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As autonomous driving systems mature, motion forecasting has receivedincreasing attention as a critical requirement for planning. Of particularimportance are interactive situations such as merges, unprotected turns, etc.,where predicting individual object motion is not sufficient. Joint predictionsof multiple objects are required for effective route planning. There has been acritical need for high-quality motion data that is rich in both interactionsand annotation to develop motion planning models. In this work, we introducethe most diverse interactive motion dataset to our knowledge, and providespecific labels for interacting objects suitable for developing jointprediction models. With over 100,000 scenes, each 20 seconds long at 10 Hz, ournew dataset contains more than 570 hours of unique data over 1750 km ofroadways. It was collected by mining for interesting interactions betweenvehicles, pedestrians, and cyclists across six cities within the United States.We use a high-accuracy 3D auto-labeling system to generate high quality 3Dbounding boxes for each road agent, and provide corresponding high definition3D maps for each scene. Furthermore, we introduce a new set of metrics thatprovides a comprehensive evaluation of both single agent and joint agentinteraction motion forecasting models. Finally, we provide strong baselinemodels for individual-agent prediction and joint-prediction. We hope that thisnew large-scale interactive motion dataset will provide new opportunities foradvancing motion forecasting models.

Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset