端到端量产这件「小事」，做过的人才知道有多痛

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].