arXiv (All)
open access
Edge Deployable Lightweight Framework for Real-Time Driver Safety System with Integrated Lane Departure and Traffic Sign Recognition
Lane departure and traffic-sign negligence are leading contributors to road accidents, particularly in regions with non-standardized signage and limited annotated data. Existing Advanced Driver Assistance Systems (ADAS) solutions are often developed as disjointed submodules, are rarely validated for
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Comparative Study of Driving Performance Among Adolescents With Autism Spectrum Disorder and Neurotypical Adolescents#1127257
- Automated ADAS Testing Using Automata Learning: A Case Study in Learning-Based Testing#1360652
- Adapting Vision-Language Models for Human-Readable XAI in Industrial Object Detection#1115646
- Augmentor: a cognitive-inspired approach for context-aware adaptive learning with human guidance#990040
- Importancia del mantenimiento vehicular en el desarrollo de las empresas de transporte de carga pesada#658270
Record · ID 1400170
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive