作者 | 李众力 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1975401888179586737 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 系列小结： 前四篇更多在搭"地基"和"管道"。从这一篇开始，我们正式走到中枢神经—— 异常事件如何被自动发现、自动归因、自动长成问题单，并最终汇总成头部问题。 这一切的核心，就是一个三端统一的 Trigger 框架。 一、从"看 log 找 bug"到"数据自己长成问题单" 在很多自动驾驶团队里，问题排查的原始形态大概是这样： 这种方式的问题你自己也体会过： 1. 强依赖少数"老法师" 2. 云 / 车 / 仿真三套逻辑各写一遍 3. 问题聚类和头部问题发现很难做 如果把整个组织类比成一个"强化学习系统"： 01：最重要的第一步——给整个组织选对 Loss Function（MPI / MPS / MPD）。 02：L4 无人车的实时打点与业务心跳。 03：自动驾驶数据闭 ...

作者 | 李众力 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1973693169792213913 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 前言：看到一个问题有感而发写了一个关于数据闭环的整体的文章，发现引起了很多同学的共鸣，那就再写一写里面我认为很关键的点（踩坑记录），希望也给还在 做自动驾驶的各位同学一些不一样的思路。 原问题：目前各家做的自动驾驶数据闭环平台真的闭环了吗？ 【数据闭环驱动问题解决·01】 把自动驾驶团队当成一个强化学习模型： 为什么我放弃 MPI，改用 MPS / MPD 做"损失函数"？ 2025 年都快结束了，我也来分享一下我们在做数据闭环过程中，踩过的一些坑。 先简单自报下家门： 我在自动驾驶行业做数据相关已经 7 年多了，从最早拿着硬盘从工控机里拷数据，一路做到现在负责一条 L4 物流无人车线上的 数据闭环 & 质量体系。 这几年做下来，我越来越确信一件事： 如果把整个自动驾驶组 ...

Core Viewpoint - The article emphasizes the increasing importance of high-quality data in the development of autonomous driving technology, highlighting the challenges and advancements in automated 4D annotation processes [2][5]. Group 1: Data Requirements and Challenges - The demand for high-quality data has surged with the advancement of autonomous driving technologies, as seen in the full rollout of Li Auto's AD Max V13 model, which utilizes 10 million clips of training data [2]. - The complexity of data annotation has increased, necessitating synchronized sensor data for dynamic and static elements, which poses challenges in ensuring data completeness [2][5]. - Key challenges in automated annotation include high spatiotemporal consistency requirements, complex multi-modal data fusion, and the need for generalization in dynamic scenes [5][6]. Group 2: Automated Annotation Difficulties - The difficulties in automated annotation stem from the need for precise tracking of dynamic targets across frames, which can be disrupted by occlusions and interactions in complex environments [5]. - The integration of various sensor data (LiDAR, cameras, radar) requires addressing issues like coordinate alignment and semantic unification [5]. - The high cost and time associated with manual verification of large datasets hinder the efficiency of high-precision 4D automated annotation [5][6]. Group 3: Course Offerings and Learning Objectives - A new course on 4D automated annotation algorithms has been introduced to address the learning challenges faced by newcomers in the field, covering the entire process and core algorithms [6][21]. - The course aims to equip participants with practical skills in dynamic obstacle detection, SLAM reconstruction, and end-to-end truth generation [6][21]. - The curriculum includes hands-on practice and real-world algorithm applications, focusing on enhancing algorithmic capabilities in the context of autonomous driving [6][21]. Group 4: Course Structure and Target Audience - The course is structured into several chapters, each focusing on different aspects of 4D automated annotation, including dynamic obstacles, SLAM, and static elements [7][9][10][12][15]. - It is designed for individuals with a foundational understanding of deep learning and autonomous driving perception algorithms, including researchers, technical teams, and those looking to transition into data closure roles [23]. - The course will be delivered through online live sessions, code explanations, and Q&A, with materials available for one year post-purchase [21][22].

Core Viewpoint - The article emphasizes the increasing investment in automated labeling by autonomous driving companies, highlighting the challenges and requirements for end-to-end automated labeling in the context of intelligent driving [1][2]. Group 1: Challenges in Automated Labeling - The main challenges in 4D automated labeling include high spatial-temporal consistency requirements, complex multi-modal data fusion, difficulties in generalizing dynamic scenes, contradictions between labeling efficiency and cost, and high requirements for scene generalization in mass production [2][3]. Group 2: Course Overview - The course offers a comprehensive tutorial on the entire process of 4D automated labeling, covering dynamic obstacle detection, SLAM reconstruction, static element labeling, and end-to-end truth generation [3][4][6]. - It includes practical exercises to enhance algorithm capabilities and addresses real-world engineering challenges [2][3]. Group 3: Detailed Course Structure - Chapter 1 introduces the basics of 4D automated labeling, its applications, and the necessary data and algorithms [4]. - Chapter 2 focuses on the process of dynamic obstacle labeling, including offline 3D target detection algorithms and solutions to common engineering issues [6]. - Chapter 3 discusses laser and visual SLAM reconstruction, explaining its importance and common algorithms [7]. - Chapter 4 addresses static element labeling based on reconstruction outputs [9]. - Chapter 5 covers the general obstacle OCC labeling, detailing the input-output requirements and optimization techniques [10]. - Chapter 6 is dedicated to end-to-end truth generation, integrating various elements into a cohesive process [12]. - Chapter 7 provides insights into data scaling laws, industry pain points, and interview preparation for relevant positions [14]. Group 4: Target Audience and Prerequisites - The course is suitable for researchers, students, and professionals looking to transition into the data closure field, requiring a foundational understanding of deep learning and autonomous driving perception algorithms [19][23].

Core Viewpoint - The article emphasizes the increasing investment in automatic labeling by autonomous driving companies, highlighting the challenges and complexities involved in 4D automatic labeling, which integrates 3D spatial data with temporal dimensions [1][2]. Group 1: Challenges in Automatic Labeling - The main difficulties in 4D automatic labeling include high requirements for temporal consistency, complex multi-modal data fusion, challenges in generalizing dynamic scenes, conflicts between labeling efficiency and cost, and high demands for scene generalization in mass production [2][3]. Group 2: Course Overview - The course offers a comprehensive tutorial on the entire process of 4D automatic labeling, covering core algorithms and practical applications, aimed at enhancing algorithmic capabilities through real-world examples [2][3][4]. - Key topics include dynamic obstacle detection, SLAM reconstruction principles, static element labeling based on reconstruction graphs, and the mainstream paradigms of end-to-end labeling [3][4][5][6]. Group 3: Detailed Course Structure - Chapter 1 introduces the basics of 4D automatic labeling, its applications, required data, and algorithms involved, focusing on system time-space synchronization and sensor calibration [4]. - Chapter 2 delves into the process of dynamic obstacle labeling, covering offline 3D target detection algorithms and practical solutions to common engineering challenges [6]. - Chapter 3 focuses on laser and visual SLAM reconstruction, discussing its importance and the basic modules of reconstruction algorithms [7]. - Chapter 4 addresses the automation of static element labeling, emphasizing the need for accurate detection and tracking [9]. - Chapter 5 centers on the OCC labeling of general obstacles, detailing the input-output requirements and the processes for generating ground truth [10]. - Chapter 6 is dedicated to end-to-end ground truth generation, integrating various elements into a cohesive process [12]. - Chapter 7 discusses the data closed-loop topic, sharing insights on industry pain points and interview preparation for relevant positions [14]. Group 4: Target Audience and Course Benefits - The course is designed for researchers, students, and professionals looking to deepen their understanding of 4D automatic labeling and enhance their algorithm development capabilities [19][23]. - Participants will gain practical skills in 4D automatic labeling, including knowledge of cutting-edge algorithms and the ability to solve real-world problems [19].

