Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions
This work addresses the safety challenge for automated vehicles by improving motion planning around pedestrians, though it is incremental as it builds on existing gap acceptance models.
The paper tackled the problem of long-term pedestrian trajectory prediction at crosswalks for automated vehicles by developing a hybrid systems model, achieving applicability for predictions over 5 seconds and showing similarities between virtual and real-world pedestrian behaviors.
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.