• International Journal of Automotive Technology
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• v.4 no.2
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.112-121
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• 2021

Unlike a typical small-sized robot navigating in a free space, an autonomous vehicle has to travel in a designated road which has lanes to follow and traffic rules to obey. High-Definition (HD) maps, which include road markings, traffic signs, and traffic lights with high location accuracy, can help an autonomous vehicle avoid the need to detect such challenging road surroundings. With space constraints and a pre-built HD map, a new type of path planning algorithm can be conceived as a substitute for conventional grid-based path planning algorithms, which require substantial planning time to cover large-scale free space. In this paper, we propose an obstacle-avoiding, cost-based planning algorithm in a continuous space that aims to pursue a globally-planned path with the help of HD map information. Experimentally, the proposed algorithm is shown to outperform other state-of-the-art path planning algorithms in terms of computation complexity in a typical urban road setting, thereby achieving real-time performance and safe avoidance of obstacles.

• Smart Structures and Systems
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• v.32 no.5
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.254-260
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• 2023

A sensor system has been developed that uses an ultrasonic sensor to detect the downhill slope on the side of a forest road and prevents a vehicle-type forest machine from rolling down a mountainside. A specular reflection of ultrasonic wave might cause severe issues in measuring distances to targets. By investigating the installation angle of the sensor to minimize the negative effects of specular reflection, the installation angle of lateral monitoring ultrasonic sensor could be determined based on the width of road shoulder. Obstacles such as small rocks or piece of log in a forest road may cause the forest machine to be overturned while the machine riding over due to excessive its posture change. It was determined that the laser sensor could be a part of a sensor system capable of specifying the location and size of small obstacles. Not only this sensor system including ultrasonic and laser sensors can issue a warning of dangerous sections to drivers in forest forwarders currently in use, but also it can be used as a driving safety sensor in autonomous forest machine or remote-control forest machine in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.81-90
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• 2016

As an autonomous driving vehicle (AV) need to cope with external road conditions by itself, its perception performance for road environment should be better than that of a human driver. A vision sensor, one of AV sensors, performs lane detection function to percept road environment for performing safe vehicle steering, which relates to define vehicle heading and lane departure prevention. Performance standards for a vision sensor in an ADAS(Advanced Driver Assistance System) focus on the function of 'driver assistance', not on the perception of 'independent situation'. So the performance requirements for a vision sensor in AV may different from those in an ADAS. In assuming that an AV keep previous steering due to lane detection failure, this study calculated lane departure distances between the AV location following curved road alignment and the other one driving to the straight in a curved section. We analysed lane departure distance and time with respect to the allowance of lane detection malfunction of an AV vision sensor. With the results, we found that an AV would encounter a critical lane departure situation if a vision sensor loses lane detection over 1 second. Therefore, it is concluded that the performance standards for an AV should contain more severe lane departure situations than those of an ADAS.

• Journal of Environmental Impact Assessment
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• v.14 no.6
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• pp.427-441
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• 2005

In order to choose proper dispersion model and emission factors suitable in Korea in evaluating the effect of pollutants emitted by the vehicles in highway on nearby area, various road dispersion models and vehicle emission factors were reviewed. With theoretical inter-comparisons of the exiting models for line source, CALINE 3 and CALINE 4 models which were suggested by US EPA were selected as the road dispersion models for further evaluation with the measurement. The emission factors suggested by Korean Ministry of Environment was turned out to be appropriate since the classification of vehicle kinds was simple and easy to apply in Korea. The comparisons of predicted concentrations by CALINE 3 and 4 models with the measurements in flat, fill and bridge road types showed that CO and PM-10 were in good agreements with experiments and the differences between CALINE 3 and 4 models are negligible. The model concentrations of $NO_2$ by CALINE 4 were also in good agreement with the measurement but those by CALINE 3 were over-predicted. The discrepancies in CALINE 3 model were due to rapid decay reaction of $NO_2$ near the highway, which was not included in CALINE 3 model. For the road type with one & two side cutting grounds, the similar patterns as the flat & fill road type for CO, PM10, & $NO_2$ were observed but the number of data for comparison in these cases were not enough to draw the conclusion. These results lead to the conclusion that CALINE4 model is proper in road environmental impact assessment near the highway in flat, fill and bridge road types.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.57-65
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• 2011

In this study, estimated the minimum lane width for the curved section by analyzing of lateral sway distance and compared the lane width for result of this study and a precedent study for straight section on urban roads. Then suggested minimum lane width of road alignments and vehicle classes. The lane width of curved section that was investigated was 2.79m~3.40m. Analysis of frequency distribute and cumulative frequency distribution for lateral sway distance on the basis of 85% of the suggested vehicles. The result of study, minimum lane width for the curved section was 2.31m~2.58m in the case of small size car and 2.80m~3.27m in the case of large size car. Result of this study is judged that it is necessary to case for introduction of green transit, during road construction and construct a road for small size car. Expect result of this study can be used for the application of flexible design standard according the purpose of road designer.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.595-603
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• 2015

With the advances of the big-data process technology, acquiring the real-time information from the massive image data taken by a mobile device inside a vehicle will be possible in the near future. Among the information that can be found around the vehicle, the route information is needed for safe driving. In this study, the automatic extraction of route information from the road sign imagery was dealt with. The scope of the route information in this study included the route number, route type, and their relationship with the driving direction. For the recognition of the route number, the modified Tesseract OCR (Optical Character Recognition) engine was used after extracting the rectangular-road-sign area with the Freeman chain code tracing algorithm. The route types (expressway, highway, rural highway, and municipal road) are recognized using the proposed algorithms, which are acquired from colour space analysis. Those road signs provide information about the route number as well as the roads that may be encountered along the way. In this study, such information was called “OTW (on the way)” or “TTW (to the way)” which between the two should be indicated is determined using direction information. Finally, the route number is matched with the direction information. Experiments are carried out with the road sign imagery taken inside a car. As a result, route numbers, route number type, OTW or TTW are successfully recognized, however some errors occurred in the process of matching TTW number with the direction.

• 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.

• Korean Journal of Agricultural Science
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• v.42 no.4
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• pp.341-346
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• 2015

This study was conducted to investigate the awareness tendency of the government employees who manage forest roads about problems of forest road use and how to improve them in the future using surveys. The results show there are more silvicultural operations than harvesting operations using forest roads. The main purpose of using forest roads is to transport workers; however, the road will be used more for harvesting operations in the future. The respondents wanted to be accessibility for forest operations within 200 m from road in both present and future. For silvicultural operations 1-ton vehicle is used at present, and 5-ton vehicles are expected to be used in the future. For harvesting operations 5-ton vehicle is used at present, and 10-ton vehicles are will be used in the future. The roadway width is 3 - 4 m at present, but should be 4 - 4.5 m in the future. The longitudinal gradient of roads is 7 - 10% at present, and will be below 10% in the future. The minimum curve radius of roads is 12 - 15 m at present, and will be 15 - 20 m in the future. The results provide basic data for making future forest road policies.