• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.58-64
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• 2004

In order to determine the level of safety on highway driving, the relationship between the psychophysiological signal and driving condition was investigated. In particular, a Demand-Effort model was conceptualized and used in this study to diagnose the suitability of driving by reading the patterns of psychophysiological signals. To run the model, threshold values were determined to categorize the high, moderate and low level of effort. To examine the sensitivity of the model, a cross-validation process was performed by collecting additional data. Further investigation need to be conducted to improve the sensitivity of the model for practical application.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.127-135
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• 2009

Most traffic information that drivers receive while driving are stored in their short-term memory and disappear within a few seconds. Contemporary modeling approaches using a dummy variable can't fully explain this phenomenon. As such, this study proposes to use utility functions of real-time in-vehicle traffic safety information (IVTSI), analyzing its safety impacts based on empirical data from an on-site driving experiment at signalized intersection approach with a limited visibility. For this, a driving stability evaluation model is developed based on driver's driving speed choice, applying an ordered probit model. To estimate the specified utility functions, the model simultaneously accounts for various factors, such as traffic operation, geometry, road environment, and driver's characteristics. The results show three significant facts. First, a normal density function (exponential function) is appropriate to explain the utility of IVTSI proposed under study over time. Second, the IVTSI remains in driver's short-term memory for up to nearly 22 second after provision, decreasing over time. Three, IVTSI provision appears more important than the geometry factor but less than the traffic operation factor.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.5
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• pp.125-135
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• 2004

The validity of simulation has been well-established for decades in areas such as computer and communication system. Recently, the technique has become entrenched in specific areas such as transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and their driver's characteristics, even though it is known that they are important factors for any traffic flow analysis, have never been considered sufficiently. In this paper, the traffic simulation using a multi-agent approach with considering vehicle dynamics is proposed. The multi-agent system is constructed with the traffic environment and the agents of vehicle and driver. The traffic environment consists of multi-lane roads, nodes, virtual lanes, and signals. To ensure the fast calculation, the agents are performed on the based of the rules to regulate their behaviors. The communication frameworks are proposed for the agents to share the information of vehicles' velocity and position. The model of a driver agent which controls a vehicle agent is described in the companion paper. The vehicle model contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation has proceeded for an interrupted and uninterrupted flow model. The result has shown that the driver agent performs human-like behavior ranging from slow and careful to fast and aggressive driving behavior, and that the change of the traffic state is closely related with the distance and the signal delay between intersections. The system developed shows the effectiveness and the practical usefulness of the traffic simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.149-155
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• 2020

The Korea Highway Traffic Authority provides statistics that analyze the causes of traffic accidents that occurred since 2015 using the Traffic Accident Analysis System (TAAS). it was reported Through TAAS that the driver's forward carelessness was the main cause of traffic accidents in 2018. As statistics on the cause of traffic accidents, 51.2 percent used mobile phones and watched DMB while driving, 14 percent did not secure safe distance, and 3.6 percent violated their duty to protect pedestrians, representing a total of 68.8 percent. In this paper, we propose a system that has improved the advanced driver assistance system ADAS (Advanced Driver Assistance Systems) by utilizing CNN (Convolutional Neural Network) among the algorithms of Deep Learning. The proposed system learns a model that classifies the movement of the driver's face and eyes using Conv2D techniques which are mainly used for Image processing, while recognizing and detecting objects around the vehicle with cameras attached to the front of the vehicle to recognize the driving environment. Then, using the learned visual steering model and driving environment data, the hazard is classified and detected in three stages, depending on the driver's view and driving environment to assist the driver with the forward and blind spots.

• 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 Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.286-290
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• 2001

This paper presents a full-digital low-level controller for a robotic material transfer system which has been developed for a computer-integrated manufacturing model plant. Compared to conventional analog or hybrid type controllers in current industrial environments, this controller system has some advantages such as strong noise-immunity, easy control algorithm implementation, etc The servo-controller consists of two modules, a position controller and a DC servo motor driver. The position controller operates position feedback routines by receiving position encoder data and sending control outputs to the driver. The position controller is implemented in a full-digital way using a recently introduced microcontroller. The DC servomotor driver controls speeds and torques. The driver consists of a micro-controller and insulated-gate-bipolar-transistors (IGBT). The micro-controller provides control signals, and the IGBT's amplifies the control signals and sends them to the motor.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.1
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• pp.101-109
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• 1993

Dynamic characteristics of an electro-magnetic clutch transmission system were investigated by using Bondgraph modeling method. Simulation results showed that when the rotor engaged with the armature, the response time of the current, the driver torque, the rotational speed and the relative sliding time between the driver and the driven side decreased, as the gap size between the rotor and the armature decreased and the number of coil turns increased. Also, when the rotor disengaged with the armature, the delay time increased with the decreased gap size and the increased number of coil turns. It was found that the experimental results of the current, the driver torque, the rotational speeds were in good accordance with the theoretical results. The results of this study can be used as basic design materials of the electro-magnetic clutch.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.611-618
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• 2000

The test facility of the 1/60-scale models for the train-tunnel interactions was recently developed to investigate the effects of entry portal shapes, hood shapes and air-shafts for reducing the micro-pressure waves radiating to the surroundings of the tunnel exits by KRRI in Korea. The launching system of train model was chosen as air-gun type. In present test rig, after train model is launched, the blast wave by the driver did not enter to inside of the tunnel model. The train model is guided on the one-wire system from air-gun driver to the brake parts of test facility end. Some cases of the experiments were compared with numerical simulations to prove the test facility.

• Proceedings of the KSR Conference
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• 1999.11a
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• pp.146-154
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• 1999

The test facility of the 1/60-scale models for the train-tunnel interactions was recently developed to investigate the effects of entry portal shapes, flood shapes and air-shafts for reducing the micro-pressure waves radiating to the surroundings of the tunnel exits by KRRI in Korea. The launching system of train model was chosen as air-gun type. In present test rig, after train model is launched, the blast wave by the driver did not enter to inside of the tunnel model. The train model is guided on the one-wire system from air-gun driver to the brake parts of test facility end. Some cases of the experiments were compared with numerical simulations to prove the test facility.

• 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.