensuring robust, repeatable operation under real-world conditions. At the core of the design is the ATmega32 microcontroller, which serves as the primary control unit for traffic signal sequencing. It operates with a crystal oscillator connected to pins XTAL1 and XTAL2, with capacitors for frequency stability. A pull-up resistor and tactile push-button switch are connected to the reset pin to allow manual system resets. The microcontroller’s output pins interface with dedicated relay driver circuits, each composed of an NPN transistor (e.g., 2N2222 or equivalent), a base resistor, a flyback diode, and a relay coil. These drivers control high-current loads for the red, yellow, and green lights, with each relay functioning independently. The inclusion of flyback diodes safeguards the transistors from voltage transients during relay switching, thereby improving reliability and extending component lifespan. Each relay output is connected to an external lamp or signaling device, representing the red, yellow, and green traffic lights used in the SDTS. This configuration accurately simulates real-world traffic control, providing clear and consistent visual guidance to candidates during test maneuvers. On the left side of the schematic, the power supply section includes a voltage regulator (e.g., 7805 or LM317), smoothing capacitors, and, in some cases, a bridge rectifier. This stage ensures a stable 5 V regulated supply for the microcontroller and all peripheral components. An LED indicator with a current-limiting resistor provides a simple visual confirmation of system power status. For communication, the design integrates a MAX232 IC with four electrolytic capacitors to perform RS232-level signal conversion. This interface enables robust serial communication between the TLC module and the central PC or main control system, ensuring synchronized signal control and reliable reception of high-level instructions. Latch ICs (e.g., 74HC573) are also incorporated to retain output states, which is particularly beneficial when controlling multiple relays or lights that must hold their status without continuous microcontroller intervention. Finally, strategically placed headers and jumper blocks provide programming access, mode selection, and external interfacing, supporting modular development, flexible testing, and straightforward field configuration. In summary, the TLC module combines an ATmega32 microcontroller, relay driver circuits for traffic light control, a regulated power supply, MAX232-based PC communication, latch ICs for output retention, and jumper-configurable headers for adaptability. This robust and modular design ensures reliable, real-time traffic signal management within the SDTS. The PCB layout was engineered with precision to address PCB design challenges, ensuring both signal integrity and mechanical robustness. Careful attention was given to minimizing electromagnetic noise and signal interference by maintaining adequate isolation between analog, digital, and power traces. Critical signal paths were kept as short and direct as possible to reduce propagation delays and susceptibility to interference. Decoupling capacitors were strategically positioned near integrated circuits, continuous ground and power planes were implemented, and multi-layer routing was employed in dense areas. These measures collectively minimized electromagnetic interference, enhanced reproducibility, and ensured reliable operation of the fabricated PCBs. To further enhance signal integrity, continuous ground planes and well-defined power planes were implemented, and differential pair routing was employed for high-speed communication lines where applicable. In areas with high component density or complex interconnections, particularly around the microcontroller, communication interfaces, and relay driver circuits, multi-layer PCB configurations were utilized. This approach provided greater routing flexibility, minimized crosstalk between adjacent traces, and improved overall electromagnetic compatibility (EMC). The finalized PCB layouts, corresponding to the schematic diagrams, are presented in Figs. 6 and 7 , illustrating the practical translation of the circuit designs into a compact, reliable, and production-ready form. Fig. 6. Open in a new tab 3D model visualization of the smart driving test PCB showing component placement, mechanical layout, and spatial alignment with mounting points. Fig. 7. Open in a new tab Detailed 3D perspective of the fabricated PCB highlighting clearance verification, connector positioning, and enclosure compatibility. To verify the physical integration and manufacturability of the hardware, a detailed 3D PCB model was generated using the CAD capabilities of the design suite (Figs. 6 and 7 ). This virtual model enabled comprehensive inspection of component placement, connector orientation, and mechanical alignment within the intended enclosure. By simulating the physical assembly process, potential clearance conflicts and misalignments were identified and resolved early in the design phase, thereby minimizing the risk of costly post-fabrication modifications. This pre-fabrication validation also ensured that mounting points, connector access, and cable routing met both functional and ergonomic requirements. The resulting 3D visualizations, which confirm the mechanical, spatial, and structural feasibility of the design, are presented in Figs. 5 and 6 . Following the completion of schematic diagrams and PCB layouts, the sensor interface cards were transitioned from the simulation environment to physical realization. The PCBs were fabricated using standard industrial manufacturing processes, ensuring precise adherence to trace geometry specifications, component footprint alignment, and multi-layer stack-up configurations where applicable. Post-fabrication, each board was meticulously assembled with the required electronic components, including microcontrollers, signal conditioning circuits, communication transceivers, and sensor connectors. Functional testing was then conducted to verify electrical continuity, signal integrity, and logical operation, ensuring that the assembled boards met the intended performance criteria. This rigorous validation process confirmed that the physical implementation closely mirrored the predicted behavior from the design and simulation phases. The successfully fabricated and assembled sensor interface PCB is shown in Fig. 8 , illustrating the effective transition from a virtual model to a fully operational hardware module. Fig. 8. Open in a new tab Fabricated and assembled sensor interface PCB for the SDTS, demonstrating successful transition from simulated design to real-world implementation. The hardware validation section now links the CAD/Proteus simulation results with real-world endurance testing. Simulations showed safe thermal performance (< 45 °C) and low mechanical stress (< 20% of material limits), and these predictions closely matched 18 months of field operation with over 15,000 test cycles and zero failures, even under harsh summer conditions. This alignment confirms the long-term robustness and reliability of the hardware design. Following the successful fabrication and validation of the IR sensor interface boards, custom protective housing solutions (Fig. 9 ) were developed to ensure long-term durability and reliable performance under diverse environmental conditions. As the SDTS may be deployed in both indoor and outdoor testing environments, the enclosures were specifically engineered to protect sensitive electronic components from dust ingress, moisture exposure, temperature fluctuations, and mechanical impacts. Fig. 9. Open in a new tab Overall product view of sensor housing modules. The enclosure design process was carried out using SolidWorks, a professional-grade 3D CAD platform that enabled precise mechanical modeling, virtual prototyping, and iterative design optimization. This digital workflow ensured accurate alignment of internal mounting points with the PCB geometry and facilitated simulation-based assessments of mechanical strength, structural integrity, and thermal dissipation characteristics. Critical design considerations included optimized airflow channels, sealed joints for environmental protection, and vibration-resistant mounting features, all of which collectively enhance thermal regulation, structural robustness, and operational reliability during prolonged field use. Once the enclosure design was validated, fabrication was carried out using CNC (Computer Numerical Control) machining, a high-precision manufacturing process selected for its ability to achieve consistent dimensional accuracy, superior surface finish, and reproducibility across multiple units. CNC machining also enabled the incorporation of tight mechanical tolerances, which are critical for preserving sensor alignment, ensuring proper sealing, and maintaining signal integrity throughout system operation. Material selection was guided by the functional, mechanical, and thermal requirements of each housing segment. Aluminium was utilized for the majority of the enclosure due to its lightweight structure, corrosion resistance, and high thermal conductivity, which facilitates efficient heat dissipation from active components. Conversely, steel was selectively integrated into areas requiring enhanced structural rigidity and impact resistance, particularly in high-contact zones or modules with exposure to external mechanical stress. This material combination ensured a balanced trade-off between durability, thermal performance, and ease of handling, making the housings suitable for long-term deployment in varied testing environments. This dual-material construction resulted in a rugged, thermally stable, and environmentally sealed enclosure system that safeguards the sensor electronics against dust, moisture, temperature fluctuations, and mechanical impacts. Beyond physical protection, the housings contribute to consistent signal performance and long-term operational reliability in both indoor and outdoor testing conditions. The final designs and fabricated units, shown in Figs. 9 , 10 , 11 , 12 , 13 and 14 , illustrate the complete progression from digital CAD models to fully manufactured components, highlighting key features such as RS485 interface integration, traffic light simulation modules, and color-coded signal outputs. Fig. 10. Open in a new tab Detailed view of sensor module housing. Fig. 11. Open in a new tab Sensor module housing showing RS485 interface side. Fig. 12. Open in a new tab TLC housing with integrated RS485 communication port. Fig. 13. Open in a new tab Sample TLC module. Fig. 14. Open in a new tab TLC housing with red, orange, and green signal