Cooperative Autonomous Driving

Cooperative autonomous driving (CAD) aims to improve traffic efficiency and safety by enabling autonomous vehicles (AVs) to communicate and coordinate their actions. Current research heavily emphasizes end-to-end learning approaches, often utilizing large multimodal models (including vision-language models) and deep reinforcement learning, to integrate perception, decision-making, and motion planning into unified frameworks. These advancements leverage vehicle-to-everything (V2X) communication and data fusion techniques, addressing challenges like robust perception in complex environments and efficient multi-agent path planning. The ultimate goal is to create safer and more efficient transportation systems through enhanced situational awareness and coordinated vehicle behavior.