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Method, system and computer readable medium for evaluating influence of an … — Leddartech Inc. (US20240288550A1)

Leddartech Inc. · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US20240288550A1, Leddartech Inc., Ricardo Ferreira, en, 2024

ABSTRACT

Abstract

The present disclosure provides a non-transitory computer readable medium having instructions tangibly stored thereon, wherein when executed by a processing entity, the instructions cause the processing entity to carry out a method of evaluating influence of an action performed by an external entity, the method comprising: receiving sensor data; determining a signal reliability factor for the received sensor data, wherein the signal reliability factor represents a statistical quality indication between the received sensor data and an expected value or an expected range of values; and associating the signal reliability factor with the received sensor data. There is also provided a system configured to communicate with an autonomous driving system.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent application Ser. No. 18/514,827, filed Nov. 20, 2023, which is a continuation of U.S. patent application Ser. No. 18/318,538, filed May 16, 2023, which is a continuation of U.S. patent application Ser. No. 16/809,587, filed on Mar. 5, 2020, now U.S. Pat. No. 11,726,184, which claims priority from and the benefit of: (i) German Application No.: 10 2019 205 514.1, filed on Apr. 16, 2019, (ii) German Application No.: 10 2019 214 455.1, filed on Sep. 23, 2019, (iii) German Application No.: 10 2019 216 362.9, filed on Oct. 24, 2019, (iv) German Application No.: 10 2020 201 577.5, filed on Feb. 10, 2020, (v) German Application No.: 10 2019 217 097.8, filed on Nov. 6, 2019, (vi) German Application No.: 10 2020 202 374.3, filed on Feb. 25, 2020, (vii) German Application No.: 10 2020 201 900.2, filed on Feb. 17, 2020, (viii) German Application No.: 10 2019 203 175.7, filed on Mar. 8, 2019, (ix) German Application No.: 10 2019 218 025.6, filed on Nov. 22, 2019, (x) German Application No.: 10 2019 219 775.2, filed on Dec. 17, 2019, (xi) German Application No.: 10 2020 200 833.7, filed on Jan. 24, 2020, (xii) German Application No.: 10 2019 208 489.3, filed on Jun. 12, 2019, (xiii) German Application No.: 10 2019 210 528.9, filed on Jul. 17, 2019, (xiv) German Application No.: 10 2019 206 939.8, filed on is May 14, 2019, and (xv) German Application No.: 10 2019 213 210.3, filed on Sep. 2, 2019. The contents of each of the aforementioned U.S. and German applications are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The technical field of the present disclosure relates generally to sensor systems and, more specifically, to a method, system and computer readable medium for evaluating influence of an action performed by an external entity.

BACKGROUND

There are numerous studies and market forecasts, which predict that future mobility and transportation will shift from vehicles supervised by a human operator to vehicles with an increasing level of autonomy towards fully autonomous, self-driving vehicles. This shift, however, will not be an abrupt change but rather a gradual transition with different levels of autonomy, defined for example by SAE International (Society of Automotive Engineers) in SAE J3016 in-between. Furthermore, this transition will not take place in a simple linear manner, advancing from one level to the next level, while rendering all previous levels dispensable. Instead, it is expected that these levels of different extent of autonomy will co-exist over longer periods of time and that many vehicles and their respective sensor systems will be able to support more than one of these levels.

Depending on various factors, a human operator may actively switch for example between different SAE levels, depending on the vehicle's capabilities, or the vehicles operation system may request or initiate such a switch, typically with a timely information and acceptance period to possible human operators of the vehicles. These factors may include internal factors such as individual preference, level of driving experience or the biological state of a human driver and external factors such as a change of environmental conditions like weather, traffic density or unexpected traffic complexities.

It is important to note that the above-described scenario for a future is not a theoretical, far-away eventuality. In fact, already today, a large variety of so-called Advanced Driver Assistance Systems (ADAS) has been implemented in modern vehicles, which clearly exhibit characteristics of autonomous vehicle control. Current ADAS systems may be configured for example to alert a human operator in dangerous situations (e.g. lane departure warning) but in specific driving situations, some ADAS systems are able to takeover control and perform vehicle steering operations without active selection or intervention by a human operator. Examples may include convenience-driven situations such as adaptive cruise control, but also hazardous situations like in the case of lane keep assistants and emergency break assistants.

The above-described scenarios all require vehicles and transportation systems with a tremendously increased capacity to perceive, interpret and react on their surroundings. Therefore, it is not surprising that remote environmental sensing systems will be at the heart of future mobility.

Since modern traffic can be extremely complex due to a large number of heterogeneous traffic participants, changing environments or insufficiently mapped or even unmapped environments, and due to rapid, interrelated dynamics, such sensing systems will have to be able to cover a broad range of different tasks, which have to be performed with a high level of accuracy and reliability. It turns out that there is not a single âone fits allâ sensing system that can meet all the required features relevant for semi-autonomous or fully autonomous vehicles. Instead, future mobility requires different sensing technologies and concepts with different advantages and disadvantages. Differences between sensing systems may be related to perception range, vertical and horizontal field of view (FOV), spatial and temporal resolution, speed of data acquisition, etc. Therefore, sensor fusion and data interpretation, possibly assisted by Deep Neuronal Learning (DNL) methods and other Neural Processor Units (NFU) methods for more complex tasks, like judgment of a traffic situation and generation of derived vehicle control functions, may be necessary to cope with such complexities. Furthermore, driving and steering of autonomous vehicles may require a set of ethical rules and commonly accepted traffic regulations.

