Pedestrian Crossing Intention Prediction Method Based on Multi-Feature Fusion

Abstract

Pedestrians are important traffic participants and prediction of pedestrian crossing intention can help reduce pedestrian–vehicle collisions. For the problem of predicting an individual pedestrian’s action where there is crossing potential, a pedestrian crossing intention prediction method that considers multi-feature fusion is proposed in this study, which integrates information affecting pedestrians’ actions, such as pedestrian action and traffic environment. This study is based on the BPI dataset for training and validation, and the test results show that the model has good data fitting and generalization ability; the test set has good prediction accuracy of 89.5% in the model, with an AUC of 0.992. In the specific scenario, the method proposed in this study can predict pedestrian crossing intention when the longitudinal relative distance between a pedestrian and vehicle is about 20 m and about 0.6 s before the pedestrian crossing, which can provide useful information for decision making in intelligent vehicles.