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Register a new userAnalysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions
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Added by
Lionel Robert
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavior aware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior using a previously developed pedestrian crossing model. The model incorporates pedestrians gap acceptance behavior and utilizes minimal pedestrian information, namely their position and speed, to predict pedestrians crossing behaviors. The BMPC controller is validated through simulations and compared to a rule-based controller. By incorporating predictions of pedestrian behavior, the B-MPC controller is able to efficiently plan for longer horizons and handle a wider range of pedestrian interaction scenarios than the rule-based controller. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios.
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