Workflow
自动驾驶4D标注
icon
Search documents
通用障碍物的锅又丢给了4D标注。。。
自动驾驶之心· 2025-08-18 01:32
Core Viewpoint - The article discusses the challenges and methodologies in automating the labeling of occupancy data for autonomous driving, emphasizing the importance of the Occupancy Network (OCC) in enhancing model generalization and safety in various driving conditions [2][10]. Group 1: OCC and Its Importance - The Occupancy Network (OCC) is crucial for modeling irregular obstacles such as fallen trees and other non-standard objects, as well as background elements like road surfaces [5][19]. - Since Tesla's announcement of OCC in 2022, it has become a standard feature in visual autonomous driving solutions, leading to a high demand for training data labeling [2][19]. Group 2: Challenges in Automated Labeling - The automation of labeling in the 4D data loop faces several challenges, including high spatial-temporal consistency requirements, complex multi-modal data fusion, and the difficulty of generalizing in dynamic scenes [11][12]. - The need for high precision in 4D automatic labeling often leads to a conflict between labeling efficiency and cost, as manual verification is still required despite the volume of data [11][12]. Group 3: Training Data Generation and Quality Control - The common process for generating training data truth values involves three main methods: 2D-3D object detection consistency, comparison with edge models, and manual intervention for quality control [9][10]. - High-quality automated labeling data can be used for training both vehicle models and cloud-based large models, facilitating continuous optimization [10][12]. Group 4: Course Offerings and Learning Opportunities - The article promotes a course on 4D automatic labeling, which covers the entire process and core algorithms, aiming to address entry-level challenges and optimize advanced learning [10][12]. - The course includes practical exercises and real-world algorithm applications, focusing on dynamic obstacle detection, SLAM reconstruction, and the overall data loop [12][13][20]. Group 5: Instructor and Target Audience - The course is led by an industry expert with extensive experience in data loop algorithms for autonomous driving, having participated in multiple production delivery projects [24]. - The target audience includes researchers, students, and professionals looking to transition into the field of data loops, requiring a foundational understanding of deep learning and autonomous driving perception algorithms [26][31].