Core Insights - The article emphasizes the importance of automated 4D annotation data in enhancing autonomous driving capabilities, driven by the need for complex training data formats [1] - It highlights the challenges faced in automated annotation, including sensor calibration, occlusion handling, and quality control of annotations [3] Group 1: Automated 4D Annotation - The backbone of autonomous driving capabilities is the vast amount of training data generated through automated 4D annotation processes [1] - The complexity of training data requirements has increased, necessitating synchronized annotations of dynamic and static elements, occlusions, and trajectories [1] - The significance of automated 4D annotation is growing due to the rising complexity of annotation demands [1] Group 2: Challenges in Automated Annotation - Key challenges in automated annotation include calibrating and synchronizing different sensors across various driving scenarios [3] - Issues such as occlusion between sensors and maintaining algorithm generalization are critical pain points in the industry [3] - The need for high-quality annotation results and effective automated quality checks is paramount [3] Group 3: Course Offering - A course titled "Automated Driving 4D Annotation Algorithm Employment Class" is being offered to address these challenges, featuring insights from industry leaders [3][4] - The course aims to provide a comprehensive understanding of the entire process of 4D automated annotation and core algorithms, along with practical exercises [6] - Key topics include dynamic obstacle detection, static element annotation, and mainstream paradigms for end-to-end annotation [6]

Group 1 - The article introduces a significant learning package for the new academic season, including a 299 yuan discount card that offers a 30% discount on all platform courses for one year [3][5]. - Various course benefits are highlighted, such as a 1000 yuan purchase giving access to two selected courses, and discounts on specific classes and hardware [3][6]. - The focus is on cutting-edge autonomous driving technologies for 2025, particularly end-to-end (E2E) and VLA (Vision-Language Alignment) autonomous driving systems [5][6]. Group 2 - End-to-end autonomous driving is emphasized as a core algorithm for mass production, with a notable mention of the competition sparked by the UniAD paper winning the CVPR Best Paper award [6][7]. - The article discusses the challenges faced by beginners in mastering multi-modal large models and the fragmented nature of knowledge in the field, which can lead to discouragement [7][8]. - A course on automated 4D annotation algorithms is introduced, addressing the increasing complexity of training data requirements for autonomous driving systems [11][12]. Group 3 - The article outlines a course on multi-modal large models and practical applications in autonomous driving, reflecting the rapid growth and demand for expertise in this area [15][16]. - It mentions the increasing job opportunities in the field, with companies actively seeking talent and offering competitive salaries [15][16]. - The course aims to provide a systematic learning platform, covering topics from general multi-modal large models to fine-tuning for end-to-end autonomous driving applications [16][18]. Group 4 - The article emphasizes the importance of community and communication in the learning process, with dedicated VIP groups for course participants to discuss challenges and share insights [29]. - It highlights the need for practical guidance in transitioning from theory to practice, particularly in the context of real-world applications and job readiness [29][31]. - The article also mentions the availability of specialized small group courses to address specific industry needs and enhance practical skills [23][24].

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].