Among these sensing systems, LIDAR sensing systems are expected to play a vital role, as well as camera-based systems, possibly supported by radar and ultrasonic systems. With respect to a specific perception task, these systems may operate more or less independently of each other. However, in order to increase the level of perception (e.g. in terms of accuracy and range), signals and data acquired by different sensing systems may be brought together in so-called sensor fusion systems. Merging of sensor data is not only necessary to refine and consolidate the measured results but also to increase the confidence in sensor results by resolving possible inconsistencies and contradictories and by providing a certain level of redundancy. Unintended spurious signals and intentional adversarial attacks may play a role in this context as well.

For an accurate and reliable perception of a vehicle's surrounding, not only vehicle-internal sensing systems and measurement data may be considered but also data and information from vehicle-external sources. Such vehicle-external sources may include sensing systems connected to other traffic participants, such as preceding and oncoming vehicles, pedestrians, and cyclists, but also sensing systems mounted on road infrastructure elements like traffic lights, traffic signals, bridges, elements of road construction sites and central traffic surveillance structures. Furthermore, data and information may come from far-away sources such as traffic teleoperators and satellites of global positioning systems (e.g. GPS).

Therefore, apart from sensing and perception capabilities, future mobility will also heavily rely on capabilities to communicate with a wide range of communication partners. Communication may be unilateral or bilateral and may include various wireless transmission technologies, such as WLAN, Bluetooth and communication based on radio frequencies and visual or non-visual light signals. It is to be noted that some sensing systems, for example LIDAR sensing systems, may be utilized for both sensing and communication tasks, which makes them particularly interesting for future mobility concepts. Data safety and security and unambiguous identification of communication partners are examples where light-based technologies have intrinsic advantages over other wireless communication technologies. Communication may need to be encrypted and tamper-proof.

From the above description, it becomes clear also that future mobility has to be able to handle vast amounts of data, as several tens of gigabytes may be generated per driving hour. This means that autonomous driving systems have to acquire, collect and store data at very high speed, usually complying with real-time conditions. Furthermore, future vehicles have to be able to interpret these data, i.e. to derive some kind of contextual meaning within a short period of time in order to plan and execute required driving maneuvers. This demands complex software solutions, making use of is advanced algorithms. It is expected that autonomous driving systems will including more and more elements of artificial intelligence, machine and self-learning, as well as Deep Neural Networks (DNN) for certain tasks, e.g. visual image recognition, and other Neural Processor Units (NFU) methods for more complex tasks, like judgment of a traffic situation and generation of derived vehicle control functions, and the like. Data calculation, handling, storing and retrieving may require a large amount of processing power and hence electrical power.

In an attempt to summarize and conclude the above paragraphs, future mobility will involve sensing systems, communication units, data storage devices, data computing and signal processing electronics as well as advanced algorithms and software solutions that may include and offer various ethical settings. The combination of all these elements is constituting a cyber-physical world, usually denoted as the Internet of things (IoT). In that respect, future vehicles represent some kind of IoT device as well and may be called âMobile IoT devicesâ.

Such âMobile IoT devicesâ may be suited to transport people and cargo and to gain or provide information. It may be noted that future vehicles are sometimes also called âsmartphones on wheelsâ, a term which surely reflects some of the capabilities of future vehicles. However, the term implies a certain focus towards consumer-related new features and gimmicks. Although these aspects may certainly play a role, it does not necessarily reflect the huge range of future business models, in particular data-driven business models, that can be envisioned only at the present moment of time, but which are likely to center not only on personal, convenience driven features but include also commercial, industrial or legal aspects.

New data-driven business models will focus on smart, location-based services, utilizing for example self-learning and prediction aspects, as well as gesture and language processing with Artificial Intelligence as one of the key drivers. All this is fueled by data, which will be generated in vast amounts in automotive industry by a large fleet of future vehicles acting as mobile digital platforms and by connectivity networks linking together mobile and stationary IoT devices.

New mobility services including station-based and free-floating car sharing, as well as ride-sharing propositions have already started to disrupt traditional business fields. This trend will continue, finally providing robo-taxi services and sophisticated Transportation-as-a-Service (TaaS) and Mobility-as-a-Service (MaaS) solutions.

Electrification, another game-changing trend with respect to future mobility, has to be considered as well. Hence, future sensing systems will have to pay close attention to system efficiency, weight and energy-consumption aspects. In addition to an overall minimization of energy consumption, also context-specific optimization strategies, depending for example on situation-specific or location-specific factors, may play an important role.

Energy consumption may impose a limiting factor for autonomously driving electrical vehicles. There are quite a number of energy consuming devices like sensors, for example RADAR, LIDAR, camera, ultrasound, Global Navigation Satellite System (GNSS/GPS), sensor fusion equipment, processing power, mobile entertainment equipment, heater, fans, Heating, Ventilation and Air Conditioning (HVAC), Car-to-Car (C2C) and Car-to-Environment (C2X) communication, data encryption and decryption, and many more, all leading up to a high power consumption. Especially data processing units are very power hungry. Therefore, it is necessary to optimize all equipment and use such devices in intelligent ways so that a higher battery mileage can be sustained.

