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Image-Based Visibility Estimation Algorithm for Intelligent Transportation Systems
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DOI：10.1109/ACCESS.2018.2884225.png)
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摘要
En 中文
Posted road speed limits contribute to the safety of driving, yet when certain driving conditions occur, such as fog or severe darkness, they become less meaningful to the drivers. To overcome this limitation, there is a need for adaptive speed limits system to improve road safety under varying driving conditions. In that vein, a visibility range estimation algorithm for real-time adaptive speed limits control in intelligent transportation systems is proposed in this paper. The information required to specify the speed limit is captured via a road side unit that collects environmental data and captures road images, which are then analyzed locally or on the cloud. The proposed analysis is performed using two image processing algorithms, namely, the improved dark channel prior (DCP) and weighted image entropy (WIE), and the support vector machine (SVM) classifier is used to produce a visibility indicator in real-time. Results obtained from the analysis of various parts of highways in Canada, provided by the Ministry of Transportation of Ontario, show that the proposed technique can generate credible visibility indicators to motorists. The analytical results corroborated by extensive field measurements confirmed the advantage of the proposed system when compared to other visibility estimation methods such as the conventional DCP and WIE, where the proposed system results exhibit about 25% accuracy enhancement over the other considered techniques. Moreover, the proposed DCP is about 26% faster than the conventional DCP. The obtained promising results potentiate the integration of the proposed technique in real-life scenarios.
Keyword:
Visibility
machine learning
image processing
intelligent transportation system
smart cities
dark channel prior
entropy
SVM
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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