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

• 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.

• Journal of Korean Society of Transportation
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• v.33 no.2
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• pp.159-169
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• 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
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• v.22 no.11
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• pp.163-171
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• 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 Korean Society of Industry Convergence
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• v.5 no.3
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• pp.239-246
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• 2002

The propose of this study is to analysis driver's behavior of traveler information. This research made an attempt to explore driver's route change behavior in the en-route stage. Model were developed for each analysis with LIMDEP software which was developed by Willams H Greene. Commuters' transportation change in before trip stage are affected by their income, travel time, and incident information and constant of this model showed their reluctance of change mode. This was resulted from the inappropriateness of traffic information to general commuters which is the main target of traffic information.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.173-182
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• 2021

In this paper, we analyzed driver's and passenger's motions that cause driver's distraction, and recognized 10 driver's behaviors related to mobile phones. First, distraction-inducing behaviors were classified into environments and factors, and related recent papers were analyzed. Based on the analyzed papers, 10 driver's behaviors related to cell phones, which are the main causes of distraction, were recognized. The experiment was conducted based on about 100,000 image data. Features were extracted through SURF and tested with three models (CNN, ResNet-101, and improved ResNet-101). The improved ResNet-101 model reduced training and validation errors by 8.2 times and 44.6 times compared to CNN, and the average precision and f1-score were maintained at a high level of 0.98. In addition, using CAM (class activation maps), it was reviewed whether the deep learning model used the cell phone object and location as the decisive cause when judging the driver's distraction behavior.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.5
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• pp.136-145
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• 2004

In companion paper, the composition and structure of the traffic environment is derived. Rules to regulate agent behaviors and the frameworks to communicate between the agents are proposed. In this paper, the model of a driver agent which controls a vehicle agent is constructed. The driver agent is capable of having different driving styles. That is, each driver agent has individual behavior settings of the yielding index and the passing index. The yielding index can be defined as how often the agent yields in case of lane changes, and the passing index can be defined as how often the agent passes ahead. According to these indices, the agents overtake or make their lanes for other vehicles. Similarly, the vehicle agents can have various vehicle dynamic models. According to their dynamic characteristics, the vehicle agent shows its own behavior. The vehicle model of the vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation has proceeded for an interrupted flow model. The result has shown that it is possible to express the characteristics of each vehicle and its driver in a traffic flow, and that the change of the traffic state is closely related with the distance and the signal delay between intersections. The system developed in this paper shows the effectiveness and the practical usefulness of the traffic simulation.

• 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.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.3
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• pp.209-219
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• 1997

A fuzzy driver model based on a preview-predictor and yaw rate is developed. The model is used to investigate the handling performance of two wheel steering system(2WS) and four wheel steering system(4WS) vehicles. The two degree-of- freedom model which has yaw and lateral motion predicts the path of the vehicles. Based upon the yaw rate and lateral deviations, the fuzzy engine describes the human driver's complicated control behavior which is adjusted for the driving environment. Both typical single lane change maneuver and double lane change maneuver are adopted to demonstrate the feasibility of fuzzy driver model.