• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.75-84
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• 2001

The reliable data relating to the condition of road surface is of increasing importance to deliver the road condition to driver and road management authority. This paper describes the development of a new high-speed. automatic, road data collection system, which collects the longitudinal road data with ${\sim}30cm$ interval covering full width of the road at 100km/h speed. The system calculates the international roughness index (IRI) from the collected data and displays the IRI and road profile data on the screen. To develope the system, we implement an optical range finder, advanced distance and motion detectors, and signal processing and display modules. The measurement accuracy of the system at 70km/h operation speed shows ${\pm}0.1m/km$ in the IRI for the standard road. To confirm the performance of the developed system, we also measure the IRI of a deployed highway road and compare the results with a conventional system and human eye measurement results.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.14-14
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• 2017

본 연구는 트랙터 주행노면의 토양 특성이 트랙터 승차진동에 미치는 영향을 분석하는 것을 목표로 하였다. 여러 토양조건에서 실제 트랙터 주행실험을 진행하는 것은 어렵기 때문에, 상용 CAE 프로그램을 이용하여 트랙터를 모델링하고 시뮬레이션을 진행하였다. 이를 통해 여러 토양조건에 따른 승차진동의 특성을 비교, 분석하였다. 모델의 유의성은 트랙터 아스팔트 주행실험을 통해 진동데이터를 획득하고 실측 진동 데이터와 시뮬레이션을 통해 얻어진 진동 데이터를 분석해 검토하였다. 이 때, 노면 토양의 특성에 따라서 트랙터가 장애물을 만났을 때 순간적인 진동 특성을 분석하기 위해서 트랙터 주행 노면을 사인범퍼(sine-bumper) 노면으로 설정하고 주행실험 및 시뮬레이션을 진행하였다. 본 연구에서는 강체 노면, Heavy clay, North Gower Clay Loam 토양, Grenvile Loam 토양의 네 가지 노면 조건을 분석하였다. 분석 결과 침하가 발생하지 않는 강체 노면보다 침하가 발생하는 세 가지 토양 조건에서 장애물에 따른 순간적인 진동이 더 크게 나타나는 것을 확인할 수 있었다. 다만 승차진동이 토양 파라미터와 단순히 선형적인 관계만을 보이는 것은 아니라는 것도 확인할 수 있었다. 이 후, North Gower Clay Loam 토양 조건에서 트랙터 속도에 따른 시뮬레이션 진행 결과를 분석하였을 때, 토양 침하가 발생하는 토양 조건에서도 속도가 증가함에 따라서 진동 크기가 증가하는 것을 확인할 수 있었다.

• 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 Korean Society of Computer Information Conference
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• 2016.01a
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• pp.121-122
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• 2016

본 논문에서는 노면상에 발생하는 다양한 유형의 유고정보를 센싱 및 처리하기 위한 미들웨어를 제안한다. 유고정보란 도로 및 노면에서 발생될 수 있는 센싱자료들을 분석하여 제공하는 정보로서 유고정보 처리 및 분석을 위한 기초 데이터들을 수집하는 목적지향 미들웨어 시스템을 구축하였다. 유고정보 분석을 위해 인터넷과 센서 수집을 통하여 미들웨어로 데이터를 수집한다. 이때 인터넷을 통한 수집을 위해 공개키를 사용하여 인터넷의 공공데이터들을 수집한다. 또한 수집된 데이터들을 미들웨어에서 관리 및 제어를 할 수 있다.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.71-79
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• 2019

By using convolutional neural networks (CNNs) based on semantic segmentation, road surface damage detection has being studied. In order to generate the CNN model, it is essential to collect the input and the corresponding labeled images. Unfortunately, such collecting pairs of the dataset requires a great deal of time and costs. In this paper, we proposed a road surface damage detection technique based on semi-supervised learning using pseudo labels to mitigate such problem. The model is updated by properly mixing labeled and unlabeled datasets, and compares the performance against existing model using only labeled dataset. As a subjective result, it was confirmed that the recall was slightly degraded, but the precision was considerably improved. In addition, the $F_1-score$ was also evaluated as a high value.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1631-1638
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• 2013

Distress data, IRI, etc. are important factors in the evaluation of pavement condition. Recently, the need to consider tire-pavement noise in PMS (pavement management system) is raised. Generally, tire-pavement noise highly depends on the characteristics of pavement texture. Therefore, estimation of texture characteristics may give useful information to predict tire-pavement noise. Measurement of MPD (Mean Profile Depth) by using PLP (Portable Laser Profiler) provide very fast. The texture characteristics by means of MPD can be in a short time. hence, It can be a good alternative to give noise information, if MPD and tire-pavement noise have robust relationship. In this study, MPD and tire-pavement noise were simultaneously collected on the number of asphalt section to evaluate the tire-pavement noise due to the pavement texture characteristics. A set of statistical analysis was performed to propose relationship between tire-pavement noise and MPD for asphalt concrete pavement.

• 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.

• Journal of the Korean Society of Propulsion Engineers
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• v.14 no.2
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• pp.39-45
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• 2010

In the recent years, it is important to evaluate the durability and the reliability of the vehicle, aircraft, and structure. Especially, in case of the vehicle, the durability and reliability are tested by driving test after making prototype vehicles. However, these methods require many costs and efforts for the experiment are needed to react the defects of product. This problems can be settled by simulator which supplies the realistic environments. In this parer, four-axial road simulator with hydraulic power and driving program to operate are made up. The displacement road profile is realized by accelerometers. For the verification the real-vehicle experiment is executed and road profile obtained from the experiment is verified by four-axial road simulator.

• 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%.