Detection of Motorway Disorders by Processing and Classification of Smartphone Signals Using Artificial Neural Networks

Potholes, debris, sunken manhole covers and others are common street safety hazards which drivers experience daily as they bump into them unexpectedly while driving. Pavement roughness is usually evaluated based on the International Roughness Index (IRI), which is considered the most prevalent metric. In this work, IRI values are collected by using a smartphone, with built-in vibration sensor, placed on the car’s dashboard while driving around the city. The classification process of IRI values is primarily performed using an Artificial Neural Network (ANN) for the detection of diverse predefined street safety hazards. The designed ANN is a backpropagation pattern classifier, that must be trained to yield either a detected “disorder” area of the road or “normal” area based on the IRI data collected. The process of preparing the training and testing datasets involves a number of pre-processing operations. The IRI values are pre-processed in order to extract the most effective features. Then the network is trained with the normalized feature set by using supervised learning method. The performance of the designed network is compared to a similar works in the literature. Results show that the designed network can successfully classify the street conditions by using IRI values with a success rate that outperforms the classification rates obtained by other works.