Autonomous Vehicle Vision Processing

via “physics-aware policy learning from high-dimensional visual observations”

* ⭐ 02/2022: [Magnetic control of tokamak plasmas through deep reinforcement learning](https://www.nature.com/articles/s41586-021-04301-9%E2%80%A6)

Unique: Trains end-to-end CNN policies directly on high-resolution camera images by leveraging Gran Turismo's differentiable physics engine, enabling gradient-based optimization of visual perception and control jointly rather than using separate perception and planning modules

vs others: Achieves better sample efficiency and generalization than modular approaches (separate perception + planning) because the visual features are optimized directly for control relevance rather than generic object detection

via “vision-based perception and processing”

via “autonomous-obstacle-detection-and-avoidance”

via “perception-based autonomous control”

via “autonomous-vehicle-specific-labeling”