Core Insights - The article discusses the advancements and applications of world models in autonomous driving, particularly focusing on the reconstruction and generation techniques utilized by companies like Li Auto [2][3] - It highlights the importance of understanding world models for newcomers in the field, emphasizing the challenges faced in grasping the concepts and practical applications [4][5] Summary by Sections Section 1: Introduction to World Models - The first chapter provides an overview of world models and their connection to end-to-end autonomous driving, detailing the historical development and current applications [7] - It categorizes different types of world models, including purely simulated models, simulation combined with planning, and those generating sensor inputs and perception results [7] Section 2: Background Knowledge of World Models - The second chapter covers foundational knowledge related to world models, including scene representation, Transformer technology, and BEV perception [8][13] - It emphasizes the significance of these concepts in preparing for advanced discussions on world models [8] Section 3: General World Model Exploration - The third chapter focuses on general world models and recent popular works in autonomous driving, discussing models like Marble, Genie 3, and DriveVLA-W0 [9] Section 4: Video Generation-Based World Models - The fourth chapter delves into video generation algorithms, which are currently the most researched area in both academia and industry, starting with notable works like GAIA-1 & GAIA-2 [10] Section 5: OCC-Based World Models - The fifth chapter centers on OCC generation methods, explaining their potential for extending to vehicle trajectory planning and achieving end-to-end solutions [10] Section 6: World Model Job Topics - The sixth chapter shares practical insights from industry experience, addressing the application of world models, industry pain points, and interview preparation for related positions [11] Course Overview - The course aims to provide a comprehensive understanding of world models, targeting individuals interested in advancing their knowledge and skills in autonomous driving technology [12][15] - It includes a structured schedule with specific topics covered in each chapter, starting from foundational concepts to advanced applications [16][17]

Core Viewpoint - GeoDrive is a next-generation world model system for autonomous driving, developed collaboratively by Peking University, Berkeley AI Research (BAIR), and Li Auto, addressing the limitations of existing methods that rely on 2D modeling and lack 3D spatial perception, which can lead to unreasonable trajectories and distorted dynamic interactions [11][14]. Group 1: Key Innovations - **Geometric Condition-Driven Generation**: Utilizes 3D rendering to replace numerical control signals, effectively solving the action drift problem [6]. - **Dynamic Editing Mechanism**: Injects controllable motion into static point clouds, balancing efficiency and flexibility [7]. - **Minimized Training Cost**: Freezes the backbone model and employs lightweight adapters for efficient data training [8]. - **Pioneering Applications**: Achieves real-time scene editing and VLA (Vision-Language-Action) collaborative planning within the driving world model for the first time [9][10]. Group 2: Technical Details - **3D Geometry Integration**: The system constructs a 3D representation from single RGB images, ensuring spatial consistency and coherence in scene structure [12][18]. - **Dynamic Editing Module**: Enhances the realism of multi-vehicle interaction scenarios during training by allowing flexible adjustments of movable objects [12]. - **Video Diffusion Architecture**: Combines rendered conditional sequences with noise features to enhance 3D geometric fidelity while maintaining photorealistic quality [12][33]. Group 3: Performance Metrics - GeoDrive significantly improves controllability of driving world models, reducing trajectory tracking error by 42% compared to the Vista model, and shows superior performance across various video quality metrics [19][34]. - The model demonstrates effective generalization to new perspective synthesis tasks, outperforming existing models like StreetGaussian in video quality [19][38]. Group 4: Conclusion - GeoDrive sets a new benchmark in autonomous driving by enhancing action controllability and spatial accuracy through explicit trajectory control and direct visual condition input, while also supporting applications like non-ego vehicle perspective generation and scene editing [41].