Prediction of a cartographic readjustment parameter by deep learning

• Main Authors: Garcia, Lucien, Vigneau, Willy
• Format: Patent
• Language: English
• Published: 14.05.2024
• Subjects: ANALOGOUS ARRANGEMENTS USING OTHER WAVES; CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; ELECTRIC DIGITAL DATA PROCESSING; GYROSCOPIC INSTRUMENTS; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION ORRERADIATION OF RADIO WAVES; MEASURING; MEASURING DISTANCES, LEVELS OR BEARINGS; NAVIGATION; PHOTOGRAMMETRY OR VIDEOGRAMMETRY; PHYSICS; RADIO DIRECTION-FINDING; RADIO NAVIGATION; SURVEYING; TESTING

Subjects of the present disclosure are methods for training deep learning models, methods for predicting a map matching parameter, methods for updating a digital road map, and a computer program making it possible to implement the methods and devices for updating a digital road map. The general principle is based on the use of machine learning. Accordingly, a statistical deep learning model is trained according to a "supervised" machine learning scheme. Thereafter, the pretrained statistical deep learning model is used to predict a map matching parameter on the basis of a measurement of geographic coordinates and of an identifier of the position sensor that has performed the measurement of geographic coordinates. Finally, the map matching parameter can be used to update a digital road map.