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：我们提出了一种新颖的运动预测框架，该框架融合了双重一致性约束与多伪目标监督。运动预测任务通过整合过去时刻的时空信息来预测车辆的未来轨迹。DCMS的核心设计在于，在训练阶段引入双重一致性约束，对空间扰动和时间扰动下的预测轨迹进行正则化。此外，我们设计了一种新颖的自集成方案，通过显式利用多个目标进行监督（即多伪目标监督）来获取精确的伪目标，从而对运动预测中的多模态特性进行建模。在Argoverse运动预测基准上的实验结果表明，DCMS显著优于现有最先进方法，在排行榜上位列第一。我们还证明，所提出的策略可作为通用训练方案集成到其他运动预测方法中。