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기록

METHOD AND SYSTEM FOR MONITORING THE ROAD CONDITION BY MEANS OF A MACHINE LEARNING SYSTEM, AND METHOD FOR TRAINING THE MACHINE LEARNING SYSTEM

발명심사 중
16청구항 · 2 독립항
§ Ⅰ

개요

발명자

Sighard Schräbler; Niels Christmann; Pia Dreiseitel; Erwin Kraft; Bernd Hartmann; Florian Geis

IPC 분류

G6V 10/774G1J 5/10G1N 21/55G6V 10/26G6V 10/82G6V 10/94G6V 20/56

CPC 분류

G6V10/7747G6V10/26G6V10/82G6V10/95G6V20/588G1J5/10G1N21/55

The present disclosure relates to a method and system for monitoring the road condition by a machine learning system and to a method for training the machine learning system. The methods include: providing or acquiring data by a sensor system of a vehicle, wherein the sensor system captures the surroundings of the vehicle () as training input data X; providing or acquiring data which characterize the road condition by means of a reference sensor fitted in or on the vehicle as training target values, and training the machine learning system. Training data, which include training input data X and training target values corresponding to these training input data X, are provided. The training data are used to adjust parameters of the machine learning system in such a manner that the machine learning system generates output data similar to the training target values when the training input data are input.

원문 (중국어)

The present disclosure relates to a method and system for monitoring the road condition by a machine learning system and to a method for training the machine learning system. The methods include: providing or acquiring data by a sensor system of a vehicle, wherein the sensor system captures the surroundings of the vehicle () as training input data X; providing or acquiring data which characterize the road condition by means of a reference sensor fitted in or on the vehicle as training target values, and training the machine learning system. Training data, which include training input data X and training target values corresponding to these training input data X, are provided. The training data are used to adjust parameters of the machine learning system in such a manner that the machine learning system generates output data similar to the training target values when the training input data are input.