PhysWAM: Physically Consistent World Action Model for Autonomous Driving

arXiv:2609.37970 · cs.RO, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/OpenDriveLab/OpenScene

Terminology

• World Model — 30× in this paper · explained in 7 episodes
• Consistency — 15× in this paper · explained in 3 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Calibration — 10× in this paper · explained in 7 episodes
• Flow Matching — 9× in this paper · explained in 7 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Coupled Point Projection — 6× in this paper
• Embeddings — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Distillation — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Latent World Models — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-consistency — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Cosmos World Foundation Model Platform for Physical AI
• Cosmos 3: Omnimodal World Models for Physical AI
• Qwen3-VL Technical Report
• NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
• Pseudo-Simulation for Autonomous Driving
• GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
• 4D-WAM: 4D Consistent World Modeling for Autonomous Driving
• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
• DriveFuture: Future-Aware Latent World Models for Autonomous Driving
• GAIA-1: A Generative World Model for Autonomous Driving
• LoRA: Low-Rank Adaptation of Large Language Models
• CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving
• OmniNWM: Omniscient Driving Navigation World Models
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• UniFuture: A 4D Driving World Model for Future Generation and Perception
• Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models
• Depth Anything 3: Recovering the Visual Space from Any Views
• Flow Matching for Generative Modeling
• DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
• RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving