CMU-Drive and V2V-VLA: Cooperative Multi-agent Unified Driving with Reasoning Benchmark and Vehicle-to-Vehicle Vision-Language-Action Models
Advancing autonomous safety through multi-agent collaboration and unified vision-language-action reasoning.
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 pl...
ArXiv CS.AI
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This research marks a significant shift from isolated autonomous agents to a cooperative framework where vehicles communicate and reason together. By integrating multi-agent perception with language-based planning, V2V-VLA paves the way for safer, more synchronized traffic management in complex real-world scenarios.
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