• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.547-554
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• 2021

Cancer patients can have different kinds of cancer driver genes, and identification of these patient-specific cancer driver genes is an important step in the development of personalized cancer treatment and drug development. Several bioinformatic methods have been proposed for this purpose, but there is room for improvement in terms of accuracy. In this paper, we propose NPD (Network based Patient-specific Driver gene identification) for identifying patient-specific cancer driver genes. NPD consists of three steps, constructing a patient-specific gene network, applying the modified PageRank algorithm to assign scores to genes, and identifying cancer driver genes through a score comparison method. We applied NPD on six cancer types of TCGA data, and found that NPD showed generally higher F1 score compared to existing patient-specific cancer driver gene identification methods.

• International Journal of Highway Engineering
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• v.19 no.6
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• pp.139-145
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• 2017

PURPOSES : This study analyzed the difference in a driver's workload between using a driving simulator and field driving in tunnel, highway. METHODS : Based on the literature review, it was found that a driver's workload could be quantified using biosignals. This study analyzed the biosignal data of 30 participants using data collected while they were using a driving simulator and during a field test involving tunnel driving. Relative energy parameter was used for biosignal analysis. RESULTS : The driver's workload was different between the driving simulator and field driving in tunnels, highway. Compared with the driving simulator test, the driver's workload exhibited high value in field driving. This result was significant at the 0.05 level. The same result was observed before the tunnel entrance section and 200 m after the entrance section. CONCLUSIONS : This study demonstrates the driving simulator effect that drivers feel safer and more comfortable using a driving simulator than during a field test. Future studies should be designed considering the result of this study, age, type of simulator, study site and so on.

• Journal of Auto-vehicle Safety Association
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• v.9 no.3
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• pp.19-23
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• 2017

This paper presents a lateral driver model with neuromuscular system to evaluate the performance of electric power steering (EPS). Output of most previously developed driver models is steering angle. However, in order to evaluate EPS system, driver model which results in steering torque output is needed. The proposed lateral driver model mainly consists of 2 parts: desired steering angle calculation and conversion of steering angle into steering torque. Desired steering angle calculation part results in steering angle to track desired yaw rate for path tracking. Conversion of steering angle into torque is consideration with neuromuscular system. The proposed driver model is investigated via actual driving data. Compared to other algorithms, the proposed algorithm shows similar pattern of steering angle with human driver. The proposed driver can be utilized to efficiently evaluate EPS system in simulation level.

• 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.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1986.10a
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• pp.157-164
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• 1986

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1435-1442
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• 2009

This paper proposes a music recommendation system for a driver in vehicle. The proposed system provides (selects and plays) a music to a driver in vehicle in real-time manner by inferring his preference based on physical, environmental, and personal information. Pulse data as physical information, age and biorhythm as personal information, and time as environmental information are used to infer a driver's and thus recommend a music. Experimental results showed that the proposed system could provide better satisfaction to a driver on the recommended music compared to the conventional approach.

• Journal of the Korean Society of Safety
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• v.24 no.1
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• pp.69-77
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• 2009

This paper reports the aging driver traffic accident severity modeling results. For the modeling, Poisson regression approach is applied using the data set obtained from the Korea Transportation Safety Authority's simulator-based driver aptitude test results. The test items include the estimations of moving objects' speed and stopping distance, drivers' multi-task capability, and kinetic depth perception and so on. The resulting model with the response variable of equivalent property damage only(EPDO) indicated that EPDO is significantly influenced by moving objects' speed estimation and drivers' multi-task capabilities. More interestingly, a comparison with the younger driver model revealed that the degradation of such capabilities may result in severer crashes for older drivers as suggested by the higher estimated parameters for the older driver model.