Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings
<|reference_start|>Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings: 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.<|reference_end|>
@article{zhang2024predicting,
    title={Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized
  Crossings},
    author={Chi Zhang (1), Janis Sprenger (2), Zhongjun Ni (3), and Christian
  Berger (1) ((1) Department of Computer Science and Engineering, University of
  Gothenburg, Sweden, (2) German Research Center for Artificial Intelligence
  (DFKI), Saarland Informatics Campus, Germany, (3) Department of Science and
  Technology, Link"oping University, Campus Norrk"oping, Sweden)},
    journal={arXiv preprint arXiv:2404.09574},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09574},
    primaryClass={cs.LG cs.AI}
}