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Preliminary Study on Road Slipperiness Detection Using Real-Time Digital Tachograph Data

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Preliminary Study on Road Slipperiness Detection Using Real-Time Digital Tachograph Data Preliminary Study on Road Slipperiness Detection using Real-Time Digital Tachograph Data Jinhwan Jang Dept. of Highway Res., Korea Inst. of Civil Eng. and Building Tech., 283 Goyangdae-ro, Goyang, South Korea Keywords: Road Slipperiness, Digital Tachograph, Wheel Slip. Abstract: Faced with the high rate of commercial vehicle-related traffic accidents, digital tachographs (DTGs) are mandatorily installed in commercial vehicles in Korea. However, the current DTGs do not seem to be effective for reducing accidents. One reason for this can be attributed to the absence of useful information for drivers under dangerous road conditions such as black ice. In this study, an innovative technique to identify slippery spots on the road using DTG data is proposed. The DTG can collect two types of vehicle speed: one is wheel rotational speed and the other is vehicle transitional speed. The difference between the two speeds is referred to as wheel slip, which can be exploited as a surrogate measure for detecting road slipperiness. A confidence interval of wheel slip was established using data collected in dry road conditions; if any data point that exceeds the predefined confidence interval is observed, a slippery road spot can be identified. The proposed method was preliminarily tested in four types of winter road conditions and showed satisfactory results. 1 INTRODUCTION are getting connected for various purposes, a new cost-effective approach to detecting road slipperiness Traffic accidents caused by slippery road conditions using probe vehicles as a mobile sensing platform has are of great concern to society. According to Korean been garnering attention worldwide. statistics, 7,849 traffic accidents that caused 221 Several studies have been performed concerning fatalities and 13,736 injuries occurred on slippery weather-related hazardous road conditions using roads over the last three years (KNPA, 2017). The probe cars. A probe-based road weather data fatality rate on icy roads is 27% higher than on dry collection system in Finland was developed by Pilli- roads. One study in Sweden insisted that only 14% of Sihvola (2000). The system gathered various road drivers maintain appropriate acceleration and weather-related data, including temperature, deceleration under slippery road conditions and 50% humidity, friction, and video images. Probe cars for of drivers misjudge slippery road surfaces as normal the system were fitted with various devices—a ones (Bogren, 2010). The risk of traffic accidents is friction tester, a GPS antenna, an infrared sensor, a known to increase nine and twenty times under snowy combined air temperature and humidity sensor, a and icy road conditions, respectively (Luque, 2013). video recorder, and a cellular modem. A real-time To mitigate the tragic numbers, early detection of tire-road friction coefficient estimation system using slippery spots on the road followed by quick probe vehicles equipped with a vehicle motion sensor notification to drivers and road managers seems to be and a differential GPS module was invented by Wang imperative. To this end, a road weather information et al. (2004). A novel methodology for tire-road system (RWIS) has conventionally been deployed to friction estimation using a genetic algorithm and the gather road weather data (Boon, 2002). However, as unscented Kalman filter combined with tire-models RWIS can only collect spot data, its utility could be and probe vehicle data was proposed by Nakatsuji et restricted, given that road slipperiness is known to al. (2005 and 2007). A probe application for the vary even in short roadway sections (Nakatsuji, identification of road slipperiness and coarseness 2003). Due to this limitation coupled with the high using vehicle body and wheel rotation speeds was cost, RWIS has not been extensively deployed in demonstrated by Dong et al. (2006). The potential use Korea. Recently, as increasing numbers of vehicles of data gathered from a Vehicle Infrastructure Integration (VII)-enabled probe vehicle in weather- 263 Jang, J. Preliminary Study on Road Slipperiness Detection using Real-Time Digital Tachograph Data. DOI: 10.5220/0006954702630267 In Proceedings of the 5th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2019), pages 263-267 ISBN: 978-989-758-374-2 Copyright c 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved VEHITS 2019 - 5th International Conference on Vehicle Technology and Intelligent Transport Systems related applications and products for surface The two speeds are theoretically identical under transportation was proposed by Petty et al. (2007). A dry road surface conditions. However, they show probe-based weather-specific field study in Detroit some discrepancy due to reasons such as was conducted in 2009 and 2010 by Drobot et al. measurement errors of sensors, the