With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence, visual comput-ing, radar, monitoring equipment and GPS, whichenables computers to operate motor vehicles auto-matically and safely without human interference.However, the large-scale dataset for training andsystem evaluation is still a hot potato in the devel-opment of robust perception models. In this paper,we present the NEOLIX dataset and its applica-tions in the autonomous driving area. Our datasetincludes about 30,000 frames with point cloud la-bels, and more than 600k 3D bounding boxes withannotations. The data collection covers multipleregions, and various driving conditions, includingday, night, dawn, dusk and sunny day. In orderto label this complete dataset, we developed vari-ous tools and algorithms specified for each task tospeed up the labelling process. It is expected thatour dataset and related algorithms can support andmotivate researchers for the further developmentof autonomous driving in the field of computer vi-sion.
翻译:随着5G技术的逐渐成熟,自主驾驶技术在研究通信中吸引了越来越多的关注。自主驾驶车辆依靠人工智能、视觉计算、雷达、监测设备和全球定位系统的合作,这些设备使计算机能够自动和安全地操作机动车辆而不受人类干扰。然而,用于培训和系统评估的大型数据集仍然是在强大的认知模型破灭过程中的热土。本文介绍了近地天体LIX数据集及其在自主驾驶区的复制品。我们的数据集包括大约30,000个带有点云彩、雷达、监测设备和全球定位系统的合作。数据收集涵盖多个区域,以及各种驾驶条件,包括白天、夜间、黎明、黄昏和阳光明日。为了给这一完整的数据集贴上标签,我们开发了为加快标签进程而每件任务指定的变异工具和算法。预计我们的数据集和相关算法能够支持和感动研究人员在计算机静脉冲领域进一步自主驾驶。