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].

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 通用障碍物自动标注怎么做？ 小林是一名主机厂的云端模型算法工程师，工作已经四五年了。这两天接到了车端报的Case，恶劣天气倒在地上的树干漏检，行车时紧急刹停差点酿成事故。。。不 出意外，原因最后排查到数据的问题，要求数据团队紧急补充训练数据。 小林为难的挠了挠头，这种异常case也太难解决了。检测没办法解决这种异常的占用问题，标注数据也从未见过，看来只能靠OCC来做下兜底了。想到这里，小林觉得 需要精标一小批数据提供车端先使用，再配合挖掘大模型和云端模型的自动标注模型快速迭代数据量才能保证车端模型的泛化，看来又要有一段时间的苦日子 了。。。 自从2022年特斯拉宣布Occupancy Network上车以来，当下占用网络已经作为各家纯视觉智驾方案的标配。目前OCC作为行车和泊车中的重要感知模块，对训练数据的 标注需求也十分旺盛的，尤其是OCC需要更昂贵的点云标注，因此业内很多公司都在推进OCC的自动化标注，以期快速迭代模型的泛化性能。 简单来说，占用网络的目的将空间划分成小网格，预测每个网格的占用情况，解决异 ...