Dynamic Prediction Model of Pedestrian Crossing Orientation Probability Based on Human-vehicle Interaction

doi:10.3901/JME.2024.10.040

Abstract

Abstract: Pedestrian safety is one of the most important indicators of road traffic, due to the changeable trajectory of pedestrians, increasing the difficulty of active obstacle avoidance and trajectory planning of autonomous vehicles, accurate and efficient pedestrian trajectory prediction methods will help improve the efficiency of driver assistance systems and the success rate of obstacle avoidance, thereby improving the safety of pedestrians and vehicles. Therefore, this study proposes a dynamic prediction model of pedestrian crossing orientation probability based on human-vehicle interaction, which realizes efficient and accurate prediction of pedestrian crossing orientation dynamics considering the interaction between people and vehicles. Firstly, based on the influence of vehicles on pedestrians crossing the street, a human-vehicle interaction risk field model is established, and the influence of vehicle size, speed and distance from pedestrians on the risk field is determined. Secondly, the pedestrian crossing benefit function of pedestrian-vehicle distance, pedestrian-target point distance and pedestrian crossing steps is constructed, and a pedestrian dynamic crossing azimuth probability model method based on nested probability model is proposed. Then, the influence of different vehicle types, speeds and other characteristics on pedestrian crossing parameters is analyzed. Finally, the data of pedestrian crossing and vehicle trajectory in a certain road section are collected and analyzed through experiments, and the law of pedestrian crossing is analyzed. Comparing experimental and simulation results, it shows that the proposed method can accurately and efficiently predict the orientation of pedestrian crossings, and compared with the prediction method of long short-term memory(LSTM), the calculation efficiency is increased by 93.4% and the accuracy is improved by 31.8%.

Key words: human-vehicle interaction risk field, pedestrian crossing orientation probability, pested model, benefit function

CLC Number:

U461

WU Wenguang, ZHANG Bin, HU Lin, ZHANG Zhiyong. Dynamic Prediction Model of Pedestrian Crossing Orientation Probability Based on Human-vehicle Interaction[J]. Journal of Mechanical Engineering, 2024, 60(10): 48-50.

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