RMP: A Random Mask Pretrain Framework for Motion Prediction
Yi Yang Qingwen Zhang Thomas Gilles Nazre Batool John Folkesson
As the pretraining technique is growing in popularity, little work has been
done on pretrained learning-based motion prediction methods in autonomous
driving. In this paper, we propose a framework to formalize the pretraining
task for trajectory prediction of traffic participants. Within our framework,
inspired by the random masked model in natural language processing (NLP) and
computer vision (CV), objects' positions at random timesteps are masked and
then filled in by the learned neural network (NN). By changing the mask
profile, our framework can easily switch among a range of motion-related tasks.
We show that our proposed pretraining framework is able to deal with noisy
inputs and improves the motion prediction accuracy and miss rate, especially
for objects occluded over time by evaluating it on Argoverse and NuScenes
datasets.