Intent-Aware Pedestrian Prediction for Adaptive Crowd Navigation

Abstract

Mobile robots capable of navigating seamlessly and safely in pedestrian rich environments promise to bring robotic assistance closer to our daily lives. In this paper we draw on insights of how humans move in crowded spaces to explore how to recognize pedestrian navigation intent, how to predict pedestrian motion and how a robot may adapt its navigation policy dynamically when facing unexpected human movements. Our approach is to develop algorithms that replicate this behavior. We experimentally demonstrate the effectiveness of our prediction algorithm using real-world pedestrian datasets and achieve comparable or better prediction accuracy compared to several state-of-the-art approaches. Moreover, we show that confidence of pedestrian prediction can be used to adjust the risk of a navigation policy adaptively to afford the most comfortable level as measured by the frequency of personal space violation in comparison with baselines. Furthermore, our adaptive navigation policy is able to reduce the number of collisions by 43% in the presence of novel pedestrian motion not seen during training.