Discrimination of the road condition toward understanding of vehicle driving environments

Detection of vehicle driving environments is necessary to secure transport facilities safe from accidents and to keep the performance smooth. Road condition is one of the most important factors toward the detection of vehicle driving environments. Conventional road condition discrimination methods involved the use of optical or ultrasonic sensors. However, since these sensors could only provide spot information, detected results do not always reflect the spacious condition. To deal with this problem, a new algorithm that employs image analysis technology for discrimination of the road condition is proposed in this paper. In this algorithm, for discrimination of the road condition, we focused on features related to water and snow on the road and we extracted these features by image analysis. Features related to water were extracted by the ratio of horizontal polarization image intensity to vertical polarization image intensity for each pixel. Features related to snow were extracted by texture analysis using the co-occurrence matrix. We employ a multivariate analysis to discriminate five kinds of the road conditions.