Multi Agent Autonomous Driving

Multi-agent autonomous driving research focuses on developing algorithms enabling multiple self-driving vehicles to safely and efficiently navigate complex environments, requiring sophisticated coordination and interaction models. Current efforts concentrate on improving perception and prediction using techniques like conditional latent ODEs and hierarchical multi-agent systems incorporating large language models for high-level planning and lightweight controllers for low-level execution. This field is crucial for advancing the safety and efficiency of autonomous vehicle technology, impacting both the development of robust driving policies and the creation of realistic simulation environments for training and testing.