• Journal of Korea Game Society
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• v.15 no.5
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• pp.131-142
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• 2015

A self-driving car is an autonomous vehicle capable of fulfilling the main transportation capabilities of a traditional car. It must be capable of sensing its environment and navigating without human input. In this paper, we design the agent that can simulate these self-driving cars and develop a prototype for it. To do this, we analyze the requirements for the self-driving car, and then the agent is designed to be suitable for traditional multi-agent system. The key point of the design is that agents move along the steering forces only. The prototype of the designed agent was implemented by using Unity 3D. From simulation results using the prototype, movements of the agents were very realistic. However, in the case of increasing the number of the agent the performance was seriously degraded, and so the alternatives of the problem were suggested.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.26-41
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• 2024

To ensure safer road conditions, understanding the human factors influencing driving behavior is crucial. However, there are many difficulties in deriving the characteristics of individual human factors that affect actual driving behaviors. Therefore, this study analyzes self-reported dangerous-driving propensities in order to explore potential correlations with drivers' behaviors. The goal is to propose a method for assessing driving tendencies based on varying traffic scenarios. The study employed a questionnaire to gauge participants' propensity to drive dangerously, utilizing a simulator to analyze their driving behaviors. The aim is to determine any notable connections between dangerous-driving propensity and specific driving behaviors. Results indicate that individuals exhibiting a high propensity for reckless driving, as identified by the Korean DBQ, tend to drive at higher speeds and display more aggressive acceleration patterns. These findings contribute to a potential method for assessing reckless driving drivers.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.75-82
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• 2014

The purpose of this study is to identify the effects of sensation seeking and driving confidence level on reckless driving behavior in motor sports club's members. Data was collected from 265participants of the Trackday, driving school. Subjects answered the questionnaire using convenient sampling method. Data which is obtained through self-administration was analyzed by using the frequency analysis and multiple regression. Results are as follows. First sensation seeking has influenced on reckless driving behavior such as, drunken driving and errors. Second, driving confidence level has influenced on reckless driving behavior such as, drunken driving, overspeed driving and errors.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.161-167
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• 2018

The improvement of the social environment and the rapid development of medicine are making possible the age of 100. So a great number of countries including Korea are rapidly becoming the aged society or the super aged society. The elderly are accompanied by discomfort and disability. A variety of systems are developed and distributed to overcome them. The electric wheelchair is an electric motorized system for people who can not manipulate a manual wheelchair. In this paper, we propose an autonomous driving platform for an electric wheelchair. Here we use QR (Quick Response) code for self-localization. We also present real test results of the proposed system.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.119-126
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• 2022

This paper presents a method to reduce the vibration-induced noise effect of an inertial measurement device mounted on a self-driving vehicle. The inertial sensor used in the GNSS/INS integrated navigation system of a self-driving vehicle is fixed directly on the chassis of vehicle body so that its navigation output is affected by the vibration of the vehicle's engine, resulting in the degradation of the navigational performance. Therefore, these effects must be considered when mounting the inertial sensor. In order to solve this problem, this paper proposes to use an in-house manufactured vibration suppression device and analyzes its impact on reducing the vibration effect. Experimental test results in a static scenario show that the vibration-induced noise effect is more clearly observed in the lateral direction of the vehicle, but can be effectively suppressed by using the proposed vibration suppression device compared to the case without it. In addition, the dynamic positioning test scenario shows the position, speed, and posture errors are reduced to 74%, 67%, and 14% levels, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.402-404
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• 2022

Recent research has shown that deep learning models are vulnerable to adversarial attacks not only in the digital but also in the physical domain. This becomes very critical for applications that have a very high safety concern, such as self-driving cars. In this study, we propose a physical adversarial attack technique for one of the common tasks in self-driving cars, namely segmentation of the urban scene. Our method can create a texture on a wall so that it can be misclassified as a road. The demonstration of the technique on a state-of-the-art cityscape pretrained model shows a fairly high success rate, which should raise awareness of more potential attacks in self-driving cars.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 1998.05a
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• pp.307-312
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• 1998

In forklift operations, awkward postures due to backward driving may put drivers to the risk of CTD or low back pain. In this research, 6 forklift drivers were surveyed with OWAS for objective posture evaluation and bodymaps for self-report evaluation. The backward driving happened more frequently than forward driving as expected, and, as work hours passed by, the drivers naturally tended to assume the easier work postures in inverse proportion to the frequency of the backward operations. According to the results of OWAS, 60 % of the work postures in the forklift operations belonged to the category II, III, and IV classified serious. Especially, in the backward driving, the postures with the neck twisted over $45^{\circ}$ occupied 82.4 %. In addition, discomfort on the neck, left shoulder, and low back was frequently reported in the self-reports.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.65-72
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• 2008

The Manchester Driver Behavior Questionnaire (DBQ) is the most commonly used frameworks for investigating the relationship between self-reported driving behavior and accident involvement. After Reason et al. introduced DBQ, there were many studies replicating the research of Reason et al. in many countries. There was, however, no study replication of the Reason's research in Korea. The aim of this study is to replicate the distinction among errors, lapses and violations, and to evaluate the relationship of these behaviors with road traffic accidents on Korean drivers. 223 Korean drivers completed the Korean version of original DBQ with questions regarding background information, such as age, gender, annual mileage and accident involvement. Participants answered self-assessment questions, also. Factor analysis revealed three factors like Reason et al. The three factors were dangerous errors, violations and relatively harmless errors.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.25-34
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• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.651-658
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• 2015

Recently, lots of research works have been actively focused on the self-driving car. In order to implement the self-driving car, lots of fusion techniques should be merged and, specially, it is noted that a vehicle-attached camera can provide several useful functionalities such as traffic lights recognition, pedestrian detection, stop-line recognition including simple driving records. Accordingly, as one of the efficient tools for the self-driving car implementation, this paper proposes a mathematical model for estimating effectively the road distance with a vehicle-attached black box camera. The proposed model can be effectively used for estimating the road distance by using the height of black box camera or the widths of the referenced road line and the observed road line. Through several simulations, it is shown that the proposed model is effective in estimating the road distance.