Safety or efficiency? Estimating crossing motivations of intoxicated pedestrians by leveraging the inverse reinforcement learning

Background: Intoxicated pedestrians are particularly vulnerable while crossing roads because of their impaired cognitive and decision-making abilities. A deeper understanding of the crossing behaviors of pedestrians under the influence serves as the foundations for formulation of tailor-made countermeasures. Methods: In this study an experiment based on the immersive virtual reality was conducted, by which 53 samples of Hong Kong pedestrians' crossing trajectories before and after alcohol intake were collected. The K-means algorithm was first used to classify pedestrians into two distinct types, namely the risky and cautious, according to the post-encroachment time during all street crossings. The cutting-edging inverse reinforcement learning was then harnessed to uncover the safety and efficiency motivations underlying crossing behaviors impacted by alcohol. The results were validated by comparing the observed behaviors with those generated by reinforcement learning. Results: Our results revealed substantial differences in safety and efficiency motivations between the two types of pedestrians. Notably, the cautious type emphasized safety more than the risky. Under the influence of alcohol, both types of pedestrians exhibited a shift in motivations from safety to efficiency. In addition, road markings hardly influenced pedestrian crossing motivations, whereas traffic directions significantly altered the motivations of cautious pedestrians under sober conditions. Conclusions: Our study sheds more lights on unobserved motivations guiding crossing behaviors of pedestrians under the influence. The inverse reinforcement learning is proven promising in imitating complex pedestrian crossing behaviors under a quantifiable, reliable manner.