Risk prediction algorithm based on image texture extraction using mobile vehicle road scanning system as support for autonomous driving

Abstract

We present an algorithm for risk prediction of road surface grip where skidding and sliding occur as main road surface problems. Prediction is done by defining a fine texture classification of the properties of road aggregate. In an experimental setup, data acquisition is performed with a supervised mobile vehicle scanning system, using a vehicle equipped with a camera and temperature sensor during movement along an arterial road. Image processing is performed by testing four texture feature extraction methods: Gabor filters, wavelet transform, gray level co-occurence matrix, and edge histogram descriptor, among which the Gabor transform shows the best results. The extraction of texture feature vectors follows by statistical algorithms for measuring feature vector similarity and reference vector selection, leading to image texture classification. The algorithm itself is upgraded by incorporating simultaneous surface temperature measurements in order to create and validate the final fine surface texture classification. The roads are classified and segmented into high-, medium-, and low-risk roads according to skid danger, enabling the creation of a map of high-risk zones. We validate our risk prediction algorithm by referring to crash rate data from the Road Traffic Safety Agency of Serbia database. This algorithm enables the location and mapping of high-risk zones and can be used as a support for autonomous driving and navigation.