• 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.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1092-1096
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• 2011

The "standard regulation" of the vehicles from "urban transit law" are being adopted to electric multiple unit in domestic. In the standard regulation, there are two types for EMU. One is heavy EMU for Seoul. The other is large EMU for Pusan, Daegu, Gwangju, Daejeon, Incheon. Korea Railroad Research Institute, with the assistance of the Ministry of Land, Transport and Maritime Affairs, "advanced EMU development project" are progressed, and 6th year started at September, one unit to six cars is completed. Now "urban transit vehicle performance tests are on the current progress. The main characteristics of AUTS(Advanced Urban Transit System) are as follows. One inverter control one motor, DDM(Dircet Drive Motor), no driving gear, plug door and steps, mounting and maintenance costs down, passenger convenience improvement. This paper describes the key features the next generation EMU, and performance test results, and the commercial success method of national R&D business.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.4
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• pp.163-172
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• 2012

A driver assistance system is critical to improve a convenience and stability of vehicle driving. Several systems have been already commercialized such as adaptive cruise control system and forward collision warning system. Efficient vehicle detection is very important to improve such driver assistance systems. Most existing vehicle detection systems are based on a radar system, which measures distance between a host and leading (or oncoming) vehicles under various weather conditions. However, it requires high deployment cost and complexity overload when there are many vehicles. A camera based vehicle detection technique is also good alternative method because of low cost and simple implementation. In general, night time vehicle detection is more complicated than day time vehicle detection, because it is much more difficult to distinguish the vehicle's features such as outline and color under the dim environment. This paper proposes a method to detect vehicles at night time using analysis of a captured color space with reduction of reflection and other light sources in images. Four colors spaces, namely RGB, YCbCr, normalized RGB and Ruta-RGB, are compared each other and evaluated. A suboptimal threshold value is determined by Otsu algorithm and applied to extract candidates of taillights of leading vehicles. Statistical features such as mean, variance, skewness, kurtosis, and entropy are extracted from the candidate regions and used as feature vector for SVM(Support Vector Machine) classifier. According to our simulation results, the proposed statistical feature based SVM provides relatively high performances of leading vehicle detection with various distances in variable nighttime environments.

• Journal of the Korean Society of Safety
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• v.33 no.1
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• pp.148-161
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• 2018

A traffic fatality by young people marked average annual decrease of 4.5% since 2011. Meanwhile, a traffic fatality by senior over 65 years old marked average annual increase of 7.9% for the last five years which means that the annual increase of traffic fatality by senior will be a serious problem. This study started questioning that senior drivers over 65 years old did not retain the same causal factor of fatal traffic accidents and thus extensively analyzed a risk of it by age group quantitatively, dividing the senior driver group into the early, middle and latter stages. Depending on the aging level, the risk of traffic fatality showed a wide difference in seven different types of traffic accidents generally, and happened to increase with latter and middle parts of the senior driver more than the early part. Therefore, this study proposes four policy suggestions: 1) The senior driver need to be offered customized driving educations and the improvement of road environment is also recommended. 2) Political assistance is needed to support and guide a safety related technology installation for the new or existing car. 3) Renewal of driving license and an aptitude test(physical examination, cognitive test) for drivers over 75 years old should take in a less than 3 years and an additional road test is needed as occasion demands. 4) Like the United States and Europe, development and extension of customized treatment guidebook for medical teams who examine senior drivers is needed and establishment of education and administration system that a supervisor of driving license renewal can impose safety restriction and American anonymity reporting system is considered to institutionalize in the medium to longer term.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.907-917
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• 2023

Recently, a self-driving car have applied deep learning technology to advanced driver assistance system can provide convenience to drivers, but it is shown deep that learning technology is vulnerable to adversarial evasion attacks. In this paper, we performed five adversarial evasion attacks, including MI-FGSM(Momentum Iterative-Fast Gradient Sign Method), targeting the object detection algorithm YOLOv5 (You Only Look Once), and measured the object detection performance in terms of mAP(mean Average Precision). In particular, we present a method applying morphology operations for YOLO to detect objects normally by removing noise and extracting boundary. As a result of analyzing its performance through experiments, when an adversarial attack was performed, YOLO's mAP dropped by at least 7.9%. The YOLO applied our proposed method can detect objects up to 87.3% of mAP performance.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.33-41
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• 2015

