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].