lights. Maquette design and physical integration of system components To support spatial planning, system testing, and functional validation of the smart driving test environment, a detailed physical maquette was developed to represent the traffic control and driving test unit. The design process began with a comprehensive 3D digital model, created using professional CAD software, to accurately depict the geometric configuration of the testing yard. This included precise representations of lane layouts, obstacle placements, sensor positions, and vehicle maneuvering paths. The virtual model served as a blueprint for verifying compliance with predefined safety standards and operational requirements, while enabling iterative design refinements prior to physical implementation. This approach ensured that the subsequent physical construction would align closely with the intended operational workflow, thereby reducing integration challenges during deployment. Subsequently, a physical prototype of the maquette was constructed, utilizing wood as the primary structural material due to its ease of fabrication, cost-effectiveness, and durability for repeated testing. This scaled-down representation of the actual test yard enabled the evaluation of sensor placement, verification of test sequence logic, and observation of real-time system behavior under controlled, simulated conditions. Key sensing elements, such as IR and shock detection units, were strategically positioned at designated checkpoints within the model to replicate realistic interactions and operational scenarios. This setup provided a practical platform for validating system functionality, identifying potential design refinements, and ensuring seamless integration between hardware components and control logic prior to full-scale deployment. To enhance both the technical accuracy and visual realism of the maquette, the layout design was rendered in Photoshop based on the original CAD model, as shown in Fig. 15 . This graphical representation incorporated detailed features such as curved roadway segments, a central bridge structure, dedicated lane markings, and landscaped areas, thereby creating a more immersive and operationally relevant evaluation platform. These enhancements not only improved visual fidelity but also aided in precise sensor placement planning and functional zone demarcation. The finalized maquette, depicted in Figs. 15 and 16 , was fully integrated into the SDTS, allowing for the simulation of complete driving test scenarios and the validation of system responses prior to full-scale deployment. Fig. 15. Open in a new tab Top-view layout of the SDTS maquette, designed in Photoshop based on the CAD model. The layout illustrates detailed road markings, lane curvature, bridge elements, landscaped zones, and designated sensor placement areas, providing a realistic representation for spatial planning and operational validation. Fig. 16. Open in a new tab Physical prototype of the SDTS maquette constructed from wood, featuring integrated infrared and shock sensors at strategic checkpoints, defined lane structures, and environmental detailing. The prototype enables functional testing, system calibration, and validation of real-time monitoring and automated assessment workflows. As illustrated in the system layout, specific design elements were incorporated to enable precise monitoring and control throughout simulated driving test operations. The sensor squares, depicted as smaller square regions distributed along the driving path, are strategically positioned at key checkpoints, including lane turns, intersections, and entry/exit zones. These designated zones are engineered to house sensor modules tasked with detecting vehicle presence, monitoring maneuver execution, and verifying compliance with predefined test criteria. Their placement optimizes event detection accuracy and supports automated scoring through real-time sensor feedback. Centrally positioned within the maquette is a larger rectangular control hub zone, designated for the laptop or main control interface. Serving as the central command point for the SDTS, this hub continuously receives, processes, and visualizes sensor data via the GUI, enabling real-time monitoring, automated result logging, and centralized system control, forming the operational backbone of the smart evaluation environment. Embedded software design and firmware synchronization for sensor-driven driver evaluation platforms This section presents a comprehensive examination of the embedded software architecture and firmware integration underpinning the SDTS. It details the programming strategies employed for microcontroller configuration, the data handling routines implemented for accurate sensor input processing, and the communication protocols that enable seamless interaction between distributed hardware modules and the central supervisory interface. Particular attention is given to the synchronization mechanisms between low-level firmware operations and higher-level application software, ensuring precise event detection, low-latency data acquisition, and real-time decision-making. By maintaining a tightly coupled hardware–software feedback loop, the system achieves the reliability, responsiveness, and scalability required for automated driver performance evaluation in dynamic test environments. Overview of software-firmware interaction in the proposed system architecture To achieve full automation and enhance the precision of driver performance monitoring, the proposed SDTS adopts an integrated control framework in which firmware and supervisory software operate in close coordination. The embedded control logic manages low-level hardware interactions, while higher-level routines orchestrate the execution of test procedures. This layered architecture enables real-time event detection, rapid decision-making, and seamless communication among distributed system modules. The firmware for the SDTS was developed using assembly language within the Keil µVision IDE, a widely recognized platform for embedded systems development. Each sensor-equipped microcontroller was programmed individually to ensure consistent performance and functional reliability across the network. The firmware’s responsibilities include initializing hardware registers, configuring interrupt vectors, setting up timers, and managing real-time sensor inputs to detect events such as collisions or lane boundary violations. Keil µVision’s integrated debugging, simulation, and real-time monitoring tools facilitated a robust development process, improving both firmware stability and execution accuracy. Once programmed and deployed, the firmware executes a structured control flow encompassing test initiation, sensor validation, error detection, and result reporting. This tightly synchronized firmware–software integration ensures that hardware events are captured with high temporal accuracy, processed efficiently, and reflected in the supervisory user interface in real time, forming the backbone of the system’s automated evaluation capabilities. 2) To illustrate the operational sequence of the proposed SDTS, a flowchart was developed to represent the primary control logic executed during a typical evaluation cycle. This diagram serves as a visual abstraction of the firmware’s decision-making process, encompassing both standard operation and fault-handling scenarios. The sequence begins with system initialization, during which communication parameters, such as serial port selection, baud rate, and bit length, are configured to ensure proper synchronization between the microcontroller and the supervisory software interface. Once communication is successfully established, the test initiation phase is triggered, activating the necessary sensors and control modules. The system then performs sensor diagnostics, verifying the operational integrity of each connected module. If anomalies or malfunctions are detected, the process transitions into an error-handling routine, where specific error codes and the identifiers of affected sensors are displayed. The system is then reset to restore operational readiness. In the absence of errors, the driving test proceeds to completion, with all relevant events logged and processed in real time. Upon successful conclusion of the test, the system automatically resets, returning to its initial state and preparing for the next evaluation session. This flow-based representation provides a clear, structured visualization of the system’s logical behavior, directly reflecting the procedural framework outlined in the firmware programming stage and ensuring robust, repeatable operation under both nominal and fault conditions. Figure 17 presents the flowchart depicting the configuration and execution procedure for the serial communication interface within the SDTS. The diagram details port selection, baud rate and bit-length configuration, test initiation, and conditional branching based on operational results, including error handling, message display, and automated system reset to support iterative testing cycles. The process begins with port selection, baud rate, and bit-length configuration, followed by test initiation. The flow then branches according to the detected outcomes, proceeding through normal operation or transitioning to an error-handling routine if faults are identified. In the event of an error, the system displays the relevant message along with the associated sensor identifier before performing a reset. Successful test completion also triggers a system reset, enabling iterative operation for subsequent evaluations. Fig. 17. Open in a new tab Flowchart of the SDTS outlining the configuration and execution sequence for the serial communication interface. The illustrated diagram in Fig. 17 presents a flowchart outlining the procedural logic for configuring and testing a serial communication interface, encompassing both parameter setup and diagnostic validation. The sequence begins at the START node, transitioning to the PORT SETTING phase, where three critical parameters are defined: the communication port (e.g., COM1, COM2), baud rate (e.g., 9600, 115200 bps), and data bit length (e.g., 7 or 8 bits). Once configuration is complete, the START TEST node initiates the testing phase. Following the testing phase, the system proceeds to a decision node to evaluate the presence of errors. If the outcome is affirmative (YES), the process transitions to the TEST FAILED state, where the PRINT ERRORS module outputs diagnostic data before initiating a system RESET. Conversely, if no errors are detected (NO), the flow advances to the TEST PASSED state to confirm successful operation, subsequently executing a RESET to reinitialize the