Definition and scope of ADAS – Classification of automation levels (SAE) – Sensor technologies: camera, radar, lidar, ultrasonic – Sensor fusion methods in modern vehicles – Automotive safety standards (ISO 26262) – Features: lane departure, adaptive cruise, automated emergency braking, parking assist – Actuator roles in automated driving – Vehicle architecture for driver assistance systems – Case studies: Tesla Autopilot, Waymo Driver

Camera-based object recognition – Pedestrian and traffic sign detection – Image datasets for vehicle training: KITTI, Waymo, nuScenes – Machine learning and deep learning use in recognition – Automated annotation and data labelling tools – Model testing in various lighting and weather conditions – Generative AI for scenario creation and training – Interpretation of AI outputs for safety – Pedestrian alert system application

Rule-based control systems – Trajectory generation algorithms – Adaptive cruise with environment sensing – Lane centering and lane change logic – Collision avoidance strategies based on agentic AI – Multi-vehicle communication (V2V, V2I) – Control improvement through reinforcement learning – Operational performance metrics for ADAS – Overtaking assistant system simulation

Simulation platforms: MATLAB, CARLA, ROS – Hardware-in-the-loop integration – Data collection protocols for automotive AI – Cybersecurity measures in connected vehicles – CAN, Ethernet, and FlexRay network applications – Vehicle-to-everything (V2X) communication – Standard validation and verification procedures – Case examples from industry for system integration – Blind spot detection and warning implementation

Night vision and panoramic camera applications – Alert systems for drowsiness and distraction – Infotainment and voice personalization using AI – Smart prognostics for maintenance prediction – Electric vehicle and autonomous system convergence – OTA (over-the-air) software update systems – Regulatory guidelines for autonomous vehicles – Collaborative human–AI interfaces – Deployment of AI safety functions in electric mobility