Roadside pedestrian motion prediction using Bayesian methods and particle filter

Abstract

Accidents between vehicles and pedestrians account for a large partition of severe traffic accidents. So, pedestrian motion prediction becomes a major concern for intelligent vehicles. However, current researches often neglect pedestrian behaviour and/or intention in motion prediction. Meanwhile, related works are scattered and divided into many small fields. No integrated system is proposed to connect the task of perception and decision. To solve these problems, a pedestrian motion prediction model is proposed in this paper. The proposed method predicts pedestrian motion based on the combination of pedestrian crossing behaviour and intention. Pedestrian behaviour is recognized using the Bayesian posterior model, and pedestrian intention is recognized by the dynamic Bayesian network. A modified particle filter and a behavioural motion model are used to integrate the behaviour and intention into motion prediction. The effectiveness of the proposed method is verified in our provided BPI dataset with eight typical scenarios defined by road type, vehicle velocity etc. The results show that this method can give an accurate distribution of pedestrians' future trajectories.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Passing‐yielding intention estimation during lane change conflict: A semantic‐based Bayesian inference method

“…The output of SIIE is the vehicle's pass‐yield intention, attached with the motion modal for each intention. A particle‐filter (PF) method is applied from [45] to generate a multi‐modal trajectory accordingly. Specifically, for each intention, the motion modal provides an expected range for vehicle position at each time frame.…”