Core Insights - The article emphasizes the importance of end-to-end production in the automotive industry, highlighting the scarcity of qualified talent in this area [1][3] - A newly designed advanced course on end-to-end production has been developed to address the industry's needs, focusing on practical applications and real-world scenarios [3][5] Course Overview - The course covers essential algorithms such as one-stage and two-stage end-to-end frameworks, reinforcement learning applications, and trajectory optimization techniques [5][10] - It aims to provide hands-on experience and insights into production challenges, making it suitable for individuals looking to advance or transition in their careers [5][18] Course Structure - Chapter 1 introduces the overview of end-to-end tasks, focusing on the integration of perception and control algorithms [10] - Chapter 2 discusses the two-stage end-to-end algorithm framework, including its modeling and information transfer methods [11] - Chapter 3 covers the one-stage end-to-end algorithm framework, emphasizing its advantages in information transmission [12] - Chapter 4 focuses on the application of navigation information in autonomous driving, detailing map formats and encoding methods [13] - Chapter 5 introduces reinforcement learning algorithms, highlighting their necessity alongside imitation learning [14] - Chapter 6 provides practical experience in trajectory output optimization, combining imitation and reinforcement learning [15] - Chapter 7 discusses fallback strategies for trajectory smoothing and reliability in production [16] - Chapter 8 shares production experiences from various perspectives, including data and model optimization [17] Target Audience - The course is designed for advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [18][19] Course Logistics - The course starts on November 30 and spans three months, featuring offline video lectures and online Q&A sessions [20]

Core Viewpoint - The article emphasizes the importance of end-to-end algorithms in autonomous driving, highlighting the shift from rule-based algorithms to learning-based approaches, particularly in the context of congestion and dynamic obstacle scenarios [4][7]. Summary by Sections Overview of End-to-End Tasks - The transition to end-to-end systems merges perception tasks and emphasizes the learning-based approach for control algorithms, which is now a mainstream requirement for companies [7]. Two-Stage End-to-End Algorithm Framework - The two-stage framework is discussed, including its modeling methods and the information transfer between perception and planning, navigation, and control (PNC) [8]. One-Stage End-to-End Algorithm - The one-stage framework allows for lossless information transfer, providing superior performance compared to the two-stage approach. Various one-stage frameworks, including those based on VLA and diffusion methods, are introduced [9]. Navigation Information in Production - Navigation information is crucial for guiding and selecting routes in autonomous driving. The chapter covers mainstream navigation map formats and how to effectively encode and embed navigation maps in end-to-end models [10]. Introduction to Reinforcement Learning Algorithms - The necessity of integrating reinforcement learning with imitation learning is highlighted, as it helps machines learn causal relationships and generalize better in diverse driving scenarios [11]. End-to-End Trajectory Output Optimization - This section focuses on practical projects involving trajectory planning, emphasizing the combination of imitation learning and reinforcement learning techniques [12]. Safety Net Solutions - Spatiotemporal Joint Planning - The importance of post-processing logic to ensure model accuracy is discussed, including trajectory smoothing algorithms to enhance stability and reliability [13]. Experience Sharing in End-to-End Production - The final chapter shares insights on production experiences from various perspectives, including data, models, scenarios, and rules, to improve system capabilities [14]. Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [15][16].