Besides new services and data-driven business opportunities, future mobility is expected also to provide a significant reduction in traffic-related accidents. Based on data from the Federal Statistical Office of is Germany (Destatis, 2018), over 98% of traffic accidents are caused, at least in part by humans. Statistics from other countries display similarly clear correlations.

Nevertheless, it has to be kept in mind that automated vehicles will also introduce new types of risks, which have not existed before. This applies to so far unseen traffic scenarios, involving only a single automated driving system as well as for complex scenarios resulting from dynamic interactions between a plurality of automated driving system. As a consequence, realistic scenarios aim at an overall positive risk balance for automated driving as compared to human driving performance with a reduced number of accidents, while tolerating to a certain extent some slightly negative impacts in cases of rare and unforeseeable driving situations. This may be regulated by ethical standards that are possibly implemented in soft- and hardware.

Any risk assessment for automated driving has to deal with both, safety and security related aspects: safety in this context is focusing on passive adversaries for example due to malfunctioning systems or system components, while security is focusing on active adversaries for example due to intentional attacks by third parties.

In the following a non-exhaustive enumeration is given for safety-related and security-related factors, with reference to âSafety first for Automated Drivingâ, a white paper published in 2019 by authors from various Automotive OEM, Tier-1 and Tier-2 suppliers.

Safety assessment: to meet the targeted safety goals, methods of verification and validation have to be implemented and executed for all relevant systems and components. Safety assessment may include safety by design principles, quality audits of the development and production processes, the use of redundant sensing and analysis components and many other concepts and methods.

Safe operation: any sensor system or otherwise safety-related system might be prone to degradation, i.e. system performance may decrease over time or a system may even fail completely (e.g. being unavailable). To ensure safe operation, the system has to be able to compensate for such performance losses for example via redundant sensor systems. In any case, the system has to be configured to transfer the vehicle into a safe condition with acceptable risk. One possibility may include a safe transition of the vehicle control to a human vehicle operator.

Operational design domain: every safety-relevant system has an operational domain (e.g. with respect to environmental conditions such as temperature or weather conditions including rain, snow and fog) inside which a proper operation of the system has been specified and validated. As soon as the system gets outside of this domain, the system has to be able to compensate for such a situation or has to execute a safe transition of the vehicle control to a human vehicle operator.

Safe layer: the automated driving system needs to recognize system limits in order to ensure that it operates only within these specified and verified l

CROSS-REFERENCE TO RELATED APPLICATIONS

TECHNICAL FIELD

BACKGROUND

Safe layer: the automated driving system needs to recognize system limits in order to ensure that it operates only within these specified and verified limits. This includes also recognizing limitations with respect to a safe transition of control to the vehicle operator.

User responsibility: it must be clear at all times which driving tasks remain under the user's responsibility. In addition, the system has to be able to determine factors, which represent the biological state of the user (e.g. state of alertness) and keep the user informed about their responsibility with respect to the user's remaining driving tasks.

Human Operator-initiated handover: there have to be clear rules and explicit instructions in case that a human operator requests an engaging or disengaging of the automated driving system.

Vehicle-initiated handover: requests for such handover operations have to be clear and manageable by the human operator, including a sufficiently long time period for the operator to adapt to the current traffic situation. In case it turns out that the human operator is not available or not capable of a safe takeover, the automated driving system must be able to perform a minimal-risk maneuver.

Behavior in traffic: automated driving systems have to act and react in an easy-to-understand way so that their behavior is predictable for other road users. This may include that automated driving systems have to observe and follow traffic rules and that automated driving systems inform other road users about their intended behavior, for example via dedicated indicator signals (optical, acoustic).

Security: the automated driving system has to be protected against security threats (e.g. cyber-attacks), including for example unauthorized access to the system by third party attackers. Furthermore, the system has to be able to secure data integrity and to detect data corruption, as well as data forging. Identification of trustworthy data sources and communication partners is another important aspect. Therefore, security aspects are, in general, strongly linked to cryptographic concepts and methods.

Data recording: relevant data related to the status of the automated driving system have to be recorded, at least in well-defined cases. In addition, traceability of data has to be ensured, making strategies for data management a necessity, including concepts of bookkeeping and tagging. Tagging may comprise, for example, to correlate data with location information, e.g. GPS-information.

In the following disclosure, various aspects are disclosed which may be related to the technologies, concepts and scenarios presented in the section âBACKGROUNDâ. This disclosure is focusing on LIDAR Sensor Systems, Controlled LIDAR Sensor Systems and LIDAR Sensor Devices as well as Methods for LIDAR Sensor Management. As illustrated in the above remarks, automated driving systems are extremely complex systems including a huge variety of interrelated sensing systems, communication units, data storage devices, data computing and signal processing electronics as well as advanced algorithms and software solutions.

SUMMARY

A first aspect of the present disclosure provides a non-transitory computer readable medium, which has instructions tangibly stored thereon, wherein when executed by a processing entity, the instructions cause the processing entity to carry out a method of evaluating influence of an action performed by an external entity, the method comprising: receiving sensor data; determining a signal reliability factor for the received sensor data, wherein the signal reliability factor represents a statistical quality indication between the received sensor data and an expected value or an expected range of values; and associating the signal reliability factor with the received sensor data.

