Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model

Suresh Kumaar Jayaraman, Lionel P. Robert, Xi Jessie Yang, Anuj K. Pradhan, Dawn M. Tilbury

Abstract: 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 B-MPC 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.