  The prediction of road users' future motion is a critical task in supporting
advanced driver-assistance systems (ADAS). It plays an even more crucial role
for autonomous driving (AD) in enabling the planning and execution of safe
driving maneuvers. Based on our previous work, Context-Aware Scene Prediction
Network (CASPNet), an improved system, CASPNet++, is proposed. In this work, we
focus on further enhancing the interaction modeling and scene understanding to
support the joint prediction of all road users in a scene using spatiotemporal
grids to model future occupancy. Moreover, an instance-based output head is
introduced to provide multi-modal trajectories for agents of interest. In
extensive quantitative and qualitative analysis, we demonstrate the scalability
of CASPNet++ in utilizing and fusing diverse environmental input sources such
as HD maps, Radar detection, and Lidar segmentation. Tested on the
urban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-art
performance. The model has been deployed in a testing vehicle, running in
real-time with moderate computational resources.
