International Journal of Science, Engineering and TechnologyAuthors: Wajage Aryan Sujit, Prof. S. S. Bhor, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Advanced Driver Assistance Systems (ADAS) use cameras, radar, LiDAR, ultrasonic sensors and artificial intelligence to monitor a vehicle's surroundings and help drivers avoid crashes. Features such as automatic emergency braking, lane departure warning and lane keeping, adaptive cruise control, blind-spot detection, parking assistance and collision avoidance form the building blocks of vehicle automation, which ranges from basic assistance to highly automated driving. This paper reviews the role of AI in ADAS and vehicle automation. It explains the levels of driving automation, the sensor suite and its trade-offs, and the AI techniques used for lane detection, object detection on camera images and LiDAR point clouds, bird's-eye-view perception and multi sensor fusion. Evidence on the safety benefits of ADAS features is discussed, together with the difficulty of demonstrating the reliability of highly automated systems. Safety and security engineering, including functional safety, safety of the intended functionality, cybersecurity standards and adversarial attacks on perception, are examined. A layered ADAS architecture covering sensing, perception, fusion and prediction, planning and decision, and control and human–machine interface is proposed, with research gaps, objectives and a comparison of sensors. The review concludes that AI-driven ADAS can greatly reduce road accidents, provided that cost, adverse weather, processing demands and cyber-physical security are addressed.
DOI: https://doi.org/10.5281/zenodo.24217776
