• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.53-64
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• 2013

The car-following model is one of core models in Advanced Vehicle & Highway Systems (AVHS). The car-following model has been developed in aspects such as human factor and reduction error rates. However, the consideration of safety depending on weather condition has not been completed yet. In this paper, therefore, changes of driving condition for car-following due to different road condition were dealt with, and optimal safety distance corresponding to road condition such as dry, wet and snowy were computed. The GMIT(GM Model with Instantaneous T) model was picked over for simulation of adaptive cruise control applied the suggested optimal safety distance. As the results, the 1.7 times longer safety distance was required for wet road condition than dry road condition, and the 5.6 times longer safety distance was required for snowy road condition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.391-392
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• 2010

The frozen road is effected as major cause of car accident in winter. Especially, road surface freezing on the highway can lead to fatal accident. The accident by frozen road can effectively reduced by prevent road surface freezing before it frozen as evaluate road surface condition. Therefore, this study installed thermometer in each depth of concrete pavement for evaluate road surface conditions which freezing chronically. The result of this study will be used as preliminary data for predict before freezing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.813-819
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• 2006

Skid Resistance is a index to represent the friction between tire and road surface, which influences driving safety. Skid resistance varies with the conditions of tire, abrasion of road surface, vehicle speed, drying, wet and freezing condition of road surfaces. Especially, freezing occurs when temperature drops below $0^{\circ}C$ followed by snow or rain causes decrease of skid resistance. To recover the decreased skid resistance deicing work is applied. As a results of deicing works, freezing condition is changed into wet condition. However the wet road surfaces containing the remaining deicings agents may not show the skid resistance of normal wet condition. In this study, skid resistances in the condition of freezing, deicing process and deicing agents remained after snow-removal are evaluated. The test results, skid resistance recover quickly when Pre-wetted salt spreading and NaCl was used as deicing method. Skid resistance of Deicing agents remained on the road surface showed that concrete is higher than asphalt. superior effect. Recovery rate of skid resistance by comparison wet condition is 54~80%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.5
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• pp.1-12
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• 2013

Road's accidents caused by Ice, snow, Wet of roads surface conditions and weather conditions situations that are constantly occurring. That is, driver's negligence and safe driving ability of individuals due to lack of awareness, and Road management main agent(the government and the public, etc.) due to road conditions, if there is insufficient information. So Related research needs is a trend that is required. In this study, gather Camera(Stereo camera)'s image data, and analysis polarization coefficients and wavelet transform. And unlike traditional single-dimensional classification algorithms as multi-dimensional analysis by using SVM classification techniques, develop an algorithm to determine road conditions. Four on the road conditions (dry, wet, snow, ice) recognition success rate for the detection and analysis of experiments.

• Journal of Korean Society of Transportation
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• v.15 no.2
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• pp.25-34
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• 1997

본 연구에서는 아스팔트 포장과 콘크리트 포장의 습윤시 교통사고 가능성을 비교하 였다. 다양한 노후도를 가진 두 포장재료에 대해 마찰력 측정장비(BPT, Britsh Pendulum Tester)로 건조 및 습윤상태에서 노면마찰력을 측정하였으며, 측정결과와 두 포장재료에 대 한 실제 교통사고율을 비교하였다. 본 연구의 결과를 요약하면 다음과 같다. (1) 노면 습윤 에 따른 마찰력 저하정도는 아스팔트 포장이 콘크리트 포장보다 컸다. (2) 노면 습윤에 따른 실제 교통사고율도 아스팔트 포장에서 훨씬 큰 것으로 나타나 (1)의 결론과 일관된 결과를 보여주었다. (3) 노면마찰력은 건조상태에서는 노면마모 정도에 크게 영향을 받지 않으나 일 단 습윤상태가 되면 노면마모가 심할수록 마찰력 저하폭이 큰 것으로 나타났다.

• Journal of the Korean Applied Science and Technology
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• v.37 no.6
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• pp.1507-1516
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• 2020

This study analyzed the scenario for road freezing section by applying the road freezing evaluation algorithm. To apply road freezing algorithm, the influencing factors on road freezing were reviewed. Observation data from four points, Mokgam IC, Jeongneung tunnel, Seongsan bridge, and Yeomchang bridge were used for analysis. All observatories are installed on the expressway, and they are classified for the analysis of road freezing characteristics. When the difference between the road surface temperature and dew-point temperature of the road freezing evaluation algorithm was 3℃ or less, the section where road freezing occurred was checked. In addition, road freezing evaluation was derived through the change of the road surface condition and water film thickness of the freezing section.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.14-15
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• 2023

본 논문은 상용 특장차의 한 종류인 텔레핸들러가 주행 중에 전방 노면의 평탄하지 못한 상태, 방지턱 같은 장애물, 기울기가 큰 오르막길, 내리막길 등과 같은 상태를 자동으로 판단하여 운전자에게 도움을 주는 방법론을 제안한다. Stereolabs 사의 ZED 2i 카메라를 사용하여 카메라를 원점으로 하는 임의의 점들의 X, Y, Z 를 찾아내고 해당 점들의 원점으로부터 거리를 이용하여 전방 노면 상태를 판단하였다. 이는 추후 자율주행로봇에도 적용이 가능할 것으로 보인다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.5
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• pp.113-121
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• 2013

The road icing forecast and snow detection system using state evaluation algorithm of multi sensor optimizes snow melting system to control spread time and amount of chemical spread application This system operates integrated of contact/non-contact sensor and infrared camera. The state evaluation algorithm of the system evaluates road freezing danger condition and snowfall condition (snowfall intensity also) using acquired data such as temperature/humidity, moisture detection and result of image signal processing from field video footage. In the field experiment, it proved excellent and reliable evaluated result of snowfall state detection rate of 89% and wet state detection rate of 94%.