Abstract: The present invention relates to a method and apparatus for estimating a road surface type by using an ultrasonic signal and, more particularly, to a method for estimating a road surface type by using an artificial neural network model machine-learned with respect to a reflected ultrasonic signal and an apparatus for performing same. According to the present invention, provided are a method and apparatus for providing highly accurate road surface information at low cost, by machine-learning both characteristics of an ultrasonic signal reflected from a road surface and a road surface state, establishing a model between the two, and then estimating the type of the road surface by utilizing the model. In particular, even a road surface where thin ice, that is, black ice, is formed, which was not detectable in the conventional method for estimating a road-surface friction coefficient, may be accurately estimated, thereby contributing to safer driving.

Abstract: In accordance with an embodiment, a method of controlling a vehicle for one-pedal driving assistance includes acquiring real-time driving information of a subject vehicle detected by a driving information detection unit and real-time vehicle-ahead information detected by a front detection unit; determining whether the subject vehicle has entered an automatic control inspection section using relative distance information with respect to a vehicle ahead in the acquired real-time vehicle-ahead information; starting an automatic distance control mode upon determining that the subject vehicle has entered the automatic control inspection section; and controlling a relative speed with respect to the vehicle ahead so that a relative distance with respect to the vehicle ahead is allowed to be adjusted to a target relative distance with respect to the vehicle ahead based on the real-time driving information and the real-time vehicle-ahead information during the automatic distance control mode.

Abstract: The present invention relates to a method and apparatus for estimating a road surface type by using an ultrasonic signal and, more particularly, to a method for estimating a road surface type by using an artificial neural network model machine-learned with respect to a reflected ultrasonic signal and an apparatus for performing same. According to the present invention, provided are a method and apparatus for providing highly accurate road surface information at low cost, by machine-learning both characteristics of an ultrasonic signal reflected from a road surface and a road surface state, establishing a model between the two, and then estimating the type of the road surface by utilizing the model. In particular, even a road surface where thin ice, that is, black ice, is formed, which was not detectable in the conventional method for estimating a road-surface friction coefficient, may be accurately estimated, thereby contributing to safer driving.