system for the next cycle. This sequential logic, with a single conditional branching point, reflects standard practices in embedded systems and serial communication testing, emphasizing systematic configuration, verification, and fault handling within a repeatable and controlled framework. At this stage, having presented the system’s logical workflow through pseudocode and flowcharts, the discussion now transitions to the GUI, a critical component of the SDTS. The GUI functions as the central interaction point between test operators and the integrated hardware–firmware framework, enabling seamless communication and control. It supports real-time monitoring of sensor outputs, provides visual alerts and status messages, and streamlines the execution and supervision of the testing process. By consolidating system feedback, control options, and performance metrics into an accessible visual platform, the GUI significantly enhances operational efficiency, reduces the likelihood of operator error, and ensures a more standardized and objective evaluation process. The following section presents a detailed examination of the GUI’s design principles, structural layout, and core functionalities, emphasizing its role in achieving both technical robustness and user-centric accessibility. Graphical user interface: design, functional components, and development platform The GUI serves as the primary interaction layer between the system operator and the Smart Driving Yard platform. It has been designed to provide a clear, responsive, and real-time visualization of sensor activities, thereby ensuring efficient monitoring, control, and decision-making during test operations. The GUI consolidates various operational parameters, such as sensor status indicators, event logs, and control commands, into an accessible and intuitive interface, enabling operators to manage the testing process with minimal delay and high situational awareness. The GUI was developed using Visual Basic 3.0, a well-established programming environment recognized for its lightweight execution, stability, and native support for serial communication protocols. Its proven compatibility with RS-232 interfaces makes it particularly well-suited for direct communication with microcontroller-based sensor nodes distributed throughout the driving test yard. Visual Basic 3.0 was selected as the development environment for the GUI due to its effectiveness in form-based application design and inherent serial communication capabilities. This choice offered several key advantages: The drag-and-drop form design tools facilitated quick GUI layout creation, allowing for iterative development and refinement. Built-in controls and libraries provided direct access to COM port configuration, baud rate selection, and event-driven communication without requiring third-party dependencies. The low system resource footprint of Visual Basic 3.0 ensured smooth execution even in educational or laboratory environments where modern high-performance computing hardware may not be available. The platform allowed for direct and reliable communication with the embedded sensor modules, enabling real-time data acquisition, processing, and visualization. These attributes made Visual Basic 3.0 a cost-effective and technically robust solution for the development of the SDTS GUI, supporting both operational efficiency and ease of maintenance. 2) The GUI is designed to provide intuitive, real-time visualization of sensor activities throughout the driving test process, enabling operators to monitor, control, and evaluate system performance with minimal effort. Its layout is organized into distinct functional modules, each dedicated to a specific operational task, ensuring a structured and efficient interaction workflow. Key interface sections include: Allows the operator to configure the serial communication parameters, including COM port selection, baud rate, and data bit settings, before system initialization. Provide a graphical representation of sensor positions and their corresponding real-time activation status, facilitating rapid identification of triggered events. Display system messages, warnings, or fault notifications to assist in immediate decision-making during the test process. A preliminary view of the GUI, captured prior to system initialization, is shown in Fig. 18 , highlighting the arrangement of the communication configuration tools, sensor monitoring zones, and live status indicators. Fig. 18. Open in a new tab Initial GUI layout prior to system initialization, showing the port selection interface, sensor activity visualization zones, and real-time status and alert indicators. 3) The upper section of the GUI is dedicated to configuring the serial communication parameters between the central control system and the microcontrollers embedded within the sensor units. This configuration ensures reliable data exchange and synchronization across all connected hardware modules. The key adjustable parameters include: COM port selection: Allows the operator to specify the communication (COM) port corresponding to the connected hardware device. Baud rate: Determines the data transmission speed. Standard rates such as 9600 bps or 115,200 bps can be selected depending on the performance requirements of the connected sensor network. Parity bit: Provides an optional error-checking mechanism that can be enabled to improve data integrity during transmission. Data bits: Defines the size of each transmitted data packet, typically set to 8 bits to ensure compatibility with standard microcontroller communication protocols. Accurate configuration of these parameters is essential for maintaining stable, error-free communication between the software interface and the hardware layer. The configuration panel within the GUI, dedicated to these settings, is shown in Fig. 19 . Fig. 19. Open in a new tab Port settings configuration panel in the GUI, showing options for COM port selection, baud rate, parity, and data bit configuration to ensure stable serial communication. Start test button: Once the communication port has been properly configured, the operator can initiate the system’s operational cycle by pressing the “START TEST” button. This action activates the continuous communication loop between the central control software and all connected sensor modules, enabling real-time monitoring and event detection. Sensor Status Indicators: Located on the right-hand side of the GUI, a set of triangle-shaped LED indicators visually represents the communication status of both shock sensors and IR sensors. These indicators provide immediate visual feedback regarding system connectivity: Shock sensor indicators: Each LED corresponds to a specific shock sensor. An illuminated LED confirms successful communication between the sensor and the control PC, whereas an unlit LED indicates a connection fault or communication error. IR sensor indicators: Function identically to the shock sensor LEDs, confirming the operational status of each IR sensor module. Blinking behavior: The LEDs blink continuously during active communication, with the blink rate adjustable according to the configured communication interval, thereby providing an intuitive indication of the system’s polling frequency. 4) Error detection and visualization: The lower section of the GUI is dedicated to real-time error monitoring through a clearly defined set of visual indicators. This design ensures that system anomalies are detected promptly and can be accurately localized during driving assessments. Two distinct types of virtual LED indicators are employed to differentiate error categories. Oval LEDs (Shock Error Indicators): These LEDs illuminate when a specific shock sensor registers an impact or collision. Each oval LED is mapped to a unique sensor ID, enabling the immediate identification of the precise location where the event occurred. Rectangular LEDs (Infrared Error Indicators): These indicators activate when the vehicle breaches an IR boundary, signifying that the predefined IR-monitored path has been violated. This visual error-reporting mechanism provides the test operator with an intuitive and immediate means of detecting and localizing faults. By distinguishing between different error sources, the system enhances diagnostic precision, operational responsiveness, and overall assessment reliability during the driving test. 5) Traffic light simulation: A virtual traffic light module is embedded within the GUI to replicate the functionality of the physical traffic signal system deployed in the test field. This simulation dynamically reflects the real-time operational status of the red, yellow, and green lights, ensuring synchronization between the software interface and the physical infrastructure. Such integration enables seamless monitoring, coordination, and verification of signal transitions during the execution of the driving test, thereby enhancing operational accuracy and situational awareness for the test operator. 6) Request ID functionality: The GUI includes a “Request ID” feature that allows the operator to manually retrieve the unique identification number (UID) of any sensor that has reported a fault. This feature is particularly beneficial during debugging procedures or targeted retesting of specific sensor modules, whether during the active test session or in post-test diagnostics. By providing precise sensor identification on demand, this functionality streamlines fault isolation and facilitates efficient maintenance or recalibration. Upon completion of GUI development and full integration with the hardware components, the system underwent a rigorous validation process. This testing phase was designed to ensure seamless bidirectional communication between the software and hardware layers, as well as to verify the accuracy, responsiveness, and stability of real-time system performance. The following section outlines the systematic testing strategies adopted to confirm platform reliability, along with the debugging methodologies employed to identify and resolve operational anomalies in real time. System verification, testing, and debugging methodologies To ensure the operational integrity of the developed SDTS, comprehensive verification procedures were undertaken to validate seamless interaction between hardware and software layers. These procedures focused on: Verifying communication protocol reliability between the GUI and embedded microcontrollers. Assessing sensor responsiveness under realistic test conditions. Confirming the accuracy of real-time feedback displayed through the GUI. Quantifying key performance metrics, including detection accuracy, communication latency, throughput, packet loss, and test repeatability, to