• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.51-65
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• 2018

Intentional aggressive driving (IAD) is defined as a hazardous driving event that the aggressive driver intentionally threatens neighbor drivers with abrupt longitudinal and lateral maneuvering. This study developed a methodology for detecting IAD events based on the analysis of interactions between aggressive driver and normal driver. Three major aggressive events including rear-close following, side-close driving, and sudden deceleration were analyzed to develop the algorithm. Then, driving simulation experiments were conducted using a multi-agent driving simulator to obtain data to be used for the development of the detection algorithm. In order to detect the driver's intention to attack, a relative evaluation index (Erratic Driving Index, EDI) reflecting the driving pattern was derived. The derived IAD event detection algorithm utilizes both the existing absolute detection method and the relative detection method. It is expected that the proposed methodology can be effectively used for detecting IAD events in support of in-vehicle data recorder technology in practice.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.21-29
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• 2015

The purpose of this paper was to investigate various factors influencing the intentions of drink-driving from multiple perspectives, in order to uncover ways to reduce the number of motor accidents caused by drink-driving. We examined sociopsychological aspects as well as driver's life styles such as smoking habit and exercise habit. Perception of behaviour controls among drink-driver' sociopsychological characteristics had the highest influence on the intentions of drink-driving, followed by influence of smoking and exercise on the intentions of drink-driving. This finding indicates that driver' life style such as smoking habit or exercise habit influences more on the intentions of drink-driving than attitude toward drink-driving or subjective regulations, which affirms that driver' life style such as smoking habit or exercise habit has significant effects on the intentions of drink-driving. Therefore, it is concluded that rehabilitative curriculum for drink-drivers should include a program to diminish drink-driving through nonsmoking and exercise habit.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.247-258
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• 2017

Since 2016 when the regulations related to vehicle structure and device modification were drastically revised, the car tuning market has been growing rapidly. Particularly, many drivers are showing interest in changing the interior and exterior according to their preference, or improving the specifications of their cars by changing the engine and powertrain, among others. Also, as the initial engine settings such as horse power and torque of the vehicle are made for stable driving of the vehicle, it is possible to change the engine performance, via Engine Control Unit (ECU) mapping, to the driver's preference. However, traditionally, ECU mapping could be only performed by professional car engineers and the settings were also decided by them. Therefore, this study proposed a system that collects data related to the driver's driving habits for a certain period and sends them to a cloud server in order to analyze them and recommend ECU mapping values. The traditional mapping method only aimed to improve the car's performance and, therefore, if the changes were not compatible with the driver's driving habits, could cause problems such as incomplete combustion or low fuel efficiency. However, the proposed system allows drivers to set legally permitted ECU mapping based on analysis of their driving habits, and, therefore, different drivers can set it differently according to the vehicle specifications and driving habits. As a result, the system can optimize the car performance by improving output, fuel efficiency, etc. within the range that is legally permitted.

• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.167-175
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• 2012

For establishing a standard of design element of the smart highway, this study investigated driver's anxiety EEG according to running speeds and geometric designs. Also, the experiment was implemented on 60 subjects. Based on running speed data and brainwave data, which were obtained from the experiment, this study analyzes anxiety EEG according to running speeds and geometric designs, and finally draws a forecasting model of anxiety EEG by selecting affecting factors of anxiety EEG. Forecasting model shows that left curve is the most influential on anxiety EEG figure. The reason is because when driver is driving on the first-lane, his or her visibility is impeded by a median strip. For this reason, anxiety EEG figure increases. And also steep downward slope and large radius of curve are heavily influential on driver's anxiety EEG figure. It is judged that anxiety EEG figure is increased by high speed on those section. Thus, the forecasting model of anxiety EEG suggested on this study will be utilized for design phase, and will decide the design speed on the superhighway. So, it will be used to make practical and safety road.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.29-38
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• 2023

Although the biosignal is the best way to represent the human condition, it is difficult to acquire the biosignal of a driver driving for detecting driver's condition. As one of the methods to overcome this limitation, this paper proposes a driving stress monitoring system based on information provided by OBD-II(on-board diagnostics version II). The driving information and EDA(Electrodermal activity) data are obtained through the OBD-II scanner and E4 wristband, respectively. EDA data is used as ground truth to distinguish whether driver is stressed or not. MLP(multi-layer perceptron) neural network is used as a model to detect driving stress and is trained using driving data for about a month. To evaluate the proposed system, we used about 1 hour of driving data and the accuracy is 92%.

