Safety Critical Autonomous Driving

Safety-critical autonomous driving focuses on developing reliable and verifiable systems for self-driving vehicles, prioritizing safety above all else. Current research emphasizes hybrid approaches combining model-based planning and control with machine learning components, such as graph neural networks for scene understanding and trajectory optimization algorithms incorporating safety constraints (e.g., barrier functions). These advancements aim to address limitations of purely end-to-end learning methods and improve the robustness and explainability of autonomous driving systems, ultimately contributing to safer and more reliable deployment of self-driving technology.