用自监督预训练统一车辆预测与规划，提升轨迹生成的合理性与安全性。
Self-supervised Pretraining for Integrated Prediction and Planning of Automated Vehicles
- 通过掩码建模重建道路、轨迹和路线，融合空间与社交理解。
- 在大规模数据集上显著优于现有方法，规划指标提升明显。
- 适合需要联合预测与规划的自动驾驶系统研发者使用。
自动驾驶中准确预测周围交通参与者的行为并规划安全、目标导向的行驶轨迹至关重要。现有方法多依赖模仿学习优化与真实轨迹的匹配，常忽略场景理解对生成更全面轨迹的作用。本文提出Plan-MAE，一种基于掩码自编码器的统一预测与规划预训练框架。该框架通过三项任务融合关键上下文信息：重建被遮蔽的道路网络以学习空间关联、重构交通参与者轨迹以建模社会交互、重建导航路径以捕捉目的地意图。为进一步对齐车辆动力学与安全约束，引入局部子规划任务，基于前期轨迹段预测车辆近程轨迹片段。该预训练模型在下游任务中微调，实现预测与规划轨迹的联合生成。在大规模数据集上的实验表明，Plan-MAE在规划指标上大幅超越现有方法，可作为基于学习的运动规划器的重要预训练步骤。
原文摘要 · Abstract (English)
Predicting the future of surrounding agents and accordingly planning a safe, goal-directed trajectory are crucial for automated vehicles. Current methods typically rely on imitation learning to optimize metrics against the ground truth, often overlooking how scene understanding could enable more holistic trajectories. In this paper, we propose Plan-MAE, a unified pretraining framework for prediction and planning that capitalizes on masked autoencoders. Plan-MAE fuses critical contextual understanding via three dedicated tasks: reconstructing masked road networks to learn spatial correlations, agent trajectories to model social interactions, and navigation routes to capture destination intents. To further align vehicle dynamics and safety constraints, we incorporate a local sub-planning task predicting the ego-vehicle's near-term trajectory segment conditioned on earlier segment. This pretrained model is subsequently fine-tuned on downstream tasks to jointly generate the prediction and planning trajectories. Experiments on large-scale datasets demonstrate that Plan-MAE outperforms current methods on the planning metrics by a large margin and can serve as an important pre-training step for learning-based motion planner.
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