通用障碍物漏检，得升级下Occ自动标注模型了。。。

Core Viewpoint - The article discusses the challenges and methodologies related to the automation of occupancy network (OCC) data labeling in the context of autonomous driving, emphasizing the need for high-quality training data to improve model generalization and safety. Group 1: OCC Data Labeling Challenges - The need for high-quality training data is highlighted due to incidents caused by undetected obstacles, such as fallen tree branches during adverse weather conditions [2]. - The OCC network is essential for modeling irregular obstacles and background elements, which increases the demand for accurate data labeling [5]. - The automation of OCC data labeling is being pursued by many companies to enhance model performance and reduce costs associated with manual labeling [2][10]. Group 2: Automation Techniques - The common process for generating OCC training ground truth involves three main methods: 2D-3D object detection consistency, comparison with edge models, and manual intervention for quality control [9]. - High-quality automated labeling data can be used for both vehicle model training and cloud model optimization, facilitating continuous iteration [10]. Group 3: 4D Automated Labeling Course - A course is introduced that covers the entire process of 4D automated labeling, including dynamic and static object detection, and the challenges faced in real-world applications [10][12]. - The course aims to address the difficulties in learning and advancing in the field of automated driving data labeling, providing a comprehensive understanding of core algorithms and practical applications [10][11]. Group 4: Key Learning Outcomes - Participants will gain knowledge of the entire 4D automated labeling process, including dynamic obstacle detection, SLAM reconstruction, and the generation of end-to-end ground truth [12][20]. - The course also focuses on the practical implementation of algorithms and the resolution of common issues encountered in the industry [15][22]. Group 5: Target Audience - The course is designed for various groups, including researchers, students, and professionals looking to transition into the field of data closure in autonomous driving [26][31].