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Perception and prediction are two separate modules in the existing autonomousdriving systems. They interact with each other via hand-picked features such asagent bounding boxes and trajectories. Due to this separation, prediction, as adownstream module, only receives limited information from the perceptionmodule. To make matters worse, errors from the perception modules can propagateand accumulate, adversely affecting the prediction results. In this work, wepropose ViP3D, a query-based visual trajectory prediction pipeline thatexploits rich information from raw videos to directly predict futuretrajectories of agents in a scene. ViP3D employs sparse agent queries todetect, track, and predict throughout the pipeline, making it the first fullydifferentiable vision-based trajectory prediction approach. Instead of usinghistorical feature maps and trajectories, useful information from previoustimestamps is encoded in agent queries, which makes ViP3D a concise streamingprediction method. Furthermore, extensive experimental results on the nuScenesdataset show the strong vision-based prediction performance of ViP3D overtraditional pipelines and previous end-to-end models.

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