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For safe navigation around pedestrians, automated vehicles (AVs) need to plantheir motion by accurately predicting pedestrians trajectories over long timehorizons. Current approaches to AV motion planning around crosswalks predictonly for short time horizons (1-2 s) and are based on data from pedestrianinteractions with human-driven vehicles (HDVs). In this paper, we develop ahybrid systems model that uses pedestrians gap acceptance behavior and constantvelocity dynamics for long-term pedestrian trajectory prediction wheninteracting with AVs. Results demonstrate the applicability of the model forlong-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further wecompared measures of pedestrian crossing behaviors in the immersive virtualenvironment (when interacting with AVs) to that in the real world (results ofpublished studies of pedestrians interacting with HDVs), and found similaritiesbetween the two. These similarities demonstrate the applicability of the hybridmodel of AV interactions developed from an immersive virtual environment (IVE)for real-world scenarios for both AVs and HDVs.

Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions