Core Viewpoint - The article discusses the challenges and methodologies in automating the labeling of training data for occupancy networks (OCC) in autonomous driving, emphasizing the need for high-quality data to improve model generalization and safety [2][10]. Group 1: OCC and Its Importance - The occupancy network aims to partition space into small grids to predict occupancy, addressing irregular obstacles like fallen trees and other background elements [3][4]. - Since Tesla's announcement of OCC in 2022, it has become a standard in pure vision autonomous driving solutions, leading to a high demand for training data labeling [2][4]. Group 2: Challenges in Automated Labeling - The main challenges in 4D automated labeling include: 1. High temporal and spatial consistency requirements for tracking dynamic objects across frames [9]. 2. Complexity in fusing multi-modal data from various sensors [9]. 3. Difficulty in generalizing to dynamic scenes due to unpredictable behaviors of traffic participants [9]. 4. The contradiction between labeling efficiency and cost, as high precision requires manual verification [9]. 5. High requirements for generalization in production scenarios, necessitating data extraction from diverse environments [9]. Group 3: Training Data Generation Process - The common process for generating OCC training ground truth involves: 1. Ensuring consistency between 2D and 3D object detection [8]. 2. Comparing with edge models [8]. 3. Involving manual labeling for quality control [8]. Group 4: Course Offerings - The article promotes a course on 4D automated labeling, covering the entire process and core algorithms, aimed at learners interested in the autonomous driving data loop [10][26]. - The course includes practical exercises and addresses real-world challenges in the field, enhancing algorithmic capabilities [10][26]. Group 5: Course Structure - The course is structured into several chapters, including: 1. Basics of 4D automated labeling [11]. 2. Dynamic obstacle labeling [13]. 3. Laser and visual SLAM reconstruction [14]. 4. Static element labeling based on reconstruction [16]. 5. General obstacle OCC labeling [18]. 6. End-to-end ground truth labeling [19]. 7. Data loop topics, addressing industry pain points and interview preparation [21].

Core Viewpoint - The article emphasizes the importance of high-quality 4D data automatic annotation in the development of autonomous driving systems, highlighting that model algorithms are crucial for initial development but not sufficient for advanced capabilities [3][4]. Group 1: Industry Trends - A new player in the autonomous driving sector has rapidly advanced its intelligent driving capabilities, surpassing competitors like Xiaopeng within six months, leading to a talent war for engineers in the industry [2]. - The industry consensus indicates that the future of intelligent driving relies on vast amounts of automatically annotated data, marking a shift towards high-quality 4D data annotation as a critical component for mass production [3][4]. Group 2: Challenges in Data Annotation - The main challenges in 4D automatic annotation include high requirements for spatiotemporal consistency, complex multi-modal data fusion, difficulties in generalizing dynamic scenes, and the contradiction between annotation efficiency and cost [8][9]. - The automation of dynamic object annotation involves several steps, including offline 3D detection, tracking, post-processing optimization, and sensor occlusion optimization [5][6]. Group 3: Educational Initiatives - The article introduces a course aimed at addressing the challenges of entering the field of 4D automatic annotation, covering the entire process and core algorithms, and providing practical exercises [9][24]. - The course is designed for various audiences, including researchers, students, and professionals looking to transition into the data closure field, requiring a foundational understanding of deep learning and autonomous driving perception algorithms [25].

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15个 方向 学习 路线 千万级4D标注方案应该怎么做？ 最近有幸和很多业内的小伙伴交流，大家普遍形成了一个共识： 模型算法只是智驾能力从0到10的关键，却不是从10到100的核心。未来是海量自动标注数据的时代！ 智能驾驶的量产开发已经到了深水区，各家都投入了大量的精力去做量产落地。其中泛化的核心关键便是如何高效&高质量的获取4D数据自动标注。一方面人工精标 周期长、成本贵，对于量产泛化的关键周期是非常大的阻力，因此高质量的4D自动标注是业内非常重要的一环，无论是3D动态目标、OCC、静态标注还是端到端标 注。 相比于车端的感知算法，自动标注系统更像是一个不同模块组成的系统， 充分利用离线的算力和时序信息，才能得到更好的感知结果， 实际落地的时候，对于工程师 的能力要求上了一个档次，想要把这些大模型大系统玩转的好和高效，也是非常不容易的。 而自从端到端和大语言LLM横空出世以来，大规模无监督的预训练 + 高质量数据集做具体任务的微调， 可能也会成为量产感知算法下一阶段需要发力的方向。同时数 据的联合标注也是当下各家训练模型的实际刚需， ...

Core Viewpoint - The article discusses the evolving landscape of 4D annotation in autonomous driving, emphasizing the shift from traditional SLAM techniques to more advanced methods for static element reconstruction and automatic labeling [1][4]. Group 1: Purpose and Applications of Reconstruction - The primary purposes of reconstruction are to create 3D maps from lidar or multiple cameras and to output vector lane lines and categories [5][6]. - The application of 4D annotation in static elements remains broad, with a focus on lane markings and static obstacles, which require 2D spatial annotations at each timestamp [1][6]. Group 2: Challenges in Automatic Annotation - The challenges in 4D automatic annotation include high temporal consistency requirements, complex multi-modal data fusion, difficulties in generalizing dynamic scenes, conflicts between annotation efficiency and cost, and high demands for scene generalization in production [8][9]. - These challenges hinder the iterative efficiency of data loops in autonomous driving, impacting the system's generalization capabilities and safety [8]. Group 3: Course Structure and Content - The course on 4D automatic annotation covers a comprehensive curriculum, including dynamic obstacle detection, SLAM reconstruction principles, static element annotation based on reconstruction graphs, and the end-to-end truth generation process [9][10][17]. - Each chapter includes practical exercises to enhance understanding and application of the algorithms discussed [9][10]. Group 4: Instructor and Target Audience - The course is led by an industry expert with extensive experience in multi-modal 3D perception and data loop algorithms, having participated in multiple production delivery projects [21]. - The target audience includes researchers, students, and professionals looking to transition into the data loop field, requiring a foundational understanding of deep learning and autonomous driving perception algorithms [24][25].