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].