We propose 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.
翻译:我们提议了一个联合未来预测模型,即联合未来预测模型,可以学习如何生成准确和一致的多试剂未来轨迹。对于这项任务,提出了许多不同的方法,以捕捉模型编码部分的社会互动,然而,对于显示解码器和产出阶段的互动,重点却少得多。因此,预测轨迹不一定彼此一致,往往造成不现实的轨迹重叠。相比之下,我们提议了一个端到端可受训模型,直接学习两组物剂在结构化、图形模型的配方中的互动,以便产生一致的未来轨迹。它为互动环境设定了“Waymo Openmotion数据集”(WOMD)的新最新设计结果。我们还调查了WOMD和一个更大的内部数据集更为复杂的多试器设置,我们在这方面的方法大大改进了轨迹重叠指标,同时在单剂轨迹轨迹指标上取得分数或更好的性能。