We present a novel framework for motion forecasting with Dual Consistency Constraints and Multi-Pseudo-Target supervision. The motion forecasting task predicts future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of DCMS is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during the training stage. In addition, we design a novel self-ensembling scheme to obtain accurate pseudo targets to model the multi-modality in motion forecasting through supervision with multiple targets explicitly, namely Multi-Pseudo-Target supervision. Our experimental results on the Argoverse motion forecasting benchmark show that DCMS significantly outperforms the state-of-the-art methods, achieving 1st place on the leaderboard. We also demonstrate that our proposed strategies can be incorporated into other motion forecasting approaches as general training schemes.

翻译：我们提出了一种基于双重一致性约束与多伪目标监督的运动预测框架DCMS。运动预测任务通过整合历史时空信息预测车辆的未来轨迹。DCMS的核心创新在于提出了双重一致性约束机制，在训练阶段通过对预测轨迹施加时空扰动下的正则化约束。此外，我们设计了一种新颖的自集成方案，通过显式多目标监督（即多伪目标监督）获得精确的伪目标，以有效建模运动预测中的多模态特性。在Argoverse运动预测基准上的实验结果表明，DCMS显著超越当前最优方法，荣登排行榜首位。我们还证明提出的策略可作为通用训练方案融入其他运动预测方法。