We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints (MISC). The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of MISC is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during training. Also, to model the multi-modality in motion forecasting, we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision. With explicit constraints from multiple teacher targets, we observe a clear improvement in the prediction performance. Extensive experiments on the Argoverse motion forecasting benchmark and Waymo Open Motion dataset show that MISC significantly outperforms the state-of-the-art methods. As the proposed strategies are general and can be easily incorporated into other motion forecasting approaches, we also demonstrate that our proposed scheme consistently improves the prediction performance of several existing methods.

翻译：我们提出了一种创新的自洽约束引导运动预测框架（MISC）。运动预测任务旨在通过整合历史时空信息来预测车辆的未来轨迹。MISC的核心设计是双一致性约束机制，该机制在训练过程中对预测轨迹施加时空扰动正则化。为建模运动预测中的多模态特性，我们设计了一种新型自集成方案，通过获取精确的教师目标来实施带有模态监督的自洽约束。借助多教师目标的显式约束，我们观察到预测性能的显著提升。在Argoverse运动预测基准和Waymo开放运动数据集上的大量实验表明，MISC明显超越了现有最优方法。由于所提策略具有普适性且易于集成到其他运动预测框架中，我们还证明了该方案能持续提升多种现有方法的预测性能。