最近才明白，智能驾驶量产的核心不止是模型算法。。。

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, where high-precision 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]. Educational Course on 4D Annotation - The article promotes a course designed to address the challenges of entering the field of 4D automatic annotation, covering the entire process and core algorithms [8][9]. - The course includes practical exercises and focuses on dynamic obstacle detection, tracking, optimization, and data quality inspection [11][12]. - It also covers SLAM reconstruction, static element annotation, and OCC marking, providing a comprehensive understanding of the field [13][15][16]. Instructor and Course Structure - The course is taught by an industry expert with extensive experience in data closure algorithms and has participated in multiple mass production projects [20]. - The course is suitable for researchers, students, and professionals looking to enhance their skills in 4D automatic annotation [23][24].