Recognition and Prediction of Pedestrian Hazardous Crossing Intentions in Visual Field Obstruction Areas Based on IPVO-LSTM

Abstract

Pedestrians who suddenly cross the street from within the blind spot of a vehicle’s field of view can pose a significant threat to traffic safety. The dangerous pedestrian crossing intentions in view-obscured scenarios have not received as much attention as the prediction of pedestrian crossing intentions. In this paper, we present a method for recognizing and predicting the dangerous crossing intention of pedestrians in a view-obscured region based on the interference, pose, velocity observation–long short-term memory (IPVO-LSTM) algorithm from a road-based view. In the first step, the road-based camera captures the pedestrian’s image. Then, we construct a pedestrian interference state feature module, pedestrian three-dimensional pose feature module, pedestrian velocity feature module, and pedestrian blind observation state feature module and extract the corresponding features of the studied pedestrians. Finally, the pedestrian hazard crossing intention prediction module based on a feature-fused LSTM (ff-LSTM) and attention mechanism is used to fuse and process the above features in a cell state process to recognize and predict the pedestrian hazard crossing intention in the blind visual area. Experiments are compared with current common algorithms in terms of the input parameter selection, intention recognition algorithm, and intention prediction time range, and the experimental results validate our state-of-the-art method.