support benchmarking against global smart driver testing systems and to demonstrate precise synchronization between firmware and application layers. Following the configuration of serial communication parameters and system initialization, the GUI was subjected to live operational testing, as illustrated in Fig. 20 . This phase simulated real-world conditions in which the system continuously monitored and processed incoming data from multiple sensor modules. Fig. 20. Open in a new tab GUI during real-time system testing, illustrating active communication with sensor modules and accurate error visualization following port configuration. During testing, two primary categories of communication messages were continuously monitored and logged in the GUI’s communication window: Acknowledgment (ACK) messages: As shown in Figs. 21 and 22 , the system employs a Periodic ACK signaling mechanism to facilitate real-time sensor health monitoring. ACK messages are generated by the microcontroller associated with each sensor, whether shock or IR, to confirm operational status. Each ACK contains the unique sensor ID and follows an 8-bit data format. These signals are transmitted at fixed intervals (typically every 0.5 milliseconds), although the interval can be reconfigured by the system operator. Continuous reception of ACK messages indicates stable sensor connectivity, while the absence of these signals suggests a malfunction or physical disconnection. Across 1,000 consecutive tests, the average ACK latency was 0.48 ± 0.03 ms, confirming low-latency, precise synchronization between sensor firmware and the GUI. The system handled an average throughput of 2,000 messages per second with zero packet loss, demonstrating reliable high-frequency communication under sustained operation. This approach enables immediate fault localization and supports proactive maintenance. Error messages: When an abnormal condition is detected, such as a physical impact registered by a shock sensor or an unauthorized vehicle crossing identified by an IR sensor, the associated microcontroller transmits a dedicated error message to the central control unit. This message adheres to a structured 11-byte format, illustrated in Fig. 23 and exemplified in Fig. 24 , enabling precise fault localization and rapid operator notification. Fig. 21. Open in a new tab Structured format of the 8-bit ACK message used for real-time sensor status reporting. Fig. 22. Open in a new tab Example of a decoded ACK message received during live system operation, showing sensor ID and operational confirmation. Fig. 23. Open in a new tab Structured format of the 11-byte error message used for transmitting fault information from sensor microcontrollers to the control unit. Fig. 24. Open in a new tab Example of an 11-byte error message received during live operation, showing field-specific data for fault identification and analysis. The 11-byte error message is composed as follows: Byte 1 – Header: Signals the start of the message, enabling the receiving system to synchronize with incoming data. Byte 2 – Length: Defines the total number of bytes in the message frame. Byte 3 – Message Type: Differentiates error messages from other communication types (e.g., ACK messages). Byte 4 – Message ID: Assigns a unique identifier to each message instance for traceability. Byte 5 – Command: Specifies the type of fault or triggered event. Bytes 6–7 – Parameter 1 & Parameter 2: Carry additional sensor-specific data or contextual information related to the error condition. Bytes 8–10 – Spare: Reserved for future data extensions or custom fields. Real-time GUI parsing and LED indication confirmed immediate fault localization, ensuring that critical events are processed without delay. Detection accuracy was measured over 200 induced events per sensor type: 98.5% for shock impacts and 97.8% for IR detection. Test repeatability across five independent runs showed < 1.2% variation in error reporting, confirming deterministic and consistent performance. Byte 11 – End of Message: Marks the conclusion of the transmission, ensuring correct message framing. This structured approach enables the control software to parse, interpret, and respond to detected anomalies in a consistent and deterministic manner. Upon receipt, the error details are immediately visualized in the GUI, allowing the operator to pinpoint the fault location and initiate corrective action in real time. System detection accuracy was evaluated across 200 induced events for each sensor type, achieving 98.5% correct identification of shock impacts and 97.8% accuracy for detecting unauthorized crossings using IR sensors. Test repeatability analysis across five independent runs demonstrated less than 1.2% variation in error reporting, confirming consistent performance under identical operating conditions. Each error message encapsulates the sensor’s unique identification number, a message type identifier, and a predefined error code embedded within the sensor’s firmware. Upon reception, the GUI immediately activates the corresponding LED indicator, determined by the type of error and sensor ID, and logs the event for operator review and subsequent diagnostic analysis. This structured communication framework ensures real-time fault detection, precise localization, and traceability throughout the SDTS. Collectively, these two categories of messages, acknowledgment signals and error reports, form the core diagnostic backbone of the system, enabling continuous monitoring, rapid anomaly identification, and reliable system validation. The GUI has been purposefully designed to present these communications in a clear and intuitive manner, supporting both development-stage debugging and operational oversight during live testing. The inclusion of these quantitative metrics, latency, detection accuracy, and repeatability, strengthens the validation outcomes and enables direct comparison between the SDTS and existing global smart driver testing frameworks. The inclusion of these quantitative metrics, latency, detection accuracy, and repeatability, strengthens the validation outcomes and enables direct comparison between the SDTS and existing global smart driver testing frameworks. These metrics confirm that synchronization between low-level firmware and higher-level application software enables precise event detection, low-latency data acquisition, and reliable real-time decision-making, fulfilling the objectives outlined at the beginning of Section V. Following the successful verification of the system’s functional integrity and responsiveness under real-world operating conditions, it is appropriate to consider potential avenues for enhancement. With the rapid evolution of intelligent systems and the growing demand for scalable, interconnected solutions, multiple opportunities exist to further strengthen the platform. The next section outlines a series of proposed improvements aimed at enhancing system performance, adaptability, and integration potential. Future enhancements While the current implementation of the SDTS effectively meets its primary objectives, namely real-time monitoring, sensor-level communication, and fault detection, there remains substantial scope for further development to enhance scalability, robustness, and user experience. One promising improvement involves augmenting the GUI to incorporate real-time spatial mapping of sensor positions. This feature would provide an intuitive visual representation of sensor activity and precise fault locations, enabling faster decision-making during examinations. Integrating this functionality into a dashboard-style interface could further consolidate key system metrics, offering examiners a comprehensive, at-a-glance view of operational status throughout the test cycle. In terms of communication infrastructure, the existing serial-based architecture could be upgraded to leverage advanced protocols such as the CAN Bus or wireless technologies including ZigBee or Wi-Fi. Such enhancements would remove wiring limitations, simplify scalability, and facilitate deployment in larger or more complex testing environments. Furthermore, incorporating a comprehensive logging and analytics framework would enable continuous data capture throughout test sessions. This data could support the generation of automated performance reports, the identification of recurring fault patterns, and the long-term assessment of both candidate performance and sensor reliability. These capabilities would not only strengthen the system’s operational resilience but also open pathways for predictive maintenance and data-driven optimization. Integrating machine learning algorithms represents another promising direction. By leveraging historical sensor data, the system could be trained to classify and predict common driving errors, thereby enabling a more adaptive and intelligent assessment process in line with emerging trends in AI-driven driver education. Future iterations may also incorporate multi-language support, cloud-based record storage, and direct integration with national licensing databases. These enhancements would improve accessibility, ensure secure and traceable recordkeeping, and streamline administrative workflows. Collectively, these advancements would transform the system from a functional testing platform into a comprehensive, scalable, and future-ready solution, aligning it with the global movement toward digital transformation and automation in driver evaluation and licensing processes. Feasibility analysis of the proposed SDTS The proposed SDTS is conceived to enhance the accuracy, impartiality, and operational efficiency of conventional driving test procedures through full automation of the evaluation process. Traditional, manually administered assessments are often susceptible to human error, subjective judgment, and procedural inefficiencies. To mitigate these shortcomings, the proposed system integrates microcontrollers, sensor arrays, and serial communication protocols to enable objective, real-time monitoring of driver performance within a structured and controlled test environment. This feasibility analysis evaluates the system’s viability across multiple dimensions, including measurable technical performance metrics such as latency, throughput, packet loss, detection accuracy, and repeatability, technical practicality, economic cost-effectiveness, operational integration, legal compliance, and implementation scheduling, to determine its potential for successful real-world deployment. The technical feasibility examines the adequacy of current technologies to support the system’s functional and performance objectives. This includes evaluating whether the selected hardware components, sensor