• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.23-31
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• 2023

In recent years, the technology for autonomous driving has been advancing rapidly, ADAS (Advanced Driver Assistance System) functions, which improve driver convenience and safety performance, are mostly equipped in recently released vehicles and range from level 0 to level 2 in autonomous driving technology. Among the various functions of ADAS, AEBS (Autonomous Emergency Braking System), which analyzes traffic accidents, is the most closely related to the vehicle's braking. This study developed a simulation technique for reproducing accidents related to AEBS based on real vehicle experimental data, and it was applied to the analysis of actual ADAS vehicle accidents to identify the causes of accidents.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.233-233
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• 1996

• Journal of Auto-vehicle Safety Association
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• v.7 no.2
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• pp.44-49
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• 2015

This paper presents an evaluation scenario of safety performance for extraordinary service permission of autonomous vehicle driving on a motorway. Based on advanced driver assistance system (ADAS) which is already mass-production, an autonomous vehicle driving on motorway is tested on the public roads and also getting close to mass-production. Before the autonomous vehicle tested, the safety of autonomous driving system should be evaluated based on a proper test scenario. Prior to develop the test scenario, this paper reviews the licensing standards for an autonomous vehicle in California and Nevada, and the international regulations of each ADAS. To develop the scenario, the driving conditions of motorway are categorized into five modes and fundamental evaluation requirements of elements of autonomous driving system are derived. An evaluation scenario, which represents the real driving conditions, has been developed to assess the safety of autonomous vehicle. This scenario has validated by computer simulation using model predictive control (MPC) based autonomous driving algorithm.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.996-1003
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• 2012

We present a nearby vehicle detection method in the adjacent lane using in-vehicle front view camera. Nearby vehicles in adjacent lanes show various appearances according to their relative positions to the host vehicle. Therefore, most conventional methods use motion information for detecting nearby vehicles in adjacent lanes. However, these methods can only detect overtaking vehicles which have faster speed than the host vehicle. To solve this problem, we use the feature of regions where nearby vehicle can appear. Consequently, our method cannot only detect nearby overtaking vehicles but also stationary and same speed vehicles in adjacent lanes. In our experiment, we validated our method through various whether, road conditions and real-time implementation.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.6-16
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• 2022

Recent, the issue of the fourth industrial revolution triggered by technological advances has changed the automobile industry centered on internal combustion engines, and quantitative growth of the global automobile market, which has grown rapidly, has been slowing since 2015. These advances in technology are expected to develop beyond the advanced driver assistance system to autonomous driving technology. According to SAE-J3016 published by the Society of Automotive Engineers, the technology of autonomous vehicles is divided into a total of six stages according to the driver's intervention and automation level from 0 to 5. Securing safety for autonomous vehicles is important. But, research on safety evaluation theory and autonomous vehicle evaluation method based on real vehicle test is insufficient. In this study, the longitudinal distance theory equation and continuous test scenario were proposed for the test method of autonomous vehicles for fixed targets, and the real vehicle test was conducted. When comparing the theoretical values compared to the measured values, it was determined that it was reliable with a minimum error rate of 0.484% and a maximum error rate of 7.391%. Using the proposed theoretical equation, it is judged that it can be used as a safety evaluation method in an environment where real vehicle test is not possible because it can grasp the trend in the longitudinal direction in the development stage.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.4
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• pp.371-379
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• 2015

This paper represents the structural design of the light weight axle beam for medium duty commercial vehicle using hot press. To reduce the weight of the axle, axle beam of solid type was replaced by hollow type which was made by hot press. According to the change of axle beam structure and manufacturing method, we have to investigate the structural strength and fatigue performance. To verify the axle beam performance, the structural analysis was carried out by simplified axle beam model and various design parameters that are axle beam height, thickness and width. From the analysis results, the light weight axle beam structure was founded and applied the full model analysis. This study will be used as a guidance in development of the light weight axle for medium duty commercial vehicle.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.255-263
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• 2016

Lane Keeping Assistance System (LKAS) is a kind of Advanced Driver Assistance Systems (ADAS) which are developed to automate/ adapt/ enhance vehicle systems for safety and better driving. The main system function of LKAS is to support the driver in keeping the vehicle within the current lane. LKAS acquires information on the position of the vehicle within the lane and, when required, sends commands to actuators to influence the lateral movement of the vehicle. Recently, the vehicles equipped with LKAS are commercially available in a few vehicle-advanced countries and the installation of LKAS increases for safety enhancement. The test procedures for LKAS evaluations are being discussed and developed in the international committees such as ISO (the International Organization for Standardization) and UNECE (United Nations Economic Commission for Europe). In Korea, the evaluations of LKAS for vehicle safety are planned to be introduced in 2016 KNCAP (Korean New Car Assessment Program). Therefore, the test procedures of LKAS suitable for domestic road and traffic conditions, which accommodate international standards, should be developed. In this paper, some bullet points of the test procedures for LKAS are discussed and proposed by extensive researches of previous documents and reports, which are released in public in regard to lateral test procedures including LKAS and Lane Departure Warning System (LDWS). And then, to evaluate the validity of the proposed test procedures, a series of experiments were conducted using commercially available two vehicles equipped with LKAS. Later, it can be helpful to make a draft considering domestic traffic situations for test procedures of LKAS.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.30-39
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• 2007

This paper is concerned with development of system to detect an obstructive vehicle which is an essential prerequisite for autonomous driving system of ASV(Advanced Safety Vehicle). First, the boundary of driving lanes is detected by a Kalman filter through the front image obtained by a CCD camera. Then, lanes are recognized by regression analysis of the detected boundary. Second, parameters of road curvature within the detected lane are used as input in error-BP algorithm to recognize the driving direction. Finally, an obstructive vehicle that enters into the detection region can be detected through setting detection fields of the front and lateral side. The experimental results showed that the proposed system has high accuracy more than 90% in the recognition rate of driving direction and the detection rate of an obstructive vehicle.