• Journal of Institute of Control, Robotics and Systems
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• v.13 no.12
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• pp.1186-1191
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• 2007

Human driver monitoring system is one of the most important systems for the safety in driving vehicles, and therefore driver assistance system has gained much attention during the last decade. This paper proposed an intelligent driver assistance system which monitors human driver's states from bio-signals such as ECG and Blood Pressure. The proposed system used mamdani fuzzy inference to evaluate the driver's mental strain and generated warning signals to the driver. The approach using bio-signals in driver assistance system is the main issue of the related systems and the preliminary results showed the possibility of further research topics in developing more intelligent embedded systems with bio-signal feedback.

• Journal of Drive and Control
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• v.19 no.3
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• pp.47-52
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• 2022

The C-ITS (Cooperative-intelligent Transport System) is a driver assistance system that prevents car accidents and enhances traffic conditions, via sharing traffic information between vehicles and roadway infrastructures. A CLAS (changing lane assistance system) for unprotected left-turn, is a C-ITS that assists a driver with safely changing lanes. This system addresses a driver's critical gap, that enables the system to express a driver's uncertainty. A driver's critical gap is a time that can be used in a threshold, to change a lane or not. Unfortunately, a driver's critical gap is difficult to use in a CLAS directly. This paper addresses a driver's critical gap, and how it can be applied in a CLAS for an unprotected left-turn.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.6
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• pp.564-571
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• 2014

A single-stage current-balancing multi-channel light-emitting diode (LED) driver is proposed in this study. The conventional LED driver system consists of two cascaded power conversion stages, i.e., an isolation DC/DC converter and LED driver. LED driver is usually implemented with the same number of expensive boost converters as those of LED channels to tightly control the current through each LED channel. Therefore, its overall system size is not only bulky, but the cost is rather high. By contrast, the proposed LED driver system is composed of a single power stage with the DC/DC converter and LED driver merged. Although the current balancing circuit of the proposed LED driver requires only passive devices instead of expensive boost converters, all currents through multi-channel LEDs can be well balanced. Therefore, the proposed LED driver features a small system size, improved efficiency, and low cost. To confirm the validity of the proposed driver, its operation and performance are verified on a prototype for a 46" LED TV.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.31-40
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• 2023

Given recent accidents involving autonomous vehicles, driver monitoring technology related to the transition of control in autonomous vehicles is gaining prominence. Driver status monitoring systems recognize the driver's level of alertness and identify possible impairments in the driving ability owing to conditions including drowsiness and distraction. In autonomous vehicles, predictive factors for the transition to manual driving should also be included. During traditional human driving, monitoring the driver's status is relatively straightforward owing to the consistency of crucial cues, such as the driver's location, head orientation, gaze direction, and hand placement. However, monitoring becomes more challenging during autonomous driving because of the absence of direct manual control and the driver's engagement in other activities, which may obscure the accurate assessment of the driver's readiness to intervene. Hence, safety-ensuring technology must be balanced with user experience in autonomous driving. We explore relevant global and domestic regulations, the new car assessment program, and related standards to extract requirements for driver status monitoring. This kind of monitoring can both enhance the autonomous driving performance and contribute to the overall safety of autonomous vehicles on the road.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.11
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• pp.40-47
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• 2009

In this paper, we present the design of a source driver of QVGA scale TFT-LCD driver IC which uses a mixed driving method and performs $\gamma$-correction to improve image. The source driver with 240 RGB ${\times}$ 320 dots resolution drives a TFT-LCD panel through 720 channels and implements 262k colors using 18-bit RGB data format. The mixed driving method is a mixture the channel amp. driving method with high drivability and the gray amp. driving method with small area, which remarkably reduces channel driver areas. The driver has been designed using the $0.35{\mu}m$ Magnachip embedded DRAM technology and simulated using the HSPICE simulator. The results show that our source driver operates well with y-correction and the channel driver has $17{\mu}s$ channel driving time with only 78 driving amplifiers and control logic.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.371-377
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• 2012

