Name
Abstract | ||
|---|---|---|
As an extremely complicated task, road detection is of vital importance for the traveling comfort and driving safety. While high-end automobiles are already equipped with road detection function, most mid-range cars can only detect and evaluate road conditions leveraging remodeled or additional hardware devices built on vehicles, thereby constraining the road quality detection. With the growing popularity of smartphones, detections based on built-in sensors emerge. Most detections on built-in sensors, nevertheless, are on the basis of Euclidean distance, thus neglecting the correlation between characteristics in road quality, i.e., the acceleration sensor and gyroscope have obvious fluctuations when the vehicle passes through the larger pothole, and there is a connection between them. In this paper, we propose a novel road detection approach based on Mahalanobis Taguchi system (MTS), leveraging smartphones for data collection and involving the correlation between characteristics. We develop an application to collect and process the data, and then classify road quality conditions. The experimental test was carried out on city roads in Xi'an, Shaanxi. Experiment results reveal that the road surface conditions, including manhole cover, pothole, and speed bump, can be well differentiated with the method based on MTS. To a certain extent, the strategy of marking road conditions to the navigation map can effectively improve not only driving experience and traveling comfort but also driving safety, thereby providing more supports for the maintenance units. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2839765 | IEEE ACCESS |
Keywords | Field | DocType |
Road quality detection,smartphone,Mahalanobis-Taguchi system (MTS),road surface condition,driving safety | Speed bump,Data collection,Gyroscope,Computer science,Crowdsourcing,Euclidean distance,Mahalanobis distance,Real-time computing,Road surface,Acceleration,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 2 |
PageRank | References | Authors |
0.39 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huaijun Wang | 1 | 20 | 13.02 |
| Na Huo | 2 | 2 | 0.39 |
| Junhuai Li | 3 | 39 | 16.44 |
| Kan Wang | 4 | 2 | 0.73 |
| Wang Zhi-xiao | 5 | 37 | 12.28 |