Autonomous Traffic

Autonomous traffic research focuses on developing safe and efficient systems for self-driving vehicles, addressing challenges in trajectory planning, human-vehicle interaction, and traffic management. Current research employs diverse methods including reinforcement learning (particularly multi-agent approaches like MAPPO), game theory for modeling human driver behavior, and attention mechanisms for improved perception (e.g., road defect detection). These advancements aim to improve traffic flow, enhance safety by mitigating robotic uncertainties, and personalize driving experiences, ultimately impacting transportation systems and urban planning.