title: CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous
    Driving
summary: Vehicle-to-vehicle (V2V) cooperative autonomous driving holds great promise
  for improving safety by addressing the perception and prediction uncertainties
  inherent in single-agent systems. However, traditional cooperative methods are
  constrained by rigid collaboration protocols and limited generalization to
  unseen interactive scenarios. While LLM-based approaches offer generalized
  reasoning capabilities, their challenges in spatial planning and unstable
  inference latency hinder their direct application in cooperative driving. To
  address these limitations, we propose CoLMDriver, the first full-pipeline
  LLM-based cooperative driving system, enabling effective language-based
  negotiation and real-time driving control. CoLMDriver features a parallel
  driving pipeline with two key components: (i) an LLM-based negotiation module
  under an actor-critic paradigm, which continuously refines cooperation policies
  through feedback from previous decisions of all vehicles; and (ii) an
  intention-guided waypoint generator, which translates negotiation outcomes into
  executable waypoints. Additionally, we introduce InterDrive, a CARLA-based
  simulation benchmark comprising 10 challenging interactive driving scenarios
  for evaluating V2V cooperation. Experimental results demonstrate that
  CoLMDriver significantly outperforms existing approaches, achieving an 11%
  higher success rate across diverse highly interactive V2V driving scenarios.
  Code will be released on https://github.com/cxliu0314/CoLMDriver.
paper_authors._id: 67da5a69f182e9e349a04ca5
paper_authors.name: Changxing Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-19T09:44:07.149Z
paper_authors._id: 66484213e744424922b2edd0
paper_authors.avatarUrl: /avatars/5318ba5edd7cab27649fdc012ab9118e.svg
paper_authors.fullname: Changxing Liu
paper_authors.type: user
paper_authors.user: cxliu0314
paper_authors._id: 67da5a69f182e9e349a04ca6
paper_authors.name: Genjia Liu
paper_authors._id: 67da5a69f182e9e349a04ca7
paper_authors.name: Zijun Wang
paper_authors._id: 67da5a69f182e9e349a04ca8
paper_authors.name: Jinchang Yang
paper_authors._id: 67da5a69f182e9e349a04ca9
paper_authors.name: Siheng Chen
paper_title: CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous
    Driving
paper_summary: Vehicle-to-vehicle (V2V) cooperative autonomous driving holds great promise
  for improving safety by addressing the perception and prediction uncertainties
  inherent in single-agent systems. However, traditional cooperative methods are
  constrained by rigid collaboration protocols and limited generalization to
  unseen interactive scenarios. While LLM-based approaches offer generalized
  reasoning capabilities, their challenges in spatial planning and unstable
  inference latency hinder their direct application in cooperative driving. To
  address these limitations, we propose CoLMDriver, the first full-pipeline
  LLM-based cooperative driving system, enabling effective language-based
  negotiation and real-time driving control. CoLMDriver features a parallel
  driving pipeline with two key components: (i) an LLM-based negotiation module
  under an actor-critic paradigm, which continuously refines cooperation policies
  through feedback from previous decisions of all vehicles; and (ii) an
  intention-guided waypoint generator, which translates negotiation outcomes into
  executable waypoints. Additionally, we introduce InterDrive, a CARLA-based
  simulation benchmark comprising 10 challenging interactive driving scenarios
  for evaluating V2V cooperation. Experimental results demonstrate that
  CoLMDriver significantly outperforms existing approaches, achieving an 11%
  higher success rate across diverse highly interactive V2V driving scenarios.
  Code will be released on https://github.com/cxliu0314/CoLMDriver.
paper_ai_summary: CoLMDriver, an LLM-based system, enhances cooperative driving by enabling language-based negotiation and real-time control, outperforming existing methods in V2V scenarios.
paper_ai_keywords: LLM-based actor-critic paradigm intention-guided waypoint generator CARLA-based simulation benchmark