CMU-Drive and V2V-VLA: New Benchmark and Model for Cooperative Autonomous Driving
A recent paper on arXiv presents Cooperative Multi-agent Unified Driving with Reasoning (CMU-Drive), a comprehensive closed-loop benchmark designed to assess cooperative autonomous driving involving multiple connected autonomous vehicles (CAVs) in critical safety situations with surrounding traffic. Additionally, the authors introduce Vehicle-to-Vehicle Vision-Language-Action (V2V-VLA), a model that merges cooperative driving into a unified forward pass, simultaneously producing driving actions, future waypoints, language reasoning, and communication strategies. The experiments conducted on CMU-Drive establish the initial benchmark and baseline for cooperative VLA driving. This paper can be found on arXiv under the identifier 2608.07621.
Key facts
- CMU-Drive is a closed-loop end-to-end benchmark for cooperative autonomous driving.
- It evaluates multiple connected autonomous vehicles (CAVs) in safety-critical scenarios with background traffic.
- V2V-VLA is a cooperative Vision-Language-Action model.
- V2V-VLA integrates cooperative driving into a single forward pass.
- It jointly generates driving actions, future waypoints, language reasoning, and communication policies.
- Experiments on CMU-Drive establish the first benchmark and baseline for cooperative VLA driving.
- The paper is published on arXiv with identifier 2608.07621.
- The announcement type is new.
Entities
Institutions
- arXiv