Abstract
Background: Faced with the high rate of traffic accidents under slippery road conditions, agencies attempt to quickly identify slippery spots on the road and drivers want to receive information on the impending dangerous slippery spot, also known as “black ice.” Methods: In this study, wheel slip, defined as the difference between both speeds of vehicular transition and wheel rotation, was used to detect road slipperiness. Three types of experiment cars were repeatedly driven on snowy and dry surfaces to obtain wheel slip data. Three approaches, including regression analysis, support vector machine (SVM), and deep learning, were explored to categorize into two states-slippery or non-slippery. Results: Results indicated that a deep learning model resulted in the best performance with accuracy of 0.972, only where sufficient data were obtained. SVM models universally showed good performance, with average accuracy of 0.965, regardless of sample size. Conclusion: The proposed models can be applied to any connected devices including digital tachographs and on-board units for cooperative ITS projects that gather wheel and transition speeds of a moving vehicle to enhance road safety in winter season though collecting followed by providing dangerous slippery spots on the road.
Highlights
Road slipperiness is one of the major concerns for road safety
Fig. (5) shows the driving pattern of the experiment vehicles outfitted with digital tachographs (DTGs)
According to a report from the U.S DOT, around 80% of road accidents could be preventable if drivers have a chance to be informed of the impending dangerous situations on the road ahead [24]
Summary
In South Korea, 8,849 traffic accidents that caused 221 fatalities and 13,736 injuries occurred on slippery roads over the three years, which was 27% higher than on dry conditions [1]. In the U.S, slippery roads brought about over 1,300 fatalities and 116,800 injuries, which accounts for 24% of weather-associated road casualties [2]. As a growing number of fleets such as trucks, buses, taxis, rental cars, patrol cars, and maintenance cars are getting connected for their own purposes, an innovative approach to identifying slippery spots on the road through processing in-vehicle sensors is attracting interest worldwide. Faced with the high rate of traffic accidents under slippery road conditions, agencies attempt to quickly identify slippery spots on the road and drivers want to receive information on the impending dangerous slippery spot, known as “black ice.”
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