  Environment perception including detection, classification, tracking, and
motion prediction are key enablers for automated driving systems and
intelligent transportation applications. Fueled by the advances in sensing
technologies and machine learning techniques, LiDAR-based sensing systems have
become a promising solution. The current challenges of this solution are how to
effectively combine different perception tasks into a single backbone and how
to efficiently learn the spatiotemporal features directly from point cloud
sequences. In this research, we propose a novel spatiotemporal attention
network based on a transformer self-attention mechanism for joint semantic
segmentation and motion prediction within a point cloud at the voxel level. The
network is trained to simultaneously outputs the voxel level class and
predicted motion by learning directly from a sequence of point cloud datasets.
The proposed backbone includes both a temporal attention module (TAM) and a
spatial attention module (SAM) to learn and extract the complex spatiotemporal
features. This approach has been evaluated with the nuScenes dataset, and
promising performance has been achieved.