In accordance with the preceding aspect, the method may further comprise determining if the signal reliability factor satisfies criteria; and discarding the received sensor data or using the received sensor data to generate one or more commands which are configured to control a central control system, depending on whether the signal reliability factor satisfies the criteria.

In accordance with the preceding aspect, the criteria may be satisfied if the signal reliability factor is less than a predefined threshold or the signal reliability factor is between a lower threshold and an upper threshold.

In accordance with the preceding aspect, the expected value or the expected range of values may be based on immediately preceding sensor data, the immediately preceding sensor data being received at an antecedent time point with respect to a time point when the sensor data is received.

In accordance with the preceding aspect, a sensor system that generates the immediately preceding sensor data may be identical to a sensor system that generates the received sensor data.

In accordance with the preceding aspect, in response to the signal reliability factor indicating that the received sensor data is incoherent with the immediately preceding sensor data, the method may further comprise evaluating influence of the action performed by the external entity based on the signal reliability factor.

In accordance with the preceding aspect, the received sensor data may be first sensor data generated by a first sensor system, and the method may further comprise receiving second sensor data from a second sensor system; and statistically inferring the expected value or the expected range of values based on the second sensor data.

In accordance with the preceding aspect, the processing entity may be associated with a vehicle, and the method may further comprise determining a vehicle condition associated with the vehicle; and statistically inferring the expected value or the expected range of values based on the vehicle condition.

In accordance with the preceding aspect, the vehicle condition may include at least one of a location selective category, an environmental setting, and a driving status.

In accordance with the preceding aspect, the method may further comprise determining a sensor fusion priority corresponding to the vehicle condition, wherein the sensor fusion priority defines a particular number and combination of sensor systems to be used.

In accordance with the preceding aspect, the sensor data may be generated by one or more sensor systems, and the method may further comprise changing a configuration of the one or more sensor systems based on the signal reliability factor.

In accordance with the preceding aspect, changing the configuration of the one or more sensor systems may comprise deactivating and/or deprioritizing at least one of the one or more sensor systems.

In accordance with the preceding aspect, the signal reliability factor may indicate a probability of the received sensor data being influenced by the action performed by the external entity.

In accordance with the preceding aspect, determining the signal reliability factor may comprise applying statistical rules to the received sensor data and the expected value or the expected range to generate the statistical quality indication, wherein the statistical rules include Bayesian rulings or position-coded rulings.

A second aspect of the present disclosure provides a system comprising: a processor in communication with a memory, the processor configured to execute instructions in the memory to cause the system to: receive sensor data; determine a signal reliability factor for the received sensor data, wherein the signal reliability factor represents a statistical quality indication between the received sensor data and an expected value or an expected range of values; and associating the signal reliability factor with the received sensor data.

In accordance with the preceding aspect, the processor may be further configured to execute the instructions in the memory to cause the system to determine if the signal reliability factor satisfies criteria; and to discard the received sensor data or to use the received sensor data to generate one or more commands which are configured to control a central control system, depending on whether the signal reliability factor satisfies the criteria.

In accordance with the preceding aspect, the expected value or the expected range of values may be based on immediately preceding sensor data, the immediately preceding data being received at an antecedent time point with respect to a time point when the sensor data is received.

In accordance with the preceding aspect, the processor may be further configured to execute the instructions in the memory to cause the system to, in response to the signal reliability factor indicating that the received sensor data is incoherent with the immediately preceding sensor data, evaluate influence of the action performed by the external entity based on the signal reliability factor.

In accordance with the preceding aspect, the received sensor data may be first sensor data generated by a first sensor system, and the processor may be further configured to execute the instructions in the memory to cause the system to receive second sensor data from a second sensor system; and statistically infer the expected value or the expected range of values based on the second sensor data.

In accordance with the preceding aspect, the system is associated with a vehicle, and the processor may be further configured to execute the instructions in the memory to cause the system to determine a vehicle condition associated with the vehicle; and statistically infer the expected value or the expected range of values based on the vehicle condition.

In accordance with the preceding aspect, the processor may be further configured to execute the instructions in the memory to cause the system to determine the signal reliability factor by applying statistical rules to the received sensor data and the expected value or the expected range of values to generate the statistical quality indication, wherein the statistical rules include Bayesian rulings or position-coded rulings.

Non-limiting embodiments can be found in the independent and dependent claims and in the entire disclosure, wherein in the description and representation of the features is not always differentiated in detail between the different claim categories; In any case implicitly, the disclosure is always directed both to the method and to appropriately equipped motor vehicles (LIDAR Sensor Devices) and/or a corresponding computer program product.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanying figures. The use of the same reference number in different instances in the description and the figure may indicate a similar or identical item. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the present disclosure.

In the following description, various embodiments of the present disclosure are described with reference to the following drawings, in which:

FIG. 1 shows schematically an embodiment of the proposed to LIDAR Sensor System, Controlled LIDAR Sensor System and LIDAR Sensor Device;

FIG. 2 shows a method in accordance with various non-limiting embodiments;

FIGS. 3 A and 3 B show each a system in a schematic view in accordance with various non-limiting embodiments;

FIG. 4 shows a system and a signal path in a schematic view in accordance with various non-limiting embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

LIDAR Sensor System and LIDAR Sensor Device

The LIDAR Sensor System according to the present disclosure may be combined with a LIDAR Sensor Device for illumination of an environmental space connected to a light control unit.

