用逆强化学习统一行人路径预测，提升自动驾驶安全
Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
- 将STGAT模型改造成统一框架，支持多种决策方式
- 在多个基准数据集上优于传统监督学习，提升预测精度
- 适合研究智能驾驶与图神经网络的开发者参考
行人路径预测对提升自动驾驶车辆和高级辅助驾驶系统的安全性至关重要。以往研究探讨了不同学习任务范式并用浅层神经网络进行比较，但未扩展到更复杂的深度学习模型。本文将时空图注意力网络（STGAT）适配至统一的行人路径预测框架，并引入针对STGAT的状态与动作定义。该框架支持确定性与随机策略、单次与序列决策，以及REINFORCE和近端策略优化等强化学习算法。相较于标准监督学习范式，所提学习任务范式在选定基准数据集上均取得更优预测性能。结果表明，重构决策过程与训练目标可显著提升先进轨迹预测架构的表现，也为改进其他基于图的预测模型提供新路径。
原文摘要 · Abstract (English)
Pedestrian path prediction is crucial for enhancing the safety of autonomous vehicles and advanced driver-assistance systems. Previous studies explored different learning-task formulations for pedestrian path prediction and compared these formulations using shallow neural networks, but did not extend this analysis to more complex deep-learning models. This paper adapts the Spatial-Temporal Graph Attention Network (STGAT) to a unified pedestrian path prediction framework and introduces state and action definitions specific to STGAT. The resulting formulations support deterministic and stochastic policies, one-time and sequential decision-making, and reinforcement-learning algorithms including REINFORCE and proximal policy optimization. The proposed learning-task formulations improve prediction performance across the selected benchmark datasets compared with the standard supervised-learning formulation. These results demonstrate that reformulating the decision process and training objective can improve an advanced pedestrian trajectory prediction architecture and may provide a path toward improving other graph-based prediction models.
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