Core Viewpoint - The article emphasizes the increasing importance of high-quality data in the development of autonomous driving technology, highlighting the challenges and advancements in automated 4D annotation processes [2][5]. Group 1: Data Requirements and Challenges - The demand for high-quality data has surged with the advancement of autonomous driving technologies, as seen in the full rollout of Li Auto's AD Max V13 model, which utilizes 10 million clips of training data [2]. - The complexity of data annotation has increased, necessitating synchronized sensor data for dynamic and static elements, which poses challenges in ensuring data completeness [2][5]. - Key challenges in automated annotation include high spatiotemporal consistency requirements, complex multi-modal data fusion, and the need for generalization in dynamic scenes [5][6]. Group 2: Automated Annotation Difficulties - The difficulties in automated annotation stem from the need for precise tracking of dynamic targets across frames, which can be disrupted by occlusions and interactions in complex environments [5]. - The integration of various sensor data (LiDAR, cameras, radar) requires addressing issues like coordinate alignment and semantic unification [5]. - The high cost and time associated with manual verification of large datasets hinder the efficiency of high-precision 4D automated annotation [5][6]. Group 3: Course Offerings and Learning Objectives - A new course on 4D automated annotation algorithms has been introduced to address the learning challenges faced by newcomers in the field, covering the entire process and core algorithms [6][21]. - The course aims to equip participants with practical skills in dynamic obstacle detection, SLAM reconstruction, and end-to-end truth generation [6][21]. - The curriculum includes hands-on practice and real-world algorithm applications, focusing on enhancing algorithmic capabilities in the context of autonomous driving [6][21]. Group 4: Course Structure and Target Audience - The course is structured into several chapters, each focusing on different aspects of 4D automated annotation, including dynamic obstacles, SLAM, and static elements [7][9][10][12][15]. - It is designed for individuals with a foundational understanding of deep learning and autonomous driving perception algorithms, including researchers, technical teams, and those looking to transition into data closure roles [23]. - The course will be delivered through online live sessions, code explanations, and Q&A, with materials available for one year post-purchase [21][22].
4D标注与数据闭环，对一家自动驾驶公司究竟有多么重要？
自动驾驶之心·2025-09-21 23:32