The LIDAR Sensor System may comprise at least one light module. Said one light module has a light source and a driver connected to the light source. The LIDAR Sensor System further has an interface unit, in particular a hardware interface, configured to receive, emit, and/or store data signals. The interface unit may connect to the driver and/or to the light source for controlling the operation state of the driver and/or the operation of the light source.

The light source may be configured to emit radiation in the visible and/or the non-visible spectral range, as for example in the far-red range of the electromagnetic spectrum. It may be configured to emit monochromatic laser light. The light source may be an integral part of the LIDAR Sensor System as well as a remote yet connected element. It may be placed in various geometrical patterns, distance pitches and may be configured for alternating of color or wavelength emission or intensity or beam angle. The LIDAR Sensor System and/or light sources may be mounted such that they are moveable or can be inclined, rotated, tilted etc. The LIDAR Sensor System and/or light source may be configured to be installed inside a LIDAR Sensor Device (e.g. vehicle) or exterior to a LIDAR Sensor Device (e.g. vehicle). In particular, it is possible that the LIDAR light source or selected LIDAR light sources are mounted such or adapted to being automatically controllable, in some implementations remotely, in their orientation, movement, light emission, light spectrum, sensor etc.

The light source may be selected from the following group or a combination thereof: light emitting diode (LED), super-luminescent laser diode (LD), VSECL laser diode array.

In some embodiments, the LIDAR Sensor System may comprise a sensor, such as a resistive, a capacitive, an inductive, a magnetic, an optical and/or a chemical sensor. It may comprise a voltage or current sensor. The sensor may connect to the interface unit and/or the driver of the LIDAR light source.

In some embodiments, the LIDAR Sensor System and/or LIDAR Sensor Device comprise a brightness sensor, for example for sensing environmental light conditions in proximity of vehicle objects, such as houses, bridges, sign posts, and the like. It may be used for sensing daylight conditions and the sensed brightness signal may e.g. be used to improve surveillance efficiency and accuracy. That way, it may be enabled to provide the environment with a required amount of light of a predefined wavelength.

In some embodiments, the LIDAR Sensor System and/or LIDAR Sensor Device comprises a sensor for vehicle movement, position and orientation. Such sensor data may allow a better prediction, as to whether the vehicle steering conditions and methods are sufficient.

The LIDAR Sensor System and/or LIDAR Sensor Device may also comprise a presence sensor. This may allow to adapt the emitted light to the presence of another traffic participant including pedestrians in order to provide sufficient illumination, prohibit or minimize eye damage or skin irritation or such due to illumination in harmful or invisible wavelength regions, such as UV or IR. It may also be enabled to provide light of a wavelength that may warn or frighten away unwanted presences, e.g. the presence of animals such as pets or insects.

In some embodiments, the LIDAR Sensor System and/or LIDAR Sensor Device comprises a sensor or multi-sensor for predictive maintenance and/or operation of the LIDAR Sensor System and/or LIDAR Sensor Device failure.

In some embodiments, the LIDAR Sensor System and/or LIDAR Sensor Device comprises an operating hour meter. The operating hour meter may connect to the driver.

The LIDAR Sensor System may comprise one or more actuators for adjusting the environmental surveillance conditions for the LIDAR Sensor Device (e.g. vehicle). For instance, it may comprise actuators that allow adjusting for instance, laser pulse shape, temporal length, rise- and fall times, polarization, laser power, laser type (IR-diode, VCSEL), Field of View (FOV), laser wavelength, beam changing device (MEMS, DMD, DLP, LCD, Fiber), beam and/or sensor aperture, sensor type (PN-diode, APD, SPAD).

While the sensor or actuator has been described as part of the LIDAR Sensor System and/or LIDAR Sensor Device, it is understood, that any sensor or actuator may be an individual element or may form part of a different element of the LIDAR Sensor System. As well, it may be possible to provide an additional sensor or actuator, being configured to perform or performing any of the described activities as individual element or as part of an additional element of the LIDAR Sensor System.

In some embodiments, the LIDAR Sensor System and/or LIDAR Light Device further comprises a light control unit that connects to the interface unit.

The light control unit may be configured to control the at least one light module for operating in at least one of the following operation modes: dimming, pulsed, PWM, boost, irradiation patterns, including illuminating and non-illuminating periods, light communication (including C2C and C2X), synchronization with other elements of the LIDAR Sensor System, such as a second LIDAR Sensor Device.

The interface unit of the LIDAR Sensor System and/or LIDAR Sensor Device may comprise a gateway, such as a wireless gateway, that may connect to the light control unit. It may comprise a beacon, such as a Bluetoothâ¢ beacon.

The interface unit may be configured to connect to other elements of the LIDAR Sensor System, e.g. one or more other LIDAR Sensor Systems and/or LIDAR Sensor Devices and/or to one or more sensors and/or one or more actuators of the LIDAR Sensor System.

The interface unit may be configured to be connected by any wireless or wireline connectivity, including radio and/or optical connectivity.

