  Self-supervised learning (SSL) is an emerging technique that has been
successfully employed to train convolutional neural networks (CNNs) and graph
neural networks (GNNs) for more transferable, generalizable, and robust
representation learning. However its potential in motion forecasting for
autonomous driving has rarely been explored. In this study, we report the first
systematic exploration and assessment of incorporating self-supervision into
motion forecasting. We first propose to investigate four novel self-supervised
learning tasks for motion forecasting with theoretical rationale and
quantitative and qualitative comparisons on the challenging large-scale
Argoverse dataset. Secondly, we point out that our auxiliary SSL-based learning
setup not only outperforms forecasting methods which use transformers,
complicated fusion mechanisms and sophisticated online dense goal candidate
optimization algorithms in terms of performance accuracy, but also has low
inference time and architectural complexity. Lastly, we conduct several
experiments to understand why SSL improves motion forecasting. Code is
open-sourced at \url{https://github.com/AutoVision-cloud/SSL-Lanes}.
