• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.402-409
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• 2007

Markov Chains has proven to be effective in predicting human behaviors in the areas of web site assess, multimedia educational system, and driving environment. In order to extend an application area of predicting human behaviors using Markov Chains, this study was conducted to investigate whether Markov Chains could be used to predict human behavior in selecting mobile phone menu item. Compared to the aforementioned application areas, this study has different aspects in using Markov Chains : m-order 1-step Markov Model and the concept of Power Law of Learning. The results showed that human behaviors in predicting mobile phone menu selection were well fitted into with m-order 1-step Markov Model and Power Law of Learning in allocating history path vector weights. In other words, prediction of mobile phone menu selection with Markov Chains was capable of user's actual menu selection.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.65-70
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• 2022

Lane change in urban environments is a challenge for both human-driving and automated driving due to their complexity and non-linearity. With the recent development of deep-learning, the use of the RNN network, which uses time series data, has become the mainstream in this field. Many researches using RNN show high accuracy in highway environments, but still do not for urban environments where the surrounding situation is complex and rapidly changing. Therefore, this paper proposes a lane change possibility decision network by adopting Attention layer, which is an SOTA in the field of seq2seq. By weighting each time step within a given time horizon, the context of the road situation is more human-like. A total 7D vectors of x, y distances and longitudinal relative speed of side front and rear vehicles, and longitudinal speed of ego vehicle were used as input. A total 5,614 expert data of 4,098 yield cases and 1,516 non-yield cases were used for training, and the performance of this network was tested through 1,817 data. Our network achieves 99.641% of test accuracy, which is about 4% higher than a network using only LSTM in an urban environment. Furthermore, it shows robust behavior to false-positive or true-negative objects.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.122-131
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• 2013

The MPI CyberMotion Simulator provides a unique motion platform, as it features an anthropomorphic robot with a large workspace, combined with an actuated cabin and a linear track for lateral movement. This paper introduces the simulator as a tool for studying human perception, and compares its characteristics to conventional Stewart platforms. Furthermore, an experimental evaluation is presented in which multimodal human control behavior is studied by identifying the visual and vestibular responses of participants in a roll-lateral helicopter hover task. The results show that the simulator motion allows participants to increase tracking performance by changing their control strategy, shifting from reliance on visual error perception to reliance on simulator motion cues. The MPI CyberMotion Simulator has proven to be a state-of-the-art motion simulator for psychophysical research to study humans with various experimental paradigms, ranging from passive perception experiments to active control tasks, such as driving a car or flying a helicopter.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.332-347
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• 2017

Analysis of the interaction between the automated vehicles and manual vehicles is very important in analyzing the performance of automated cooperative driving environments. In particular, the automated vehicle platooning can affect the driving behavior of adjacent manual vehicles. The purpose of this study is to analyze the lane change behavior of the manual vehicles in automated vehicle platonning environment and to conduct the experiment and questionnaire surveys in three stages. In the first stage, a video questionnaire survey was conducted, and responsive behaviors of manual vehicles were investigated. In second stage, the driving simulator experiments were conducted to investigate the lane change behaviors of in automated vehicle platonning environments. To analyze the lane change behavior of the manual vehicles, lane change durations and acceleration noise, which are indicators of traffic flow stability, were used. The driving behavior of manual vehicles were compared across different market penetration rates (MPR) of automated vehicles and human factors. Lastly, NASA-TLX (NASA Task Load Index) was used to evaluate the workload of the manual vehicle drivers. As a result of the analysis, it was identified that manual vehicle drivers had psychological burdens while driving in automated vehicle platonning environments. Lane change durations were longer when the MPR of the automated vehicles increased, and acceleration noise were increased in the case of 30-40 years old or female drivers. The results from this study can be used as a fundamental for more realistic traffic simulations reflecting the interaction between the automated vehicles and manual vehicles. It is also expected to effectively support the establishment of valuable transportation management strategy in automated vehicle environments.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.39-47
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• 2023

