Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving industry, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new advanced course focused on end-to-end production in autonomous driving has been designed, emphasizing practical applications and real-world experience [2][4] Course Overview - The course is structured into eight chapters, covering various aspects of end-to-end algorithms, including task overview, two-stage and one-stage frameworks, navigation information applications, reinforcement learning, trajectory optimization, and production experience sharing [5][7][8][9][10][11][12][13][14] - The first chapter introduces the integration of perception tasks and learning-based control algorithms, which are essential skills for companies in the end-to-end era [7] - The second chapter focuses on the two-stage end-to-end algorithm framework, discussing its modeling and information transfer between perception and planning [8] - The third chapter covers one-stage end-to-end algorithms, emphasizing their performance advantages and various frameworks [9] - The fourth chapter highlights the critical role of navigation information in autonomous driving and its integration into end-to-end models [10] - The fifth chapter introduces reinforcement learning algorithms, addressing the limitations of imitation learning and the need for generalization [11] - The sixth chapter involves practical projects on trajectory output optimization, combining imitation and reinforcement learning [12] - The seventh chapter discusses post-processing logic for trajectory smoothing and reliability in production [13] - The final chapter shares production experiences from multiple perspectives, focusing on tools and strategies for real-world applications [14] Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [15][17]

Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving industry, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new course titled "End-to-End Practical Class for Mass Production" has been designed to address the skills gap in the industry, focusing on practical applications and mass production experiences [2][4] Course Overview - The course aims to cover core algorithms such as one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - It is structured into eight chapters, each focusing on different aspects of end-to-end autonomous driving systems, including task overview, algorithm frameworks, navigation applications, and production experiences [5][7][8][9][10][11][12][13][14] Target Audience - The course is designed for advanced learners with a background in autonomous driving perception, reinforcement learning, and programming languages like Python and PyTorch [15][16] - It emphasizes practical skills and aims to prepare participants for real-world applications in the autonomous driving sector [2][15] Course Schedule - The course will commence on November 30, with a duration of approximately three months, featuring offline video lectures and online Q&A sessions [15][17]