BE-STI: Spatial-Temporal Integrated Network for Class-Agnostic Motion Prediction With Bidirectional Enhancement

Yunlong Wang, Hongyu Pan, Jun Zhu, Yu-Huan Wu, Xin Zhan, Kun Jiang, Diange Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 17093-17102

Determining the motion behavior of inexhaustible categories of traffic participants is critical for autonomous driving. In recent years, there has been a rising concern in performing class-agnostic motion prediction directly from the captured sensor data, like LiDAR point clouds or the combination of point clouds and images. Current motion prediction frameworks tend to perform joint semantic segmentation and motion prediction and face the trade-off between the performance of these two tasks. In this paper, we propose a novel Spatial-Temporal Integrated network with Bidirectional Enhancement, BE-STI, to improve the temporal motion prediction performance by spatial semantic features, which points out an efficient way to combine semantic segmentation and motion prediction. Specifically, we propose to enhance the spatial features of each individual point cloud with the similarity among temporal neighboring frames and enhance the global temporal features with the spatial difference among non-adjacent frames in a coarse-to-fine fashion. Extensive experiments on nuScenes and Waymo Open Dataset show that our proposed framework outperforms all state-of-the-art LiDAR-based and RGB+LiDAR-based methods with remarkable margins by using only point clouds as input.