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 this https URL

通过引入 Forecast-MAE，一种专为自我监督学习运动预测任务设计的掩模自编码器框架的扩展，利用标准 Transformer 块以及最小的内在偏差，我们在具有挑战性的 Argoverse 2 运动预测基准测试上进行的实验表明，Forecast-MAE 取得了与依赖于监督学习和复杂设计的最先进方法竞争性的性能，并且明显优于以前的自我监督学习方法。