PillarVTP: vehicle trajectory prediction method based on local point cloud aggregation and receptive field expansion

Vehicle trajectory prediction plays a crucial role in the control and safety warning of autonomous vehicles. Existing methods often depend on costly high definition (HD) maps for generating trajectories to fit their scenarios, or involve inefficient aggregation of local point clouds into voxels. Therefore, an end-to-end vehicle trajectory prediction method (PillarVTP) is proposed based on local point cloud aggregation and receptive field expansion. Firstly, we construct a novel pillar-based object detection network, introducing SPPCSPC which uses max pooling layers with multiple kernel sizes on a single feature level as the neck for extracting multi-scale features, and improving ResNet-18 by adding a depth stage to expand the receptive field at multiple levels. Then, we present performing feature upsampling to improve performance before predicting vehicle positions. And a shallow convolutional network is utilized to implement the future feature learning network, which learns future features from the previous features for predicting vehicle positions in future frames. Subsequently, the positions of vehicles are matched greedily from future frames to the current frame, and the matched future trajectories are associated with the vehicles detected in the current frame. Finally, the proposed PillarVTP is evaluated on the nuScenes and Argoverse 1 datasets. Experimental results demonstrate that PillarVTP outperforms recent end-to-end prediction method based on point cloud data, FutureDet, by 3.4% and surpasses traditional multi-stage method, Trajectron + + , by 13.7%. Furthermore, PillarVTP shows good robustness under various weather conditions.