• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.831-838
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• 2006

An experimental/analytic study has been conducted to understand the adverse effects of low vehicle speed, high axle load and high tire pressure on the performance of asphalt pavements. Of 33 asphalt sections at KHC test road, two sections having different base layer thickness (180 mm versus 280 mm) are adopted for rollover tests. During the test, a standard three-axle dump truck maintains a steady state condition as moving along the wheel path of a passing lane, and lateral offsets and real travel speed are measured with a laser-based wandering system. Test results suggest that vehicle speed affects both longitudinal and transverse strains at the bottom of asphalt layer (290 mm and 390 mm below the surface), and even slightly influences the measured vertical stresses at the top of subbase and subgrade due to the dynamic effect of rolling vehicle. Since the anisotropic nature of asphalt-aggregate mixtures, the difference between longitudinal and transverse strains appears prominent throughout the measurements. As the thickness of asphalt pavement increases, the measured lateral strains become larger than its corresponding longitudinal strains. Over the limited testing conditions, it is concluded that higher axle weight and higher tire pressures induce more strains and vertical stresses, leading to a premature deterioration of pavements. Finally, a layered elastic analysis overestimates the maximum strains measured under the 1st axle load, while underestimating the maximum vertical stress in both pavement sections.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.395-402
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• 2010

The seriousness of a traffic accident appears relatively higher on the curve sections compared with the straight sections due to a change in speed caused by a change in the driver's sight. In particular, the visual distortion phenomenon, one of the dangerous factors taking place on the curve sections, appears different according to the road's geometric design. Although it is a genuinely principal design factor which should be necessarily considered in designing a road, the previous researches on establishing the design standards for it have been insufficiently conducted. As a result, the establishment of the road design standards for the curve sections considering the sight distortion phenomenon is desperately required. This research examined the previous researches on the driver's behaviors, the driver's sight characteristics and the perceived curve radius on the curve sections, and developed the theoretical model of perceived curve radius to which a mathematical technique is applied in consideration of the visual distortion phenomenon on the two-lane curve sections in a local area. In addition, after the theoretical visual distortion was calculated on the basis of the theoretical model of perceived curve radius, the range of error on the theoretical recognition radius model formula was verified through comparing it with the previous researches' experiential visual distortion level and analyzing both of them. As a result, it was observed that as the curve radius practically increases in the theoretical recognition curve radius, the range of error tends to go down, which reflects well the characteristics of the curve sections on the road. Based on this research, it is expected that this research will be helpful to eliminate the safety defects when designing the curve sections and contribute to develop the road design standards considering human factors in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5A
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• pp.673-683
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• 2008

This study improved the existing method of using the longitudinal strain and concept of influence line to develop Bridge Weigh-in-Motion system without axle detector using the dynamic strain of the bridge girders and concrete slab. This paper first describes the considered algorithms of extracting passing vehicle information from the dynamic strain signal measured at the bridge slab, girders, and cross beams. Two different analysis methods of 1) influence line method, and 2) neural network method are considered, and parameter study of measurement locations is also performed. Then the procedures and the results of field tests are described. The field tests are performed to acquire training sets and test sets for neural networks, and also to verify and compare performances of the considered algorithms. Finally, comparison between the results of different algorithms and discussions are followed. For a PSC I-girder bridge, vehicle weight can be calculated within a reasonable error range using the dynamic strain gauge installed on the girders. The passing lane and passing speed of the vehicle can be accurately estimated using the strain signal from the concrete slab. The passing speed and peak duration were added to the input variables to reflect the influence of the dynamic interaction between the bridge and vehicles, and impact of the distance between axles, respectively; thus improving the accuracy of the weight calculation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6D
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• pp.569-575
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• 2010

The occupancy based O/D is essential for measuring efficiency of various transportation policies like HOV/HOT lane, ramp metering, and public parking station. There has been many studies on occupancy survey methodology and O/D estimation using TCS (Toll Collection System) data separately. The occupancy O/D estimation methodology using TCS data has not been attempted thus far. An overall process from data collection stage to the occupancy O/D estimation stage has been suggested. Field survey was performed at the northbound Seoul toll station of Gyeongbu Expressway by each 2 hours of AM peak, PM non-peak, PM peak, midnight periods on a day. The process of matching the TCS data and field survey data classified by tollbooth ID, car type/mode, and arrival time was also performed. One typical output of the results showed that the ratio of single occupancy vehicles bounding for Seoul during the AM peak amounted to 60%. With the key output of this study and the specific O/D estimation methodology suggested, the whole centroid-to-centroid occupancy O/D of the country could be available, and then various applications in which the occupancy information is required could be possible.

