CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models

Researchers introduced CMU-Drive, a closed-loop end-to-end benchmark for evaluating cooperative autonomous driving with multiple connected autonomous vehicles in safety-critical scenarios, and V2V-VLA, a cooperative Vision-Language-Action model that jointly generates driving actions, future waypoints, language reasoning, and communication policies. The team will publicly release the code, benchmark, and model checkpoint to support open-source research.

arXiv:2608.07621v1 Announce Type: new Abstract: Vision-Language-Action VLA models have recently achieved impressive performance for end-to-end autonomous driving, yet existing approaches are primarily designed for an individual single autonomous driving agent with limited support for cooperative perception, reasoning, and planning. We present Cooperative Multi-agent Unified Driving with Reasoning CMU-Drive , a closed-loop end-to-end benchmark for evaluating cooperative autonomous driving with multiple connected autonomous vehicles CAVs operating in safety-critical driving scenarios with background traffic participants. We further propose Vehicle-to-Vehicle Vision-Language-Action V2V-VLA , a cooperative VLA model that integrates cooperative driving into a single forward pass by jointly generating driving actions, future waypoints, language reasoning, and communication policies. Experiments on CMU-Drive establish the first benchmark and baseline for cooperative VLA driving and provide a foundation for future research on multi-agent, closed-loop, end-to-end cooperative autonomous driving. Our code, benchmark, and model checkpoint will be publicly released to facilitate open-source research.