Crossing-Road Pedestrian Trajectory Prediction Based on Intention and Behavior Identification

Haoran Wu, Likun Wang, Sifa Zheng, Qing Xu, Jian-qiang Wang

Abstract: Pedestrian trajectory prediction plays an important role in both pedestrian collision avoidance systems and autonomous driving. However, most of the previous works have ignored the interaction between traffic participants or only take it into account implicitly based on neural networks, which need a large number of training data and hold poor scenario adaptability. Meanwhile, pedestrian changeable behaviors are also always overlooked in trajectory prediction. In this paper, we present a novel pedestrian trajectory prediction method that involves pedestrian intention and behavior information into prediction. Verification of this method has been conducted in our provided BPI dataset. Without previous training of pedestrian trajectories, the method shows good scenario adaptability and provides accurate path prediction results for eight defined typical pedestrian crossing-road scenarios in Is prediction horizon, especially for stopping scenarios.