Abstract: A system and a method for measuring a road surface input load for a vehicle, may include a plurality of strain gauges mounted on a surface of a hub bearing in the vehicle; a storage connected to the plurality of strain gauges and configured to store a deep learning artificial neural network model which learns road surface input load data of the vehicle according to the pieces of output data of the plurality of strain gauges; and a processor connected to the storage and the plurality of strain gauges and configured to perform calculation which is performed in each layer of the deep learning artificial neural network model stored in the storage and derive the road surface input load data of the vehicle according to the pieces of output data of the plurality of strain gauges.

Abstract: A road surface detecting apparatus may include a camera configured to obtain an image of a front side of a vehicle including a road surface, and a processor configured to classify a road surface event based on a road surface detection model which is generated by performing training based on the obtained image. Accordingly, a stable riding feeling may be provided for the user by quantitatively detecting a road surface event through training and controlling the vehicle based on the detected road surface event.

Abstract: A road surface detecting apparatus may include a camera configured to obtain an image of a front side of a vehicle including a road surface, and a processor configured to classify a road surface event based on a road surface detection model which is generated by performing training based on the obtained image. Accordingly, a stable riding feeling may be provided for the user by quantitatively detecting a road surface event through training and controlling the vehicle based on the detected road surface event.