• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.116-128
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• 2017

Traffic accidents can be defined as a physical collision event of vehicles occurred instantaneously when drivers do not perceive the surrounding vehicles and roadway environments properly. Therefore, detecting the high potential events that cause traffic accidents with monitoring the interactions among the surroundings continuously by driver is the prerequisite for prevention the traffic accidents. For the analysis, basic data were collected to analyze interactions using a test vehicle which is equipped the GPS(Global Positioning System)-IMU(Inertial Measurement Unit), camera, radar and RiDAR. From the collected data, highway geometric information and the surrounding traffic situation were analyzed and then safety evaluation algorithm for driving vehicle was developed. In order to detect a dangerous event of interaction with surrounding vehicles, locations and speed data of surrounding vehicles acquired from the radar sensor were used. Using the collected data, the tangent and curve section were divided and the driving safety evaluation algorithm which is considered the highway geometric characteristic were developed. This study also proposed an algorithm that can assess the possibility of collision against surrounding vehicles considering the characteristics of geometric road structure. The methodology proposed in this study is expected to be utilized in the fields of autonomous vehicles in the future since this methodology can assess the driving safety using collectible data from vehicle's sensors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.628-637
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• 2017

The automobile industry has developed Advanced Driver Assistance Systems (ADASs) to prevent traffic accidents and reduce the burden for drivers. One example is the Lane Keeping Assistance System (LKAS), which was developed for automotive vehicle systems for safety and better driving. The main system of the LKAS supports the driver while maintaining the vehicle within a lane. LKAS uses a radar sensor and camera sensor to collect information about the vehicle's position in the lane and send commands to the actuator to influence the lateral movement of the vehicle if necessary. Recently, vehicles equipped with LKAS have become commercially available. Test procedures for international LKAS evaluation are being discussed and developed by international committees, such as the International Organization for Standardization and United Nations Economic Commission for Europe. In Korea, an evaluation of LKASs for car safety is being planned by the Korean New Car Assessment Program. Therefore, test procedures should be developed for LKASs that are suitable for the domestic road environment while accommodating international standards. We developed a test scenario for LKASs and propose a formula for obtaining the target relative distance. To validate the methods, a series of experiments were conducted using commercially available vehicles equipped with LKAS.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.222-234
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• 2020

Autonomous vehicles are equipped with a wide rage of sensors such as GPS, RADAR, LIDAR, camera, IMU, etc. and are driven by recognizing and judging various transportation systems at intersections in the city. The accident ratio of the intersection of the autonomous vehicles is 88% of all accidents due to the limitation of prediction and judgment of an area outside the sensing distance. Not only research on non-signalized intersection collision avoidance strategies through V2V and V2I is underway, but also research on safe intersection driving in failure situations is underway, but verification and fragments through simple intersection scenarios Only typical V2V failures are presented. In this paper, we analyzed the architecture of the V2V module, analyzed the causal factors for each V2V module, and defined the failure mode. We presented intersection scenarios for various road conditions and traffic volumes. we used the ISO-26262 Part3 Process and performed HARA (Hazard Analysis and Risk Assessment) to analyze the risk of autonomous vehicle based on the simulation. We presented ASIL, which is the result of risk analysis, proposed a monitoring concept for each component of the V2V module, and presented monitoring coverage.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.55-69
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• 2017

Recently, the IoT (Internet of Things) environment is being developed rapidly through network which is linked to intellectual objects. Through the IoT, it is possible for human to intercommunicate with objects and objects to objects. Also, the IoT provides artificial intelligent service mixed with knowledge of situational awareness. One of the industries based on the IoT is a car industry. Nowadays, a self-driving vehicle which is not only fuel-efficient, smooth for traffic, but also puts top priority on eventual safety for humans became the most important conversation topic. Since several years ago, a research on the recognition of the surrounding environment for self-driving vehicles using sensors, lidar, camera, and radar techniques has been progressed actively. Currently, based on the WAVE (Wireless Access in Vehicular Environment), the research is being boosted by forming networking between vehicles, vehicle and infrastructures. In this paper, a research on the recognition of a traffic signs on highway was processed as a part of the awareness of the surrounding environment for self-driving vehicles. Through the traffic signs which have features of fixed standard and installation location, we provided a learning theory and a corresponding results of experiment about the way that a vehicle is aware of traffic signs and additional informations on it.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.78-96
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• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.