• 대한인간공학회지
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• 제31권2호
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• pp.371-377
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• 2012

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.496-499
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• 1997

This paper describes the operating software of a motion system developed for a driving simulator, consisting of a six degree of freedom Stewart platform driven hydraulically. The drive logic, consisting of an washout algorithm, inverse kinematic analysis, and a control algorithm, has been developed and applied for creating high fidelity motion cues. The basic environment of the operating software is based on LabVIEW 4.0 and DLL modules compiled by Fortran.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2005년도 춘계학술대회 논문집
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• pp.175-180
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• 2005

As the high performance computer system increases, improving of reality and usefulness causes the virtual environment of simulator to be used widely as training and assessment tool. Although some domestic companies have developed train driving simulators since about mid of 1990s, accumulation of technology and experience is not yet sufficient compared to foreign makers. This paper describes system composition, training and assessment regimes for high level train driving simulator. When the subsystems are designed, comprehension of train system is emphasized and the functions that simulator should provide are discussed.

• 한국융합학회논문지
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• 제9권10호
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• pp.249-255
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• 2018

본 논문은 자율주행 개인화를 위한 역 충돌시간 및 차두시간 융합 기반 인간중심 제어 알고리즘 개발에 관한 것이다. 운전자 및 탑승자의 자율주행에 대한 이질감 최소화를 위해 인간중심적 주행제어 기술이 필요하다. 운전자가 선행차량과 함께 주행하는 조건에서 운전자의 주행특성을 분석하고, 분석된 결과를 종방향 자율주행 제어에 반영하였다. 주행특성으로 가속도, 역 충돌시간, 차두시간 분포가 분석되었고, 운전자의 주행특성이 반영된 제어기 구성을 위해 역 충돌시간 및 차두시간을 이용한 종방향 제어기를 구성하였다. 본 연구에서 제안된 제어 알고리즘은 Matlab/Simulink 환경에서 구성되었으며 실 주행데이터 기반 성능평가가 수행되었다.

• International Journal of Automotive Technology
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• 제4권2호
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• pp.101-108
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• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• 한국자동차공학회논문집
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• 제25권3호
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• pp.309-316
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• 2017

In shuttle vans designed to transport children, the recognition of a child's approach and departure is very important. Ultrasonic sensors are generally used for a short distance around a vehicle. Although ultrasonic sensors are cheaper than other ADAS sensors, the number of sensors installed in a van should be optimized. In order to recognize the presence of a child around a shuttle van, this paper proposes the placement of ultrasonic sensors in the van. Considering the turning radius of the van and the distance from each sensor to a child, collision risk is classified as 'safe', 'warning', and 'danger'. The sensor placement and the recognition algorithm are verified in a virtual driving environment.

• 센서학회지
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• 제22권2호
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• pp.111-117
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• 2013

The autonomous driving method using magnetic sensors recognizes the position by measuring magnetic fields in autonomous robots or vehicles after installing magnetic markers in a moving path. The Position estimate method using magnetic sensors has an advantage of being affected less by variation of driving environment such as oil, water and dust due to the use of magnetic field. It also has the advantages that we can use the magnet as an indicator and there is no consideration for power and communication environment. In this paper, we propose an efficient sensor system for an autonomous driving vehicle supplemented for existing disadvantage. In order to efficiently eliminate geomagnetism, we analyze the components of the horizontal and vertical magnetic field. We propose an algorithm for position estimation and geomagnetic elimination to ease analysis, and also propose an initialization method for sensor applied in the vehicle. We measured and analyzed the developed system in various environments, and we verify the advantages of proposed methods.

• 자동차안전학회지
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• 제13권1호
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• pp.26-30
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• 2021

K-city is an experimental area for developing self-driving cars. V2X communications such as WAVE, C-V2X and 5G are an essential technology for autonomous driving above level 4. In this paper, the research on the V2V communication environment was carried out through BSM receiving level analysis on the driving route in K-city. A stationary vehicle communicated with a test vehicle moving along urban area and suburban road in two different scenarios. The communication range and receiving levels obtained from this study will be used to develop and verify various safety scenarios using V2V communication within K-city in the future.

• 한국도로학회논문집
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• 제19권1호
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• pp.107-119
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• 2017

PURPOSES : The desire of drivers to increase their driving speeds is increasing in response to the technological advancements in vehicles and roads. Therefore, studies are being conducted to increase the maximum design speed in Korea to 140 km/h. The stopping sight distance (SSD) is an important criterion for acquiring sustained road safety in road design. Moreover, although the perception-reaction time (PRT) is a critical variable in the calculation of the SSD, there are not many current studies on PRT. Prior to increasing the design speed, it is necessary to confirm whether the domestic PRT standard (2.5 s) is applicable to high-speed driving. Thus, in this study, we have investigated the influence of high-speed driving on PRT. METHODS : A driving simulator was used to record the PRT of drivers. A virtual driving map was composed using UC-Win/Road software. Experiments were carried out at speeds of 100, 120, and 140 km/h while assuming the following three driving scenarios according to driver expectation: Expected, Unexpected, and Surprised. Lastly, we analyzed the gaze position of the driver as they drove in the simulated environment using Smarteye. RESULTS : Driving simulator experimental results showed that the PRT of drivers decreased as driving speed increased from 100 km/h to 140 km/h. Furthermore, the gaze position analysis results demonstrated that the decrease in PRT of drivers as the driving speed increased was directly related to their level of concentration. CONCLUSIONS : In the experimental results, 85% of drivers responded within 2.0 s at a driving speed of 140 km/h. Thus, the results obtained here verify that the current domestic standard of 2.5 s can be applied in the highways designated to have 140 km/h maximum speed.