Outline

Camera, LiDAR, Radar, Ultrasonic, and GPS hardware overview • Multi-modal sensor fusion for robust environmental understanding • Sensor calibration, synchronization, and data acquisition • Perception challenges in adverse weather and dynamic conditions

Outline

Deep learning architectures for mobility systems • Object detection, lane tracking, and semantic segmentation • Scene understanding and human behavior recognition • Reliability, safety, and model evaluation metrics

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Principles of vehicle positioning and localization • Simultaneous Localization and Mapping (SLAM) techniques • High-Definition (HD) maps and route planning layers • Navigation in structured urban and unstructured off-road environments

Outline

Motion planning and trajectory generation algorithms • Vehicle control: steering, braking, and acceleration feedback loops • AI decision-making in complex, high-traffic scenarios • Obstacle avoidance and road-user interaction modeling

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Real-time computing and low-latency architecture • Embedded AI hardware (NVIDIA Jetson, SoC) and software stacks • Edge inference optimization and model pruning • System integration: power, memory, and thermal constraints

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Functional safety and fail-safe system design • Cybersecurity for connected and autonomous fleets • Ethical decision-making and AI accountability • Regulatory frameworks, global standards, and validation

Outline

Case studies: Autonomous cars vs. delivery robots • AI in driver monitoring and advanced ADAS • Virtual validation and hardware-in-the-loop (HIL) workflows • Future: V2X (Vehicle-to-Everything) and connected mobility