The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.169-179
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• 2020

This study examines the drivers and implementation of intrapreneurship strategy in a property service company. Using a qualitative case study approach, the study interviewed four managers involved in related intrapreneurship initiatives. The data was validated by an expert and a practitioner from a different company. The implementation of the company's intrapreneurship strategy is limited to improving new ways of working and developing products and services. However, business development and the creation of new business models are still limited. From several intrapreneurship driving factors, it was observed that the company practices are considered adequate with regard to top management support, leadership, flexibility in carrying out work, as well as in fairly harmonious arrangements for ongoing business relationships with the intrapreneurship projects. On the other hand, human resources with entrepreneurial behavior are still minimal. Similarly, the driving factors in reward and training that promote entrepreneurial behavior are also considered to be insufficient. The application of intrapreneurship as a strategy requires understanding and commitment from all parties in the organization. This study provides insight into the Indonesian context and proposes that intrapreneurship initiatives are less likely to succeed if they are not supported by developing a more systematic entrepreneurial mindset, behavior, and culture.

• 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.

• Journal of the Ergonomics Society of Korea
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• v.29 no.6
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• pp.959-964
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• 2010

Seat belt is an important means to protect drivers and passengers from the damages by car accidents. Many ways to increase the seat belt wearing rate have been proposed through human factors researches. The primary ways to increase seat belt use rate have emphasized the intention-behavior cycle. This study focused on the gap between intention and behavior. The gap may be bridged by the habit for seat belt use behavior. Divers following a desirable car starting sequence, from sitting on the chair, fastening seat belt, starting engine to moving a car, reported that higher belt wearing rate and unconscious behavior (automated response). That is, the habitualized procedure knowledge prevented drivers from forgetting to fasten their seat belt. The reminder systems such as warning light and warning sound could not significantly give an influence in remembering to fasten seat belt. In order to increase the seat belt use rate, the desirable car starting procedure should be included in the driving education program.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2747-2752
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• 2003

This paper has develops a new framework to understand the human’s driving maneuver based on the expression as HDS focusing on the driver’s stopping maneuver. The driving data has been collected by using the three-dimensional driving simulator based on CAVE, which provides three-dimensional visual information. In our modeling, the relationship between the measured information such as distance to the stop line, its first and second derivatives and the braking amount has been expressed by the PWPS model, which is a class of HDS. The key idea to solve the identification problem was to formulate the problem as the MILP with replacing the switching conditions by binary variables. From the obtained results, it is found that the driver appropriately switches the ‘control law’ according to the following scenario: At the beginning of the stopping behavior (just after finding the stopping point), the driver decelerate the vehicle based on the acceleration information, and then switch to the control law based on the distance to the stop line.

• The Korean Journal of Emergency Medical Services
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• v.6 no.1
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• pp.199-207
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• 2002

This is a study to evaluate the effects of the safety of ambulance driving and the occurrence of ambulance traffic accidents and to provide basic information for the description of various factors to reduce the ambulance traffic accidents. The major instruments of this study were Korean Self-Analysis Driver Opinionnaire. Questionnaire contains 8 items which measure driver's opinions or attitudees: driving courtesy, emotion, traffic law, speed, vehicle condition, the use of drugs, high-risk behavior, human factor. To take the analysis of data, the total of 187 drivers were investigated ambulance drivers in Gwangju Metropolitan City from 2002. 1. September to 2002. 20. September. The data were analyzed by the path analysis SPSS program. The result are as follows : 1. There was desirable attitude group(58.4%) and undesirable attitude group(41.7%) on safety ambulance driving. 2. It have suggested that rist factors of ambulance traffic accident much affected with emotion and speed control on safety ambulance driving(Y(Accident) = -2.00 + 0.6 X1(Emotion Control) + 0.4 $X_2$(Speed control) + E). 3. Almost 92.1% of respondents have agreed to necessity of emergency medical technics for ambulance drivers.