CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous
  Driving
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.
67da5a69f182e9e349a04ca5
Changxing Liu
claimed_verified
2025-03-19T09:44:07.149Z
66484213e744424922b2edd0
/avatars/5318ba5edd7cab27649fdc012ab9118e.svg
Changxing Liu
user
cxliu0314
67da5a69f182e9e349a04ca6
Genjia Liu
67da5a69f182e9e349a04ca7
Zijun Wang
67da5a69f182e9e349a04ca8
Jinchang Yang
67da5a69f182e9e349a04ca9
Siheng Chen
CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous
  Driving
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.
CoLMDriver, an LLM-based system, enhances cooperative driving by enabling language-based negotiation and real-time control, outperforming existing methods in V2V scenarios.
LLM-based actor-critic paradigm intention-guided waypoint generator CARLA-based simulation benchmark