Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with
  Masked Autoencoders
Jie Cheng Xiaodong Mei Ming Liu
This study explores the application of self-supervised learning (SSL) to the
task of motion forecasting, an area that has not yet been extensively
investigated despite the widespread success of SSL in computer vision and
natural language processing. To address this gap, we introduce Forecast-MAE, an
extension of the mask autoencoders framework that is specifically designed for
self-supervised learning of the motion forecasting task. Our approach includes
a novel masking strategy that leverages the strong interconnections between
agents' trajectories and road networks, involving complementary masking of
agents' future or history trajectories and random masking of lane segments. Our
experiments on the challenging Argoverse 2 motion forecasting benchmark show
that Forecast-MAE, which utilizes standard Transformer blocks with minimal
inductive bias, achieves competitive performance compared to state-of-the-art
methods that rely on supervised learning and sophisticated designs. Moreover,
it outperforms the previous self-supervised learning method by a significant
margin. Code is available at https://github.com/jchengai/forecast-mae.