Motion forecasting is an essential task for autonomous driving, and utilizing information from infrastructure and other vehicles can enhance forecasting capabilities. Existing research mainly focuses on leveraging single-frame cooperative information to enhance the limited perception capability of the ego vehicle, while underutilizing the motion and interaction context of traffic participants observed from cooperative devices. In this paper, we propose a forecasting-oriented representation paradigm to utilize motion and interaction features from cooperative information. Specifically, we present V2X-Graph, a representative framework to achieve interpretable and end-to-end trajectory feature fusion for cooperative motion forecasting. V2X-Graph is evaluated on V2X-Seq in vehicle-to-infrastructure (V2I) scenarios. To further evaluate on vehicle-to-everything (V2X) scenario, we construct the first real-world V2X motion forecasting dataset V2X-Traj, which contains multiple autonomous vehicles and infrastructure in every scenario. Experimental results on both V2X-Seq and V2X-Traj show the advantage of our method. We hope both V2X-Graph and V2X-Traj will benefit the further development of cooperative motion forecasting. Find the project at https://github.com/AIR-THU/V2X-Graph.

翻译：运动预测是自动驾驶中的一项关键任务，利用来自基础设施和其他车辆的信息可以提升预测能力。现有研究主要集中于利用单帧协同信息来增强自车有限的感知能力，而对从协同设备观测到的交通参与者的运动与交互上下文信息利用不足。本文提出一种面向预测的表征范式，以利用协同信息中的运动与交互特征。具体而言，我们提出了V2X-Graph，这是一个实现可解释、端到端协同运动预测轨迹特征融合的代表性框架。V2X-Graph在车对基础设施（V2I）场景下的V2X-Seq数据集上进行了评估。为了进一步在车对万物（V2X）场景下进行评估，我们构建了首个真实世界V2X运动预测数据集V2X-Traj，其中每个场景均包含多辆自动驾驶车辆与基础设施。在V2X-Seq和V2X-Traj上的实验结果表明了所提方法的优势。我们希望V2X-Graph与V2X-Traj均能有益于协同运动预测的进一步发展。项目地址：https://github.com/AIR-THU/V2X-Graph。