Core Viewpoint - The article emphasizes the importance of end-to-end production in the automotive industry, highlighting the scarcity of qualified talent and the need for comprehensive training programs to address various challenges in this field [1][3]. Course Overview - The course is designed to cover essential algorithms related to end-to-end production, including single-stage and two-stage frameworks, reinforcement learning applications, and trajectory optimization [3][9]. - It aims to provide practical experience and insights into production challenges, focusing on real-world applications and expert guidance [3][16]. Course Structure - Chapter 1 introduces the overview of end-to-end tasks, discussing the integration of perception and control algorithms, and the importance of efficient data handling [9]. - Chapter 2 focuses on the two-stage end-to-end algorithm framework, explaining its modeling and information transfer processes [10]. - Chapter 3 covers the single-stage end-to-end algorithm framework, emphasizing its advantages in information transmission and performance [11]. - Chapter 4 discusses the application of navigation information in autonomous driving, detailing the formats and encoding methods of navigation maps [12]. - Chapter 5 introduces reinforcement learning algorithms, highlighting their necessity in complementing imitation learning for better generalization [13]. - Chapter 6 involves practical projects on trajectory output optimization, combining imitation and reinforcement learning techniques [14]. - Chapter 7 presents fallback strategies for trajectory planning, focusing on smoothing algorithms to enhance output reliability [15]. - Chapter 8 shares production experiences from various perspectives, offering strategies for optimizing system capabilities [16]. Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [17][18].