ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries

Junru Gu, Chenxu Hu, Tianyuan Zhang, Xuanyao Chen, Yilun Wang, Yue Wang, Hang Zhao

Abstract: In the existing autonomous driving systems, perception and prediction are two separate modules. The two modules communicate via hand-picked features such as agent boxes and trajectories as interfaces. Due to this separation, the prediction module only receives limited information from the perception module. Even worse, errors from the perception modules can propagate and accumulate, adversely affecting the prediction results. In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to predict future trajectories of agents in a scene. ViP3D employs sparse agent queries throughout the pipeline, making it fully differentiable and interpretable. Extensive experimental results on the nuScenes dataset show the strong performance of ViP3D over traditional pipelines and previous end-to-end models.