We introduce RMP-YOLO, a unified framework designed to provide robust motion predictions even with incomplete input data. Our key insight stems from the observation that complete and reliable historical trajectory data plays a pivotal role in ensuring accurate motion prediction. Therefore, we propose a new paradigm that prioritizes the reconstruction of intact historical trajectories before feeding them into the prediction modules. Our approach introduces a novel scene tokenization module to enhance the extraction and fusion of spatial and temporal features. Following this, our proposed recovery module reconstructs agents' incomplete historical trajectories by leveraging local map topology and interactions with nearby agents. The reconstructed, clean historical data is then integrated into the downstream prediction modules. Our framework is able to effectively handle missing data of varying lengths and remains robust against observation noise, while maintaining high prediction accuracy. Furthermore, our recovery module is compatible with existing prediction models, ensuring seamless integration. Extensive experiments validate the effectiveness of our approach, and deployment in real-world autonomous vehicles confirms its practical utility. In the 2024 Waymo Motion Prediction Competition, our method, RMP-YOLO, achieves state-of-the-art performance, securing third place.

翻译：我们提出了RMP-YOLO，这是一个统一的框架，旨在即使输入数据不完整也能提供鲁棒的运动预测。我们的核心洞见源于观察到完整可靠的历史轨迹数据对于确保准确运动预测起着关键作用。因此，我们提出了一种新的范式，优先在将数据输入预测模块之前重建完整的历史轨迹。我们的方法引入了一种新颖的场景令牌化模块，以增强时空特征的提取与融合。随后，我们提出的恢复模块通过利用局部地图拓扑结构以及与邻近智能体的交互，重建智能体不完整的历史轨迹。重建后的干净历史数据随后被整合到下游预测模块中。我们的框架能够有效处理不同长度的缺失数据，并在存在观测噪声时保持鲁棒性，同时维持高预测精度。此外，我们的恢复模块与现有预测模型兼容，确保了无缝集成。大量实验验证了我们方法的有效性，在实际自动驾驶车辆中的部署也证实了其实用价值。在2024年Waymo运动预测竞赛中，我们的方法RMP-YOLO取得了最先进的性能，获得了第三名。