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 model 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 on the OPV2V and V2V4Real datasets, we demonstrate the effectiveness of our method in cooperative perception, tracking, and motion prediction. In particular, CMP reduces the average prediction error by 16.4\% with fewer missing detections compared with the no cooperation setting and by 12.3\% compared with the strongest baseline. Our work marks a significant step forward in the cooperative capabilities of CAVs, showcasing enhanced performance in complex scenarios. The code can be found on the project website: https://cmp-cooperative-prediction.github.io/.

翻译：随着自动驾驶汽车技术的进步与车联网通信技术的成熟，协同式网联自动驾驶汽车已成为可能。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。我们提出的CMP方法以激光雷达信号作为模型输入，以增强跟踪与预测能力。与以往分别关注协同感知或运动预测的研究不同，据我们所知，我们的框架首次解决了CAV在感知与预测模块中共享信息的统一问题。我们的设计具备独特能力，能够在处理庞大感知表征的同时，容忍实际V2X带宽限制与传输延迟。我们还提出了预测聚合模块，用于统合不同CAV获得的预测结果并生成最终预测。通过在OPV2V和V2V4Real数据集上的大量实验与消融研究，我们验证了该方法在协同感知、跟踪与运动预测方面的有效性。特别地，与无协同设置相比，CMP在减少漏检的同时将平均预测误差降低了16.4%；与最强基线相比降低了12.3%。我们的工作标志着CAV协同能力向前迈出了重要一步，在复杂场景中展现出增强的性能。代码可在项目网站获取：https://cmp-cooperative-prediction.github.io/。