Pedestrian trajectory prediction by deep learning considering obstacles in the surrounding environment

Abstract

In this paper, we propose a pedestrian trajectory prediction method for autonomous mobile robots. In many cases, there are many pedestrians in the environment in which the autonomous mobile robot runs. In such an environment, the robot needs to run safely for pedestrians. In order to avoid collisions with pedestrians and drive safely, it is important to predict future movements of pedestrians. In the conventional prediction method, the trajectory of a future pedestrian is often predicted from the position of a pedestrian in the past. However, in such cases, it is difficult to predict the movement to avoid obstacles such as walls and pillars around the pedestrian. In this study, point cloud data acquired by LiDAR is used to predict the behavior of pedestrians avoiding surrounding static obstacles. Based on the point cloud data, distances between the pedestrians and the static obstacles is calculated at each time. Then, input it into the network together with the transition of the pedestrian’s position to predict the future pedestrian’s position. In addition, we use attention mechanisms to model interactions between pedestrians. This makes predictions that consider not only static obstacles but also the effects of other pedestrians. The usefulness of this study is shown by performing accuracy evaluation using the dataset created in the simulation environment and the publicly available dataset.

Keywords

• trajectory prediction
• mobile robot
• static obstacle
• attention mechanism
• spatial interaction