The LIDAR Sensor System and/or LIDAR Sensor Device may be configured to enable customer-specific and/or vehicle-specific light spectra. The LIDAR Sensor Device may be configured to change the form and/or position and/or orientation of the at least one LIDAR Sensor System. Further, the LIDAR Sensor System and/or LIDAR Sensor Device may be configured to change the light specifications of the light emitted by the light source, such as direction of emission, angle of emission, beam divergence, color, wavelength, and intensity as well as other characteristics like laser pulse shape, temporal length, rise- and fall times, polarization, pulse synchronization, pulse synchronization, laser power, laser type (IR-diode, VCSEL), Field of View (FOV), laser wavelength, beam changing device (MEMS, DMD, DLP, LCD, Fiber), beam and/or sensor aperture, sensor type (PN-diode, APD, SPAD).

In some embodiments, the LIDAR Sensor System and/or LIDAR Sensor Device may comprise a data processing unit. The data processing unit may connect to the LIDAR light driver and/or to the interface unit. It may be configured for data processing, for data and/or signal conversion and/or data storage. The data processing unit may advantageously be provided for communication with local, network-based or web-based platforms, data sources or providers, in order to transmit, store or collect relevant information on the light module, the road to be travelled, or other aspects connected with the LIDAR Sensor System and/or LIDAR Sensor Device.

In some embodiments, the LIDAR Sensor Device can encompass one or many LIDAR Sensor Systems that themselves can be comprised of infrared or visible light emitting modules, photoelectric sensors, optical components, interfaces for data communication, actuators, like MEMS mirror systems, computing and data storage devices, software and software databank, communication systems for communication with IoT, edge or cloud systems.

The LIDAR Sensor System and/or LIDAR Sensor Device can further include light emitting and light sensing elements that can be used for illumination purposes, like road lighting, or for data communication purposes, for example car-to-car, car-to-environment (for example drones, pedestrian, traffic signs, traffic posts etc.).

The LIDAR Sensor Device can further comprise one or more LIDAR Sensor Systems as well as other sensor systems, like optical camera sensor systems (CCD; CMOS), RADAR sensing system, and ultrasonic sensing systems.

The LIDAR Sensor Device can be functionally designed as vehicle headlight, rear light, side light, daytime running light (DRL), corner light etc. and comprise LIDAR sensing functions as well as visible illuminating and signaling functions.

The LIDAR Sensor System may further comprise a control unit (Controlled LIDAR Sensor System). The control unit may be configured for operating a management system. It is configured to connect to one or more LIDAR Sensor Systems and/or LIDAR Sensor Devices. It may connect to a data bus. The data bus may be configured to connect to an interface unit of an LIDAR Sensor Device. As part of the management system, the control unit may be configured for controlling an operating state of the LIDAR Sensor System and/or LIDAR Sensor Device.

The LIDAR Sensor Management System may comprise a light control system which may comprise any of the following elements: monitoring and/or controlling the status of the at least one LIDAR Sensor System and/or LIDAR Sensor Device, monitoring and/or controlling the use of the at least one LIDAR Sensor System and/or LIDAR Sensor Device, scheduling the lighting of the at least one LIDAR Sensor System and/or LIDAR Sensor Device, adjusting the light spectrum of the at least one LIDAR Sensor System and/or LIDAR Sensor Device, defining the light spectrum of the at least one LIDAR Sensor System and/or LIDAR Sensor Device, monitoring and/or controlling the use of at least one sensor of the at least one LIDAR Sensor System and/or LIDAR Sensor Device.

In some embodiments, the method for LIDAR Sensor System can be configured and designed to select, operate and control, based on internal or external data input, laser power, pulse shapes, pulse length, measurement time windows, wavelength, single wavelength or multiple wavelength approach, day and night settings, sensor type, sensor fusion, as well as laser safety functions according to relevant safety regulations.

The method for LIDAR Sensor Management System can be configured to initiate data encryption, data decryption and data communication protocols.

LIDAR Sensor System, Controlled LIDAR Sensor System, LIDAR Sensor Management System and Software

In a Controlled LIDAR Sensor System according to the present disclosure, the computing device may be locally based, network based, and/or cloud-based. That means, the computing may be performed in the Controlled LIDAR Sensor System or on any directly or indirectly connected entities. In the latter case, the Controlled LIDAR Sensor System is provided with some connecting means, which allow establishment of at least a data connection with such connected entities.

In some embodiments, the Controlled LIDAR Sensor System comprises a LIDAR Sensor Management System connected to the at least one hardware interface. The LIDAR Sensor Management System may comprise one or more actuators for adjusting the surveillance conditions for the environment. Surveillance conditions may, for instance, be vehicle speed, vehicle road density, vehicle distance to other objects, object type, object classification, emergency situations, weather conditions, day or night conditions, day or night time, vehicle and environmental temperatures, and driver biofeedback signals.

The present disclosure further comprises an LIDAR Sensor Management Software. The present disclosure further comprises a data storage device with the LIDAR Sensor Management Software, wherein the data storage device is enabled to run the LIDAR Sensor Management Software. The data storage device may either comprise be a hard disk, a RAM, or other common data storage utilities such as USB storage devices, CDs, DVDs and similar.

The LIDAR Sensor System, in particular the LIDAR Sensor Management Software, may be configured to control the steering of Automatically Guided Vehicles (AGV).

