THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling

• URL: http://arxiv.org/abs/2110.06607v1
• Date: Wed, 13 Oct 2021 10:05:47 GMT
• Title: THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
• Authors: Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde
• Abstract summary: We present a unified model architecture for fast and simultaneous agent future heatmap estimation. generating scene-consistent predictions goes beyond the mere generation of collision-free trajectories. We report our results on the Interaction multi-agent prediction challenge and rank $1st$ on the online test leaderboard.
• Score: 2.424910201171407
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for efficient and consistent prediction of multi-agent multi-modal trajectories. We present a unified model architecture for fast and simultaneous agent future heatmap estimation leveraging hierarchical and sparse image generation. We demonstrate that heatmap output enables a higher level of control on the predicted trajectories compared to vanilla multi-modal trajectory regression, allowing to incorporate additional constraints for tighter sampling or collision-free predictions in a deterministic way. However, we also highlight that generating scene-consistent predictions goes beyond the mere generation of collision-free trajectories. We therefore propose a learnable trajectory recombination model that takes as input a set of predicted trajectories for each agent and outputs its consistent reordered recombination. We report our results on the Interaction multi-agent prediction challenge and rank $1^{st}$ on the online test leaderboard.

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