  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.
