• Journal of the Korean Society for Railway
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• v.10 no.5
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• pp.473-479
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• 2007

Lateral vibration at the tail of KTX train was found during the acceptance test. In order to settle the problem of lateral vibration, the wheel conicity was changed 1/40 to 1/20. However, we should evaluate the running safety of vehicle with 1/20 wheel conicity because modification of wheel conicity may cause the running performance to be worse and critical speed to reduce. In this paper, we calculate critical speed of KTX bogie as wheel conicity increase and analyze the running safety for KTX that has 20 car trainset formation using VAMPIRE. and compare with the test results of KHST to validate analysis results on high speed line. A analysis results show that critical speed of 0.3 wheel conicity is over 375km/h and curving performance of 1/20wheel conicity is better than 1/40. Also, we examinate the running performance of KTX to check out possibility to increase speed of KTX on conventional line. A analysis results show that it is possible to increase up to 10% the speed of KTX on tangent line but KTX on a curved line should be operated with the speed of conventional train.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.1
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• pp.1-10
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• 2023

With the advent of the 4th industrial revolution era, interest in autonomous driving technology is increasing. Accordingly it is necessary to seek safe driving by recognizing surrounding situations using sensors attached to autonomous vehicles along with the applicability of existing traffic facilities to autonomous driving lanes and the utilization of HD maps. In this study, in order to deduce the role of sensor only physical facilities which recognized through a laser scanner on an autonomous vehicle developed to improve road and traffic infrastructure, through comparative analysis with existing road facilities such as road signs, safety signs, and gaze guidance facilities. Sign facilities can promote driving safety by allowing autonomous vehicles to perform specific actions directly. In order to promote safe driving by recognizing sign facilities by using sensors for autonomous vehicles, it is necessary to prepare standards for installation, management, and use, and it is considered that management and supervision should be carried out continuously according to the standards.

• International Journal of Highway Engineering
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• v.14 no.2
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• pp.29-36
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• 2012

In this study, frequently passing vehicles with two, three, four, and five axles were chosen through traffic volume analysis in Kyung-In Expressway in order to analyze how the road roughness and vehicle speed affect on the dynamic loads for roads in various vehicle classes. Dynamic loads according to chosen vehicles are estimated by TruckSim program. Dynamic load amplification factor is ratio between dynamic and static loads, and it is also determined for each vehicle classes. From the result of dynamic loads estimated by the dynamic load amplification factor, it is shown that for three-axles vehicle, when IRI is 3.5 and vehicle speed is 100km/hr, asphalt pavements receive additional 36% of static loads in maximum. The analysis of the amplification factor according to each vehicle classes also indicates that the amplification factor increases as the distance between the axles becomes smaller and each axle receives more loads.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.97-100
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• 2010

Lately, becausing Life, property loss from car accident, vehicles have been used vehicle blackbox like blackbox by airplane. when the accident happened, existing car blackbox that was stored external image or video of vehicle don't know the vehicle's driving conditions. For knowing vehicle's driving conditions, vehicle is loaded sensors for Variety of measurement and control. the sensors is controlled by ECU(Electronic Control Unit) and all vehicles is used Mandatory OBD2(On-board diagnostics) protocol for communication between ECUs since 2006. Using ODB2 protocol, driver use blackbox data by various driving data to occur vehicle' ECU and can be obtained more definitive information. In this paper, there implement smart blackbox system to use exact vehicle's data using OBD2 protocol rather than blackbox to store external image or video.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1047-1053
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• 2017

The range data acquired by 2D/3D LIDAR, a core sensor for autonomous navigation of an unmanned ground vehicle, is effectively used for ground modeling and obstacle detection. Within the ambiguous boundary of a road environment, however, LIDAR does not provide enough information to analyze the traversable region. This paper presents a new method to analyze a candidate area using the characteristics of LIDAR reflectivity for better detection of a traversable region. We detected a candidate traversable area through the front zone of the vehicle using the learning process of LIDAR reflectivity, after calibration of the reflectivity of each channel. We validated the proposed method of a candidate traversable region detection by performing experiments in the real operating environment of the unmanned ground vehicle.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.122-134
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• 2020

To improve traffic congestion, reduce fuel consumption, and improve the stability of truck operations, truck platooning, in which several trucks are organized in a single platoon, is being actively researched globally. Compared to the operation of a single truck, the operation of a truck platoon requires more caution before the actual operation because an accident of one vehicle in the platoon can lead to an accident with all the vehicles in the platoon. Therefore, this study examined the types of trucks and cargo suitable for truck platooning to prevent safety accidents. The review showed that a closed-van-type truck is appropriate for truck platooning to prevent falling objects during driving. In the case of cargo types, it is necessary to exclude liquids and dangerous goods defined in related laws from truck platooning.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.327-333
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• 2019

The Fuel-cut driving is started when the acceleration pedal released with transmission gear engaged. Fuel economy of the vehicle improves by active fuel-cut driving. A deep-learning technique is proposed to predict fuel-cut driving with vehicle speed, acceleration and road gradient data in the study. It's 3~10 of hidden layers and 10~20 of variables and is applied to the 9600 data obtained in the test driving of a vehicle in the road of 12km. Its accuracy is about 84.5% with 10 variables, 7 hidden layers and Relu as activation function. Its error is regarded from the fact that the change rate of input data is higher than the rate of fuel consumption data. Therefore the accuracy can be better by the normalizing process of input data. It's unnecessary to get the signal of vehicle injector or OBD, and a deep-learning technique applied to the data to be got easily, like GPS. It can contribute to eco-drive for the computing time small.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.173-188
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• 2024

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.