• Spatial Information Research
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• v.22 no.2
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• pp.89-97
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• 2014

This study investigated how much the spread of traffic control information affect the traffic congestion in order to identify the behavior of the individual drivers that impacts on the entire transport system. For this purpose, agent-based transportation model was constructed. GIS data were directly used for the transportation model and the processing steps of the simulation results are presented. The results showed that the average speed was not lowered when the traffic information was provided to 30 to 70% of total drivers. In contrast, the driver's average speed is reduced when he traffic information was provided to less than 20% or 80% or more. In summary, the provision of traffic information to drivers has an influence on the traffic flow and bypassing vehicles can generate local congestion. This results can be used as a basis for the future direction of road transport policy.

• Journal of Korean Society of Transportation
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• v.15 no.1
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• pp.17-26
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• 1997

최근에 이르러 자동차의 대수는 급격히 증가해 왔다. 이런 경향성의 결과로 나타난 교통사고의 인적 및 물질적인 손실이 최악의 상태에 이르른 것이 사실상 오늘날 우리들의 불행한 현실이다. 대다수의 교통사고가 운전자의 교통법규 위반에 기인되고 있다는 전제하 에 본 논문은 심리학적인 관점에서 그와같은 위반적인 운전자 행동의 이론적 배경을 비판적으로 분석하고자 시도한다. 아울러 이 논문에서는 운전자의 법규 위반적 행동에 대해 취할 수 있는 기본적이 대책을 마련 하는데 있어 특별히 기여할 수 있을 "인간-환경-경험" 모델을 구축하는 기초를 마련하고자 노력하고 있다.자 노력하고 있다.

• Journal of Industrial Technology
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• v.18
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• pp.241-248
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• 1998

In this study, a modeling method of power-assisted steering systems and driver models for vehicle dynamic analysis using AUTODYN7 is presented. Pressure-flow relations of flow control valve are derived, and the equations of motion of a steering gear are obtained. Combining pressure-flow relations and equations of motion, the steering force can be represented as a function of steering wheel angle or torque. Driver model was modeled based on a PID controller and forward target method. With the steering systems and driver model, various driving tests are conducted using AUTODYN7.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.115-122
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• 2016

This paper proposes a system to detect driver's cognitive state by internal and external information of vehicle. The proposed system can measure driver's eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver's cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver's characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver's face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1539-1540
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• 2008

운전자의 경험과 판단에 전적으로 의존하는 빙축열 시스템의 기존 운전방식에서는 운전자의 그릇된 판단과 미숙한 운전으로 인해 과잉 축열이나 냉방공급량 부족현상이 자주 초래된다. 본 논문에서는 경제적이고 효율적인 빙축열 시스템의 운용을 위해 다음날의 구간별 온도, 습도와 냉방부하를 예측하는 자기구성퍼지모델 구축방안을 제안한다. 제안된 방법의 성능과 실제 적용가능성을 검증학기 위하여 한국전력 속초 생활연수원을 대상으로 제안된 방법과 신경회로망, 퍼지모델, 선형회귀모델 등을 이용한 기존의 방법을 적용하여 구한 냉방부하, 온도, 습도의 예측정확도를 비교 분석한다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.244-244
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• 2003

• Journal of Industrial Technology
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• v.21 no.B
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• pp.273-280
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• 2001

In this study, a driver model based on the lead-lag controller for stable maneuver of a highly nonlinear, multi-dimensional, numerically stiff multibody vehicle model according to the various handling test requirements such as steady-state cornering, double lange change, etc. is presented The lead-lag controller is developed with lead and lag compensation. which use the transfer function with cross-over frequency by frequency response method. The proposed driver model is applied to a vehicle model in steady-state and slalom maneuver to verify its effectiveness and validity. The results show that the proposed path control strategy is excellent both in pursuing the desired course and stability of the vehicle.

• Journal of Biosystems Engineering
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• v.9 no.1
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• pp.1-4
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• 1984

노외차량(路外車輛)의 승차(乘車) 시뮬레이션을 위한 착석(着席) 상태(狀態)의 운전자(運轉者) 모델을 개발(開發)하였다. 운전자(運轉者) 모델은 머리, 몸체, 신부(腎部)로 크게 나누어 자유도(自由度) 3의 진동체로 가정(假定)하고 머리와 몸체, 몸체와 신부(腎部)는 각각 목과 장기(臟器)를 나타내는 탄성체로서 연결하였다. 또한 인체의 해부학적(解部學的) 구조(構造)와 일치(一致)하도록 머리와 신부(腎部)사이에는 척추(脊椎) 부분(部分)을 포함시켰다. 모델의 각 변수(變數)들은 기존(旣存)의 실험(實驗) 결과(結果)를 이용(利用)하여 머리부의 진동(振動)이 실험(實驗) 결과(結果)와 일치(一致)하도록 시뮬레이션 방법(方法)으로 결정(決定)하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1228-1229
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• 2023

현대 도시 모빌리티의 중요한 구성 요소로 자리 잡은 전동킥보드는 편리한 이동 수단으로 인기를 얻고 있으나, 이에 따른 안전사고 증가로 운전자와 보행자의 안전이 심각하게 위협받고 있다. 본 논문에서는 전동킥보드 운전 중에 발생한 사고의 원인을 객관적으로 분석하고, 사고가 운전자의 부주의로 인한 것인지를 판별하며, 이로 인한 배상 책임을 정확하게 결정하기 위한 모델을 제안한다. 운전 중 수집된 센서 데이터를 활용하여 Deep SVDD (Deep Support Vector Data Description) 모델을 구축하고, 이상치 탐지를 통해 운전 패턴을 분류하며 운전자의 부주의로 인한 사고를 파악한다. 이를 통해, 정확하고 공정한 배상 책임 판단을 지원하며, 도시 모빌리티 분야에서 안전사고 감소에 기여할 것으로 기대된다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.