Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized
  Crossings
Chi Zhang Janis Sprenger Zhongjun Ni Christian Berger
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.