technologies, communication protocols, and development platforms can reliably meet the system’s requirements for precision, responsiveness, low-latency data acquisition, and real-time synchronization between firmware and application layers. The analysis further considers the availability of compatible tools for firmware development, GUI implementation, and integration testing to ensure seamless interoperability between the hardware and software layers. Hardware components AT89C52 microcontroller: Serves as the core processing unit for sensor interfacing and local data handling. It offers sufficient I/O ports for multi-sensor integration, supports UART communication for Modbus RTU protocol implementation, and provides a cost-effective solution for embedded control. Shock sensors: Detect sudden impacts or vibrations, enabling prompt identification of collision events and activation of relevant error-handling routines. Detection accuracy measured at 98.5% for induced shock events. IR sensors: Employed for monitoring vehicle position, obstacle detection, and boundary-crossing events, ensuring accurate spatial tracking during test execution. Detection accuracy measured at 97.8% for unauthorized crossings. Power supply: A 12 V battery, coupled with voltage regulation circuitry, ensures stable and uninterrupted operation of all sensor modules and microcontrollers. Electronic components: Supporting elements such as resistors, capacitors, and connectors are incorporated to ensure robust circuit stability, noise reduction, and reliable signal transmission. Communication protocol RS485 with Modbus RTU: Selected for its robustness in electrically noisy environments, RS485 ensures stable long-distance serial communication between the central control unit and distributed sensor nodes. The Modbus RTU protocol facilitates a structured master–slave communication framework, enabling efficient data exchange, high noise immunity, and minimal signal degradation across extended cabling distances. Measured latency for ACK messages: 0.48 ± 0.03 ms; throughput: ~2,000 messages/sec; zero packet loss during sustained operation. For a typical driving track setup, the total cabling length is estimated at 50–70 m, depending on sensor placement. Software & interface The development and implementation of the SDTS relied on commercially available, widely adopted software tools to ensure reliable system operation and reproducibility: Keil µVision 3.0: Programming the microcontroller, implementing data acquisition routines, and executing control logic for sensor management. MiniPro Programmer: Microcontroller firmware flashing, debugging, and iterative code refinement. Proteus 8 Professional: Electronic circuit design and simulation, validating hardware configurations and communication protocols prior to deployment. AutoCAD & SketchUp 3D: Producing precise 2D/3D technical drawings and intuitive 3D modeling of test yard layouts. Autodesk SolidWorks: Parametric 3D CAD modeling and mechanical design of custom hardware components. All selected software tools are commercially available, well-documented, and widely used in engineering and development workflows. Their adoption ensures technical feasibility, reliability, and reproducibility of the proposed system, while remaining compatible with standard development platforms. Real-time event acquisition and firmware-to-GUI synchronization were validated through continuous logging of ACK and 11-byte error messages, confirming deterministic and low-latency operation. Economic feasibility This subsection evaluates the financial viability of deploying the proposed SDTS by analyzing its cost-effectiveness, resource requirements, and potential return on investment (ROI). A detailed cost analysis was conducted to determine the total expenditure required for system implementation, as summarized in Table 6 . The breakdown includes expenses for hardware components, fabrication, auxiliary materials, and supporting tools. Table 6. Estimated cost breakdown for the proposed system. Component Approx. Cost (EGP) Electronic components 1,200 Enclosure fabrication (Housing) 4,000 IR transmitter 150 IR receiver 55 Cables and connectors 600 Shock sensor 1,500 Car 2,000 PCB 2,500 Printing A0 Maquette 200 Maquette 5,000 Adaptors 350 Tools 700 Wires 400 Chassis painting 500 Total cost 23,000 Open in a new tab It is noteworthy that for large-scale deployments, the per-unit cost is expected to decrease significantly due to economies of scale achieved through bulk procurement and component standardization. This cost optimization would further enhance the project’s economic attractiveness, particularly for widespread adoption in training and testing facilities. The proposed system offers notable economic advantages by minimizing the need for manual labor, thereby reducing associated staffing costs, and by shortening the duration of each driving test, which increases candidate throughput. Furthermore, it enhances evaluation consistency and objectivity, thereby lowering the likelihood of disputes and retests. The integration of digital recordkeeping and performance analytics streamlines administrative processes, reducing paperwork and operational overhead. To provide a more comprehensive operational evaluation, the system incorporates defined maintenance and calibration schedules. Sensor modules are recalibrated every 6–12 months; software and firmware updates, including latency and throughput optimizations, are scheduled periodically. Measured repeatability across five test runs remained < 1.2%. Sensor modules are designed for recalibration every 6–12 months depending on usage intensity, while regular functional checks and preventive maintenance for mechanical and electronic components are recommended every 3–6 months. Software and firmware updates, including performance optimizations and protocol upgrades, are projected at low incremental costs, typically limited to labor and minimal hardware replacement for extended lifecycle support. These lifecycle measures ensure sustained accuracy, reliability, and cost-effectiveness across multiple test centers, enabling scalable deployment with predictable operational expenditure. Installation labor for a standard setup is estimated at 16–20 person-hours for initial wiring, sensor placement, and system configuration. These lifecycle and installation measures ensure sustained accuracy, reliability, and cost-effectiveness across multiple test centers, enabling scalable deployment with predictable operational expenditure. Considering the relatively modest initial investment, the system’s long-term savings, and the structured maintenance framework, the implementation is deemed economically feasible, sustainable, and resilient for multi-center adoption. Operational feasibility assessment of the SDTS Operational feasibility assesses the system’s capacity to integrate seamlessly into existing infrastructures and workflows without causing significant disruption. The system’s low-latency, synchronized data acquisition ensures real-time event detection and decision-making, supporting examiner oversight and safety interventions. The SDTS is specifically designed for deployment within predefined driving tracks at driving schools or government testing facilities, where lane markings, checkpoints, and other essential physical infrastructure are already established. The system requires minimal training for operational staff, as examiners can monitor candidate performance through an intuitive, user-friendly interface that provides real-time sensor feedback and event alerts. Examiners retain the ability to intervene when necessary, ensuring safety and maintaining oversight. Routine maintenance is limited to periodic sensor calibration and scheduled software updates, both of which can be performed efficiently. A key operational advantage is the inclusion of remote diagnostics via Wi-Fi, enabling technical issues to be identified and addressed without requiring on-site support in most cases. This feature significantly reduces downtime and maintenance costs while enhancing overall system reliability. User adoption is anticipated to be highly positive: Candidates benefit from fair, unbiased, and consistent evaluations. Examiners gain improved operational efficiency and reduced subjectivity in assessments. Administrators receive actionable data and automated recordkeeping for streamlined reporting and decision-making. Overall, the SDTS exhibits strong operational feasibility, supporting a smooth transition from manual to automated testing with minimal disruption to established procedures. These operational and installation details, together with cost data, support a realistic Total Cost of Ownership (TCO) estimate for multi-center deployment scenarios. Legal and regulatory feasibility The implementation of the SDTS demonstrates strong legal and regulatory viability under current conditions. The system is designed to comply with established data protection and privacy standards by ensuring that all candidate performance data is securely stored and transmitted over encrypted Wi-Fi channels. Additional security measures, such as TLS/SSL encryption, can be integrated to further safeguard data integrity and confidentiality. From a road and vehicle safety perspective, the hardware and sensor modules are non-invasive and do not interfere with normal vehicle operation, thereby maintaining full compliance with safety requirements. All integrated components are positioned to avoid creating physical hazards or obstructing driver visibility, ensuring adherence to standard vehicle inspection criteria. The use of open-source software libraries and commercially available hardware minimizes the risk of intellectual property disputes or licensing conflicts, simplifying the adoption process. Nevertheless, for the system to be deployed in official, government-certified testing facilities, it may be necessary to obtain formal approvals, operational certifications, or compliance verification from relevant transportation authorities. In summary, while no significant legal obstacles are anticipated during development and pilot testing, full-scale deployment will require proactive coordination with national regulatory bodies to ensure adherence to all applicable traffic, safety, and data governance regulations. Feasibility evaluation and deployment recommendation for the SDTS The feasibility assessment confirms that the proposed smart automated driving test evaluation system is a practical and sustainable solution across all major evaluation dimensions. The system employs cost-effective, commercially available components and well-established integration practices, minimizing implementation risks and ensuring maintainability. The system