• Korean Journal of Culture and Social Issue
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• v.15 no.4
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• pp.547-563
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• 2009

This study was conducted to examine the stress effects of time pressure and induced driver-anger on driving performance. The participants in the four different stress conditions(i.e., control condition, time pressure, induced anger, and mixed condition where induced-anger and time pressure were combined) were asked to drive the driving simulator, and their driving performances(i. e., lane crossing, signal violation, speeding, and deviation form designated path) were measured as the dependent variable. The results can be summarized as followings. (1) Induced-anger alone and the mixed driver stresses tended to yield deteriorated driving performances as well as awareness for designated path, (2) Time pressure alone appeared to have only limited effect both on the driving and path awareness. And (3) the effects of induced-anger alone and the mixed condition on driving performance and path awareness did not show ant significant difference. The results of the present study indicated that drivers' basic vehicle control and keeping awareness to destination could be affected differently by the types of driver stress.

• Animal Bioscience
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• v.36 no.11
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• pp.1709-1717
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• 2023

Objective: A pivotal study was designed to investigate the effect of Hydroxy (HYC) Cu, Zn, and Mn on egg quality and laying performance of chickens under tropical conditions. Methods: A total of 1,260 Babcock White laying hens (20-wk-old) were randomly assigned to one of 4 treatments with 15 replicates of 21 hens each in a Randomized Complete Block Design. The birds were reared for 16 weeks and were fed the corn-soybean meal diets supplemented with one of the following mineral treatments: T1, inorganic (INO, 15 ppm CuSO4, 80ppm MnSO4 and 80 ppm ZnO); T2, Hydroxy-nutritional level (HYC-Nut, 15 ppm Cu, 80 ppm Mn, 80 ppm Zn from Hydroxy); T3, Hydroxy-Low (HYC-Low, 15 ppm Cu, 60 ppm Mn, 60 ppm Zn from Hydroxy); T4, Hydroxy plus inorganic (HYC+INO, 7.5 ppm HYC Cu+7.5 ppm CuSO4, 40 ppm HYC ZnO+40 ppm ZnSO4, 40 ppm HYC Mn+40 ppm MnSO4). The egg production was recorded daily, while the feed consumption, feed conversion ratio (FCR) and egg mass were determined at the end of each laying period. The egg quality parameters were assayed in eggs collected over 48 h in each laying period. Results: Overall, no significant effect of treatments was observed on percent egg production, egg weight and FCR (p>0.05). Feed intake was significantly lower in birds fed Hydroxy plus inorganic (p<0.05) diet. The supplementation of HYC-Low significantly increased the egg mass compared to the other treatments (p<0.05). HYC supplementation alone or in combination with INO elicited a positive effect on shell thickness, shell weight, shell weight per unit surface area, yolk colour, albumen and yolk index for a certain period (p<0.05), but not throughout the whole laying period. Conclusion: Dietary supplementation of HYC-Low (15-60-60 mg/kg) showed similar effects on production performance and egg quality characteristics in laying hens as compared to 15-80-80 mg/kg of Cu-Zn-Mn from inorganic sources. This indicates that sulphate based inorganic trace minerals can effectively be substituted by lower concentration of hydroxyl minerals.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.148-162
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• 2023

The objective of this study is to derive the appropriate perception location for changes in driving behavior of autonomous vehicles in urban road environments based on traffic safety signs. For this purpose, 32 types of signs that induce changes in driving behavior were selected from currently used traffic safety signs and classified as three types according to changes in driving behavior. Based on this, three scenarios were designed: stop, speed change, and lane change scenarios. These were used to confirm the impact on traffic flow. As a result of the analysis, it was found that each scenario needs to receive information on traffic safety signs in advance to ensure changes in traffic flow and safety. Consequently, the appropriate perception location can be used as a basis for establishing standards for delivering message sets to autonomous vehicles or revising traffic safety signs for them. In addition, this study is expected to contribute to the establishment of safe and efficient driving strategies on urban roads as autonomous vehicles are introduced in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.182-199
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• 2023

Since 2021, government departments have been promoting Automated Driving Technology Development and Innovation Project as national research and development(R&D) project. The automated vehicles and service technologies developed as part of these projects are planned to be subsequently provided to the public at the selected Living Lab City. Therefore, it is important to determine a spatial area and operation section that enables safe and stable automated driving, depending on the purpose and characteristics of the target service. In this study, the static Operational Design Domain(ODD) elements for Level 4 automated driving services were reclassified by reviewing previously published papers and related literature surveys and investigating field data. Spatial analysis techniques were used to consider the reclassified ODD elements for level 4 in the real area of level 3 automated driving services because it is important to reflect the spatial factors affecting safety related to real automated driving technologies and services. Consequently, a total of six driving mode changes(disengagement) were derived through spatial information analysis techniques, and the factors affecting the safety of automated driving were crosswalk, traffic light, intersection, bicycle road, pocket lane, caution sign, and median strip. This spatial factor analysis method is expected to be useful for determining special areas for the automated driving service.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.