Papers › Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks
Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S. Refaat, Benjamin Sapp
Motion forecasting for autonomous driving is a challenging task because complex driving scenarios result in a heterogeneous mix of static and dynamic inputs. It is an open problem how best to represent and fuse information about road geometry, lane connectivity, time-varying traffic light state, and history of a dynamic set of agents and their interactions into an effective encoding. To model this diverse set of input features, many approaches proposed to design an equally complex system with a diverse set of modality specific modules. This results in systems that are difficult to scale, extend, or tune in rigorous ways to trade off quality and efficiency. In this paper, we present Wayformer, a family of attention based architectures for motion forecasting that are simple and homogeneous. Wayformer offers a compact model description consisting of an attention based scene encoder and a decoder. In the scene encoder we study the choice of early, late and hierarchical fusion of the input modalities. For each fusion type we explore strategies to tradeoff efficiency and quality via factorized attention or latent query attention. We show that early fusion, despite its simplicity of construction, is not only modality agnostic but also achieves state-of-the-art results on both Waymo Open MotionDataset (WOMD) and Argoverse leaderboards, demonstrating the effectiveness of our design philosophy
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 | Wayformer | DAC (K=6) | 0.9893 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | MR (K=1) | 0.5716 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | MR (K=6) | 0.1186 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | brier-minFDE (K=6) | 1.7408 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | minADE (K=1) | 1.636 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | minADE (K=6) | 0.7676 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | minFDE (K=1) | 3.6559 | #6 of 299 | Archive leaderboard | report |
| Motion Forecasting | Argoverse CVPR 2020 | Wayformer | minFDE (K=6) | 1.1616 | #6 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