Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.

翻译：预测行人与车辆交互时的行为是自动驾驶领域最具挑战性的问题之一。行人过街行为受多种交互因素影响，包括到达时间、行人等待时间、斑马线存在与否，以及行人与驾驶员双方的属性与个性特征。然而，这些因素在预测交互结果中尚未得到充分探索。本文利用机器学习方法预测行人在无信号交叉口与车辆交互时的过街行为，包括过街决策、过街起始时间（CIT）及过街持续时间（CD）。研究采用分布式模拟器数据进行交互因素分析与预测。与逻辑回归基线模型相比，我们提出的神经网络模型在预测准确率和F1分数上分别提升4.46%和3.23%。相较于线性回归模型，该模型将CIT和CD的均方根误差（RMSE）分别降低21.56%和30.14%。此外，我们分析了交互因素的重要性，并展示了使用较少因素构建模型的结果，为输入特征受限场景下的模型选择提供了依据。