Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model
This work addresses the challenge of safe and efficient automated vehicle control at crosswalks for urban mobility, representing an incremental improvement by integrating an existing pedestrian model into a controller.
The paper tackled the problem of automated vehicles navigating crosswalks by developing a Behavior-aware Model Predictive Controller (B-MPC) that uses a pedestrian prediction model based on minimal information (position and speed) to anticipate crossing behaviors. Results showed the B-MPC efficiently plans for longer horizons and handles more interaction scenarios than a rule-based controller, enabling safe and efficient navigation.
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.