  For safe navigation around pedestrians, automated vehicles (AVs) need to plan
their motion by accurately predicting pedestrians trajectories over long time
horizons. Current approaches to AV motion planning around crosswalks predict
only for short time horizons (1-2 s) and are based on data from pedestrian
interactions with human-driven vehicles (HDVs). In this paper, we develop a
hybrid systems model that uses pedestrians gap acceptance behavior and constant
velocity dynamics for long-term pedestrian trajectory prediction when
interacting with AVs. Results demonstrate the applicability of the model for
long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further we
compared measures of pedestrian crossing behaviors in the immersive virtual
environment (when interacting with AVs) to that in the real world (results of
published studies of pedestrians interacting with HDVs), and found similarities
between the two. These similarities demonstrate the applicability of the hybrid
model of AV interactions developed from an immersive virtual environment (IVE)
for real-world scenarios for both AVs and HDVs.
