• 한국심리학회지 : 문화 및 사회문제
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• 제20권4호
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• pp.281-305
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• 2014

본 연구에서는 고령운전자의 자기-평가 안전운전행동과 운전이동성(이동량, 안전운전능력, 상황적응능력 및 사회활동) 그리고 주관적 안녕감(삶의 만족, 긍정 정서 및 부정 정서) 사이의 상호관련성을 살펴보았다. 65세 이상의 남녀 고령운전자 142명을 대상으로 한국형 자기보고식 노인 안전운전행동 척도, 고령자 이동성 척도, 그리고 두 가지의 주관적 안녕감 척도(삶의 만족, 긍정 정서/부정 정서 척도)를 이용한 면대면 설문조사를 실시한 후 수집된 자료를 상관분석과 경로분석을 통해 분석하였다. 본 연구의 주요 결과를 요약하면 다음과 같다. 첫째, 자기-평가 안전운전행동 점수가 높은 고령운전자는 주관적 안녕감도 높았다. 둘째, 자기-평가 안전운전행동 점수가 높은 고령운전자들은 운전이동성 전체 점수뿐만 아니라 물리적 이동량을 제외한 모든 운전이동성 하위요인에서의 점수도 높았다. 셋째, 물리적 이동량을 제외한 운전이동성의 하위요인 점수들, 그리고 운전이동성 전체점수가 높을수록 삶의 만족이나 정적 정서 점수는 높은 반면 부적 정서 점수는 낮았다. 넷째, 자기-평가 안전운전행동과 주관적 안녕감 사이의 관계에서 운전이동성의 전체점수와 이동성의 하위요인 중 물리적 이동량을 제외한 안전운전능력, 상황적응능력, 사회활동의 매개효과가 유의하였는데, 구체적으로 안전운전능력과 상황적응능력은 삶의 만족과 긍정 정서를 정적으로 완전매개한 반면, 안전운전능력과 사회활동은 부정 정서를 부적으로 완전매개하였다. 본 연구의 시사점과 추후 연구 방향에 대해 기술하였다.

• 한국자동차공학회논문집
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• 제11권5호
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• pp.201-209
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• 2003

Virtual human models are widely used to save time and expense in vehicle safety studies. A human model is an essential tool to visualize and simulate a vehicle driver in virtual environments. This research is focused on creation and application of a human model fer virtual reality. The Korean anthropometric data published are selected to determine basic human model dimensions. These data are applied to GEBOD, a human body data generation program, which computes the body segment geometry, mass properties, joints locations and mechanical properties. The human model was constituted using MADYMO based on data from GEBOD. Frontal crash and bump passing test were simulated and the driver's motion data calculated were transmitted into the virtual environment. The human model was organized into scene graphs and its motion was visualized by virtual reality techniques including OpenGL Performer. The human model can be controlled by an arm master to test driver's behavior in the virtual environment.

• 대한토목학회논문집
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• 제26권3D호
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• pp.409-416
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• 2006

VMS를 통해 제공되는 실시간 교통정보는 운전자의 통행경로선택에 영향을 주는 것으로 알려져 있으며, 이에 따라 VMS 정보를 이용한 운전자 통행경로 제어를 통해 도로망 전체의 운영효율을 최적화하고자하는 다양한 연구들이 이루어져 왔다. 본 연구에서는 실시간으로 주기별로 최적화된 메시지의 내용을 VMS에 표출할 때 도로망 전체의 총 통행시간을 최소화할 수 있는 운전자 경로선택행태 모형을 개발하였다. 우선 운전자의 경로선택을 현실감 있게 반영하기 위해 Stated Preference(SP) 조사를 바탕으로 하여 개발하였다. VMS를 통해 제공되는 메시지 내용과 표출주기가 주어졌을 때 최적의 VMS 정보제공 조합은 유전자 알고리즘을 이용하여 구했으며, 최적해 산출과정에서 필요한 교통분석은 미시적 교통시뮬레이션인 파라믹스를 이용하였다. 실험결과를 살펴보면 모든 시나리오에서 본 모형이 효과적으로 최적해를 찾아가는 것으로 나타났다. VMS 설치 전후를 비교하면 VMS를 운영하였을 때 도로망의 총 통행시간을 줄일 수 있는 것으로 나타났으며, VMS 정보의 표출주기가 짧을수록 VMS 메시지 내용의 개수가 작은 것이 총 통행시간을 감소시키는데 유리한 것으로 분석되었다.