employs cost-effective, commercially available components and well-established integration practices, minimizing implementation risks and ensuring maintainability. Sensor calibration and instrumentation accuracy are rigorously implemented, with high-resolution analog-to-digital conversion and systematic verification of measurement precision. Drawing parallels with established methods in embedded instrumentation, such as the SAE 2024 study on digital fuel indicators, the system ensures repeatable, reliable measurements for all sensor modalities 35 . Moreover, the choice of sensor suite (IR, ultrasonic, LiDAR) is grounded in evidence-based literature comparing their field reliability in realistic outdoor conditions: empirical studies show complementary strengths (e.g., ultrasonic sensors achieve high accuracy at longer ranges and orthogonal target surfaces, whereas IR sensors perform better for short-range detection and angled or irregular surfaces) 85 . In dusty or “dirty” environments, ultrasonic sensors maintain robust operation even when IR sensors degrade due to particulate scattering 87 . LiDAR, while offering superior distance resolution, can be vulnerable to environmental conditions (e.g., rain, fog, reflective surfaces) that affect ranging performance 88 . This balanced configuration ensures both precision and operational resilience. Significant long-term savings are anticipated through reduced reliance on manual labor, increased candidate throughput, and improved operational efficiency. The design integrates seamlessly with existing testing procedures and infrastructure, requiring only minimal personnel training and minimal workflow adjustments. The emphasis on precise digital signal acquisition ensures operational consistency across multiple testing centers. Leveraging open-source software and non-intrusive hardware mitigates licensing and compliance risks. While no immediate regulatory barriers are expected, formal certification from relevant authorities will be necessary for official deployment. Development, integration, and initial deployment are achievable within an estimated three-month timeframe. Given these positive indicators, it is recommended to proceed with the development of a fully functional prototype, followed by controlled field testing in an operational driving test environment. This phase will validate system performance under real-world conditions, particularly testing our IR, ultrasonic, and LiDAR sensor suite under environmental stresses (dust, humidity, varying surface reflectivity) as indicated in the literature. Fine-tuning of hardware and software components, and provide actionable insights to guide large-scale deployment and scalability. Emphasizing calibration and measurement accuracy during this phase will further strengthen the reliability and precision of the SDTS instrumentation across varied operating environments. Including wiring length estimates and installation labor considerations in combination with lifecycle costs ensures the feasibility assessment now reflects both technical and economic realism for deployment planning. Future directions and potential enhancements for the SDTS Building upon the successful implementation of the current prototype, several strategic enhancements can be envisioned to expand the functionality, robustness, and scalability of the SDTS. These prospective improvements address emerging technological trends, evolving user requirements, and the need for higher adaptability in diverse operational contexts: Future iterations could leverage AI and ML algorithms to advance the system from a rule-based assessment tool to an adaptive, intelligent evaluation platform. By analyzing historical driving behavior patterns, the system could generate predictive scoring models that dynamically assess candidate performance and tailor test difficulty in real time. Moreover, adopting TinyML (i.e., machine learning on microcontrollers) would allow embedding lightweight inference models directly on the microcontroller sensor nodes, enabling real-time anomaly detection, dynamic threshold adjustment, and adaptive self-calibration without relying solely on the supervisory PC. For example, online-learning frameworks such as TinyOL enable models on MCUs to continue learning from streaming sensor data, improving over time as conditions evolve 86 . Additionally, computer vision–based perception modules could be incorporated to identify traffic signs, detect road surface anomalies, and track surrounding vehicle behavior, thereby simulating more complex and realistic driving scenarios. Object-detection TinyML models could be integrated (e.g., using Tiny convolutional neural nets) for vision-based extensions: detecting lane markings, traffic signs, or even pedestrian presence in future test-yard expansions. The feasibility of such models on microcontrollers has been demonstrated in the TinyML literature 89 . On the system-architecture side, this suggests a hybrid control model to maintain the existing deterministic, hard real-time firmware (in assembly) for collision detection, timers, and critical interrupts; layer on a lightweight ML model running on either the same MCU (if resource allows) or on a nearby edge node; and let the supervisory GUI or controller consume the ML outputs to refine scoring or flag anomalies. Integrating additional sensors such as GPS modules, IMUs, and biometric or acoustic sensors can improve vehicle localization, capture driver state, and monitor mechanical performance, enabling a more comprehensive assessment of candidate performance. Combining heterogeneous sensor data streams allows for precise performance evaluation and identification of testing anomalies, supporting evidence-based decision-making and standardized reporting. Transitioning to advanced wireless protocols, miniaturizing sensor units, and ruggedizing components can enhance system flexibility, reduce installation complexity, and ensure durability in varied testing environments. Adaptive dashboards, real-time feedback, and accessible GUI design can improve examiner efficiency and candidate experience. Cloud-based storage and analytics facilitate longitudinal tracking, automated reporting, and operational transparency for licensing authorities. Continuous refinement of system architecture and embedded software will improve response times, error handling, and test repeatability, ensuring consistent and objective evaluation outcomes. This capability would align the system with modern autonomous vehicle technologies, further enhancing its relevance and applicability. Incorporating additional sensing technologies can provide a richer and more accurate depiction of the test environment. GPS modules and Inertial Measurement Units (IMUs) could improve vehicle localization accuracy and capture precise motion dynamics, while biometric sensors such as eye trackers or heart rate monitors could assess the driver’s cognitive workload and stress levels. Acoustic sensors could detect engine irregularities or other mechanical anomalies during testing, contributing to a more comprehensive evaluation framework. The fusion of these heterogeneous data streams would enable advanced analytics and a more nuanced understanding of candidate performance. Deploying a secure, centralized cloud infrastructure would facilitate the storage, indexing, and retrieval of large volumes of test session data. This would enable longitudinal tracking of driver performance, automated generation of predictive insights, and data-driven scoring over multiple sessions. Cloud integration would also allow licensing authorities to access standardized, verified test records in real time, thereby enhancing administrative efficiency and transparency. Furthermore, data mining and analytics modules could be implemented to identify systemic issues, optimize testing protocols, and support evidence-based policy-making. As reliance on interconnected systems grows, safeguarding sensitive candidate and operational data is critical. Future versions of the system could adopt multi-layered cybersecurity strategies, including end-to-end encryption (e.g., AES-256, TLS 1.3), role-based access control (RBAC), and multi-factor authentication (MFA) for both operators and administrators. Regular penetration testing, intrusion detection systems, and blockchain-based data integrity verification could be incorporated to ensure resilience against unauthorized access or tampering. To improve deployment flexibility and reduce cabling complexity, future hardware designs could transition from wired RS485 protocols to advanced wireless communication standards such as Wi-Fi 6, ZigBee, LoRa, or 5G-based IoT modules. These technologies would facilitate greater scalability, especially in large or irregular test environments. In parallel, miniaturization and ruggedization of sensor units, using weather-resistant enclosures and shock-absorbing materials—would extend system lifespan under challenging environmental conditions. User-centric refinements to the GUI could include adaptive dashboards, customizable alerts, and real-time feedback for candidates during and immediately after testing. Visual summaries, heat maps of performance areas, and integrated replay features could further improve examiner efficiency and training effectiveness. Incorporating multilingual support and accessibility-compliant design would broaden the system’s usability across diverse demographic and linguistic groups. While RS485 is highly reliable for industrial applications, extended daisy-chain networks can experience signal degradation and timing delays. Future research could explore error correction coding, intelligent repeaters, or hybrid network architectures that combine wired and wireless communication to maintain high data fidelity in large-scale deployments. By systematically implementing these advancements, the SDTS can evolve from a functional automation tool into a fully intelligent, AI-driven, scalable, and analytics-enabled evaluation platform, capable of predictive assessment, longitudinal performance tracking, and evidence-based optimization of testing protocols. This evolution will not only enhance test precision and fairness but also align the system with global trends in smart mobility, connected infrastructure, and AI-enabled driver assessment. Conclusion The proposed SDTS system represented a significant leap forward in modernizing driver assessment by replacing conventional manual evaluation methods with an automated, sensor-driven framework. Through the seamless integration of microcontroller-based sensor arrays, reliable RS485 serial communication, and a purpose-built GUI, the system delivers objective, consistent, and real-time performance evaluation within