Papers › SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
SSL-Lanes: Self-Supervised Learning for Motion Forecasting in Autonomous Driving
Prarthana Bhattacharyya, Chengjie Huang, Krzysztof Czarnecki
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}.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | DAC (K=6) | 0.9844 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | MR (K=1) | 0.5671 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | MR (K=6) | 0.1326 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | brier-minFDE (K=6) | 1.9433 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | minADE (K=1) | 1.6342 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | minADE (K=6) | 0.8401 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | minFDE (K=1) | 3.5643 | #59 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | SSL-Lanes | minFDE (K=6) | 1.2493 | #59 of 299 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections