Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

TL;DR

This paper develops a machine learning approach to predict pedestrian crossing behavior at unsignalized crossings, considering various interaction factors, and demonstrates improved accuracy over baseline models using simulator data.

Contribution

The paper introduces a neural network model that predicts pedestrian crossing decisions, timing, and duration, incorporating multiple interaction factors, and analyzes feature importance for different scenarios.

Findings

Neural network outperforms logistic regression with 4.46% higher F1 score.

Model reduces RMSE for crossing initiation time by 21.56%.

Model reduces RMSE for crossing duration by 30.14%.

Abstract

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.