Abstract: The present invention is directed to a method of identifying road roughness by collecting sensor data from a computing device and applying deep learning techniques to estimate the road's roughness index. The present invention features a system comprising computing devices. Each computing device may be mounted to a vehicle. Each computing device may be capable of measuring, while the vehicle is driving on a road. GPS, driving speed, vertical acceleration, and angular velocity of pitch motion. The system may further comprise a neural network computing device comprising a CNN comprising convolutional layers and global average pooling layers. The CNN may be trained by a data set comprising previous data from the plurality of computing devices. The CNN may be capable of accepting the plurality of parameters from the computing devices as input and generating an international roughness index (IRI) value of the road as output.

Abstract: The present invention is directed to a method of identifying road roughness by collecting sensor data from a computing device and applying deep learning techniques to estimate the road's roughness index. The present invention features a system comprising computing devices. Each computing device may be mounted to a vehicle. Each computing device may be capable of measuring, while the vehicle is driving on a road. GPS, driving speed, vertical acceleration, and angular velocity of pitch motion. The system may further comprise a neural network computing device comprising a CNN comprising convolutional layers and global average pooling layers. The CNN may be trained by a data set comprising previous data from the plurality of computing devices. The CNN may be capable of accepting the plurality of parameters from the computing devices as input and generating an international roughness index (IRI) value of the road as output.