• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1565-1574
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• 2010

While we are driving a car, we are able to listen to musics by two ways: by selecting (manipulating) what we want and by just playing as they are given (in CD). These methods make a driver tired while he is driving or it means that a music which is provided is not concerned with a driver's preference. To improve these problems, there have been many studies about the automatic music providing systems based on driver's emotion. However, these studies have some difficult problems: the first one is that it is not easy to determine driver's emotion, and the other one is that it is hard to recommend and play the suitable music corresponding to the determined user's emotion. In this paper, to overcome the second problem mentioned above, a new mapping method between driver's emotion and music genre for automatic music providing system on vehicle is presented and two experiments are examined for the validation of the proposed method. The experimental results and discussions are explored to show the effectiveness and validity of the proposed method.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.67-78
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• 2011

This paper analyzes the characteristics of drivers' workload observed from with 30 participant drivers with respect to two physio-physiological parameters. For investigating physio-physiological characteristics of road drivers, bio-signals from brain's occipital lobe between simulation experiment and real driving experiment are collected and analyzed. The major findings from the analysis are summarized as follows: First, the drivers' physio-physiological workload is a good parameter for explaining the workload characteristics of road drivers. Secondly, the two physio-physiological workload parameters selected, i.e., beta value and relative energy parameter, are revealed to be statistically significant. Thirdly, it is also revealed to be statistically significant to select 90 percentile measurements in simulator experiment to explain the road drivers' characteristics. Finally, the maximum workload of road design driver is 31.72 in beta parameter, whereas the minimum workload is 1.296 in relative energy parameter.

• Information and Communications Magazine
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• v.25 no.7
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• pp.10-15
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• 2008

최근 고속도로의 길이와 운전 차량 수가 빠른 속도로 증가하고 있어 운전자들에게 고속도로 교통상황를 신속하고 정확하게 제공하는 것이 중요한 문제로 대두되고 있다. 고속도로통행료수납시스템(TCS: Toll Collection Systrem)은 전국 고속도로를 주행하는 차량의 통행 정보를 실시간으로 제공하므로 교통 상황 예측에 유용하게 활용될 수 있다. TCS 자료는 차량이 입구영업소를 통과한 후 출구영업소를 통과하는 데 소요된 시간으로서, 운전한 시간, 휴게소 체류시간 등을 모두 포함한 통행시간으로 운전자의 운전 특성, 통행 목적, 피로의 정도에 따라 편차가 크게 나타난다. TCS 자료의 통행시간을 기초로 예측된 정보는 이러한 불확실성을 포함하고 있기 때문에 이를 활용하기 다양한 데이터처리 기법이 필요하다. 본 논문에서는 TCS 자료의 효율적인 전처리 및 교통 예측 기법 현황에 대하여 기술하고 향후 발전 방향을 제시하였다.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.277-285
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• 1997

본 연구에서는 운전 중에 빈번히 나타나는 부작업을 모의 운전 실험하에서 선정하여 그때의 운전 수행 도와 운전자의 심리적 작업부하에미치는 영향에 대해 연구하였다. 운전의 수행도는 브레이크 작동시의 시간의 적절성을 사용하였으며, 이를 측정하기 위해도로교통안전협회에 설치되어있는 운전기능검사기기를 사용하였다. 또한 운전자의 심리적 작업부하를 측정하기 위해 심리적 작업부하 측정 방법의 하나인 NASA- TLX를 사용하였다. 다양한 운전 상황을 설정하기 위해도로의 형태를 골목길과 일반도로로 나누었으며, 부작업의 종류는 전화 걸기, 라디오 조작, 대화로 선정하였다. 실험에는 17명의 피실험자가 참가하였고 결과에 대한 통계분석(SAS)이 이루어졌다. 본 연구 결과, 운전시 전화 걸기와 라디오 조작의 경우가 다른 부작업에 비해 운전 수행도를 통계적으로 유의한 정도 저하시키는 것이 조사되었다. 부작업에따라서 3 . approx. 22%의 수행도의 저하가 나타났으며, 42 .approx. 59%의 심리적 작업부하의 증가가 나타났다. 본 연구에서는 부작업이 정도의 차이는 있으나 수행도의 감소와 심리적 작업부하의 증가를 유발시키는 것이 정량적으로 입증되었다. 차후 보다 실제상황에 가가운 모의 실험과 다양한 수행도 측정지수를 사용한 연구 를 통해 운전시 부작업이 사고에 미치는 영향이 검증될 것으로 기대된다.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.95-105
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• 2009

