Hybrid Data- and Physical Model-driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicle motion planning and control are vital components of next-generation intelligent transportation systems. Recent advances in both data- and physical model-driven methods have improved driving performance, yet current technologies still fall short of achieving human-level driving in complex, dynamic traffic scenarios. Key challenges include developing safe, efficient, and human-like motion planning strategies that can adapt to unpredictable environments. Data-driven approaches leverage deep neural networks to learn from extensive datasets, offering promising avenues for intelligent decision-making. However, these methods face issues such as covariate shift in imitation learning and difficulties in designing robust reward functions. In contrast, conventional physical model-driven techniques use rigorous mathematical formulations to generate optimal trajectories and handle dynamic constraints.</div><div class="htmlview paragraph"><b>Hybrid Data- and Physical Model-Driven Safe and Intelligent Motion Planning and Control for Autonomous Vehicles</b> presents a hybrid framework that combines data-driven insights with the robustness of physical models. It identifies key challenges in fusing these disparate methods and outlines potential solutions in developing robust fusion strategies, establishing generalized mixed dynamics models, and designing multi-objective robust control systems. In addition, the report explores future research directions to enhance learning efficiency, improve adaptability to rare but critical scenarios, and ultimately pave the way for secure, efficient, and human-like autonomous driving systems.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a>^TM<a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>