In some embodiments, the computing device is configured to perform the LIDAR Sensor Management Software.

The LIDAR Sensor Management Software may comprise any member selected from the following group or a combination thereof: software rules for adjusting light to outside conditions, adjusting the light intensity of the at least one LIDAR Sensor System and/or LIDAR Sensor Device to environmental conditions, adjusting the light spectrum of the at least one LIDAR Sensor System and/or LIDAR Sensor Device to environmental conditions, adjusting the light spectrum of the at least one LIDAR Sensor System and/or LIDAR Sensor Device to traffic density conditions, adjusting the light spectrum of the at least one LIDAR Sensor System and/or LIDAR Sensor Device according to customer specification or legal requirements.

According to some embodiments, the Controlled LIDAR Sensor System further comprises a feedback system connected to the at least one hardware interface. The feedback system may comprise one or more sensors for monitoring the state of surveillance for which the Controlled LIDAR Sensor System is provided. The state of surveillance may for example, be assessed by at least one of the following: road accidents, required driver interaction, Signal-to-Noise ratios, driver biofeedback signals, close encounters, fuel consumption, and battery status.

The Controlled LIDAR Sensor System may further comprise a feedback software.

The feedback software may in some embodiments comprise algorithms for vehicle (LIDAR Sensor Device) steering assessment on the basis of the data of the sensors.

The feedback software of the Controlled LIDAR Sensor System may in some embodiments comprise algorithms for deriving surveillance strategies and/or lighting strategies on the basis of the data of the sensors.

The feedback software of the Controlled LIDAR Sensor System may in some embodiments of the present disclosure comprise LIDAR lighting schedules and characteristics depending on any member selected from the following group or a combination thereof: road accidents, required driver interaction, Signal-to-Noise ratios, driver biofeedback signals, close encounters, road warnings, fuel consumption, battery status, other autonomously driving vehicles.

The feedback software may be configured to provide instructions to the LIDAR Sensor Management Software for adapting the surveillance conditions of the environment autonomously.

The feedback software may comprise algorithms for interpreting sensor data and suggesting corrective actions to the LIDAR Sensor Management Software.

In some embodiments of the LIDAR Sensor System, the instructions to the LIDAR Sensor Management Software are based on measured values and/or data of any member selected from the following group or a combination thereof: vehicle (LIDAR Sensor Device) speed, distance, density, vehicle specification and class.

The LIDAR Sensor System therefore may have a data interface to receive the measured values and/or data. The data interface may be provided for wire-bound transmission or wireless transmission. In particular, it is possible that the measured values or the data are received from an intermediate storage, such as a cloud-based, web-based, network-based or local type storage unit.

Further, the sensors for sensing environmental conditions may be connected with or interconnected by means of cloud-based services, often also referred to as Internet of Things.

In some embodiments, the Controlled LIDAR Sensor System comprises a software user interface (UI), particularly a graphical user interface (GUI). The software user interface may be provided for the light control software and/or the LIDAR Sensor Management Software and/or the feedback software.

The software user interface (UI) may further comprise a data communication and means for data communication for an output device, such as an augmented and/or virtual reality display.

The user interface may be implemented as an application for a mobile device, such as a smartphone, a tablet, a mobile computer or similar devices.

The Controlled LIDAR Sensor System may further comprise an application programming interface (API) for controlling the LIDAR Sensing System by third parties and/or for third party data integration, for example road or traffic conditions, street fares, energy prices, weather data, GPS.

In some embodiments, the Controlled LIDAR Sensor System comprises a software platform for providing at least one of surveillance data, vehicle (LIDAR Sensor Device) status, driving strategies, and emitted sensing light.

In some embodiments, the LIDAR Sensor System and/or the Controlled LIDAR Sensor System can include infrared or visible light emitting modules, photoelectric sensors, optical components, interfaces for data communication, and actuators, like MEMS mirror systems, a computing and data storage device, a software and software databank, a communication system for communication with IoT, edge or cloud systems.

The LIDAR Sensor System and/or the Controlled LIDAR Sensor System can include light emitting and light sensing elements that can be used for illumination or signaling purposes, like road lighting, or for data communication purposes, for example car-to-car, car-to-environment.

In some embodiments, the LIDAR Sensor System and/or the Controlled LIDAR Sensor System may be installed inside the driver cabin in order to perform driver monitoring functionalities, such as occupancy-detection, eye-tracking, face recognition, drowsiness detection, access authorization, gesture control, etc.) and/or to communicate with a Head-up-Display HUD).

The software platform may cumulate data from one's own or other vehicles (LIDAR Sensor Devices) to train machine learning algorithms for improving surveillance and car steering strategies.

The Controlled LIDAR Sensor System may also comprise a plurality of LIDAR Sensor Systems arranged in adjustable groups.

The present disclosure further refers to a vehicle (LIDAR Sensor Device) with at least one LIDAR Sensor System. The vehicle may be planned and build particularly for integration of the LIDAR Sensor System. However, it is also possible, that the Controlled LIDAR Sensor System was integrated in a pre-existing vehicle. According to the present disclosure, both cases as well as a combination of these cases shall be referred to.

