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