controlled test environments. This automation addresses key shortcomings of traditional approaches, reducing human error, eliminating subjective bias, and mitigating the potential for procedural inconsistencies or corruption. From an operational perspective, the system achieves measurable gains in efficiency and scalability, enabling shorter test cycles, higher candidate throughput, and streamlined administrative workflows. The selection of the RS485 protocol proved crucial, providing a balanced solution in terms of cost, robustness, and network scalability, ensuring reliable data transmission even in electrically noisy environments and over extended distances. The hardware architecture, underpinned by precise schematic design, optimized PCB layouts, and mechanically robust 3D-modeled enclosures, ensure system durability and reliability under repeated operational use. Complementing this, the software layer, developed in Visual Basic Version 3, offers a feature-rich, intuitive interface capable of real-time sensor state visualization, dynamic traffic light simulation, automated error detection, and event logging. The modular GUI structure allows for straightforward operation by examiners while maintaining flexibility for debugging, calibration, and system scaling. The feasibility analysis confirms the system’s technical, economic, operational, and regulatory viability, highlighting its readiness for transition from an educational prototype to a fully deployable field solution. Cost-benefit evaluation underscores its long-term economic advantages, while operational assessments demonstrate minimal disruption to existing workflows and rapid adaptability by users. Beyond its current implementation, the system provides a strong foundation for future innovation, including AI-driven adaptive testing, predictive scoring, multimodal sensor fusion, cloud-based analytics, and advanced wireless communication integration. Such enhancements will not only elevate assessment precision and scalability but also align the system with global trends in smart mobility and connected infrastructure. In conclusion, the SDTS system successfully bridges the gap between theoretical research and practical deployment, offering a cost-effective, technologically robust, and operationally efficient platform for driver evaluation. By combining engineering precision, intelligent software design, predictive analytics, and forward-looking scalability, it positions itself as a transformative solution capable of redefining driver testing standards in both public and private licensing frameworks. Acknowledgements The authors would like to acknowledge Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R197), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Automated Systems and Computing Lab (ASCL) at Prince Sultan University, Riyadh, Saudi Arabia, for their support to this work. Author contributions All authors are equally contributed. All authors read and approved the final manuscript. Funding This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2026R197), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Data availability All data are available upon request from the corresponding author. Declarations Competing interests The authors declare no competing interests. Ethics approval All authors are contributing and accepting to submit the current work. Consent to participate All authors are contributing and accepting to submit the current work. Consent to publish All authors are accepting to submit and publish the submitted work. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Sekadakis, M. Systematic review and meta-analysis of take-over time from automated driving at SAE levels 2 and 3 to manual control. Transp. Res. Part. F: Traffic Psychol. Behav. 113 , 263–306 (2025). [ Google Scholar ] 2. de Winter, J. C., Verschoor, K., Doubek, F. & Happee, R. Once a driver, always a driver—Manual driving style persists in automated driving takeover. Appl. Ergon. 121 , 104366 (2024). [ DOI ] [ PubMed ] [ Google Scholar ] 3. El-Nabi, S. A. et al. Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review. Multimedia Tools Appl. 83 (3), 9441–9477 (2024). [ Google Scholar ] 4. Qian, H., Wang, M., Zhu, M. & Wang, H. A review of multi-sensor fusion in autonomous driving. Sensors 25 (19), 6033 (2025). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Wei, C., Qin, Z., Zhang, Z., Wu, G. & Barth, M. J. Integrating multi-modal sensors: A review of fusion techniques for intelligent vehicles. Preprint at https://arXiv/org:2506.21885. (2025). 6. Nambi, A. U., Mehta, I., Ghosh, A., Lingam, V. & Padmanabhan, V. N. ALT: towards automating driver license testing using smartphones. In: Proceedings of the 17th Conference on Embedded Networked Sensor Systems (29–42). (2019), 7. Huang, H. et al. Understanding driver cognition and decision-making behaviors in high-risk scenarios: A drift diffusion perspective. Accid. Anal. Prev. 220 , 108123 (2025). [ DOI ] [ PubMed ] [ Google Scholar ] 8. Moreno-Sanfélix, A., Gragera-Peña, F. C. & Jaramillo-Morán, M. A. An improvement of the conceptual system of the sequential events model of road crashes (i-MOSES). Heliyon , 10 (18). (2024). [ DOI ] [ PMC free article ] [ PubMed ] 9. Santhakumar, D. et al. Smart driving licensing system and authorization of autonomous vehicle integrating wireless sensor networks and Iot. Telecommun. Radio Eng. , 83 (9). (2024). 10. Abd El-Nabi, S. et al. Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising (IEEE Access, 2025). 11. El-Shafai, W., El-Rabaie, S., El-Halawany, M. & Abd El-Samie, F. E. Enhancement of wireless 3d video communication using color-plus-depth error restoration algorithms and bayesian Kalman filtering. Wireless Pers. Commun. 97 (1), 245–268 (2017). [ Google Scholar ] 12. zu Westerhausen, S. M., Schneider, J. & Lachmayer, R. Reliability analysis for sensor networks and their data acquisition: A systematic literature review. Proc. Design Soc. , 3 , 3065–3074. (2023). 13. Li, W., Duan, F. & Xu, C. Design and performance evaluation of a simple semi-physical human-vehicle collaborative driving simulation system. IEEE Access. 7 , 31971–31983 (2019). [ Google Scholar ] 14. Abd El-Nabi, S., Ramadan, K. F., El-Rabaie, E. S. M., Emam, A. & El-Shafai, W. A real-time design and implementation of intelligent drowsiness and fatigue recognition system for enhancing driver safety. Eng. Appl. Artif. Intell. 162 , 112665 (2025). [ Google Scholar ] 15. Karthaus, M. & Falkenstein, M. Functional changes and driving performance in older drivers: assessment and interventions. Geriatrics 1 (2), 12 (2016). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Rogeberg, O. A meta-analysis of the crash risk of cannabis-positive drivers in culpability studies—avoiding interpretational bias. Accid. Anal. Prev. 123 , 69–78 (2019). [ DOI ] [ PubMed ] [ Google Scholar ] 17. Wang, J., Ni, D. & Li, K. RFID-based vehicle positioning and its applications in connected vehicles. Sensors 14 (3), 4225–4238 (2014). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Pan, R. et al. An RFID Tag movement trajectory tracking method based on multiple RF characteristics for electronic vehicle identification ITS applications. Sensors 23 (15), 7001 (2023). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. De Simone, M. C., Rivera, Z. B. & Guida, D. Obstacle avoidance system for unmanned ground vehicles by using ultrasonic sensors. Machines 6 (2), 18 (2018). [ Google Scholar ] 20. Obuhuma, J. I., Okoyo, H. O. & McOyowo, S. O. Shortcomings of ultrasonic obstacle detection for vehicle driver assistance and profiling. I J. Inform. Technol. Comput. Sci. 6 , 28–36. 10.5815/ijitcs.2019.06.04 (2019). [ Google Scholar ] 21. Prinsloo, J. & Malekian, R. Accurate vehicle location system using RFID, an internet of things approach. Sensors 16 (6), 825 (2016). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Smagina, A. A., Shepelev, D. A., Ershov, E. I. & Grigoryev, A. S. Obstacle detection quality as a problem-oriented approach to stereo vision algorithms estimation in road situation analysis. In: Journal of Physics: Conference Series (1096, No. 1, 012035). (IOP Publishing, 2018), 23. Jha, S. et al. The multimodal driver monitoring database: A naturalistic corpus to study driver attention. IEEE Trans. Intell. Transp. Syst. 23 (8), 10736–10752 (2021). [ Google Scholar ] 24. Rezaei, M. & Azarmi, M. Driver-Net: Multi-camera fusion for assessing driver take-over readiness in automated vehicles. In: 2025 IEEE Intelligent Vehicles Symposium (IV) (pp. 1841–1848). IEEE. (2025), June. 25. Li, Y., Moreau, J. & Ibanez-Guzman, J. Emergent visual sensors for autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 24 (5), 4716–4737 (2023). [ Google Scholar ] 26. Kim, B. & Baek, Y. Sensor-based extraction approaches of in-vehicle information for driver behavior analysis. Sensors 20 (18), 5197 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Wang, B. et al. A review of intelligent connected vehicle cooperative driving development. Mathematics 10 (19), 3635 (2022). [ Google Scholar ] 28. Mmakwena, M., Clement, M. S. & Kholofelo, R. Corruption within the traffic Police environment: challenges and possible solutions. Pakistan J. Criminol. , 15 (4). (2023). 29. Wang, C., Guo, F., Yu, R., Wang, L. & Zhang, Y. The application of driver models in the safety assessment of autonomous vehicles: Perspectives, insights, prospects. IEEE Trans. Intell. Veh. 9 (1), 2364–2381 (2023). [ Google Scholar ] 30. Shi, S. et al. VIPS: Real-time perception fusion for infrastructure-assisted autonomous driving. In: Proceedings of the 28th annual international conference on mobile computing and networking (133–146). (2022), 31. Li, Y. et al. Choose your simulator wisely: A review on open-source simulators for autonomous driving. IEEE Trans. Intell. Veh. 9 (5), 4861–4876 (2024). [ Google Scholar ] 32. Bengler, K. et al. Three decades of driver assistance systems: review and future perspectives. IEEE Intell. Transp. Syst. Mag. 6 (4), 6–22 (2014). [ Google Scholar ] 33. Liu, L., Wang, Z. & Qiu, S. Driving behavior tracking and recognition based on multisensors data fusion. IEEE Sens. J. 20 (18), 10811–10823 (2020). [ Google Scholar ] 34. Kumar, N., Acharya, D. & Lohani, D. An IoT-based vehicle accident detection and classification system using sensor fusion. IEEE Internet Things J. 8 (2), 869–880 (2020). [ Google Scholar ] 35. Mallieswaran, K., Agaramudhalvan, S., Nithya, R., Shuruti, R. & Radhika, S. Design and implementation of advanced safety features in Two-Wheeled vehicles for accident prevention and Anti-Theft system (2024). (2024-01-5229). 36. Kashinath, S. A. et al. Review of data fusion methods for real-time and multi-sensor traffic flow analysis. IEEE Access. 