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 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 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. More details can be found on the project website: https://cmp-cooperative-prediction.github.io.

翻译：自动驾驶车辆（AVs）技术的进步与车联网（V2X）通信技术的成熟，共同推动了协同网联自动驾驶车辆（CAVs）的实现。本文在协同感知的基础上，探讨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CAVs在感知与预测模块中共享信息的统一性问题。我们的设计独特地包含了容忍实际V2X传输延迟的能力，同时处理庞大的感知表征。我们还提出了一个预测聚合模块，用于统一不同CAVs获得的预测结果并生成最终预测。通过在OPV2V和V2V4Real数据集上进行的大量实验与消融研究，我们验证了该方法在协同感知、跟踪和运动预测方面的有效性。特别地，与最强的基线方法相比，CMP将平均预测误差降低了12.3%。我们的工作标志着CAVs协同能力向前迈出了重要一步，展示了在复杂场景下性能的显著提升。更多细节请访问项目网站：https://cmp-cooperative-prediction.github.io。