Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings
This research addresses pedestrian safety for automated driving systems, but it is incremental as it builds on existing methods with new simulator data.
This study tackled the problem of predicting pedestrian crossing behavior at unsignalized crossings to enhance automated driving safety, by proposing machine learning models that predict gap selection and zebra crossing usage, with results including analysis of factors like waiting time and walking speed.
Understanding and predicting pedestrian crossing behavior is essential for enhancing automated driving and improving driving safety. Predicting gap selection behavior and the use of zebra crossing enables driving systems to proactively respond and prevent potential conflicts. This task is particularly challenging at unsignalized crossings due to the ambiguous right of way, requiring pedestrians to constantly interact with vehicles and other pedestrians. This study addresses these challenges by utilizing simulator data to investigate scenarios involving multiple vehicles and pedestrians. We propose and evaluate machine learning models to predict gap selection in non-zebra scenarios and zebra crossing usage in zebra scenarios. We investigate and discuss how pedestrians' behaviors are influenced by various factors, including pedestrian waiting time, walking speed, the number of unused gaps, the largest missed gap, and the influence of other pedestrians. This research contributes to the evolution of intelligent vehicles by providing predictive models and valuable insights into pedestrian crossing behavior.