Safe Autonomous Driving

Safe autonomous driving research centers on developing systems that reliably and safely navigate complex environments. Current efforts focus on improving perception (e.g., robust object detection and motion prediction using deep learning models like transformers and convolutional neural networks), planning (e.g., trajectory optimization incorporating safety constraints and social awareness), and control (e.g., reinforcement learning with human feedback and physics-based constraints). These advancements aim to enhance the trustworthiness and robustness of autonomous vehicles, ultimately contributing to safer and more efficient transportation systems.