• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.60-69
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• 2022

Recently, the importance of simulation validation in a virtual environment for autonomous driving system validation is increasing. At the same time, interest in the advancement of the virtual driving environment is also increasing. To develop autonomous driving technology, a simulation environment similar to the real-world environment is needed. For this reason, not only the road model is configured in the virtual driving environment, but also the driving environment configuration that includes the surrounding environments -traffic, object, etc- is necessary. In this article, FMTC, which is a test bed for autonomous vehicles, is implemented in a virtual environment and advanced to form a virtual driving environment similar to that of real FMTC. In addition, the similarity of the virtual driving environment is verified through comparative analysis with the real FMTC.

• Convergence Security Journal
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• v.19 no.1
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• pp.19-30
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• 2019

As the 4th industrial revolution has recently become a hot topic, the importance of autonomous vehicles has increased and interest has been increasing worldwide, and accidents involving autonomous vehicles have also occurred. With the development of autonomous vehicles, the possibility of a cyber-hacking threat to the car network is increasing. Various countries, including the US, UK and Germany, have developed guidelines to counter cyber-hacking of autonomous vehicles, In the case of Korea, limited temporary operation of autonomous vehicles is being carried out, but the legal system to be applied in case of accidents caused by vehicle network hacking is insufficient. In this paper, based on the existing legal system, we examine the civil liability caused by the cyber hacking of the autonomous driving car, while we propose a law amendment suited to the characteristics of autonomous driving car and a legal system improvement plan that can give sustainable trust to autonomous driving car.

• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.16-26
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• 2023

Recently, the importance of simulation in a virtual driving environment as well as real road-based tests for autonomous vehicle testing is increasing. Real road tests are being actively conducted at K-City, an autonomous driving test bed located at the Korea Automobile Safety Test & Research Institute of the Transportation Safety Authority. In addition, the need to advance the K-City virtual driving environment and build a virtual environment similar to the autonomous driving system test environment in real road tests is increasing. In this study, for K-City of Korea Automobile Safety Test & Research Institute, using detailed drawings and actual field data, K-City virtual driving environment was advanced, and similarity verification was verified through comparative analysis with actual K-City.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.81-100
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• 2018

This study investigated the influential factor over the intention to buy autonomous driving car by applying extended technology acceptance model (TAM2). To this end, 117 ordinary persons experienced in driving car were analyzed by using SEM(Structural Equation Modeling). Analysis shows that the perceived usefulness and purchase intention is positively affected by social influence and recognized risk. It is found that perceived usefulness is not affected, but purchase intention is positively affected in the case of innovation. On the contrary, analysis shows that driving capability and car playfulness recognized by individual have no influence on the perceived easiness. Although the result that driving capability recognized by individual negatively affects perceived usefulness was not included in the study hypothesis, it was remarkable. Generalizing the above result, it is found that social influence, innovation and recognized risk as variables which affect the intention to buy autonomous car play the role of significant variable. This study is meaningful in that such result can foresee the perception of preliminary accommodators of new technology of the 4th industrial revolution, autonomous driving car.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5299-5320
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• 2019

Interest in self-driving vehicle research has been rapidly increasing, and related research has been continuously conducted. In such a fast-paced self-driving vehicle research area, the development of advanced technology for better convenience safety, and efficiency in road and transportation systems is expected. Here, we investigate research in self-driving vehicles and analyze the main technologies of driverless car software, including: technical aspects of autonomous vehicles, traffic infrastructure and its communications, research techniques with vision recognition, deep leaning algorithms, localization methods, existing problems, and future development directions. First, we introduce intelligent self-driving car and road infrastructure algorithms such as machine learning, image processing methods, and localizations. Second, we examine the intelligent technologies used in self-driving car projects, autonomous vehicles equipped with multiple sensors, and interactions with transport infrastructure. Finally, we highlight the future direction and challenges of self-driving vehicle transportation systems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.237-242
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• 2019

Recently, as the public interest in autonomous cars has increased, research and technology development of related companies for the commercialization of autonomous cars have been actively carried out, and the development has progressed to a stage where they are partially but actually used. However, in March 2018, Uber and Tesla cars caused two fatal accidents, and the need for a new system is emerging. Therefore, this paper suggests a new information processing model for autonomous driving car system by supplementing the cause of recognition errors caused by the cause of death by focusing on the accident of autonomous driving car.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.31-40
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• 2023

Given recent accidents involving autonomous vehicles, driver monitoring technology related to the transition of control in autonomous vehicles is gaining prominence. Driver status monitoring systems recognize the driver's level of alertness and identify possible impairments in the driving ability owing to conditions including drowsiness and distraction. In autonomous vehicles, predictive factors for the transition to manual driving should also be included. During traditional human driving, monitoring the driver's status is relatively straightforward owing to the consistency of crucial cues, such as the driver's location, head orientation, gaze direction, and hand placement. However, monitoring becomes more challenging during autonomous driving because of the absence of direct manual control and the driver's engagement in other activities, which may obscure the accurate assessment of the driver's readiness to intervene. Hence, safety-ensuring technology must be balanced with user experience in autonomous driving. We explore relevant global and domestic regulations, the new car assessment program, and related standards to extract requirements for driver status monitoring. This kind of monitoring can both enhance the autonomous driving performance and contribute to the overall safety of autonomous vehicles on the road.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.167-173
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• 2018

The Korean government announced its goal of commercializing autonomous vehicle by year 2020. With such changes, it is expecting to decrease car accident mortality by half. To commercialize autonomous car, not only worries on safety of autonomous vehicle has to be solved but at the same time, institutional system has to be clear to distinguish legal responsibilities in case of accident. This paper will present the legal improvement direction of the introduction of autonomous vehicles as follows. First, it is necessary to re-establish concept of 'driver' institutionally. Second, it is appropriate to focus on Level 3 autonomous vehicle which is about to be commercialized in year 2020 and organize legal responsibility. Third, we should have a clear understanding on how level 3 autonomous vehicle will be commercialized in the future. Fourth, it is necessary to revise The Traffic Law, Act on Special Cases concerning the Settlement of Traffic Accident, and Automobile Accident Compensation Security Law in line with level 3 autonomous vehicle. Fifth, it is necessary to review present car insurance system. Sixth, present Product Liability Law is limited to movable products (Article 2), however, it is necessary to include intangible product which is software. Seventh, we should review on making special law related to autonomous car including civil, criminal, administrative, and insurance perspectives.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.713-722
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• 2017

Recently technological drive on autonomous vehicle is on the rush. Along with the trend, researches on driver's perspective are increasing. However, previous studies have limitations in terms of study period and rich experience. In this paper, we conducted an ethnographically inspired fieldwork to observe human-autonomous car interaction. We had six participants to ride a prototype autonomous car on the real road for six days. After, we generated trust, distrust factors according to Lee & See's categorization of trust dimension: process, performance, and purpose. We derived eight distrust factors that saliently influences passenger's experience in autonomous vehicle. Our research broadens trust model into autonomous driving context based on real road field study and contributes to automotive community with design guidelines to increase trust toward autonomous vehicle.