Potential Field: Interpretable and Unified Representation for Trajectory Prediction
This provides an interpretable method for trajectory prediction in domains like autonomous driving, though it is incremental in applying physics concepts to this task.
The paper tackles trajectory prediction by modeling environmental, inertial, and social stimuli as a unified potential field representation, achieving state-of-the-art results on ETH, UCY, and Stanford Drone datasets.
Predicting an agent's future trajectory is a challenging task given the complicated stimuli (environmental/inertial/social) of motion. Prior works learn individual stimulus from different modules and fuse the representations in an end-to-end manner, which makes it hard to understand what are actually captured and how they are fused. In this work, we borrow the notion of potential field from physics as an interpretable and unified representation to model all stimuli. This allows us to not only supervise the intermediate learning process, but also have a coherent method to fuse the information of different sources. From the generated potential fields, we further estimate future motion direction and speed, which are modeled as Gaussian distributions to account for the multi-modal nature of the problem. The final prediction results are generated by recurrently moving past location based on the estimated motion direction and speed. We show state-of-the-art results on the ETH, UCY, and Stanford Drone datasets.