端到端自动驾驶需要什么样的标注数据？

Core Viewpoint - The article emphasizes the importance of high-quality data sets for autonomous driving, highlighting the need for efficient and low-cost methods to obtain these data sets through advanced 4D labeling techniques [1][2]. Group 1: Importance of 4D Labeling - The demand for automated 4D labeling is increasing due to the growing complexity of labeling tasks in autonomous driving, which includes dynamic and static object labeling, OCC labeling, and end-to-end labeling [1][2]. - Automated labeling algorithms are crucial for generating high-precision ground truth data without being limited by vehicle computing power, allowing for the optimization of results using full temporal data [1][2]. Group 2: Challenges in Automated Labeling - Key challenges in 4D automated labeling include maintaining high spatial-temporal consistency, complex multi-modal data fusion, generalization in dynamic scenes, balancing labeling efficiency with cost, and ensuring performance across various production scenarios [2][3]. Group 3: Course Offerings - The article introduces a course titled "Automated Driving 4D Labeling Employment Class," which aims to address the difficulties in learning and advancing in the field of automated labeling [2][4]. - The course covers the entire process of 4D automated labeling, including dynamic and static object labeling, OCC labeling, and end-to-end labeling, with practical exercises to enhance algorithm capabilities [4][18]. Group 4: Course Structure - The course is structured into several chapters, each focusing on different aspects of 4D automated labeling, such as the basics of 4D labeling, dynamic object labeling, SLAM reconstruction, static element labeling, OCC labeling, and end-to-end truth generation [5][7][8][10][11][12]. - Each chapter includes practical exercises and real-world applications to ensure participants not only understand the concepts but can also apply them effectively [4][18]. Group 5: Target Audience - The course is designed for a diverse audience, including researchers, students, and professionals looking to transition into the field of data closure in autonomous driving [18][19].