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.

本文提出了THOMAS，一个联合多代理轨迹预测框架，可以有效、一致地预测多代理多模态轨迹。 我们提出了一个统一的模型架构，用于同时代理的未来热力图估计，并利用分层和稀疏图像生成进行快速和内存有效的推断。我们报告了我们在Interaction multi-agent prediction 挑战中的结果，并在在线测试排行榜上排名第一。

THOMAS：基于多智能体采样学习的轨迹热力图输出