端到端落地小班课：核心算法&实战讲解（7个project）

Core Insights - The article discusses the evolving recruitment landscape in the autonomous driving sector, highlighting a shift in demand from perception roles to end-to-end, VLA, and world model positions [2] - A new advanced course focused on end-to-end production in autonomous driving has been designed, emphasizing practical applications and real-world experience [2][4] Course Overview - The course is structured to cover various core algorithms, including one-stage and two-stage end-to-end methods, navigation information applications, reinforcement learning, and trajectory optimization [2] - The course aims to provide in-depth knowledge and practical skills necessary for production in autonomous driving, with a focus on real-world applications and challenges [2][4] Chapter Summaries - Chapter 1: Overview of End-to-End Tasks Discusses the integration of perception tasks and the learning-based design of control algorithms, which are essential skills for companies in the end-to-end era [7] - Chapter 2: Two-Stage End-to-End Algorithm Framework Introduces the modeling methods of two-stage frameworks and the information transfer between perception and planning, including practical examples [8] - Chapter 3: One-Stage End-to-End Algorithm Focuses on one-stage frameworks that allow for lossless information transfer, presenting various methods and practical learning experiences [9] - Chapter 4: Production Application of Navigation Information Covers the critical role of navigation information in autonomous driving, detailing mainstream navigation map formats and their integration into models [10] - Chapter 5: Introduction to RL Algorithms in Autonomous Driving Explains the necessity of reinforcement learning in conjunction with imitation learning to enhance the model's ability to generalize [11] - Chapter 6: Trajectory Output Optimization Engages participants in practical projects focusing on algorithms based on imitation learning and reinforcement learning [12] - Chapter 7: Safety Net Solutions - Spatiotemporal Joint Planning Discusses post-processing logic to ensure model accuracy and stability in trajectory outputs, introducing common smoothing algorithms [13] - Chapter 8: Experience Sharing on End-to-End Production Provides insights on practical experiences in production, addressing data, models, scenarios, and strategies for system capability enhancement [14] Target Audience - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, reinforcement learning, and programming skills [15][17]