Abstract: The present disclosure provides a mobile communications system line number sheet management device and method. Provided, according to one aspect of the present disclosure, is a line number sheet management device and method, for automating access network-related line number sheet management by automatically collecting and updating line number information, the line number information being collected from a mobile communications base station management server (mobile communications system management server) and a fronthaul management server. Provided, according to another aspect of the present disclosure, is a fronthaul device for: acquiring unique information of a base station by receiving an optical signal from any one of a radio unit (RU) and a digital unit (DU); and transmitting the unique information of the base station to a fronthaul management server.

Abstract: A high precision position estimation method through a road shape classification-based map matching implemented by a high precision position estimation system of an autonomous vehicle includes classifying a road into a straight lane, a curved lane, and a clothoid curve lane by the lane matching between a sensor lane segment and a map lane segment, generating a movement amount value calculated by applying a geometric feature-based map matching or an optimization-based map matching to the classified lane as a covariance matrix, and calculating the latitude, longitude, and traveling direction of a vehicle to be estimated by combining the covariance matrix with sensor measurement values of a vehicle speed and a yaw rate by an extended Kalman filter.

Abstract: A high precision position estimation method through a road shape classification-based map matching implemented by a high precision position estimation system of an autonomous vehicle includes classifying a road into a straight lane, a curved lane, and a clothoid curve lane by the lane matching between a sensor lane segment and a map lane segment, generating a movement amount value calculated by applying a geometric feature-based map matching or an optimization-based map matching to the classified lane as a covariance matrix, and calculating the latitude, longitude, and traveling direction of a vehicle to be estimated by combining the covariance matrix with sensor measurement values of a vehicle speed and a yaw rate by an extended Kalman filter.