V2X-WAM: A Cooperative World Action Model for End-to-End Autonomous Driving

2026-09-29

AI Summary

Existing cooperative driving methods lack explicit modeling of the future consequences of behaviors, limiting their planning capabilities in dynamic environments. This work proposes V2X-WAM, a model that tightly couples scene understanding, action generation, and future world reasoning to optimize end-to-end planning via closed-loop feedback. Key innovations include the first approach to compressing infrastructure information into quantized messages for substantially reduced communication overhead, the construction of reliability-aware spatiotemporal representations alongside a multimodal planner, and the establishment of a closed-loop interaction mechanism between actions and future occupancy and flow prediction. Evaluated on large-scale real-world datasets, the proposed model significantly improves planning accuracy, safety, and predictive performance.

Abstract

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.

Problem

• V2X cooperation
• end-to-end autonomous driving
• world action model
• trajectory planning
• future prediction

Innovation

• Cooperative World Action Model
• V2X Communication
• End-to-End Autonomous Driving
• Future-World Reasoning
• Quantized Message Compression