Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system
IEEE Transactions on Intelligent Transportation Systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
| Hi-index | 0.00 |
The detection of vehicle driving environments is necessary to secure transport facilities safe from accidents and to keep the performance smooth. The road condition is one of the most important factors toward detection of vehicle driving environments. Conventional discrimination methods for road conditions involved the use of optical or ultrasonic sensors. However, since these sensors can 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 road conditions is proposed in this paper. In this algorithm, for discrimination of road conditions, 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: “Dry,” “Wet,” “Slushy,” “Icy” and “Snowy,” on the basis of these features extracted from the road images as well as temperature. Furthermore, we conducted field tests to verify the accuracy of this algorithm and obtained favorable discrimination accuracy rate of 92.3% on the average