  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.
