刚做了一份世界模型的学习路线图，面向初学者......

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].