Core Viewpoint - The article emphasizes the importance of end-to-end production in autonomous driving technology, highlighting the need for practical experience in various algorithms and applications to address real-world challenges in the industry [2][7]. Course Overview - The course is designed to provide in-depth knowledge on end-to-end production techniques, focusing on key algorithms such as one-stage and two-stage frameworks, reinforcement learning, and trajectory optimization [2][4]. - It includes practical projects that cover the entire process from theory to application, ensuring participants gain hands-on experience [2][12]. Instructor Background - The instructor, Wang Lu, is a top-tier algorithm expert with a strong academic background and extensive experience in developing and implementing advanced algorithms for autonomous driving [3]. Course Structure - The course consists of eight chapters, each focusing on different aspects of end-to-end algorithms, including: 1. Overview of end-to-end tasks and integration of perception and control systems [7]. 2. Two-stage end-to-end algorithm frameworks and their advantages [8]. 3. One-stage end-to-end algorithms with a focus on performance [9]. 4. Application of navigation information in autonomous driving [10]. 5. Introduction to reinforcement learning algorithms and training strategies [11]. 6. Optimization of trajectory outputs using various algorithms [12]. 7. Post-processing strategies for ensuring reliable outputs [13]. 8. Sharing of production experiences and strategies for real-world applications [14]. Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, including familiarity with reinforcement learning and diffusion models [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]

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]. Group 1: Course Overview - The course is designed to cover essential algorithms related to end-to-end production, including one-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][6]. Group 2: Course Structure - The course consists of eight chapters, each addressing different aspects of end-to-end production, such as task overview, algorithm frameworks, navigation information applications, and trajectory output optimization [9][10][11][12][13][14][15][16]. - The final chapter will share production experiences from various perspectives, including data, models, and strategies for system enhancement [16]. Group 3: Target Audience and Requirements - The course is aimed at advanced learners with a background in autonomous driving, reinforcement learning, and programming, although those with weaker foundations can still participate [17][18]. - Participants are required to have access to a GPU with recommended specifications and familiarity with relevant algorithms and programming languages [18].

Core Insights - The article emphasizes the growing demand for end-to-end production talent in the automotive industry, highlighting a paradox where job seekers are abundant, yet companies struggle to find qualified candidates [1][3]. Course Overview - A newly designed end-to-end production course aims to address the skills gap in the industry, focusing on practical applications and real-world scenarios over three months [3][5]. - 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]. Course Content - **Chapter 1: Overview of End-to-End Tasks** - Discusses the integration of perception tasks and the learning-based control algorithms that are becoming mainstream in autonomous driving [10]. - **Chapter 2: Two-Stage End-to-End Algorithms** - Introduces the two-stage framework, its modeling methods, and the flow of information between perception and planning [11]. - **Chapter 3: One-Stage End-to-End Algorithms** - Focuses on one-stage frameworks that allow for lossless information transfer, enhancing performance compared to two-stage methods [12]. - **Chapter 4: Application of Navigation Information** - Explains the critical role of navigation data in autonomous driving and how it can be effectively integrated into end-to-end models [13]. - **Chapter 5: Introduction to Reinforcement Learning Algorithms** - Highlights the necessity of reinforcement learning to complement imitation learning, enabling machines to generalize better [14]. - **Chapter 6: Trajectory Output Optimization** - Covers practical projects involving imitation learning and reinforcement learning algorithms for trajectory planning [15]. - **Chapter 7: Contingency Planning - Spatiotemporal Joint Planning** - Discusses post-processing logic to ensure reliable trajectory outputs, including smoothing algorithms [16]. - **Chapter 8: Experience Sharing in End-to-End Production** - Provides insights on practical strategies and tools for enhancing system capabilities in real-world applications [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 Schedule - The course is set to begin on November 30, with a structured timeline for unlocking chapters and providing support through offline videos and online Q&A sessions [20].

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]