2019/02/09 by Mohammad Billah, Billah, Mohammad, Farzana Rahman +10
Computer Science · Engineering · Environmental Science · #Autonomous Vehicle Technology and Safety #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.1902.03346
6 pages, 5 figures
arxiv created 2019/02/09 · openalex publication_date 2019/02/09 · arxiv updated 2019/02/12 · openalex created_date 2019/04/11 · openalex updated_date 2026/07/28
Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.