基于逆强化学习的统一行人路径预测
Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
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中文总结 AI 辅助
本文将STGAT适配到统一行人路径预测框架，提出适配的状态动作定义，结合多种强化学习算法，在基准数据集上较标准监督学习提升了预测性能，为改进图基预测模型提供方向。
中文摘要 AI 辅助
行人路径预测对提升自动驾驶汽车和高级驾驶辅助系统的安全性至关重要。过往研究针对行人路径预测探索了不同的学习任务公式，并使用浅层神经网络对这些公式进行了比较，但未将该分析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）适配到统一行人路径预测框架中，引入了针对STGAT的状态和动作定义。所得公式支持确定性和随机策略、一次性和序列决策，以及包括REINFORCE和近端策略优化在内的强化学习算法。与标准监督学习公式相比，所提出的学习任务公式在选定基准数据集上的预测性能有所提升。这些结果表明，重新制定决策过程和训练目标可以改进先进的行人轨迹预测架构，可能为改进其他基于图的预测模型提供途径。
英文摘要
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.
发表机构
- Eindhoven University of Technology（埃因霍温理工大学）
- Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
