TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification

Abstract

Vehicle trajectory classification plays an important role in intelligent transportation systems because it can be utilized in traffic flow estimation at an intersection and anomaly detection such as traffic accidents and violations of traffic regulations. In this paper, we propose a new neural network architecture for vehicle trajectory classification by modifying the PointNet architecture, which was proposed for point cloud classification and semantic segmentation. The modifications are derived based on analyzing the differences between the properties of vehicle trajectory and point cloud. We call the modified network TrajNet. It is demonstrated from experiments using three public datasets that TrajNet can classify vehicle trajectories faster and more slightly accurate than the conventional networks used in the previous studies.