  The confluence of the advancement of Autonomous Vehicles (AVs) and the
maturity of Vehicle-to-Everything (V2X) communication has enabled the
capability of cooperative connected and automated vehicles (CAVs). Building on
top of cooperative perception, this paper explores the feasibility and
effectiveness of cooperative motion prediction. Our method, CMP, takes LiDAR
signals as input to enhance tracking and prediction capabilities. Unlike
previous work that focuses separately on either cooperative perception or
motion prediction, our framework, to the best of our knowledge, is the first to
address the unified problem where CAVs share information in both perception and
prediction modules. Incorporated into our design is the unique capability to
tolerate realistic V2X bandwidth limitations and transmission delays, while
dealing with bulky perception representations. We also propose a prediction
aggregation module, which unifies the predictions obtained by different CAVs
and generates the final prediction. Through extensive experiments and ablation
studies, we demonstrate the effectiveness of our method in cooperative
perception, tracking, and motion prediction tasks. In particular, CMP reduces
the average prediction error by 17.2\% with fewer missing detections compared
with the no cooperation setting. Our work marks a significant step forward in
the cooperative capabilities of CAVs, showcasing enhanced performance in
complex scenarios.
