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。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，所以这门课程目前不打算大规模招生， 仅剩「15名」招生 名额...... 仅剩「15个」名额，扫码咨询助理！ 讲师介绍 王路, C9本科+QS50 PhD，已发表CCF-A和CCF-B论文若干。现任国内TOP tier1算法专家，目前从事大模型、世界模型等前沿算法的预研和量产，所研发算法已成功 落地并量产，拥有丰富的端到端算法研发和实战经验。 课程大纲 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 近期和业内一位做招聘的朋友聊了聊，他们反馈中游车企和Tier1 开始铺 人力和资源跟进端到端。但面试的候选人往往只懂一部分，甚至有些还停留在论文层面， 根本没有量产经验和优化能力，端到端 ...

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 最近和业内专家讨论了导航信息SD如何应用到自动驾驶中，分享给大家： 图商提供的导航信息SD/SD Pro目前已经在很多量产方案上使用了。导航可以提供车道、粗粒度的waypoint等信息，相当于给司机提供了一个粗略的全局和局部视 野，将导航信息应用到车端模型上也就顺水渠成。目前来看，导航模块的核心职责有两个： 当然还有非常重要的一part，提供参考线reference line，这是下游规控强需的信息，有了参考线，可以极大的减轻规划的压力，相当于车辆已经有一条行驶的参考路 线，只需在细化即可。 除此之外，还可以提供规划约束与优先级、路径监控和重规划。 1. 车道级的全局路径规划：搜索一条目标车道的最优lane sequence； 2. 给行为规划提供明确的语义指导，方便车辆提前准备变道、减速、让行； 具体涉及到自车定位、道路结构构建和感知定位匹配可以参考下图： 在两段式中，导航输入到感知模型中，输出navi path，navi path作为ml planner的输入进而预测自车的行驶轨迹。 本文均出自平台最新推 ...

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 最近和业内专家讨论了导航信息SD如何应用到自动驾驶中，分享给大家： 图商提供的导航信息SD/SD Pro目前已经在很多量产方案上使用了。导航可以提供车道、粗粒度的waypoint等信息，相当于给司机提供了一个粗略的全局和局部视 野，将导航信息应用到车端模型上也就顺水渠成。目前来看，导航模块的核心职责有两个： 当然还有非常重要的一part，提供参考线reference line，这是下游规控强需的信息，有了参考线，可以极大的减轻规划的压力，相当于车辆已经有一条行驶的参考路 线，只需在细化即可。 除此之外，还可以提供规划约束与优先级、路径监控和重规划。 1. 车道级的全局路径规划：搜索一条目标车道的最优lane sequence； 2. 给行为规划提供明确的语义指导，方便车辆提前准备变道、减速、让行； 具体涉及到自车定位、道路结构构建和感知定位匹配可以参考下图： 在两段式中，导航输入到感知模型中，输出navi path，navi path作为ml planner的输入进而预测自车的行驶轨迹。 在一段式框架中，SD ...

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 最近很多中游厂商联系自动驾驶之心，想要挖掘端到端、VLA方向的技术人才，明年会投入更多的资源进行落地，对于经验丰富的专家级人才，基本上都是百万年 薪起步了。 针对工业界明确的需求，自动驾驶之心联合了诸多大佬 大佬开展了 《面向量产的端到端实战小班课》、《端到端与VLA自动驾驶小班课》和《自动驾驶VLA和大 模型实战课程》！ 入门、进阶、求职全部打通！ 扫码报名！抢占课程名额 端到端与VLA自动驾驶课程 由工业界大佬带队！ 这门课程则聚焦在端到端自动驾驶的宏观领域，梳理一段式/两段式方向的重点算法和理论基础，详细讲解了BEV感知、大语言模型、扩散模 型和强化学习。课程设计了两大实战：基于扩散模型的Diffusino Planner和基于VLA的ORION算法。课程大纲如下： 课程老师介绍：Jason, C9本科+QS50 PhD，已发表CCF-A论文2篇，CCF-B论文若干。现任国内TOP主机厂算法专家，目前从事端到端、大模型、世界模型等前沿算 法的预研和量产，并已主持和完成多项自动驾驶感知和端到端算法的产品量 ...

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 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 into eight chapters, covering various aspects of end-to-end algorithms, including task overview, two-stage and one-stage frameworks, navigation information applications, reinforcement learning, trajectory optimization, and production experience sharing [5][7][8][9][10][11][12][13][14] - The first chapter introduces the integration of perception tasks and learning-based control algorithms, which are essential skills for companies in the end-to-end era [7] - The second chapter focuses on the two-stage end-to-end algorithm framework, discussing its modeling and information transfer between perception and planning [8] - The third chapter covers one-stage end-to-end algorithms, emphasizing their performance advantages and various frameworks [9] - The fourth chapter highlights the critical role of navigation information in autonomous driving and its integration into end-to-end models [10] - The fifth chapter introduces reinforcement learning algorithms, addressing the limitations of imitation learning and the need for generalization [11] - The sixth chapter involves practical projects on trajectory output optimization, combining imitation and reinforcement learning [12] - The seventh chapter discusses post-processing logic for trajectory smoothing and reliability in production [13] - The final chapter shares production experiences from multiple perspectives, focusing on tools and strategies for real-world applications [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]

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]

Core Viewpoint - The article discusses the evolving recruitment demands in the autonomous driving sector, highlighting a shift from perception roles to end-to-end, VLA, and world model positions, indicating a broader technical skill requirement for candidates [1][2]. Group 1: Course Overview - The course titled "End-to-End Practical Class for Mass Production" focuses on practical applications in autonomous driving, covering various algorithms and real-world production experiences [2][3]. - The course is designed for a limited number of participants, with only 25 spots available, emphasizing a targeted approach to training [2][3]. Group 2: Course Structure - Chapter 1 introduces the overview of end-to-end tasks, discussing the integration of perception tasks and the learning-based control algorithms that are becoming mainstream [6]. - Chapter 2 covers the two-stage end-to-end algorithm framework, explaining the modeling methods and the information transfer between perception and planning [7]. - Chapter 3 focuses on the one-stage end-to-end algorithm framework, highlighting its advantages in information transmission and introducing various one-stage framework solutions [8]. - Chapter 4 discusses the application of navigation information in autonomous driving, detailing the formats and encoding methods of navigation maps [9]. - Chapter 5 introduces reinforcement learning algorithms, emphasizing the need for these methods to complement imitation learning in autonomous driving [10]. - Chapter 6 involves practical projects on trajectory output optimization, combining imitation learning and reinforcement learning techniques [11]. - Chapter 7 presents fallback solutions through spatiotemporal planning, focusing on trajectory smoothing algorithms to enhance output reliability [12]. - Chapter 8 shares mass production experiences, analyzing how to effectively use tools and strategies to improve system capabilities [13]. Group 3: Target Audience and Requirements - The course is aimed at advanced learners with a foundational understanding of autonomous driving algorithms, though those with weaker backgrounds can still participate [14][15]. - Participants are required to have access to a GPU with recommended specifications and familiarity with various algorithms and programming languages [15].

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].