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.

翻译：运动预测是自动驾驶中的关键任务，而有效利用来自基础设施及其他车辆的信息能够增强运动预测能力。现有研究主要聚焦于利用单帧协同信息来弥补自车受限的感知能力，却未能充分挖掘协同设备观测到的交通参与者的运动与交互信息。本文首先提出了协同轨迹表示学习范式，具体而言，我们提出了V2X-Graph——首个面向协同运动预测的可解释且端到端的学习框架。V2X-Graph通过可解释图结构充分挖掘协同运动与交互上下文。在车对基础设施（V2I）运动预测数据集V2X-Seq上的实验结果表明了V2X-Graph的有效性。为进一步评估车对外界（V2X）场景，我们构建了首个真实世界V2X运动预测数据集V2X-Traj，实验结果展示了我们方法的优势。我们期望V2X-Graph与V2X-Traj能够推动协同运动预测的进一步发展。项目地址：https://github.com/AIR-THU/V2X-Graph，数据地址：https://github.com/AIR-THU/DAIR-V2X-Seq。