9 , 51258–51276 (2021). [ Google Scholar ] 37. Garefalakis, T., Katrakazas, C. & Yannis, G. Data-driven Estimation of a driving safety tolerance zone using imbalanced machine learning. Sensors 22 (14), 5309 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Blasch, E. et al. Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges. IEEE Aerosp. Electron. Syst. Mag. 36 (7), 80–93 (2021). [ Google Scholar ] 39. Anderson, J. M. et al. Autonomous Vehicle Technology: A Guide for Policymakers (Rand Corporation, 2014). 40. Kendall, A. et al. End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE international conference on computer vision (66–75). (2017). 41. Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (779–788). (2016). 42. Wang, Z., Wu, Y. & Niu, Q. Multi-sensor fusion in automated driving: A survey. Ieee Access. 8 , 2847–2868 (2019). [ Google Scholar ] 43. Kavas-Torris, O., Gelbal, S. Y., Cantas, M. R., Guvenc, A., Guvenc, L. & B., & V2X communication between connected and automated vehicles (CAVs) and unmanned aerial vehicles (UAVs). Sensors 22 (22), 8941 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Chen, S. et al. Vehicle-to-everything (V2X) services supported by LTE-based systems and 5G. IEEE Commun. Stand. Magazine . 1 (2), 70–76 (2017). [ Google Scholar ] 45. Meuser, T. et al. Revisiting edge ai: opportunities and challenges. IEEE Internet Comput. 28 (4), 49–59 (2024). [ Google Scholar ] 46. Al Barghuthi, N. B. & Said, H. Readiness, safety, and privacy on adopting autonomous vehicle technology: UAE case study. In 2019 Sixth HCT Information Technology Trends (ITT) (pp. 47–52). IEEE. (2019), November. 47. Dabla, T. K. The role of intelligent transport system to safe road in Iran, Qatar, and china: A comparative analysis. Int. J. Traffic Transp. Manage. (JTTM) , 4 (2). (2023). 48. John, S. K., Sivaraj, D. & Mugelan, R. K. Implementation Challenges and Opportunities of Smart City and Intelligent Transport Systems in India 213–235 (Internet of things and big data analytics for smart generation, 2018). 49. Oh, T., Ha, Y., Yoo, D. & Yoo, J. AD-VILS: Implementation and Reliability Validation of vehicle-in-the-loop Simulation Platform for Evaluating Autonomous Driving Systems (IEEE Access, 2024). 50. Wishart, J. et al. Literature review of verification and validation activities of automated driving systems. SAE Int. J. Connect. Autom. Veh. 3 , 267–323 (2020). [ Google Scholar ] 51. Zhao, J. et al. A survey of autonomous driving from a deep learning perspective. ACM Comput. Surveys . 57 (10), 1–60 (2025). [ Google Scholar ] 52. El-Sebah, M. I. A., Syam, F. A., Sweelem, E. A. & El-Sotouhy, M. M. A proposed controller for an autonomous vehicles embedded system. WSEAS Trans. Circuits Syst. 22 , 1–9 (2023). [ Google Scholar ] 53. Evans, D. S., Hagiu, A. & Schmalensee, R. Invisible Engines: How Software Platforms Drive Innovation and Transform Industries 408 (The MIT Press, 2008). 54. Nadkarni, S. & Haider, I. Digital transformation, operational efficiency and sustainability: innovation drivers for hospitality’s rebound in the united Arab Emirates. Worldw. Hospitality Tourism Themes . 14 (6), 572–578 (2022). [ Google Scholar ] 55. Forge, S. & Bohlin, E. Managed innovation in Korea in telecommunications–Moving towards 4G mobile at a National level. Telematics Inform. 25 (4), 292–308 (2008). [ Google Scholar ] 56. Baek, K., Moon, H. & Ko, I. Y. Vr-powered scenario-based testing for visual and acoustic web of things services. In: International Conference on Web Engineering (514–518). (Springer International Publishing, 2019). 57. Ryan, M. The future of transportation: ethical, legal, social and economic impacts of self-driving vehicles in the year 2025. Sci Eng. Ethics . 26 (3), 1185–1208 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Yamasaki, Y., Fleischer, T. & Schippl, J. Governance, policy and regulation in the field of automated driving: A focus on Japan and Germany. In Acceptance and Diffusion of Connected and Automated Driving in Japan and Germany (57–95). (Springer Nature, 2024). [ Google Scholar ] 59. Taefi, T. T., Kreutzfeldt, J., Held, T. & Fink, A. Supporting the adoption of electric vehicles in urban road freight transport–A multi-criteria analysis of policy measures in Germany. Transp. Res. Part. A: Policy Pract. 91 , 61–79 (2016). [ Google Scholar ] 60. Ronquillo-Cana, C. J., Pancardo, P., Silva, M., Hernández-Nolasco, J. A. & Garcia-Constantino, M. Fuzzy system to assess dangerous driving: A multidisciplinary approach. Sensors 22 (10), 3655 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 61. Pełka, M., Kruszewski, M., Kasprzyk, Z. & Rychlicki, M. Model for evaluating the effectiveness of practical training with the use of a high-end driving simulator. Appl. Sci. 15 (14), 8083 (2025). [ Google Scholar ] 62. Stokic, M. Evaluation of driver’s eco-driving skills based on fuzzy logic model–A realistic example of vehicle operation in real-world conditions. J. Appl. Eng. Sci. , 17 (2). (2019). 63. Mafeni Mase, J. et al. Contextual Intelligent Decisions arXiv–2202 (Expert Moderation of Machine Outputs for Fair Assessment of Commercial Driving. arXiv e-prints, 2022). 64. Ruiwen, Z., Jingli, Y., Chao, M., Yanan, Z. & Haijun, W. Research on a comprehensive evaluation system for driving behavior by introducing fuzzy evaluation based on natural driving data. Medicon Eng. Themes . 5 (1), 146–153 (2023). [ Google Scholar ] 65. Xie, L., Zhang, J. & Cheng, R. Comprehensive evaluation of freeway driving risks based on fuzzy logic. Sustainability 15 (1), 810 (2023). [ Google Scholar ] 66. Zhang, P., Zhu, B., Zhao, J., Fan, T. & Sun, Y. Performance evaluation method for automated driving system in logical scenario. Automot. Innov. 5 (3), 299–310 (2022). [ Google Scholar ] 67. Almadi, A. I. et al. A fuzzy-logic approach based on driver decision-making behavior modeling and simulation. Sustainability 14 (14), 8874 (2022). [ Google Scholar ] 68. Dkhil, M. B., Wali, A. & Alimi, A. M. A hierarchical fuzzy system for an advanced driving assistance system. Preprint at :180604611. (2018). 69. Comus International. (n.d.). AU2401-1 gold plated motion sensors. Retrieved March 10. from (2025). https://comus-intl.com/products/tilt-switches-motion-sensors/motion-sensors-switches/au2401-1/ 70. Comet Electronics. (n.d.). IR emitter, panel mount, 950nm. Retrieved March 10. from (2025). https://store.comet.bg/CatalogueFarnell/Product/1021380/ 71. Micro Ohm Electronics. (n.d.). TSOP JS-1838B infrared remote receiver: 38 kHz, high noise immunity. Retrieved March 10. from (2025). https://microohm-eg.com/ 72. Belmonte, F. J., Martín, S., Sancristobal, E., Ruipérez-Valiente, J. A. & Castro, M. Overview of embedded systems to build reliable and safe ADAS and AD systems. IEEE Intell. Transp. Syst. Mag. 13 (4), 239–250 (2020). [ Google Scholar ] 73. Ramamurthy, H., Prabhu, B. S., Gadh, R. & Madni, A. M. Wireless industrial monitoring and control using a smart sensor platform. IEEE Sens. J. 7 (5), 611–618 (2007). [ Google Scholar ] 74. Mohammed, M. S., Abduljabar, A. M., Faisal, M. M., Mahmmod, B. M., Abdulhussain, S.H., Khan, W., Hussain, A. (2023). Low-cost autonomous car level 2: Design and implementation for conventional vehicles. Res. Eng. , 17 , 100969. 75. Ovsthus, K. & Kristensen, L. M. An industrial perspective on wireless sensor networks—A survey of requirements, protocols, and challenges. IEEE Commun. Surv. Tutorials . 16 (3), 1391–1412 (2014). [ Google Scholar ] 76. World Health Organization. Global status report on road safety 2023. (2023). https://www.who.int/publications/i/item/9789240085684 77. Grabiszewski, K. & Horenstein, A. The social costs of deregulation in a fight against corruption: A case study on driver’s licenses in Mexico City. Rev. Law Econ. 20 (3), 341–355 (2024). [ Google Scholar ] 78. Krishnamurthi, R., Kumar, A., Gopinathan, D., Nayyar, A. & Qureshi, B. An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors 20 (21), 6076 (2020). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 79. Wynne, R. A., Beanland, V. & Salmon, P. M. Systematic review of driving simulator validation studies. Saf. Sci. 117 , 138–151 (2019). [ Google Scholar ] 80. Wang, Y. Navigating the evolving risk landscape of automated driving: regulatory approaches and challenges. Chinese ethical and legal perspectives on automated vehicles, 125–150. (2025). 81. Găitan, V. G., Zagan, I. & Găitan, N. C. Modbus RTU protocol timing evaluation for scattered holding register read and ModbusE-Related implementation. Processes 13 (2), 367 (2025). [ Google Scholar ] 82. Noble, P. J. Printed Circuit Board Assembly: the Complete Works (Springer Science & Business Media, 2013). 83. Patterson, D. A. & Hennessy, J. L. Computer Organization and Design ARM Edition: the Hardware Software Interface (Morgan kaufmann, 2016). 84. Vahid, F. & Givargis, T. D. Embedded System Design: a Unified hardware/software Introduction (Wiley, 2001). 85. Téllez-Garzón, J. L., Fandiño-Pelayo, J. S., Bernard, A. & Mazzini, G. Study comparing empirical data on sensors used to measure obstacle distance. Ingeniería , 30 (1). (2025). 86. Saha, S. S., Sandha, S. S. & Srivastava, M. Machine learning for microcontroller-class hardware: A review. IEEE Sens. J. 22 (22), 21362–21390 (2022). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 87. Demeke, A. Y., Younesi Heravi, M., Dola, I. S., Jang, Y., Le, C., Jeong, I., … Wang,D. (2025). Advancing highway work zone safety: a comprehensive review of sensor technologies for intrusion and proximity hazards. Transportation Res. Record , 2679 (2), 1066–1089. 88. Balestrieri, E., Daponte, P., De Vito, L. & Lamonaca, F. Sensors and measurements for unmanned systems: an overview. Sensors 21 (4), 1518 (2021). [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 89. El Zeinaty, C., Hamidouche, W., Herrou, G. & Menard, D. Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutions (ACM Computing Surveys, 2025). Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement All data are available upon request from the corresponding author. Articles from Scientific Reports are provided here courtesy of Nature Publishing Group ACTIONS View on publisher site PDF (4.6 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

Related documents

• Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation#243155
• Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove#998524
• Intermediate Vision in a Driving Simulator Environment: Comparison of the J&J EYHANCE With a Conventional Monofoc. IOL#879098
• Linking Behaviour and Perception to Evaluate Meaningful Human Control over Partially Automated Driving#151779
• CoLT-Drive: Counterfactual Long-Tail Benchmarking and Knowledge-Preserving Adaptation for Driving Affordance Prediction#819700