Abstract:

In order to improve the reliability of perception and decision fusion of vulnerable road users in future autonomous vehicles， in this paper， a pedestrian future trajectory prediction method based on target detection algorithm （YOLOv5）， multi-target tracking algorithm （Deep-Sort） and Social-Long Short-Term Memory （Social-LSTM） neural network is proposed. Combining the YOLOv5 detection and Deep-Sort tracking algorithms， the problem of target loss during pedestrian detection and tracking can be effectively solved. A specific pedestrian target history trajectory is extracted as the input boundary conditions of the prediction framework， and Social-LSTM is used to predict the pedestrian future motion trajectory. The future motion trajectory is also subjected to perspective transformation and direct linear transformation， which is then converted into position information in the world coordinate system to predict the possible future collision locations of vehicles and pedestrians. The results show that the target detection accuracy reaches 93.889% and the average accuracy reaches 96.753%. The results of the final trajectory prediction algorithm based on the high accuracy detection model show that the prediction loss is decreasing with the increase of the training step， and the final loss values are less than 1%， among which the average displacement error is reduced by 18.30% and the final displacement error is reduced by 51.90%. This study can provide a theoretical basis and reference for the development of intelligent vehicle collision avoidance strategies.

Key words: car to pedestrian crash, pedestrian trajectory prediction, target detection, multi-target tracking, Social-LSTM