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Predicting an agent's future trajectory is a challenging task given thecomplicated stimuli (environmental/inertial/social) of motion. Prior workslearn individual stimulus from different modules and fuse the representationsin an end-to-end manner, which makes it hard to understand what are actuallycaptured and how they are fused. In this work, we borrow the notion ofpotential field from physics as an interpretable and unified representation tomodel all stimuli. This allows us to not only supervise the intermediatelearning process, but also have a coherent method to fuse the information ofdifferent sources. From the generated potential fields, we further estimatefuture motion direction and speed, which are modeled as Gaussian distributionsto account for the multi-modal nature of the problem. The final predictionresults are generated by recurrently moving past location based on theestimated motion direction and speed. We show state-of-the-art results on theETH, UCY, and Stanford Drone datasets.

Potential Field: Interpretable and Unified Representation for Trajectory Prediction