• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1565-1571
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• 2010

FMCW(Frequency Modulation Continuous Wave) Radar is very useful for vehicle collision warning system because of the simplicity. In this work, a signal processing part of FMCW vehicle radar system is implemented with flexibility using DSP, FPGA, ADC, and DAC so that the system could adopt lots of algorithm and could be improved through road test. It is shown that the system meets basic requirements as designed, and finds some problems in road test. We briefly discuss the problem which are caused by shadow effect from overlapped target and the distortion of beat frequency from the nonlinearity of VCO and the RCS of vehicle.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.262-266
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• 2014

Military vehicle equipped with an antenna and a shelter for operating radar has a vibration exposure during driving time. This vibration would have influence on structure of military vehicle critically. In this paper, driving stability of the military vehicle is obtained through the vibration response analysis. And, vibration level of the military vehicle satisfied the military vibration specification through analysis and comparing the MIL-STD-810G. PSD and Grms data obtained by road test can be used for vibration test specification of cabinets and electronic equipment in shelter.

• Transactions of the Society of Information Storage Systems
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• v.11 no.2
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• pp.26-30
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• 2015

Recently, military vehicles are driven with a lot of electronic devices such as radar, antenna, and information storage devices. However, the military vehicles can be exposed to impact easily. Therefore, they have to be designed robustly in order to ensure the stability of the vehicle and the electronic devices. To achieve that, the dynamic behaviors of the military vehicle should be exactly identified. Therefore, in this research, dynamic behaviors of the vehicles were identified by carrying out road tests and we constructed finite element model to analyze the dynamic characteristics of the vehicle.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1505-1515
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• 2016

The age of autonomous vehicles has come according to development of high performance sensing and artificial intelligence technologies. And importance of the vehicle's surrounding environment sensing and observation is increasing accordingly because of its stability and control efficiency. In this paper we propose an integrated platform for efficient networking, analysis and monitoring of multiple sensing data on the vehicle that are equiped with various automotive sensors such as GPS, weather radar, automotive radar, temperature and humidity sensors. From simulation results, we could see that the proposed platform could perform realtime analysis and monitoring of various sensing data that were observed from the vehicle sensors. And we expect that our system can support drivers or autonomous vehicles to recognize optimally various sudden or danger driving environments on the road.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.1-12
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• 2015

Intrusive Vehicle Detectors have excellent detection performance compared to other types of detector, but disadvantages of high installation and maintenance costs, short life time due to greater damage to roads and paving materials. In contrast, Non-Intrusive Vehicle Detectors attached to the stationary pole have advantages because it does not damage the road surface and easy and less expensive to maintain. Despite these advantages, Non-Intrusive type detectors are still not been widely used in traffic signal control systems because of the low detection performance. In this study, a FMCW(Frequency Modulated Continuous Wave) radar Vehicle Detector was designed as an alternative detector for the signalized intersection, and the performance evaluation was presented by purpose applicability.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.121-129
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• 2015

This paper presents a multiple vehicle recognition algorithm based on radar and vision sensor fusion for lane change assistance. To determine whether the lane change is possible, it is necessary to recognize not only a primary vehicle which is located in-lane, but also other adjacent vehicles in the left and/or right lanes. With the given sensor configuration, two challenging problems are considered. One is that the guardrail detected by the front radar might be recognized as a left or right vehicle due to its genetic characteristics. This problem can be solved by a guardrail recognition algorithm based on motion and shape attributes. The other problem is that the recognition of rear vehicles in the left or right lanes might be wrong, especially on curved roads due to the low accuracy of the lateral position measured by rear radars, as well as due to a lack of knowledge of road curvature in the backward direction. In order to solve this problem, it is proposed that the road curvature measured by the front vision sensor is used to derive the road curvature toward the rear direction. Finally, the proposed algorithm for multiple vehicle recognition is validated via field test data on real roads.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.5
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• pp.258-267
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• 1999

An intelligent safety vehicle(ISV) should have an ability to predict the possibility of an accident and help a driver avoid the accident in advance. The basic function of the ISV is to alert the driver by warning when the collision is to occur. For this purpose, the ISV has to function efficiently in sensing the environmental context. While image processing provides lane information, laser radar senses road obstacles including vehicles. By applying a simple clustering algorithm to radar signals, it is possible to obtain the vehicle information. Consequently, we can identify the existence of the vehicle of interest on my lane. The reliability of the sensing algorithm is evaluated by running on the highway with a test vehicle.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.10
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• pp.797-804
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• 2016

The vehicle-mounted radar system (VMRS) including its electronic parts must be designed so that its performance is maintained under varying environmental conditions. The important aspects are typically weight and safety. Since many rotating VMRSs have been developed, discussion about the vibration and shock requirements for the transportation conditions has occurred: in addition, the dynamic unpaved, paved, and off-road effects have been emphasized with respect to lightweight designs. A lightweight-design VMRS should be capable of operating stably under the wind condition with the support of the vehicle structure. In this paper, a structural analysis regarding the support of the VMRS is performed, whereby the real-load conditions for three types of road and pressure were employed in terms of the wind condition. The structural analysis for the safety of the VMRS is performed, and the structural-integrity analytical processes of the VMRS are presented for different load conditions.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.