Collect some World Models (for Autonomous Driving) papers.

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• CVPR 2024 Workshop & Challenge | OpenDriveLab Track #4: Predictive World Model.

  Serving as an abstract spatio-temporal representation of reality, the world model can predict future states based on the current state. The learning process of world models has the potential to elevate a pre-trained foundation model to the next level. Given vision-only inputs, the neural network outputs point clouds in the future to testify its predictive capability of the world.

• CVPR 2023 Workshop on Autonomous Driving CHALLENGE 3: ARGOVERSE CHALLENGES, 3D Occupancy Forecasting using the Argoverse 2 Sensor Dataset. Predict the spacetime occupancy of the world for the next 3 seconds.

• Using Occupancy Grids for Mobile Robot Perception and Navigation [paper]

• Yann LeCun: A Path Towards Autonomous Machine Intelligence [paper] [Video]

• CVPR'23 WAD Keynote - Ashok Elluswamy, Tesla [Video]

• Wayve Introducing GAIA-1: A Cutting-Edge Generative AI Model for Autonomy [blog]

  World models are the basis for the ability to predict what might happen next, which is fundamentally important for autonomous driving. They can act as a learned simulator, or a mental “what if” thought experiment for model-based reinforcement learning (RL) or planning. By incorporating world models into our driving models, we can enable them to understand human decisions better and ultimately generalise to more real-world situations.

• A survey on multimodal large language models for autonomous driving. WACVW 2024 [Paper] [Code]
• World Models for Autonomous Driving: An Initial Survey. 2024.3, arxiv [Paper]

• [ViDAR] Visual Point Cloud Forecasting enables Scalable Autonomous Driving. CVPR 2024 [Paper] [Code]
• [GenAD] Generalized Predictive Model for Autonomous Driving. CVPR 2024 [Paper] [Data]
• [Cam4DOCC] Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications. CVPR 2024 [Paper] [Code]
• [Drive-WM] Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving. CVPR 2024 [Paper] [Code]
• [DriveWorld] DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving. CVPR 2024 [Code]
• [Panacea] Panacea: Panoramic and Controllable Video Generation for Autonomous Driving. CVPR 2024 [Paper] [Code]
• [MagicDrive] MagicDrive: Street View Generation with Diverse 3D Geometry Control. ICLR 2024 [Paper] [Code]
• [Copilot4D] Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion. ICLR 2024 [Paper]
• [SafeDreamer] SafeDreamer: Safe Reinforcement Learning with World Models. ICLR 2024 [Paper] [Code]
• [RoboDreamer] RoboDreamer: Learning Compositional World Models for Robot Imagination. 2024.4, arxiv [Paper] [Code]
• [LidarDM] LidarDM: Generative LiDAR Simulation in a Generated World. 2024.4, arxiv [Paper] [Code]
• [3D-VLA] 3D-VLA: A 3D Vision-Language-Action Generative World Model. 2024.3, arxiv [Paper]
• [DriveDreamer-2] DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation. 2024.3, arxiv [Paper] [Code]
• [Think2Drive] Think2Drive: Efficient Reinforcement Learning by Thinking in Latent World Model for Quasi-Realistic Autonomous Driving. 2024.2, arxiv [Paper]

• [TrafficBots] TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction. ICRA 2023 [Paper] [Code]
• [WoVoGen] WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation. 2023.12, arxiv [Paper] [Code]
• [CTT] Categorical Traffic Transformer: Interpretable and Diverse Behavior Prediction with Tokenized Latent. 2023.11, arxiv [Paper]
• [OccWorld] OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving. 2023.11, arxiv [Paper] [Code]
• [MUVO] MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations. 2023.11, arxiv [Paper]
• [DrivingDiffusion] DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model. 2023.10, arxiv [Paper] [Code]
• [GAIA-1] GAIA-1: A Generative World Model for Autonomous Driving. 2023.9, arxiv [Paper]
• [ADriver-I] ADriver-I: A General World Model for Autonomous Driving. 2023.9, arxiv [Paper]
• [DriveDreamer] DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving. 2023.9, arxiv [Paper] [Code]
• [UniWorld] UniWorld: Autonomous Driving Pre-training via World Models. 2023.8, arxiv [Paper] [Code]

• [MILE] Model-Based Imitation Learning for Urban Driving. NeurIPS 2022 [Paper] [Code]
• [Symphony] Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation. ICRA 2022 [Paper]
• Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving. IROS 2022 [Paper]

• [Genie] Genie: Generative Interactive Environments. DeepMind [Paper] [Blog]
• [Sora] Video generation models as world simulators. OpenAI [Technical report]
• [IWM] Learning and Leveraging World Models in Visual Representation Learning. Meta AI [Paper]
• [V-JEPA] V-JEPA: Video Joint Embedding Predictive Architecture. Meta AI [Blog] [Paper] [Code]
• [Newton] Newton™ – a first-of-its-kind foundation model for understanding the physical world. Archetype AI [Blog]
• [MAMBA] MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning. ICLR 2024 [Paper] [Code]
• [Compete and Compose] Compete and Compose: Learning Independent Mechanisms for Modular World Models. 2024.4, arxiv [Paper]
• [MagicTime] MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators. 2024.4, arxiv [Paper] [Code]
• [Dreaming of Many Worlds] Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization. 2024.3, arxiv [Paper] [Code]
• [ManiGaussian] ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation. 2024.3, arxiv [Paper] [Code]
• [LWM] World Model on Million-Length Video And Language With RingAttention. 2024.2, arxiv [Paper] [Code]
• Planning with an Ensemble of World Models. OpenReview [Paper]
• [WorldDreamer] WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens. 2024.1, arxiv [Paper] [Code]