端到端作为这两年的量产关键词，是各家车企核心的招聘岗位。但市面上真正的量产人才少之又少，模型优化、场景优化、数据优化，再到下游的规划兜底，可以 说端到端是一个全栈的岗位。 从技术的成熟度和工业界的需求来看，端到端需要攻克的难题还有很多。导航信息的引入、强化学习调优、轨迹的建模及优化都有很多门道，目前也是量产第一 线。 为此我们花了三个月的时间设计了端到端量产进阶课程，从实战到落地层层展开。 该课程涉及的核心算法包括：一段式端到端、两段式端到端、导航信息的量产应用、开闭环强化学习、扩散模型+强化学习、自回归+强化学习、时空联合规划等 等，最后分享一些实际的量产经验。很多想进阶或者跳槽的同学苦于没有专家辅导，想转行但实际工作中无法接触到实际的量产优化，简历上往往不够亮眼，遇到 问题连个请教的人都没有。 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 这门课程是自动驾驶之心联合工业界算法专家开设的《面向量产的端到端实战小班课》！课程只有一个重点：聚焦量产。从一段式、两段式、强化学习、导航应 用、轨迹优化、兜底方案再到具体量产经验分享。面向就业直击落地，所以这门课 ...

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].