• 자동차안전학회지
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• 제16권1호
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• pp.6-11
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• 2024

This paper presents a motion planning algorithm of autonomous racing vehicles for mimicking the characteristics of a human driver. Time optimal maneuver of a race car has been actively studied as a major research area over the past decades. Although the time optimization problem yields a single time series solution of minimum time maneuver inputs for the vehicle, human drivers achieve similar lap times while taking various racing lines and velocity profiles. In order to model the characteristics of a specific driver and reproduce the motion, a stochastic motion planning framework based on kernelized motion primitive is introduced. The proposed framework imitates the behavior of the generated reference motion, which is based on a small number of human demonstration laps along the racetrack using Gaussian mixture model and Gaussian mixture regression. The mean and covariance of the racing line and velocity profile mimicking the driver are obtained by accumulating the outputs tested at equidistantly sampled input points. The results confirmed that the obtained lateral and longitudinal motion simulates the driver's driving characteristics, which are feasible for actual vehicle test environments.

• 한국자동차공학회논문집
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• 제13권2호
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• pp.149-156
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• 2005

In this paper, the VR(Virtual Reality) simulation system is developed to analyze driver's perceptive response on the ASV(Advanced Safety Vehicle). The ASV is the vehicle of next generation equipped with various warning systems. For the purpose, the VR simulation system consists of VR database, vehicle dynamic model, graphic/sound system, and driving system. The VR database which generates 3D graphic and sound information is organized for the driving reality. Mathematical models of vehicle dynamic analysis are constructed to represent the dynamic behavior of a vehicle. The driving system and the graphic/sound system provide a driver with the operation of a vehicle and the feedback of a driving situation. Also, the real-time simulation algorithm synchronizes the vehicle dynamic model with the VR database. To check the validity of the developed system, a simple scenario is applied to investigate driver's perceptive response time and vehicle acceleration on an emergency situation. It is confirmed that the proposed system is useful and helpful to design the FVCWS(Forward Vehicle Collision Warning System).

• 인터넷정보학회논문지
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• 제24권1호
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2007년도 춘계학술대회 논문집
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• pp.409-412
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• 2007

A motorcycle helmet is the best means to protect the head of bike's driver and it is directly connected to a driver's life. Prior to producing of the helmet, it has to be passed the process of impact test to evaluate of its safety. This test evaluates peak acceleration and H.I.C (Head Injury Criteria). This paper analyzes impact test with finite element method to find the behavior of helmet during the test. Also, the effect of impact sites on the helmet was evaluated to improve the thickness distribution of the helmet.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1867-1870
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• 2003

The analysis and simulation are the method to study the behavior, response, and specification of the driver device. This paper proposes brushless DC drive which using the vector control technique. The encoder is used detect the rotor position and decode to Three-phase step signal. The step signal is modulated with triangle signal and change to the pulse width modulation (PWM) signal. The PWM signal is used for control the input power of the motor based on the vector control technique. The experimental results of the driver circuit and motor response performed under the following condition: as the motor was started, change the load torque, and vary the supply voltage. The experimental performs with a dynamometer and the test results are compared to the simulation method is the same result.

• 소성∙가공
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• 제16권5호통권95호
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• pp.370-374
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• 2007

A motorcycle helmet is very essential to protect the head of driver and it is directly connected to driver's life. Prior to producing the helmet, it has to be passed the process of impact test to evaluate its safety. This test evaluates peak acceleration and head injury criteria (H.I.C.). This paper simulates the impact test with finite element method to find the behavior of helmet during the test. Also, the effect of impact sites on the helmet was evaluated to improve the thickness distribution of the helmet.

• 한국안전학회지
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• 제23권2호
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• pp.45-50
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• 2008

The purpose of the present study was to investigate the age difference in driving behavior(more specifically, left-turn). The participants were instructed to report whether they can turn left their car in the T-shape road(road and other vehicles' behavior relating to driver's tasks were recorded in advance and projected the simulation screen) after the leading vehicle passed them(i.e., before the target vehicle arrived). The participants' judgment accuracy and response bias were analyzed by using signal detection theory. The results showed that the old group tended to be less sensitive but more confident in their judgement of turning left their car. In particular, both age groups appeared to more depend on the distance from observation location to approaching vehicle rather than arrival times or driving speeds of the approaching vehicle.