Abstract:

A dynamic obstacle avoidance method for unmanned vehicle based on pedestrian trajectory prediction is proposed to address the problem of poor obstacle avoidance performance caused by the lack of consideration of target motion trends during autonomous navigation of unmanned vehicle. Firstly, use depth camera and object detection algorithm to complete pedestrian recognition and position calculation, and obtain temporal information of pedestrian position through tracking. Secondly, by combining one-dimensional convolutional neural network (CNN) with bidirectional gated recurrent unit (BiGRU) and incorporating attention (ATT) mechanism, a CNN-BiGRU-ATT pedestrian trajectory prediction model is constructed, which predicts future trajectories based on the current position sequence of pedestrians. Finally, the Bresenham algorithm is used to overlay the current position and trajectory prediction results of pedestrians on the cost map, enabling global and local path planning algorithms to avoid collision risks in advance. Experiments show that the pedestrian detection and localization method based on depth camera can accurately calculate the historical trajectory of pedestrian, providing reliable input for trajectory prediction model. The pedestrian trajectory prediction model based on CNN-BiGRU-ATT has smaller prediction errors in various motion states. The cost map integrated with trajectory prediction results improves the foresight of path planning, avoids frequent changes of global and local paths by unmanned vehicle, and improves the efficiency of task execution.

Key words: autonomous navigation, trajectory prediction, cost map, dynamic obstacle avoidance