Vehicle Control

Vehicle control research focuses on developing algorithms and systems to safely and efficiently manage vehicle movement, encompassing tasks from basic path tracking to complex maneuvers in diverse traffic scenarios. Current research emphasizes integrating physics-based models with machine learning techniques, such as reinforcement learning and neural networks, to improve adaptability, robustness, and interpretability of control systems. This work is crucial for advancing autonomous driving, enhancing traffic flow, and improving vehicle safety and security, with applications ranging from collision avoidance to coordinated traffic management.