Driving Maneuver

Research on driving maneuvers focuses on developing accurate and efficient methods for predicting, planning, and executing complex driving actions in autonomous vehicles. Current efforts utilize diverse approaches, including deep learning models like transformers and diffusion models for trajectory prediction and generation, coupled with optimization algorithms such as model predictive control and ADMM for real-time planning and control. These advancements aim to improve the safety and reliability of autonomous driving systems by enabling vehicles to anticipate driver intent, navigate challenging scenarios, and execute maneuvers smoothly and safely. The resulting improvements in maneuver prediction and execution have significant implications for both the development of safer autonomous vehicles and the advancement of related fields like robotics and AI.