Core Viewpoint - The article discusses the challenges and methodologies in automating the labeling of occupancy data for autonomous driving, emphasizing the importance of the Occupancy Network (OCC) in enhancing model generalization and safety in various driving conditions [2][10]. Group 1: OCC and Its Importance - The Occupancy Network (OCC) is crucial for modeling irregular obstacles such as fallen trees and other non-standard objects, as well as background elements like road surfaces [5][19]. - Since Tesla's announcement of OCC in 2022, it has become a standard feature in visual autonomous driving solutions, leading to a high demand for training data labeling [2][19]. Group 2: Challenges in Automated Labeling - The automation of labeling in the 4D data loop faces several challenges, including high spatial-temporal consistency requirements, complex multi-modal data fusion, and the difficulty of generalizing in dynamic scenes [11][12]. - The need for high precision in 4D automatic labeling often leads to a conflict between labeling efficiency and cost, as manual verification is still required despite the volume of data [11][12]. Group 3: Training Data Generation and Quality Control - The common process for generating training data truth values involves three main methods: 2D-3D object detection consistency, comparison with edge models, and manual intervention for quality control [9][10]. - High-quality automated labeling data can be used for training both vehicle models and cloud-based large models, facilitating continuous optimization [10][12]. Group 4: Course Offerings and Learning Opportunities - The article promotes a course on 4D automatic labeling, which covers the entire process and core algorithms, aiming to address entry-level challenges and optimize advanced learning [10][12]. - The course includes practical exercises and real-world algorithm applications, focusing on dynamic obstacle detection, SLAM reconstruction, and the overall data loop [12][13][20]. Group 5: Instructor and Target Audience - The course is led by an industry expert with extensive experience in data loop algorithms for autonomous driving, having participated in multiple production delivery projects [24]. - The target audience includes researchers, students, and professionals looking to transition into the field of data loops, requiring a foundational understanding of deep learning and autonomous driving perception algorithms [26][31].

Core Viewpoint - The article emphasizes the importance of a complete data closed-loop system in autonomous driving, which includes data collection, annotation, training, simulation validation, and OTA updates. As autonomous driving evolves from Level 2 to higher levels, the volume of data increases exponentially, making the breadth and depth of scenario coverage crucial for system safety [3]. Group 1: Data Closed-Loop Challenges - The data closed-loop engineering faces three core challenges: 1. The "long tail problem," which refers to the difficulty in capturing and incorporating rare but critical extreme scenarios (e.g., extreme weather, complex road conditions, sudden obstacles) into the training system [3]. 2. Data processing efficiency, as each vehicle generates terabytes of data daily due to increased sensor quantity and precision, necessitating effective filtering, annotation, and utilization of this data [3]. 3. Verification difficulties, where traditional testing methods cannot cover all possible scenarios, highlighting the need for a scientific complement between simulation testing and real-world validation [3]. Group 2: Industry Transition - The industry is transitioning from a focus on "function stacking" to "safety-centric" approaches. The challenges of data closed-loop engineering extend beyond technology to include establishing scientific verification standards, improving data processing efficiency, and balancing iteration speed with system stability to maintain a positive feedback loop in data utilization [3]. Group 3: Expert Insights - The article mentions an invitation to a data expert, Ethan, to discuss the deep challenges faced during the mass production process of autonomous driving, focusing on the essence of engineering rather than flashy technology [3].

Core Viewpoint - The article emphasizes the critical importance of high-quality 4D automatic annotation in the development of autonomous driving technology, highlighting the challenges and complexities involved in achieving effective data annotation for dynamic and static elements in various driving scenarios [1][6][7]. Group 1: Industry Trends and Challenges - The industry consensus is that while model algorithms are essential for initial autonomous driving capabilities, they are not sufficient for scaling from basic to advanced functionalities [1]. - Current testing shows that many domestic models struggle with auxiliary driving features, with some achieving a pass rate as low as 1 in 6 [1]. - The shift towards large-scale unsupervised pre-training and high-quality datasets for fine-tuning is seen as a necessary direction for the next phase of perception algorithms in mass production [2]. Group 2: 4D Data Annotation Process - The 4D data annotation process involves multiple complex modules, particularly for dynamic obstacles, which require precise tracking and integration of data from various sensors [2][3]. - Key steps in the dynamic target automatic annotation process include offline 3D detection, tracking, post-processing optimization, and sensor occlusion optimization [4][5]. Group 3: Automation Challenges - High spatial-temporal consistency is required for accurate tracking of dynamic targets across frames, which is complicated by occlusions and interactions in complex environments [6]. - The integration of multi-modal data from different sensors presents challenges in coordinate alignment and semantic unification [6]. - The industry faces difficulties in generalizing models to various driving scenarios, including different cities and weather conditions, which impacts the performance of annotation algorithms [7]. Group 4: Educational Initiatives - The article promotes a specialized course aimed at addressing the challenges of entering the field of 4D automatic annotation, covering the entire process and core algorithms [7][8]. - The course includes practical exercises and real-world applications, focusing on dynamic obstacle detection, SLAM reconstruction, and end-to-end truth generation [10][11][15]. Group 5: Course Structure and Target Audience - The course is structured into several chapters, each focusing on different aspects of 4D automatic annotation, including dynamic obstacles, static elements, and occupancy network (OCC) marking [8][10][14]. - It is designed for individuals with a background in deep learning and autonomous driving perception algorithms, aiming to enhance their practical skills and industry competitiveness [22][23].

