工业界大佬带队！自动驾驶4D标注全流程实战（动静态/OCC）

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]