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 necessity of large-scale 4D automatic annotation in the second half of intelligent driving, highlighting the increasing demand for higher-level driving capabilities and the limitations of manual annotation methods [2][3]. Group 1: Importance of 4D Automatic Annotation - The shift towards higher-level intelligent driving capabilities necessitates millions of 4D automatic annotations to meet production demands [2]. - Manual annotation efficiency is insufficient for the growing needs of data quality and quantity, making 4D automatic annotation essential [2][3]. - The complexity of current annotation requirements, including the need for time-synchronized sensor data, underscores the importance of automated solutions [3]. Group 2: Challenges in Automatic Annotation - High requirements for spatiotemporal consistency complicate the tracking of dynamic targets across frames, leading to potential annotation errors [4]. - The integration of multimodal data from various sensors presents challenges in data synchronization and semantic unification [5]. - The unpredictability of dynamic scenes and environmental factors increases the difficulty of generalizing annotation models [5]. Group 3: Course Offerings and Learning Opportunities - The article promotes a course on 4D automatic annotation, designed to address entry-level challenges and optimize advanced learning [5][6]. - The course covers a comprehensive curriculum, including dynamic obstacle detection, SLAM reconstruction, and end-to-end annotation processes [6][7][10]. - It aims to equip learners with practical skills in 4D automatic annotation algorithms and their applications in real-world scenarios [22][25].

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 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 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 labeling tasks in autonomous driving, which includes dynamic and static object labeling, OCC labeling, and end-to-end labeling [1][2]. - Automated labeling algorithms are crucial for generating high-precision ground truth data without being limited by vehicle computing power, allowing for the optimization of results using full temporal data [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, generalization in dynamic scenes, balancing labeling efficiency with cost, and ensuring performance across various production scenarios [2][3]. Group 3: Course Offerings - The article introduces a course titled "Automated Driving 4D Labeling Employment Class," which aims to address the difficulties in learning and advancing in the field of automated labeling [2][4]. - The course covers the entire process of 4D automated labeling, including dynamic and static object labeling, OCC labeling, and end-to-end labeling, with practical exercises to enhance algorithm capabilities [4][18]. Group 4: Course Structure - The course is structured into several chapters, each focusing on different aspects of 4D automated labeling, such as the basics of 4D labeling, dynamic object labeling, SLAM reconstruction, static element labeling, OCC labeling, and end-to-end truth generation [5][7][8][10][11][12]. - Each chapter includes practical exercises and real-world applications to ensure participants not only understand the concepts but can also apply them effectively [4][18]. Group 5: Target Audience - The course is designed for a diverse audience, including researchers, students, and professionals looking to transition into the field of data closure in autonomous driving [18][19].

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 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].