Talking Vehicles: Cooperative Driving via Natural Language
Keywords: multi-agent learning, communication, llm, autonomous driving
TL;DR: We constrain the multi-vehicle communicate to be natural language and attempted to solve it through LLM self-play debriefing
Abstract: Using natural language as a vehicle-to-vehicle (V2V) communication protocol offers the potential for autonomous vehicles to drive cooperatively not only with each other but also with human drivers. Simple and effective messages for sharing critical observations or negotiating plans to achieve coordination could improve traffic safety and efficiency compared to methods without communication. In this work, we propose a suite of traffic tasks in vehicle-to-vehicle autonomous driving where vehicles in a traffic scenario need to communicate in natural language to facilitate coordination in order to avoid an imminent collision and/or support efficient traffic flow, which we model as a general-sum partially observable stochastic game. To this end, this paper introduces a novel method, LLM+Debrief, to learn a message generation and control policy for autonomous vehicles through multi-agent discussion. To evaluate our method, we developed a gym-like simulation environment that contains a range of accident-prone driving scenarios that could be alleviated by communication. Our experimental results demonstrate that our method is more effective at generating meaningful and human-understandable natural language messages to facilitate cooperation and coordination than untrained LLMs. Our anonymous code is available in supplementary materials.
Supplementary Material: zip
Primary Area: applications to robotics, autonomy, planning
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Submission Number: 569
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