Core Viewpoint - The article emphasizes the importance of high-quality data sets for autonomous driving, highlighting the need for efficient and low-cost methods to obtain these data sets through advanced 4D labeling techniques [1][2]. Group 1: Importance of 4D Labeling - The demand for automated 4D labeling is increasing due to the growing complexity of autonomous driving scenarios, which require precise tracking of dynamic and static elements [1][3]. - Automated labeling algorithms are crucial for generating high-precision ground truth data, which can optimize results using full temporal data without being limited by vehicle computing power [1][2]. Group 2: Challenges in Automated Labeling - Key challenges in 4D automated labeling include maintaining high spatial-temporal consistency, complex multi-modal data fusion, and ensuring model generalization across various driving conditions [2][3]. - The industry faces significant pain points such as sensor calibration, occlusion handling, and the need for high-quality automated labeling results [2][3]. Group 3: Course Offerings - The article introduces a course titled "Automated Driving 4D Labeling Employment Class," which aims to address the challenges of entering the field and optimizing advanced learning [2][4]. - The course covers the entire process of 4D automated labeling, including dynamic and static object labeling, occupancy labeling, and end-to-end labeling methodologies [2][4]. Group 4: Course Structure - The course is structured into several chapters, each focusing on different aspects of 4D automated labeling, such as dynamic object detection, SLAM reconstruction, and static element labeling [3][4][5]. - Practical exercises are included in each chapter to enhance understanding and application of the concepts taught [4][5]. Group 5: Target Audience - The course is designed for individuals interested in deepening their knowledge in the autonomous driving data loop, including researchers, students, and professionals looking to transition into this field [18][19].

Core Viewpoint - The article emphasizes the importance of 4D automatic annotation in the autonomous driving industry, highlighting the shift from traditional 2D static element annotation to more efficient 3D scene reconstruction methods [2][3][4]. Group 1: Traditional 2D Annotation Deficiencies - Traditional 2D static element annotation is time-consuming and labor-intensive, requiring repeated work for each timestamp [2]. - The need for 3D scene reconstruction allows for static elements to be annotated only once, significantly improving efficiency [2][3]. Group 2: 4D Automatic Annotation Process - The process of 4D automatic annotation involves several steps, including converting 3D scenes to BEV views and training cloud-based models for automatic annotation [6]. - The cloud-based pipeline is distinct from the vehicle-end model, focusing on high-quality automated annotation that can be used for vehicle model training [6]. Group 3: Challenges in Automatic Annotation - Key challenges in 4D automatic annotation include high temporal consistency requirements, complex multi-modal data fusion, and the difficulty of generalizing dynamic scenes [7]. - The industry faces issues with annotation efficiency and cost, as high-precision 4D automatic annotation often requires manual verification, leading to long cycles and high costs [7]. Group 4: Course Offerings and Learning Opportunities - The article promotes a course on 4D automatic annotation, covering dynamic and static elements, OCC, and end-to-end automation processes [8][9]. - The course aims to provide a comprehensive understanding of the algorithms and practical applications in the field of autonomous driving [8][9]. Group 5: Course Structure and Target Audience - The course is structured into multiple chapters, each focusing on different aspects of 4D automatic annotation, including dynamic obstacle marking, SLAM reconstruction, and end-to-end truth generation [9][11][12][16]. - It is designed for a diverse audience, including researchers, students, and professionals looking to transition into the data loop field [22][24].