• Journal of Korean Society of Transportation
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• v.35 no.6
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• pp.545-556
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• 2017

Various assessment indexes have been developed and utilized to evaluate the driver workload. However, existing workload assessment indexes do not fully reflect driving habits and driving patterns of individual drivers. In addition, there exists significant differences in the amount of workload experienced by a driver and the ability to overcome the driver's workload. To overcome these limitations associated with existing indexes, this study has developed a novel workload assessment index to reflect an individual driver's driving pattern. An average of the absolute values of the steering velocity for each driver are set as a threshold value in order to reflect the driving patterns of individual drivers. Further, the sum of the areas of the steering velocities exceeding the threshold value, which is defined as erratic steering area (ESA) in this study, was quantified. The developed ESA index is applied in evaluating the driver workload of manually driven vehicles in automated vehicle platooning environments. Driving simulation experiments are conducted to collect drivers' responsive behavior data which are used for exploring the relationship between the NASA-TLX score and the ESA by the correlation analysis. As a result, ESA is found to have the greatest correlation with the NASA-TLX score among the various driver workload evaluation indexes in the lane change scenario, confirming the usefulness of ESA.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.1
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• pp.73-79
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• 2016

The purpose of this study is to understand human sensory and emotional response according to car noise. This study investigated human stress levels from car noise based on the data of electro-encephalogram (EEG) and subjective evaluation analysis data. Since there are few previous works on the effect of car noise on human's reaction, more profound research about the effect of car noise on driver's emotion would be worth performing. Subjects were exposed to five different kinds of car noises. EEG data were collected while the subject was exposed to noise. In order to process EEG data, FFT analysis was used to separate the collected EEG data into ${\alpha}$-wave and ${\beta}$-wave. Also, survey was performed to carry out subjective evaluation. The results of the subjective evaluation were closely correlated to those found in the EEG signal analysis. The results from this study may provide useful information for mitigating car noise induced stress.

• Journal of Biosystems Engineering
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• v.12 no.3
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• pp.7-16
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• 1987

This study was carried out to develop a microcomputer-based data acquisition and control system which was able to collect the data of engine performance test automatically and control the speed and load of the engine. The results of the study are summarized as follows: 1. The signal processing devices, which were able to measure cylinder pressure, coolant temperature, compositions of exhaust gas, fuel consumption, engine rpm and torque etc., were developed. The results of calibration showed that all of devices had high accuracy ranging from 0.3% to 0.69% respectively. 2. The PIA (peripheral interface adapter) for interfacing digital signal and PTM (programmable timer module) for displaying real time every 0.0408 sec were designed and developed. 3. An engine-speed control system using a stepping motor and driver was developed. The control system had the stability, and faster settling time than the manual control system. 4. The automatic control system of electrical dynamometer, which was able to control the speed and load of dynamometer, was developed with a SSD (shackleton system driver) and D/A converter. 5. The computer programs, which were able to collect and process the data of engine tests, were developed using both the machine language and BASIC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.511-514
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• 2011

Recently, devices such as smartphone and vehicle blackbox and EDR(Evern Data Recorder knowed automotive real-time control and driving data to use OBD-2(in-vehicle network). when devices receive vehicle driving data, communication way use each Wifi, Bluetooth. but if user and driver change device to use OBD-2 connect, the device differ communication network way. and driver buy and change OBD-2 connect. In this paper, to remedy one's shortcomings, there integrate Bluetooth and Wifi network module and design integrability hardware as any another device know vehicle real-time control and driving data with one integrability connect.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.60-65
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• 2021

The event data recorders (EDR) have been used as a device to help understand traffic accidents. With the recent development of autonomous vehicle (AV), it has become important to prepare the new EDR for AV. Therefore, the purpose of this study is to propose the direction of EDR-AV recording. First of all, the recent EDR data elements and the data elements of AV under discussion at UNECE WP29 EDR/DSSAD (Data Storage System for Automated Driving) were analyzed. The consumer complaint database in Motor Vehicle Recall Center in Korea was analyzed in order to utilize cases of domestic traffic accidents related to advanced driver assistance systems (ADAS). Consequently, problems with existing EDR were identified through unclear accident cases related to ADAS. In the future, it was proposed to record images in which the ADAS perception systems recognize the surroundings of the accident site as an EDR-AV recording item.