Motion forecasting is an essential task for autonomous driving, and the effective information utilization from infrastructure and other vehicles can enhance motion forecasting capabilities. Existing research have primarily focused on leveraging single-frame cooperative information to enhance the limited perception capability of the ego vehicle, while underutilizing the motion and interaction information of traffic participants observed from cooperative devices. In this paper, we first propose the cooperative trajectory representations learning paradigm. Specifically, we present V2X-Graph, the first interpretable and end-to-end learning framework for cooperative motion forecasting. V2X-Graph employs an interpretable graph to fully leverage the cooperative motion and interaction contexts. Experimental results on the vehicle-to-infrastructure (V2I) motion forecasting dataset, V2X-Seq, demonstrate the effectiveness of V2X-Graph. To further evaluate on V2X scenario, we construct the first real-world vehicle-to-everything (V2X) motion forecasting dataset V2X-Traj, and the performance shows the advantage of our method. We hope both V2X-Graph and V2X-Traj can facilitate the further development of cooperative motion forecasting. Find project at https://github.com/AIR-THU/V2X-Graph, find data at https://github.com/AIR-THU/DAIR-V2X-Seq.

自动驾驶中，运动预测是一项关键任务，充分利用基础设施和其他车辆提供的有效信息可以增强运动预测能力。本研究首次提出了合作轨迹表示学习范式，通过可解释的图形结构（V2X-Graph）充分利用合作运动和互动信息，有效提升合作运动预测的效果，并构建了实际车辆网络中的运动预测数据集（V2X-Traj），验证了本方法的优势。希望V2X-Graph和V2X-Traj能促进合作运动预测的进一步发展。

学习合作轨迹表示进行运动预测