Abstract
Abstract. Over the decades, autonomous driving technology has attracted a lot of attention and is under rapid development. However, it still suffers from inadequate accuracy in a certain area, such as the urban area, Global Navigation Satellite System (GNSS) hostile area, due to the multipath interference or Non-Line-of-Sight (NLOS) reception. In order to realize fully autonomous applications, High Definition Maps (HD Maps) become extra assisted information for autonomous vehicles to improve road safety in recent years. Compared with the conventional navigation maps, the accuracy requirement in HD Maps, which is 20 cm in the horizontal direction and 30 cm in 3D space, is considerably higher than the conventional one. Additionally, HD Maps consist of rich and high accurate road traffic information and road elements. For the requirement of high accuracy, conducting a Mobile Laser Scanning (MLS) system is an appropriate method to collect the geospatial data accurately and efficiently. Nowadays, digital vector maps are constructed by digitalizing manually on the collected data. However, the manual process spends a lot of manpower and is not efficient and practical for a large field. Therefore, this paper proposes to automatically construct the crucial road elements, such as road edge, lane line, and centerline, to generate the HD Maps based on point clouds collected by the MMS from the surveying company. The RMSEs in the horizontal direction of the road edge, lane line, and centerline are all lower than 30 cm in 3D space.
Highlights
Reacting to the era of autonomous driving, called selfdriving, an industry and academia has gotten involved in the relevant researches and development for autonomous vehicles
Since the ArcGIS only supports 2D demonstration, the verified HD M aps are converted to computer-aided design (CAD) files and divided into one point per centimeter to represent the ground truth data for accuracy assessment
M oreover, the modelling road edge might contain a few points derived from the lane line points near the real road edge since the road edges are determined by the intensity difference
Summary
Reacting to the era of autonomous driving, called selfdriving, an industry and academia has gotten involved in the relevant researches and development for autonomous vehicles. There are many sensors configured on the autonomous vehicle to support the navigation and perception services These sensor systems can be separated into positioning sensors, such as Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and wheel-mounted Distance M easuring Indicator (DM I), and mapping sensors, such as camera, LiDAR, radar. The mapping sensors are inferior to inherent limitation, for example, camera is affected by the illumination, LiDAR suffers from the cost, and radar is limited to the spatial resolution To overcome this dilemma, HD M aps becomes extra assisted information for autonomous vehicle. This study takes the modelling process into consideration to fit the extracted road elements since the HD M aps should be light vector maps rather than extracted point cloud for uploading and downloading efficiency (Gwon et al, 2016). Generating the centreline and evaluating the results with verified HD M aps to guarantee the quality
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