NAVSIM 新SOTA！SimWAM：从世界建模到轨迹规划新方案（华科白翔团队&东风研发）
Ranking
Overall
69
Content
70
Popularity
68
Observed public metrics from 1 member.
Merged summary
TL;DR - SimWAM transfers traffic-dynamics priors learned through future-video prediction into a standalone trajectory planner, eliminating future-frame generation at inference. It achieves a reported 91.5 PDMS on NAVSIM navtest with lower inference overhead.
- Joint flow matching co-trains separate Video and Action Experts, using video prediction as training-only supervision.
- GRPO reinforcement learning explores candidate trajectories via an SDE and directly optimizes combined driving rewards.
- Video co-training raises PDMS from 86.6 to 90.3; reinforcement learning further improves it to 91.5.
- The modular design supports independently scaling the video and action models and shows zero-shot transfer to nuScenes.
Sources (1)
NAVSIM 新SOTA！SimWAM：从世界建模到轨迹规划新方案（华科白翔团队&东风研发）
Public signals
Hugging Face upvotes 109
TL;DR - SimWAM transfers traffic-dynamics priors learned through future-video prediction into a standalone trajectory planner, eliminating future-frame generation at inference. It achieves a reported 91.5 PDMS on NAVSIM navtest with lower inference overhead.
- Joint flow matching co-trains separate Video and Action Experts, using video prediction as training-only supervision.
- GRPO reinforcement learning explores candidate trajectories via an SDE and directly optimizes combined driving rewards.
- Video co-training raises PDMS from 86.6 to 90.3; reinforcement learning further improves it to 91.5.
- The modular design supports independently scaling the video and action models and shows zero-shot transfer to nuScenes.