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].