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

• Korean Journal of Culture and Social Issue
• /
• v.12 no.5_spc
• /
• pp.149-167
• /
• 2006

Korean society is rapidly changing to aging society comparing the other industrialized countries, however, the studies of elderly driver's driving behavior and accidents are not enough in Korea for elderly driver's accident prevention. This study focused on the elderly driver's psychological effects on elderly driver's driving behavior and traffic accidents; carefulness and aberrant driving behavior. - Elderly driver's traffic accidents The high percentage of elderly driver's accidents occurs in intersections and when turning left. There was a significant difference of the opponent vehicle's speed when left turn, between elderly driver and young driver; the elderly driver choose the higher speed of opponent vehicle than young driver when left turning. This result means that elderly driver has some problems with deciding the vehicle's speed and gap acceptance(Sunyeol Lee, Soonchul Lee, and Inseok Kim, 2006)(Table 1). - Carefulness and driving confidence In order to understand elderly driver's carefulness, this study compared the elderly driver's driving confidence. Driving confidence was consisted of 4 factors; environment of traffic condition, safe driving, driving ability and attention. Elderly driver's confidence was lower than young driver's. Elderly driver in high driving confidence group, showed longer driving history and they were tend to commit violations more frequently than elerly driver in low driving confidence group. Young driver, whose driving confidence level was high answered more driving history, annual mileage, the frequency of committing traffic violation and the experience of accident within lats 5 years(Soonchul Lee, Juseok Oh, Sunjin Park, Soonyeol Lee and Inseok Kim, 2006)(Table 2). This study examined the total time required until deciding to turn left in the no traffic signal intersection between elderly driver and young driver. The result showed that the time of elderly driver was significant longer than young driver(Sunyeol Lee et al, 2006)(Table 3). - Elderly driver's aberrant behavior Driver behavior Questionnaire(DBQ) was measured to understand the aberrant behavior; violation, error and lapse. The tend of aberrant behavior was observed by aging(Sunjin Park, Soonchul Lee, Jonghoi, Kim and Inseok Kim, 2006). Elderly driver's DBQ score was lower than young driver's(Table 4). Elderly and young driver showing longer driving history were in low DBQ score group. Elderly driver had high error score and young driver had high violation score. Young driver's aberrant driving behaviour was associated with annual mileage and the frequency of committing traffic violation. Elderly driver's aberrant driving behaviour was associated with annual mileage and experience of accident. Especially elderly driver whose violation, error and lapse score was high answered more committing experience of accident within last 5 years.

• Journal of information and communication convergence engineering
• /
• v.11 no.2
• /
• pp.95-102
• /
• 2013

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unseen/unmatched parameter spaces from individual training observations. The proposed driver behavior model was employed to anticipate pedal operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the model adaptation approach.

• Journal of Korean Society of Transportation
• /
• v.33 no.2
• /
• pp.159-169
• /
• 2015

Speeding has been the most common traffic violation which increases the risk of accidents. The purpose of this study is to examine drivers' behaviors on the speeding intention and speeding action and to identify the relationship between those causes and effects. Effects of behaviors and human characters of drivers on speeding are analyzed through a Driver Behavior Questionnaire and the cause and effect among behavior characters, speeding intention and speeding behavior are validated through the structural equation model. In order to validate the hypothesis of the study, a path analysis is conducted through structural equation model. As the result, Driver Behavior Questionnaire property that influences the speeding is revealed to be the violation while Driver Behavior Questionnaire properties that influences the speeding behavior are lapse, mistake, and violation. And the speeding intention influences the speeding behavior. The study results are compared with previous studies to reveal that Driver Behavior Questionnaire properties influencing the speeding behavior are in the order of violation, mistake and lapse. Three properties of Driver Behavior Questionnaire, lapse, mistake and violation, are behavior scales in agreement with previous studies. The results of this study based on a Driver Behavior Questionnaire are expected to be utilized as a way to predict and validate driving behaviors.

• International Journal of Computer Science & Network Security
• /
• v.22 no.11
• /
• pp.163-171
• /
• 2022

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 examine driver compliance behaviour within the dilemma zone at signalised intersection, pertaining 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 noncompliant behavior when further away from stop line. This finding could help us in the development of conceptual model of driving simulation with purpose of studying driver behavior.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.12
• /
• pp.37-44
• /
• 2015

In this paper, we propose the assistance method to enable safe driving through analysis of dangerous driving behavior using real-time alarm by vehicle speed, azimuth data and smartphone. For this method, smartphone is receiving driving data from digital tachograph using communication. Safe driving habit is a very important issue to commercial vehicle because that driver's long time driving than other vehicle type driver. Existing methods are very inefficient to improve immediately dangerous driving habits during driving because proceed driving behavior analysis after the vehicle operation. We propose the new safe driving assistance method that can prevent traffic accidents by real-time and improve the driver's wrong driving habits through real-time dangerous driving behavior analysis and notification the result to the driver. We have confirmed that the method in this paper will help to improve driving habits and can be applied through the proposed method implementation and simulation experiment.

• International Journal of Automotive Technology
• /
• v.7 no.5
• /
• pp.603-608
• /
• 2006

As an important and relatively easy to implement technology for realizing Intelligent Transportation Systems(ITS), Adaptive Cruise Control(ACC) automatically adjusts vehicle speed and distance to a preceding vehicle, thus enhancing driver comfort and safety. One of the key issues associated with ACC development is usability and user acceptance. Control parameters in ACC should be optimized in such a way that the system does not conflict with driving behavior of the driver and further that the driver feels comfortable with ACC. A driving simulator is a comprehensive research tool that can be applied to various human factor studies and vehicle system development in a safe and controlled environment. This study investigated driving behavior with ACC for drivers with different driving styles using the driving simulator. The ACC simulation system was implemented on the simulator and its performance was evaluated first. The Driving Style Questionnaire(DSQ) was used to classify the driving styles of the drivers in the simulator experiment. The experiment results show that, when driving with ACC, preferred headway-time was 1.5 seconds regardless of the driving styles, implying consistency in driving speed and safe distance. However, the lane keeping ability reduced, showing the larger deviation in vehicle lateral position and larger head and eye movement. It is suggested that integration of ACC and lateral control can enhance driver safety and comfort even further.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.7
• /
• pp.1146-1151
• /
• 2003

In this paper, results of an analysis of driving behavior characteristics and a driver-adaptive control algorithm for adaptive cruise control systems have been described. The analysis has been performed based on real-world driving data. The vehicle longitudinal control algorithm developed in our previous research has been extended based on the analysis to incorporate the driving characteristics of the human drivers into the control algorithm and to achieve natural vehicle behavior of the adaptive cruise controlled vehicle that would feel comfortable to the human driver. A driving characteristic parameters estimation algorithm has been developed. The driving characteristics parameters of a human driver have been estimated during manual driving using the recursive least-square algorithm and then the estimated ones have been used in the controller adaptation. The vehicle following characteristics of the adaptive cruise control vehicles with and without the driving behavior parameter estimation algorithm have been compared to those of the manual driving. It has been shown that the vehicle following behavior of the controlled vehicle with the adaptive control algorithm is quite close to that of the human controlled vehicles. Therefore, it can be expected that the more natural and more comfortable vehicle behavior would be achieved by the use of the driver adaptive cruise control algorithm.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.12
• /
• pp.123-129
• /
• 2014

In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

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