Advanced Driver Assistance Systems (ADAS)

Week 1: ADAS Fundamentals

• Introduction to ADAS: Evolution, SAE autonomy levels (L0-L2), and key applications like adaptive cruise control (ACC) and lane departure warning

• Core sensors overview: Cameras, radar, LiDAR, ultrasonic; principles, data types, and basic limitations

• Vehicle communication basics: CAN bus, LIN, and ECU roles in ADAS integration

• Safety standards intro: ISO 26262 ASIL levels and basic functional safety concepts

Week 2: Basic Perception and Control

• Image processing essentials: Edge detection, filtering with OpenCV/Python for lane and object recognition

• Simple algorithms: ACC logic, emergency braking (AEB), and blind-spot detection implementation

• Sensor data preprocessing: Calibration, noise reduction, and introductory fusion techniques

• Model-based design basics: MATLAB/Simulink for ADAS simulation and code generation

Week 3: Advanced Perception and AI

• Deep learning for ADAS: YOLO/TensorFlow for real-time object detection, semantic segmentation, and traffic sign recognition

• Multi-sensor fusion: Kalman filters, probabilistic methods, and AI-driven fusion for robust perception

• Behavioral planning: Path prediction, decision trees, and trajectory optimization using Python frameworks

• Edge computing: Deploying lightweight models (e.g., TensorRT) on ECUs for low-latency inference

Week 4: Validation and Deployment

• Testing frameworks: MIL/SIL/HIL/DIL with tools like Vector CANoe, dSPACE, and fault injection

• Cybersecurity in ADAS: ISO 21434 compliance, secure OTA updates, and threat modeling

• Advanced autonomy: L3+ planning with reinforcement learning, V2X communication, and end-to-end neural networks

• Capstone project: Build and validate a full ADAS pipeline (e.g., sensor fusion to control) with industry standards review