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关键的挑战之一。行人过街行为受多种交互因素影响，包括车辆到达时间、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特征和个性特征。然而，这些因素在预测交互结果方面的潜力尚未得到充分挖掘。本文利用机器学习预测行人在无信号路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)和过街持续时间(CD)。采用分布式仿真数据对交互因素进行预测与分析。与逻辑回归基线模型相比，本文提出的神经网络模型将预测准确率和F1分数分别提升4.46%和3.23%。同时，与线性回归模型相比，本模型将CIT和CD的均方根误差(RMSE)分别降低21.56%和30.14%。此外，我们分析了各交互因素的重要性，并展示了使用更少因素的模型结果，这为输入特征受限的不同场景下的模型选择提供了参考依据。