  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.
