随到随学！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 [2][4] - It highlights the challenges faced in automated annotation, including sensor calibration, occlusion handling, and quality control of annotations [4][9] Group 1: Automated 4D Annotation - The backbone of autonomous driving capabilities is the vast training data generated through automated 4D annotation, which is increasingly complex compared to traditional methods [2] - The shift towards end-to-end data requires synchronized sensor annotations of dynamic and static elements, ensuring the completeness of training data [2][4] Group 2: Challenges in Automated Annotation - Key challenges in the industry include calibrating and synchronizing different sensors, managing occlusion issues, and ensuring the generalization of algorithms [4] - The need for high-quality annotation results and effective automated quality checks are critical pain points in the current landscape [4] Group 3: Educational Initiatives - The article introduces a course focused on automated 4D annotation algorithms, aimed at addressing the industry's needs and enhancing algorithmic capabilities [4][8] - The course covers the entire process of dynamic and static object annotation, including practical exercises to reinforce learning [8]