JFP: Joint Future Prediction with Interactive Multi-Agent Modeling for Autonomous DrivingDownload PDF

Published: 10 Sept 2022, Last Modified: 05 May 2023CoRL 2022 PosterReaders: Everyone
Keywords: Self-driving, motion forecasting, interactive prediction
TL;DR: Using learned interaction among agents for multi-agent motion forecasting achieves sota results on WOMD interactive split.
Abstract: We propose \textit{JFP}, a Joint Future Prediction model that can learn to generate accurate and consistent multi-agent future trajectories. For this task, many different methods have been proposed to capture social interactions in the encoding part of the model, however, considerably less focus has been placed on representing interactions in the decoder and output stages. As a result, the predicted trajectories are not necessarily consistent with each other, and often result in unrealistic trajectory overlaps. In contrast, we propose an end-to-end trainable model that learns directly the interaction between pairs of agents in a structured, graphical model formulation in order to generate consistent future trajectories. It sets new state-of-the-art results on Waymo Open Motion Dataset (WOMD) for the interactive setting. We also investigate a more complex multi-agent setting for both WOMD and a larger internal dataset, where our approach improves significantly on the trajectory overlap metrics while obtaining on-par or better performance on single-agent trajectory metrics.
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