V2X-WAM：一种用于端到端自动驾驶的协同世界动作模型

中文摘要 AI 辅助

车路协同可以通过更广泛、更丰富的交通环境观测来补充车载感知，为端到端自动驾驶提供有价值的支持。然而，现有的协同驾驶方法主要利用路侧信息来增强当前场景的表示，而很少显式地对未来驾驶动作的后果进行建模。这限制了规划器预测其决策如何与不断演变的交通环境交互的能力。为解决这一问题，我们提出了V2X-WAM，一种协同世界动作模型，它紧密耦合了协同场景理解、动作生成和未来世界推理。V2X-WAM从车辆和路侧观测中构建一个可靠性感知的时空表示，同时将路侧信息压缩为紧凑的量化消息以实现高效通信。基于所得的协同表示，一个多模态规划器生成预期轨迹，该轨迹显式地条件化未来的占用率和动态流预测。预测的世界后果随后被反馈以优化规划的轨迹，形成动作与未来世界演化之间的闭环交互。在大规模真实世界协同驾驶数据集上的实验表明，V2X-WAM在规划准确性和安全性方面持续优于代表性的端到端协同驾驶方法，同时实现了更强的未来世界预测和显著更低的通信开销。消融研究进一步验证了所提出设计的有效性。

英文摘要

Vehicle-infrastructure cooperation can complement onboard sensing with broader and more informative observations of the traffic environment, providing valuable support for end-to-end autonomous driving. However, existing cooperative driving methods mainly exploit roadside information to enhance the representation of the current scene, while the future consequences of prospective driving actions are rarely modeled explicitly. This limits the ability of the planner to anticipate how its decisions may interact with the evolving traffic environment. To address this issue, we propose V2X-WAM, a cooperative world action model that tightly couples cooperative scene understanding, action generation, and future-world reasoning. V2X-WAM constructs a reliability-aware spatiotemporal representation from vehicle- and infrastructure-side observations, while compressing infrastructure information into a compact quantized message for efficient communication. Based on the resulting cooperative representation, a multimodal planner generates prospective trajectories, which explicitly condition future occupancy and dynamic-flow prediction. The predicted world consequences are then fed back to refine the planned trajectory, forming a closed interaction between action and future-world evolution. Experiments on a large-scale real-world cooperative driving dataset demonstrate that V2X-WAM consistently improves planning accuracy and safety over representative end-to-end cooperative driving methods, while achieving stronger future-world prediction and substantially lower communication overhead. Ablation studies further validate the effectiveness of the proposed design.

发表机构

• ITS Center, Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院智能交通系统中心）
• State Key Lab of Intelligent Transportation System, Research Institute of Highway Ministry of Transport（交通运输部公路科学研究院智能交通系统国家重点实验室）
• Department of Civil and Environmental Engineering, University of Wisconsin–Madison（威斯康星大学麦迪逊分校土木与环境工程系）
• Intelligent Transportation Systems Research Center, Wuhan University of Technology（武汉理工大学智能交通系统研究中心）
• Engineering Research Center of Transportation Information and Safety, Ministry of Education（教育部交通信息与安全工程研究中心）
• School of Transportation, Inner Mongolia University（内蒙古大学交通学院）
• College of Transportation, Tongji University（同济大学交通运输工程学院）

机构由 AI 辅助整理，请以论文原文为准。