characteristics of (2010) and Chapman et al. (2010). A technique to vehicle tires made of rubber, and so on. Fig. 1 shows estimate the maximum friction while simultaneously values of wheel slip according to vehicle speed and diagnosing the road conditions was discussed by acceleration under dry road conditions: low and high Koskinen (2010). Recently, Wyoming Department of correlation with speed and acceleration, respectively. Transportation (WyDOT) has been deploying a The high correlation with acceleration was exploited connected vehicle-based road weather information for measuring slippery road spots and the method will system as part of a connected vehicle pilot project be described in the next section. (WyDOT, 2018). As of 2011, digital tachographs (DTG) have been obligatorily installed on every commercial vehicle in Korea to reduce traffic accidents related to commercial vehicles, for which the accident rate is 1.7 times higher than for general vehicles. A DTG collects several types of vehicle data—location, speed, brake activation, time of driving, etc. A substantial number of DTGs installed on trucks are connected to a truck management center via cellular communications to transmit real-time data for (a) efficient logistics. In this study, a simple but novel technique is proposed to identify slippery road spots using the real-time DTG data. Cargo trucks are considered to be advantageous over other types of vehicles in that they are generally operated throughout the nation on a 24/7 basis. Also, truck drivers tend to choose night driving so as not to be trapped in congestion, so information on icy spots on the road ahead could be highly valued considering that black ice is not easily discerned at night. (b) Figure 1: Relationships between wheel slip and vehicle 2 METHOD dynamics: (a) speed and (b) acceleration. 2.1 Basic Concept 2.2 Confidence Interval On a slippery road surface, vehicle wheels have a The statistical confidence interval concept of a high risk of skidding while decelerating, as well as regression line (or equation) expressed in (2)–(4) was spinning in place while accelerating. This, called applied to identify road slipperiness. According to the wheel slip and expressed in (1), can be measured by identical and independent distribution assumption of calculating the difference between the wheel the error term of the regression line, the confidence rotational and vehicle transitional speeds. In a DTG interval of the i-th estimate can be determined. For platform, the wheel rotational speed can be measured this study, the dependent variable corresponds to using wheel pulse sensors installed on each wheel of wheel slip and the independent variable corresponds a vehicle and the vehicle transitional speed can be to acceleration of a vehicle. obtained using a GPS sensor installed in the DTG. 푦̂푖 = a푥푖 + 푏 + 푒푖 (2) w 푟 −푣 푠 = 푤 푤 푤, 0≤s≤1 (1) 푒 ~N(0, 푠 ) (3) w푤푟푤 푖 푒 where s=wheel slip, ww=angular velocity of wheel, ∑푛 (푦 − 푦̂)2 r =effective wheel radius, v =vehicle transitional 푠 = √ 푖=1 푖 푖 w w 푒 푛 − 2 (4) speed, and wwrw=wheel rotational speed. 264 Preliminary Study on Road Slipperiness Detection using Real-Time Digital Tachograph Data where 푦̂푖=i-th dependent variable (estimate), xi=i-th independent variable (observation), b=y-axis intercept, ei=i-the error, se=standard error of regression line, yi=i-th observation, and n=number of sample. For a desired level of false alarm rate (α), a two- sided confidence interval which includes (1- α)*100% of the wheel slip can be defined with its upper and lower limit values becoming thresholds (Thr) of the regression equation for the particular estimate of wheel slip, as expressed in (5). If any wheel slip exceeds the thresholds, a slippery road spot can be identified. −1 Thr = 푁 (푦̂푖, 푠푒, 1 − 훼/2) (5) -1 Figure 2: Algorithm for identifying slippery road spot using where N =inverse of normal cumulative distribution DTG data. function and α=significance level. Table 1 shows the result of the regression analysis of the relationship between wheel slip and 3 FIELD EXPERIMENT acceleration plotted on the right side of Fig. (1). To determine the statistical confidence interval of the To verify the proposed algorithm, field experiments wheel slip, a z-value corresponding to 99.7% was under various slippery road surface conditions were employed. performed in the 2017–2018 winter season on local and arterial roads. A passenger car operated by a Table 1: Regression analysis of the relationship between skilled male driver with more than 20 years driving wheel slip and acceleration. experience was used for collecting real-time DTG data including GPS coordinates, wheel speed, GPS Statistics Value speed, and so on. For visual verification, a digital Slope (a) -1.2 video recorder time-synchronized with the DTG was y-axis intercept (b) 0.5 employed to gather video image data. The driver was Correlation coefficient 0.8 instructed to slightly accelerate followed by Determination coefficient 0.6 decelerate while driving on the slippery road spots. Standard error (se) 4.7 Fig.
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