Objective: The aim of this study is to investigate effect of driver's cognitive distraction on driver's physiological state and driving performance, and then to determine parameters appropriate for detecting the cognitive distraction. Background: Driver distraction is a major cause of traffic accidents and poses a serious threat to traffic safety due to ever increasing use of in-vehicle information systems and mobile phones during driving. Cognitive distraction, among four different types of distractions, prevents a driver from processing traffic information correctly and adapting to change in surround vehicle behavior in time. However, the cognitive distraction is more difficult to detect because it normally does not involve significant change in driver behavior. Method: A full-scale driving simulator was used to create virtual driving environment and situations. Participants in the experiment drove the driving simulator in three different conditions: attentive driving with no secondary task, driving and conducting secondary task of adding numbers, and driving and conducting secondary task of conversing with an experimenter. Parameters related with driver's physiological state and driving performance were measured and analyzed for their change. Results: The experiment results show that driver's cognitive distraction, induced by secondary task of addition and conversation during driving, increased driver's cognitive workload, and indeed brought change in driver's physiological state and degraded driving performance. Conclusion: The galvanic skin response, pupil size, steering reversal rate, and driver reaction time are shown to be statistically significant for detecting cognitive distraction. The appropriate combination of these parameters will be used to detect the cognitive distraction and estimate risk of traffic accidents in real-time for a driver distraction warning system.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3820-3841
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• 2018

Most of the accidents occur due to drowsiness while driving, avoiding road signs and due to driver's distraction. Driver's distraction depends on various factors which include talking with passengers while driving, mood disorder, nervousness, anger, over-excitement, anxiety, loud music, illness, fatigue and different driver's head rotations due to change in yaw, pitch and roll angle. The contribution of this paper is two-fold. Firstly, a data set is generated for conducting different experiments on driver's distraction. Secondly, novel approaches are presented that use features based on facial points; especially the features computed using motion vectors and interpolation to detect a special type of driver's distraction, i.e., driver's head rotation due to change in yaw angle. These facial points are detected by Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN). Various types of classifiers are trained and tested on different frames to decide about a driver's distraction. These approaches are also scale invariant. The results show that the approach that uses the novel ideas of motion vectors and interpolation outperforms other approaches in detection of driver's head rotation. We are able to achieve a percentage accuracy of 98.45 using Neural Network.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.142-150
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• 2024

A conceptual model represents an understanding of a system that is going to be developed, which in this research, a driving simulation software to study driver behavior at signalised intersections. Therefore, video observation was conducted to study driver compliance behaviour within the dilemma zone at signalised intersection, with regards to driver's distance from the stop line during yellow light interval. The video was analysed using Thematic Analysis and the data extracted from it was analysed using Chi-Square Independent Test. The Thematic Analysis revealed two major themes which were traffic situation and driver compliance behaviour. Traffic situation is defined as traffic surrounding the driver, such as no car in front and behind, car in front, and car behind. Meanwhile, the Chi-Square Test result indicates that within the dilemma zone, there was a significant relationship between driver compliance behaviour and driver's distance from the stop line during yellow light interval. The closer the drivers were to the stop line, the more likely they were going to comply. In contrast, drivers showed higher non-compliant behavior when further away from stop line. This finding could help in the development of conceptual model of driving simulation with purpose in studying driver behavior.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.874-881
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• 2011

Driver's cab shall be designed ergonomically to provide the comfort for a driver to gather the information from the equipments and to operate the equipments effectively. The typical three design factors effected on the comfort are the access of driver's cab, freedom of movement inside the cabs and visibility condition. All control equipments shall be arranged so that those which are most often used or are of critical importance are the most convenient to the driver. The layout of driver's cab shall maximize the use of available space to be rugged and easily maintained. The visibility for the running direction along the track shall be secured. The visibility condition apply the seating position of the driver. Not only the environment condition(temperature, humidity, rattle, etc) but also the construction of driver's cab can be ergonomic design factor.