Authors: Jack Terwilliger (Center for Transportation and Logistics) , Michael Glazer (Center for Transportation and Logistics) , Henri Schmidt (Center for Transportation and Logistics) , Josh Domeyer (Toyota Collaborative Safety Research Center) , Heishiro Toyoda (Toyota Collaborative Safety Research Center) , Bruce Mehler (Center for Transportation and Logistics, MIT AgeLab & N.E. University Transportation Center) , Bryan Reimer (Center for Transportation and Logistics, MIT AgeLab & N.E. University Transportation Center) , Lex Fridman (Center for Transportation and Logistics)
Humans, as both pedestrians and drivers, generally skillfully navigate traffic intersections. Despite the uncertainty, danger, and the non-verbal nature of communication commonly found in these interactions, there are surprisingly few collisions considering the total number of interactions. As the role of automation technology in vehicles grows, it becomes increasingly critical to understand the relationship between pedestrian and driver behavior: how pedestrians perceive the actions of a vehicle/driver and how pedestrians make crossing decisions. The relationship between time-to-arrival (TTA) and pedestrian gap acceptance (i.e., whether a pedestrian chooses to cross under a given window of time to cross) has been extensively investigated. However, the dynamic nature of vehicle trajectories in the context of non-verbal communication has not been systematically explored. Our work provides evidence that trajectory dynamics, such as changes in TTA, can be powerful signals in the non-verbal communication between drivers and pedestrians. Moreover, we investigate these effects in both simulated and realworld datasets, both larger than have previously been considered in literature to the best of our knowledge.
How to Cite: Terwilliger, J. , Glazer, M. , Schmidt, H. , Domeyer, J. , Toyoda, H. , Mehler, B. , Reimer, B. & Fridman, L. (2019) “Dynamics of Pedestrian Crossing Decisions Based on Vehicle Trajectories in Large-Scale Simulated and Real-World Data”, Driving Assessment Conference. 10(2019). doi: https://doi.org/10.17077/drivingassessment.1676