Traffic accidents on highways are likely to happen when there is an imbalance in the complex relationships among key elements such as road geometries, driver related factors, and mechanical performances. The Demand-Effort Model (DEM), which evaluates highway safety, can be explained by the imbalance, which occurs when the level of demand of the driver's attention to the road environment exceeds that of the response from the driver. This study suggests a new model that improves the reliability of the current DEM through the reinterpretation on the physiological signals with the help of the Neural Network Model (NNM). The data were collected from 149 subjects, who drove a test vehicle on the Yongdong, Honam, and Seohaean Expressways in Korea. Three important results could be drawn from the recursive tests as follows; (1) Only 5 out of 10 parameters on the physiological signals which are currently used were proven to be meaningful through the Normality Test, Cluster Analysis, and Mann-Whitney Analysis. (2) The revised DEM, which internally uses the NNM, showed more reliable results than existing DEM. Group 1, which is based on the new DEM showed 80.0% of accuracy in measuring the level of driver's efforts, however, that of Group 2 based on the current DEM was 74.3%. (3) Field tests on the Honam Expressway showed lower 'type II error' with the new DEM (40.5%) than the old DEM (58.8%). The DEM is designed as a quick and easy way to determine highway safety prior to the minute road safety audit (RSA) by a professional audit team. Then a new DEM, which is based on the NNM, needs to be considered since it showed higher reliability and lower error.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.6
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• pp.271-280
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• 2017

In this paper, we propose a new safety system composed of wearable devices, driver's seat belt, and integrating controllers. The wearable device and driver's seat belt capture driver's biological information, while the integrating controller analyzes captured signal to alarm the driver or directly control the car appropriately according to the status of the driver. Previous studies regarding driver's safety from driver's seat, steering wheel, or facial camera to capture driver's physiological signal and facial information had difficulties in gathering accurate and continuous signals because the sensors required the upright posture of the driver. Utilizing wearable sensors, however, our proposed system can obtain continuous and highly accurate signals compared to the previous researches. Our advanced wearable apparatus features a sensor that measures the heart rate, skin conductivity, and skin temperature and applies filters to eliminate the noise generated by the automobile. Moreover, the acceleration sensor and the gyro sensor in our wearable device enable the reduction of the measurement errors. Based on the collected bio-signals, the criteria for identifying the driver's condition were presented. The accredited certification body has verified that the devices has the accuracy of the level of medical care. The laboratory test and the real automobile test demonstrate that our proposed system is good for the measurement of the driver's condition.

• Proceedings of The KACE
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• 2018.01a
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• pp.129-132
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• 2018

교통 체증은 차량 이용이 대중화된 지금 많은 문제를 야기하고 있다. 교통 정체로 인한 시간 허비, 장시간 운전으로 인한 운전자의 피로 증가, 매연 배출로 인한 환경 오염 촉진 등 교통 체증은 사회적으로 무시할 수 없는 여러 문제들을 불러온다. 이에 본 연구는 신호 체계 개선에 초점을 두어 유전 알고리즘을 이용한 도시 규모의 유동성 연동 신호 체계를 구현하고자 하였다. 이를 위하여 교통 시뮬레이션 프로그램 SUMO를 이용하여 실험 환경을 조성하고, 4개의 사거리와 16개의 사거리, 그리고 선릉역 인근의 실제 도로망에서의 최적 신호 주기를 탐색하였다. 실험 결과 4개의 사거리의 경우 유전 알고리즘 적용 전의 초기 설정에 비해 자동차들의 평균 이동 시간이 31.1% 감소, 16개의 사거리의 경우 6.2% 감소, 실제 도로에서는 1.1% 감소함을 확인할 수 있었다. 따라서 유전 알고리즘을 이용한 새로운 교통 체계는 교통 정체를 완화하는 효과가 있으며 본 연구를 실제에 적용한다면 도시 규모의 도로망에서 시간대 별로 교통 흐름을 최적화하는 맞춤형 신호 주기를 구할 수 있을 것이다.

• Journal of Korean Society of Transportation
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• v.35 no.5
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• pp.423-433
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• 2017

During recent 5 years, it was recorded that 20% of total accident frequency and 30% of total number of death have been occurred due to drowsy driving. Drowsy driving accident is result from the loss of driving ability due to driver's accumulated fatigue. Continuous driving time can be measured as a surrogate variable to quantify the level of fatigue. The main purpose of this research is to investigate statistical correlation between the proportion of continuous driving vehicle (more than 2 hours) and the number of drowsy accidents. To carry this out, continuous driving time was measured using GPS route-guidance trajectory data. Also, accident frequency, traffic volume and segment length were collected to estimate safety performance function (SPF) for Jungbunearuk expressway in Korea. Through various types of estimated SPFs, statistical correlation was analyzed based on estimated statistical indices. This research can provide theoretical background for enforcement to regulate commercial vehicle driver's continuous driving time. In addition, throughout the trajectory data expansion, it is expected that strategy for anti-drowsy driving facilities installation can be established based on the suggested methodology.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.481-484
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• 2011

Recent Automotive technology is driven from mechanical device to the electronic components which improve the vehicle's safety and convenience. The future competitiveness of the car will come from safety issues and energy efficiency, convenience and the application of the technologies. In this study, various techniques for driver drowsiness detection are introduced and compared with each others. The advantages and disadvantages of commercially available technologies and developed technologies are compared. To enhance the detection resolution, multiple sensing technologies are introduced in this paper. The feasibility of two drowsiness detection methods, that is, existing camera image recognition method and bio signal analysis method, are tested. The direct drowsiness detection by the camera image of eyes and driver's vital signs detected indirectly are combined and analyzed by the developed noble algorithm for stress, fatigue, drowsiness detection with a more accurate high-drowsiness detection.