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 ...

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].

作者 | 张云聪 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/28731517852 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 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 目前不少讲Flow Matching的文章都上来一大堆概念，一大堆公式，搞得人头皮发麻，但实际上这个算法没 那么复杂，代码也很容易理解。 本文不推导公式、无高深数学概念即可理解flow matching算法，并完成一个简单的代码实战。 算法原理 Related Works Flow Matching是一种 生成式模型 。 最简单的生成式模型，目标就是没输入的情况下，就能生成与给定目标集中的样本相近的样本。 举个例子，可以直接无提示的用diffusion模型来生成图片。 带提示的生成式任务是可以基于无提示的生成式任务简单实现的，这里我们先只考虑无提示的生成式任 务。 由于我们一般学的是一个映射，拿一个空输入映射成不同的样本不太符合映射的定义，因此，我们一般实 际上会生成一堆随机值作为输入， ...

Algorithm Overview - Flow Matching is a generative model that aims to generate samples similar to a given target set without any input [3][4] - The model learns a direction of movement from a source point to a target point, effectively generating new samples by iteratively adjusting the position towards the target [14][17] Training and Inference - During training, the model samples points along the line connecting source and target, learning the average slope from multiple connections [16][17] - In inference, the model starts from a noise point and moves towards the target, gradually collapsing to a specific state as it approaches the target [17][18] Code Implementation - The implementation involves generating random inputs, predicting the slope using a neural network, and applying an optimization process to minimize the loss between predicted and target slopes [18][19] - The code includes hyperparameters for dimensions, sample sizes, and training epochs, demonstrating a straightforward approach to implementing the Flow Matching algorithm [19][25] Advanced Applications - The model can be adapted to generate samples based on prompts, allowing for more controlled generation by segmenting the target distribution [24][29] - A more complex example includes generating handwritten digits from the MNIST dataset, showcasing the model's versatility in handling different types of data [30][32] Model Architecture - The architecture includes a UNet backbone for predicting the velocity field, which enhances performance through multi-scale feature fusion [32][34] - The model incorporates conditional inputs to refine the generation process, ensuring that the output aligns with specified conditions [34][35] Training Process - The training loop involves generating dynamic noise, calculating the loss based on the difference between predicted and actual images, and updating the model parameters accordingly [40][41] - The model is designed to visualize generated samples periodically, providing insights into its performance and output quality [40][41]

Core Insights - The article highlights significant developments in the autonomous driving industry, particularly the performance of Horizon HSD and the advancements in Xiaopeng's VLA2.0, indicating a shift towards end-to-end production models [1][3]. Group 1: Industry Developments - Horizon HSD's performance has exceeded expectations, marking a return to the industry's focus on one-stage end-to-end production, which has a high potential ceiling [1]. - Xiaopeng's VLA2.0, which integrates visual and language inputs, reinforces the notion that value-added (VA) capabilities are central to autonomous driving technology [1]. Group 2: Educational Initiatives - The article discusses a new course titled "Practical Class for End-to-End Production," aimed at sharing production experiences in autonomous driving, focusing on various methodologies including one-stage and two-stage frameworks, reinforcement learning, and trajectory optimization [3][8]. - The course is limited to 40 participants, emphasizing a targeted approach to skill development in the industry [3][5]. Group 3: Course Structure - The course consists of eight chapters covering topics such as end-to-end task overview, two-stage and one-stage algorithm frameworks, navigation information applications, reinforcement learning algorithms, trajectory output optimization, fallback solutions, and production experience sharing [8][9][10][11][12][13][14][15]. - Each chapter is designed to build upon the previous one, providing a comprehensive understanding of the end-to-end production process in autonomous driving [16]. Group 4: Target Audience and Requirements - The course is aimed at advanced learners with a background in autonomous driving algorithms, reinforcement learning, and programming skills, although it is also accessible to those with less experience [16][17]. - Participants are required to have a GPU with recommended specifications and a foundational understanding of relevant mathematical concepts [17].