Core Viewpoint - The article discusses the distinction between world models and end-to-end models in autonomous driving, clarifying that world models are not a specific technology but rather a category of models with certain capabilities. It emphasizes the trend in the industry towards using world models for closed-loop simulation to address the high costs associated with corner cases in autonomous driving [2]. Course Overview - The course on world models in autonomous driving is structured into six chapters, covering the introduction, background knowledge, discussions on general world models, video generation-based models, OCC-based models, and job-related insights in the industry [5][6][7][8][9]. Chapter Summaries - **Chapter 1: Introduction to World Models** This chapter outlines the relationship between world models and end-to-end autonomous driving, discussing the development history and current applications of world models, as well as various streams such as pure simulation, simulation plus planning, and generating sensor inputs [5]. - **Chapter 2: Background Knowledge** This chapter covers foundational knowledge related to world models, including scene representation, Transformer technology, and BEV perception, which are crucial for understanding subsequent chapters [6]. - **Chapter 3: General World Models** Focuses on popular general world models like Marble from Li Fei-Fei's team and Genie 3 from DeepMind, discussing their core technologies and design philosophies [7]. - **Chapter 4: Video Generation-Based World Models** This chapter delves into video generation algorithms, starting with GAIA-1 & GAIA-2 and extending to recent works like UniScene and OpenDWM, highlighting both classic and cutting-edge advancements in this area [8]. - **Chapter 5: OCC-Based World Models** Concentrates on OCC generation algorithms, discussing three major papers and a practical project, emphasizing the potential for these methods to extend into vehicle trajectory planning [9]. - **Chapter 6: World Model Job Topics** This chapter shares practical insights from the instructor's experience, addressing industry applications, pain points, and interview preparation for positions related to world models [9]. Learning Outcomes - The course aims to provide a comprehensive understanding of world models in autonomous driving, equipping participants with the knowledge to achieve a level comparable to one year of experience as a world model algorithm engineer [10].

Core Insights - The article discusses the advancements and challenges faced by Li Auto in the development of its autonomous driving technology, particularly focusing on the end-to-end model and VLA (Vision-Language-Action) integration [2][5][9]. Group 1: Model Performance and Data Utilization - The performance improvement of end-to-end models slows down after reaching a certain amount of training data, specifically after 10 million clips, where the model's MPI (Miles Per Interaction) only doubled in five months [5]. - To enhance model performance, Li Auto adjusted the training data mix, increasing the quantity of generated data, including corner cases, and implementing manual rules for safety and compliance in special scenarios [5][9]. Group 2: VLA Integration and Decision-Making - The introduction of VLA aims to enhance the decision-making capabilities of the end-to-end model, addressing issues such as illogical behavior, lack of deep thinking in decision-making, and insufficient preventive judgment based on scenarios [5][6]. - VLA incorporates spatial intelligence, linguistic intelligence, and action policy, allowing the model to understand and communicate spatial information effectively, and generate smooth driving trajectories using diffusion models [6][9]. Group 3: Simulation and Testing Efficiency - Li Auto upgraded its model evaluation methods by utilizing a world model for closed-loop simulation and testing, significantly reducing testing costs from 18.4 per kilometer to 0.53 per kilometer [9][11]. - The closed-loop training framework AD-R1 was introduced, allowing for efficient data management and reinforcement learning, with high-value data being processed through a series of steps back to the cloud platform [11][12]. Group 4: Computational Power and Resources - Li Auto's total computational power is 13 EFLOPS, with 3 EFLOPS dedicated to inference and 10 EFLOPS for training, utilizing 50,000 training and inference cards [13]. - The emphasis on inference power is crucial in the VLA era, as it is necessary for generating simulation training environments [13].