以枢轴为中心的轨迹预测：通过动态引导桥接长时程
Pivot-Centric Trajectory Prediction: Bridging Long Horizons via Dynamical Guidance
AI总结：
针对长时程轨迹预测的误差累积问题，本文提出PCTP方法，通过引入枢轴点解耦预测任务，提升了主流模型在Argoverse数据集上的预测精度，且可灵活集成到现有模型中。
AI中文摘要：
预测周围智能体的精确未来运动是自动驾驶车辆可靠运行的关键。然而，随着对更长预测时程的需求增加，现有的端点补全或迭代优化方法因引导不足和误差累积而愈发难以适用。为解决长时程预测挑战，本文提出以枢轴为中心的轨迹预测（Pivot-Centric Trajectory Prediction，PCTP）方法。通过引入“枢轴点”并聚焦于预测扩展轨迹上的枢轴点，本文将长时程预测任务划分为不同尺度的短期子任务。具体而言，PCTP将长时程轨迹预测过程解耦为两个子过程：枢轴点预测和基于枢轴点的轨迹优化。枢轴点预测过程旨在利用全局地图上下文和智能体间交互来识别这些“枢轴点”，而基于枢轴点的轨迹优化过程则聚焦于局部地图细节，并基于预测的“枢轴点”优化短期轨迹。与现有方法相比，PCTP提供了更多中间引导，同时减少了误差累积。此外，PCTP是一种灵活的方法，可集成到大多数最先进的轨迹预测模型中。实验结果表明，PCTP在对模型规模影响极小的情况下，提升了主流模型在Argoverse I和Argoverse II数据集上的预测精度。具体而言，结合QCNet的PCTP在提交时的Argoverse II排行榜上优于所有已发表的无集成方法。
英文摘要：
Forecasting precise future motion of surrounding agents is essential for reliable autonomous vehicles. However, as the demand for longer prediction horizons increases, existing endpoint-completion or iterative-refine methods increasingly struggle with weak guidance and compounding errors. To tackle the long-horizon prediction challenge, we propose Pivot-Centric Trajectory Prediction (PCTP). By introducing ``pivots'' and focusing on predicting pivot points along extended trajectories, we divide the long-term prediction task into short-term sub-tasks at various scales. Specifically, PCTP decouples the long-term trajectory predicting process into two processes: pivot prediction and pivot-based trajectory refinement. The pivot prediction process aims to utilize global map context and agent-to-agent interactions to identify these ``pivot points'', while the pivot-based trajectory refinement process focuses on local map details and refines the short-term trajectory based on predicted ``pivot points''. Compared with existing methods, PCTP provides more intermediate guidance while reducing compounding errors. Moreover, PCTP is a flexible approach that can be integrated into most state-of-the-art trajectory prediction models. Experimental results show that PCTP improves the prediction accuracy of leading models on both Argoverse I and Argoverse II datasets with minimal impact on model size. Specifically, PCTP combined with QCNet outperforms all published ensemble-free methods on the Argoverse II leaderboard at submission.
