Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

The development of intelligent vehicle systems has resulted in an increased need for a high-precision road map. However, conventional road maps that are used for vehicle navigation systems or geographical information systems (GISs) are insufficient to satisfy new requirements of intelligent vehicle systems such as autonomous driving. There are three primary road-map requirements for intelligent vehicle systems: centimeter-level accuracy, storage efficiency, and usability. However, no existing researches have met these three requirements simultaneously. In this paper, we propose a precise and efficient lane-level road-map generation system that conforms to the requirements all together. The proposed map-building process consists of three steps: 1) data acquisition, 2) data processing, and 3) road modeling. The road data acquisition and processing system captures accurate 3-D road geometry data by acquiring data with a mobile 3-D laser scanner. The road geometry data are then refined to extract meta information, and in the road modeling system, the refined data are represented as sets of piecewise polynomials to ensure storage efficiency and usability of the map. The proposed mapping system has been extensively tested and evaluated on a real urban road and highway. The experimental results show that the proposed mapping system outperforms conventional systems in terms of the road-map requirements.