Dense Traffic

Dense traffic presents a significant challenge for autonomous vehicle (AV) navigation, demanding efficient and safe decision-making in complex, dynamic environments. Current research focuses on developing advanced planning algorithms, such as model predictive control and reinforcement learning, often integrated with deep learning models for perception and prediction, to enable AVs to navigate dense traffic safely and efficiently. These methods are being enhanced through techniques like knowledge distillation for improved computational efficiency and the incorporation of game theory and social interaction models for more realistic behavior prediction. The resulting improvements in AV navigation have significant implications for traffic flow optimization, safety enhancement, and the broader development of autonomous driving technology.