Efficient and Interpretable Modular End-to-end Autonomous Driving System

The future of autonomous driving systems holds great promise, offering a solution to address the challenges associated with human errors and the mental fatigue of driving. However, there are trade-offs between the modularity (henceforth interpretability) and the efficiency in existing end-to-end modular autonomous driving models. In this PhD project, student is expected to conduct research in the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and planning modules to accurately predict driving trajectories and controls for high-fidelity autonomous driving. Additionally, student will apply sim-to-real adaptation strategies to generalize deep learning models for deployment in real-world driving scenarios.