← Blog World Model for Autonomous Driving Oct 3, 2026 · Updated Oct 4, 2026 自车规划与控制 WAM-Flow: Parallel Coarse-to-Fine Motion Planning via Discrete Flow Matching for Autonomous Driving ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving WorldRFT: Latent World Model Planning with Reinforcement Fine-Tuning for Autonomous Driving Latent Chain-of-Thought World Modeling for End-to-End Autonomous Driving OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model DynVLA: Learning World Dynamics for Action Reasoning in Autonomous Driving World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model AdaWM: Adaptive World Model based Planning for Autonomous Driving Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving (SSR) DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving CoIRL-AD: Collaborative-Competitive Imitation-Reinforcement Learning in Latent World Models for Autonomous Driving ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2) End-to-End Driving with Online Trajectory Evaluation via BEV World Model (WoTE) 联合预测与规划 DriveVA: Video Action Models are Zero-Shot Drivers DriveLaW: Unifying Planning and Video Generation in a Latent Driving World FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving (FSDrive) From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction Epona: Autoregressive Diffusion World Model for Autonomous Driving DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers 视频与多模态未来生成 UniFuture: A 4D Driving World Model for Future Generation and Perception RAYNOVA: Scale-Temporal Autoregressive World Modeling in Ray Space GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generation OmniNWM: Unifying the State-Action-Reward Triad for Closed-Loop Panoramic Driving Navigation World Models MAD: Motion Appearance Decoupling for efficient Driving World Models CausalDrive: Real-time Causal World Models for Autonomous Driving DCARL: A Divide-and-Conquer Framework for Autoregressive Long-Trajectory Video Generation DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation MaskGWM: A Generalizable Driving World Model with Video Mask Reconstruction GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control Generating Multimodal Driving Scenes via Next-Scene Prediction (UMGen) STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation Overcoming Challenges of Long-Horizon Prediction in Driving World Models (Orbis) ReSim: Reliable World Simulation for Autonomous Driving GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control 占据与语义未来预测 SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model DINO-Foresight: Looking into the Future with DINO Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers (FUTURIST) DIO: Decomposable Implicit 4D Occupancy-Flow World Model OccProphet: Pushing the Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with an Observer-Forecaster-Refiner Framework Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving (PreWorld) Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries OccTENS: 3D Occupancy World Model via Temporal Next-Scale Prediction I2-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving LiDAR时序建模 U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences LaGen: Towards Autoregressive LiDAR Scene Generation GEM: Generating LiDAR World Model via Deformable Mamba LiSTAR: Ray-Centric World Models for 4D LiDAR Sequences in Autonomous Driving Towards foundational LiDAR world models with efficient latent flow matching 多交通参与者仿真 Long-term Traffic Simulation via Structured Autoregressive Modeling SceneDiffuser++: City-Scale Traffic Simulation via a Generative World Model 3D驾驶场景生成 InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models LSD-3D: Large-Scale 3D Driving Scene Generation with Geometry Grounding 4D驾驶场景重建与新视角合成 DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration 条件场景合成与编辑 UniScene: Unified Occupancy-centric Driving Scene Generation DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks (Dream4Drive) LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control 场景理解与联合建模 GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation UniDrive-WM: Unified Understanding, Planning and Generation World Model For Autonomous Driving HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models 当前场景感知 GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction Semi-SMD: Semi-Supervised Metric Depth Estimation via Surrounding Cameras for Autonomous Driving 跨传感器生成 RadarGen: Automotive Radar Point Cloud Generation from Cameras 驾驶员动态预测 Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout 自然语言目标定位 Think Before You Drive: World Model-Inspired Multimodal Grounding 事故视频生成与理解 AVD2: Accident Video Diffusion for Accident Video Description 车辆动力学与逆问题 Unlocking Efficient Vehicle Dynamics Modeling via Analytic World Models 评测与数据资源 WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving UniOcc: A Unified Benchmark for Occupancy Forecasting and Prediction in Autonomous Driving Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models Advancing Off-Road Autonomous Driving: The Large-Scale ORAD-3D Dataset and Comprehensive Benchmarks SimWorld: A Unified Benchmark for Simulator-Conditioned Scene Generation via World Model