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].

Core Viewpoint - The article emphasizes the importance of high-quality 4D automatic annotation in the development of intelligent driving, highlighting that while model algorithms are crucial for initial capabilities, the future lies in efficiently obtaining vast amounts of automatically annotated data [2][3]. Summary by Sections 4D Data Annotation Process - The article outlines the complexity of automatically annotating dynamic obstacles, which involves multiple modules and requires advanced engineering skills to effectively utilize large models and systems [2][3]. - The process includes offline 3D target detection, tracking, post-processing optimization, and sensor occlusion optimization [4][5]. Challenges in Automatic Annotation - High requirements for spatiotemporal consistency, necessitating precise tracking of dynamic targets across frames [7]. - Complexity in multi-modal data fusion, requiring synchronization of data from various sensors [7]. - Difficulty in generalizing dynamic scenes due to unpredictable behaviors of traffic participants and environmental interferences [7]. - The contradiction between annotation efficiency and cost, as high-precision 4D automatic annotation relies on manual verification, leading to long cycles and high costs [7]. - High requirements for scene generalization in mass production, with challenges in data extraction across different cities, roads, and weather conditions [8]. Course Offerings - The article promotes a course on 4D automatic annotation, designed to address entry-level challenges and optimize advanced learning [8]. - The course covers the entire process of 4D automatic annotation and core algorithms, including practical exercises [8][9]. - Key topics include dynamic obstacle detection, SLAM reconstruction, static element annotation, and end-to-end truth generation [11][12][14][16]. Instructor Background - The course is taught by an expert with extensive experience in data closure algorithms for autonomous driving, having participated in multiple mass production projects [20]. Target Audience and Prerequisites - The course is suitable for researchers, students, and professionals looking to transition into the field of data closure, requiring a foundational understanding of deep learning and autonomous driving perception algorithms [23][24].

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].