Recent developments in robotics and intelligent vehicle area, bring interests of people in an autonomous driving ability and advanced driving assistance system. Especially fully automatic parking ability is one of the key issues of intelligent vehicles, and accurate parked vehicles detection is essential for this issue. In previous researches, many types of sensors are used for detecting vehicles, 2D LiDAR is popular since it offers accurate range information without preprocessing. The L shape feature is most popular 2D feature for vehicle detection, however it has an ambiguity on different objects such as building, bushes and this occurs misdetection problem. Therefore we propose the accurate vehicle detection method by using a 3D complete vehicle model in 3D point clouds acquired from front inclined 2D LiDAR. The proposed method is decomposed into two steps: vehicle candidate extraction, vehicle detection. By combination of L shape feature and point clouds segmentation, we extract the objects which are highly related to vehicles and apply 3D model to detect vehicles accurately. The method guarantees high detection performance and gives plentiful information for autonomous parking. To evaluate the method, we use various parking situation in complex urban scene data. Experimental results shows the qualitative and quantitative performance efficiently.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.325-336
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• 2023

With the development of autonomous driving technology, the number of external contact sensors mounted on vehicles is increasing, and the importance is also rising. The vehicular ultrasonic sensor uses the LIN protocol in the form of a bus topology and reports a status message about its surroundings through the vehicle's internal network. Since ultrasonic sensors are vulnerable to various threats due to poor security protocols, physical testing on actual vehicle is needed. Therefore, this paper developed a LIN-CAN co-analysis testbed with a jig for location-specific distance test to examine the operational relation between LIN and CAN caused by ultrasonic sensors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.849-856
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• 2020

Recently, as the number of high-height vehicles such as double-decker buses has increased, collision accidents have occurred in bridges and tunnels due to the deviation from the designated routes and driver's carelessness. In the case of the existing front collision warning system, it is limited to vehicles and pedestrians, so it is difficult to use it as a pass height warning system for the high height vehicles. In this paper, we propose a system that generates a warning by determining the correlation and time series characteristics of data for each segment using multiple lidar sensors and then determining the possibility of collision in the upper part of the vehicle. Also, the proposed system confirmed the proper operation through a real-time driving test and a system performance evaluation by the Korea Automobile Testing & Research Institute.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.219-230
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• 2023

With the development of autonomous driving technology, as the use of software in vehicles increases, the complexity of the system increases and the difficulty of development increases. Developments that meet ISO 26262 must be carried out to reduce the malfunctions that may occur in vehicles where the system is becoming more complex. ISO 26262 for the functional safety of the vehicle industry proposes to consider functional safety from the design stage to all stages of development. Specifically at the software level, the requirements to be complied with during development and the requirements to be complied with during verification are defined. However, it is not clearly expressed about specific design methods or development methods, and it is necessary to supplement development guidelines. The importance of analysis and verification of requirements is increasing due to the development of technology and the increase of system complexity. The vehicle industry must carry out developments that meet functional safety requirements while carrying out various development activities. We propose a process that reflects the perspective of system engineering to meet the smooth application and developmentrequirements of ISO 26262. In addition, the safety analysis/verification FMEA processforthe safety of the proposed ISO 26262 function was conducted based on the FCAS (Forward Collision Avoidance Assist System) function applied to autonomous vehicles and the results were confirmed. In addition, the safety analysis/verification FMEA process for the safety of the proposed ISO 26262 function was conducted based on the FCAS (Forward Collision Avoidance Assist System) function applied to the advanced driver assistance system and the results were confirmed.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.2
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• pp.75-81
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• 2018

In this paper for developing and implementing the car driver's front lane attention enhancement developed system using the car camera. The developed system automatically alarm the car driver when front cars make the dangerous situation. We use Raspberry Pi camera module V2 as car camera module, Raspberry Pi 3 board as hardware main board of implementing embedded system and develop the application library module which can be operated on the Raspberry situation. The application library module widely consist of two part, front car recognition part and dangerous situation distinguish part. Our developed system satisfy the performance test of the target system at the software test certification laboratory of TTA(Telecommunication Technology Association). We test four items as attentive car recognition ability at day and night, system performance, response time. We get the performance of developed system based on the four goal. The car driver's front lane attention enhancement system in this paper will be widely used at the ADAS(Advanced Driving Assistance System) because of the better performance and function.