CoLMIN is an LLM-based framework for cooperative autonomous driving that addresses premature convergence to suboptimal solutions in multi-solution traffic scenarios. It introduces a Multi-Intent Negotiation module that generates multiple candidate driving intentions, an Evaluation-based Shallow Reflection Module that provides feedback to accelerate consensus, and a Deep Reflection Module that mitigates cognitive fixation by reflecting on negotiation histories. Experiments in the CARLA simulation show that CoLMIN outperforms existing methods in challenging interactive driving scenarios.
By Zhe Huang, Zhaoxin Fan, Shuo Wang, Wenjun Wu, Xuan Zhao, Min Liu
arXiv:2608. 07621v1 Announce Type: new Abstract: 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 planning.
By Hsu-kuang Chiu, Stephen F. Smith
arXiv:2609.37098v1 Announce Type: cross
Abstract: Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providin...
By Junwei You, Weizhe Tang, Can Wang, Yan Zhao, Jun Hua, Haotian Shi, Wei Zhang, Lin Wang, Bin Ran
arXiv:2505. 18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other.
By Jiaxun Cui, Chen Tang, Jarrett Holtz, Janice Nguyen, Alessandro G. Allievi, Hang Qiu, Peter Stone
arXiv:2608. 20129v1 Announce Type: cross Abstract: Autonomous vehicles require robust perception and decision-making capabilities to operate in diverse and unseen scenarios.
By Mehdi Azarafza, Faezeh Pasandideh, Ali Ehteshami Bejnordi, Stefan Henkler, Achim Rettberg
arXiv:2508. 00917v2 Announce Type: replace-cross Abstract: Connected autonomous vehicles (CAVs) must simultaneously perform multiple tasks, such as perception, prediction, planning, and control, to ensure safe and reliable navigation in complex environments.
By Jiayuan Wang, Farhad Pourpanah, Q. M. Jonathan Wu, Ning Zhang
arXiv:2606. 30694v1 Announce Type: cross Abstract: Traffic signal control at urban intersections inherently introduces stop-and-go behavior, resulting in increased delays and reduced traffic efficiency, especially under high traffic demand.
By Qian Hu, Haoyang Peng, Songan Zhang, Ming Yang, Hongtei Eric Tseng
arXiv:2608.20890v1 Announce Type: new
Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving by jointly integrating perception, reasoning,...
By Jingtao Sun, Xiaohai He, Yike Zhang, Dong Huang, Yaonan Wang, Ajmal Mian, Mike Zheng Shou
arXiv:2608. 19964v1 Announce Type: new Abstract: Autonomous driving systems must operate under partial observability, where safety-critical objects may be occluded or visible only to neighboring connected vehicles.
By Bhavya Gupta, Onat Gungor, Tajana Rosing
arXiv:2609.35916v1 Announce Type: cross
Abstract: Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions fac...
By Jie Yang, Jiajun Chen, Jiazheng Zhou, Mianqiu Huang, Yining Zheng, Yuxin Wang, Xipeng Qiu
arXiv:2607. 21488v1 Announce Type: cross Abstract: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs.
By Gil Lifshits, Igal Bilik, Gilad Katz
arXiv:2609.24631v1 Announce Type: cross
Abstract: Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics...
By Zhengbao Yao, Yuanfu Luo, Kehan Xue