• International Journal of Highway Engineering
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• v.8 no.3 s.29
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• pp.21-28
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• 2006

Field or laboratory wheel tracking tests have been employed for the evaluation of the rutting potential of asphalt paving mixtures. Compared to field tests, laboratory wheel tracking tests are much less expensive and more manageable for most road projects. However, most test laboratories are not equipped to perform such tests because there does not exist any standard test procedure, and the required equipment is rather expensive. Futhermore, the size of test specimens and the relatively large quantity of test mixture required present difficulties for laboratory specimen mixing and compaction. This paper describes a project conducted to study the feasibility of replacing wheel tracking testsby a repeated-load creep test for rutting potential evaluation. Comparisons were made between the results of the two tests for different test temperatures, loading speeds and applied pressures. Three types of asphalt mixtures were studied in the test program. Favorable conclusions concerning the use of the repeated-load test for rutting potential evaluation were drawn based on the findings of the experimental test results. The correlation between the two types of tests was found to be good for all threeasphalt mixtures. Adopting the repeated-load creep test would lead to cost savings since it employs standard test equipment already available in most laboratories. It would also result in substantial time savings due to the much smaller quantity of mix needed, and the ease in specimen preparation.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.69-79
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• 2013

A system of tracking a vehicle collects the vehicle location and then transmits the data into a GIS server in order to provide several service areas. An ODU built in the vehicle savesa GPS information data and communicates with the RSU installed in the road over the wave technology. The collected data in RSU is transmitted into the GIS serverand is stored. The system based on the collected GPS data in each vehicle is providing useful and various services between RSU and OBU. In this paper indicates that the system is designed in 2 phasesbased on the collected GPS data. The result comparing to process of GPS data created in 1 phase and 2 phase over the Web GIS service indicates that the capability of process of the GPS data in 2 phase is enhanced in "50.88% ~ 51.81%". Therefore, when the accident is happened the system of tracking the vehicle over GPS tech not only receives the information properly but also provides the emergence rescue services.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.1-20
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• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• 한국도로학회:학술대회논문집
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• 2005.11a
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• pp.127-132
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• 2005

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.501-504
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• 2003

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.563-570
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• 2024

This paper proposes a method to recognize and track drivable lane areas to assist the driver. The main topic is designing a deep-based network that predicts drivable road areas using computer vision and deep learning technology based on images acquired in real time through a camera installed in the center of the windshield inside the vehicle. This study aims to develop a new model trained with data directly obtained from cameras using the YOLO algorithm. It is expected to play a role in assisting the driver's driving by visualizing the exact location of the vehicle on the actual road consistent with the actual image and displaying and tracking the drivable lane area. As a result of the experiment, it was possible to track the drivable road area in most cases, but in bad weather such as heavy rain at night, there were cases where lanes were not accurately recognized, so improvement in model performance is needed to solve this problem.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.145-148
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• 1996

In moving object tracking based on the visual sensory feedback, a prerequisite is to determine which feature or which object is to be tracked and then the feature or the object identification precedes the tracking. In this paper, we focus on the object identification not image feature identification. The target identification is realized by finding out corresponding line segments to the hypothesized model segments of the target. The key idea is the combination of the Mahalanobis distance with the geometrica relationship between model segments and extracted line segments. We demonstrate the robustness and feasibility of the proposed target identification algorithm by a moving vehicle identification and tracking in the video traffic surveillance system over images of a road scene.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.12
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• pp.651-658
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• 2000

This paper presents a new method to extract traffic information such as number of passing vehicles and average speed by a pair of stereo road images. The whole process consists of the extraction of vehicles and the tracking of the extracted vehicles. For the extraction of vehicles, the outline of each vehicle is obtained by using binary region growing technique applied to disparity map based on multi-resolution stereo matching. The Kalman filter tracking algorithm is applied to the extracted vehicle outlines to determine the flow of vehicles. Experimental results show that the proposed method significantly improved recognition rate of vehicles over the conventional methods-frame difference and background elimination methods.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1211-1212
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• 2008

In this paper, we propose image process modeling as a part of location based surveillance system for unauthorized target recognition and tracking in harbor, airport, military zone. For this, we compress and store background image in lower resolution and perform object extraction and motion tracking by using sobel edge detection and difference picture method between real images and a background image. In addition to, we use Independent Component Analysis Neural Network for moving target recognition. Experiments are performed for object extraction and tracking of moving targets on road by using static camera in 20m height building and it shows the robust results.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.12
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• pp.69-77
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• 2009

In this study an ANFIS-based trajectory tracking motion control algorithm is proposed for autonomous garage and parallel parking of a model car. The ANFIS controller is trained off-line using data set which obtained by Mandani fuzzy inference system and thereby the processing time decreases almost in half. The controller with a steering delay compensator is tuned through simulations performed under MATLAB/Simulink environment. Experiments are carried out with the model car for garage and parallel parking. The experimental results show that the trajectory tracking performance is satisfactory under various initial and road conditions