Pedestrian Path Prediction for Autonomous Driving at Un-Signalized Crosswalk Using W/CDM and MSFM

Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving, which can help autonomous vehicles have a better understanding of traffic environment and perform tasks such as risk assessment in advance. In this paper, pedestrian path prediction at a time horizon of 2s for autonomous driving is systematically investigated using waiting/crossing decision model (W/CDM) and modified social force model (MSFM), and the possible conflict between pedestrians and straight-going vehicles at an un-signalized crosswalk is focused on. First of all, a W/CDM is efficiently developed to judge pedestrians' waiting/crossing intentions when a straight-going vehicle is approaching. Then the humanoid micro-dynamic MSFM of pedestrians who have been judged to cross is characterized by taking into account the evasion with conflicting pedestrians, the collision avoidance with straight-going vehicles, and the reaction to crosswalk boundary. The influence of pedestrian heterogeneous characteristics is considered for the first time. Moreover, aerial video data of pedestrians and vehicles at an un-signalized crosswalk is collected and analyzed for model calibration. Maximum likelihood estimation (MLE) is proposed to calibrate the non-measurable parameters of the proposed models. Finally, the model validation is conducted with two cases by comparing with the existing methods. The result reveals that the integrated method (W/CDM-MSFM) outperforms the existing methods and accurately predicts the path of pedestrians, which can give us great confidence to use the current method to predict the path of pedestrian for autonomous driving with significant accuracy and highly improve pedestrian safety.

Automated summary

This paper investigates pedestrian path prediction at a 2-second time horizon for autonomous driving using a waiting/crossing decision model and a modified social force model, focusing on conflicts between pedestrians and straight-going vehicles at an un-signalized crosswalk. An integrated method, W/CDM-MSFM, is proposed to accurately predict pedestrian paths by considering factors such as pedestrians' waiting/crossing intentions, conflict avoidance, collision avoidance, and heterogeneous characteristics. The proposed method outperforms existing methods and significantly improves pedestrian safety by accurately predicting pedestrian paths for autonomous driving.