Method for a LIDAR Sensor System

According to yet another aspect of the present disclosure, a method for a LIDAR Sensor System is provided, which comprises at least one LIDAR Sensor System. The method may comprise the steps of controlling the light emitted by the at least one LIDAR Sensor System by providing light control data to the hardware interface of the Controlled LIDAR Sensor System and/or sensing the sensors and/or controlling the actuators of the Controlled LIDAR Sensor System via the LIDAR Sensor Management System.

<div id="p-0117" num="0116" cl

CLAIMS

Claims ( 21 )

1 . A non-transitory computer readable medium having instructions tangibly stored thereon, wherein when executed by a processing entity, the instructions cause the processing entity to carry out a method of evaluating influence of an action performed by an external entity, the method comprising:

receiving sensor data; determining a signal reliability factor for the received sensor data, wherein the signal reliability factor represents a statistical quality indication between the received sensor data and an expected value or an expected range of values; and associating the signal reliability factor with the received sensor data.

2 . The computer readable medium of claim 1 , the method further comprising:

determining if the signal reliability factor satisfies criteria; and discarding the received sensor data or using the received sensor data to generate one or more commands which are configured to control a central control system, depending on whether the signal reliability factor satisfies the criteria.

3 . The computer readable medium of claim 2 , wherein the criteria are satisfied if the signal reliability factor is less than a predefined threshold or the signal reliability factor is between a lower threshold and an upper threshold.

4 . The computer readable medium of claim 1 , wherein the expected value or the expected range of values is based on immediately preceding sensor data, the immediately preceding sensor data being received at an antecedent time point with respect to a time point when the sensor data is received.

5 . The computer readable medium of claim 4 , wherein a sensor system that generates the immediately preceding sensor data is identical to a sensor system that generates the received sensor data.

6 . The computer readable medium of claim 4 , in response to the signal reliability factor indicating that the received sensor data is incoherent with the immediately preceding sensor data, the method further comprises evaluating influence of the action performed by the external entity based on the signal reliability factor.

7 . The computer readable medium of claim 1 , wherein the received sensor data is first sensor data generated by a first sensor system, and the method further comprises receiving second sensor data from a second sensor system; and statistically inferring the expected value or the expected range of values based on the second sensor data.

8 . The computer readable medium of claim 1 , wherein the processing entity is associated with a vehicle, and the method further comprises determining a vehicle condition associated with the vehicle; and statistically inferring the expected value or the expected range of values based on the vehicle condition.

9 . The computer readable medium of claim 8 , wherein the vehicle condition includes at least one of a location selective category, an environmental setting, and a driving status.

10 . The computer readable medium of claim 8 , the method further comprising determining a sensor fusion priority corresponding to the vehicle condition, wherein the sensor fusion priority defines a particular number and combination of sensor systems to be used.

11 . The computer readable medium method of claim 1 , wherein the sensor data is generated by one or more sensor systems, the method further comprising changing a configuration of the one or more sensor systems based on the signal reliability factor.

12 . The computer readable medium method of claim 11 , wherein changing the configuration of the one or more sensor systems comprises deactivating and/or deprioritizing at least one of the one or more sensor systems.

13 . The computer readable medium method of claim 1 , wherein the signal reliability factor indicates a probability of the received sensor data being influenced by the action performed by the external entity.

14 . The computer readable medium method of claim 1 , wherein determining the signal reliability factor comprises applying statistical rules to the received sensor data and the expected value or the expected range to generate the statistical quality indication, wherein the statistical rules include Bayesian rulings or position-coded rulings.

15 . A system comprising:

a processor in communication with a memory, the processor configured to execute instructions in the memory to cause the system to: receive sensor data; determine a signal reliability factor for the received sensor data, wherein the signal reliability factor represents a statistical quality indication between the received sensor data and an expected value or an expected range of values; and associating the signal reliability factor with the received sensor data.

16 . The system of claim 15 , wherein the processor is further configured to execute the instructions in the memory to cause the system to determine if the signal reliability factor satisfies criteria;

and to discard the received sensor data or to use the received sensor data to generate one or more commands which are configured to control a central control system, depending on whether the signal reliability factor satisfies the criteria.

17 . The system of claim 15 , wherein the expected value or the expected range of values is based on immediately preceding sensor data, the immediately preceding data being received at an antecedent time point with respect to a time point when the sensor data is received.

18 . The system of claim 15 , wherein the processor is further configured to execute the instructions in the memory to cause the system to, in response to the signal reliability factor indicating that the received sensor data is incoherent with the immediately preceding sensor data, evaluate influence of the action performed by the external entity based on the signal reliability factor.

19 . The system of claim 15 , wherein the received sensor data is first sensor data generated by a first sensor system, and the processor is further configured to execute the instructions in the memory to cause the system to receive second sensor data from a second sensor system; and statistically infer the expected value or the expected range of values based on the second sensor data.

20 . The system of claim 15 , wherein the system is associated with a vehicle, and the processor is further configured to execute the instructions in the memory to cause the system to determine a vehicle condition associated with the vehicle; and statistically infer the expected value or the expected range of values based on the vehicle condition.

21 . The system of claim 15 , wherein the processor is further configured to execute the instructions in the memory to cause the system to determine the signal reliability factor by applying statistical rules to the received sensor data and the expected value or the expected range of values to generate the statistical quality indication, wherein the statistical rules include Bayesian rulings or position-coded rulings.

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