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.

翻译：随着自动驾驶车辆（AVs）的进步与车联万物（V2X）通信技术的成熟，协同式网联自动驾驶车辆（CAVs）的能力得以实现。在协同感知的基础上，本文探索了协同运动预测的可行性与有效性。我们的方法CMP以LiDAR信号为输入，增强跟踪与预测能力。不同于以往分别聚焦于协同感知或运动预测的研究，据我们所知，本框架首次解决了CAVs在感知与预测模块间共享信息的统一性问题。我们的设计具备独特能力：在应对庞大感知表示的同时，能容忍实际场景中V2X带宽限制与传输延迟。我们还提出了预测聚合模块，该模块整合不同CAV获得的预测结果并生成最终预测。通过大量实验与消融研究，我们证明了该方法在协同感知、跟踪与运动预测任务中的有效性。特别地，与无协同方案相比，CMP将平均预测误差降低了17.2%，同时减少了漏检次数。本研究标志着CAVs协同能力的重大进步，展示了在复杂场景中的性能提升。