• Journal of Digital Convergence
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• v.19 no.10
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• pp.241-246
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• 2021

Although there have been many studies on laws and systems for the proliferation of autonomous vehicles, studies on the activation of autonomous vehicles from a strategic perspective are insufficient. This study examines the issues and problem solving methods of autonomous vehicles. Based on this, plans to activate autonomous vehicles from a strategic point of view are proposed. In order to solve the issues and problems of autonomous vehicles, it is necessary to clearly establish legal and institutional standards based on the reinforcement of the safety of autonomous vehicles. In the event of a traffic accident, who is responsible for the accident and responsibility for compensation should be prioritized. Diffusion strategies are established according to the level of autonomous driving for the activation of autonomous vehicles in strategic perspective. In addition, governmental support policies should be used as triggers for initial activation, and marketing mix strategies should be implemented based on segmentation, targeting, and positioning strategies.

• Convergence Security Journal
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• v.18 no.4
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• pp.53-61
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• 2018

Recently 4th Industrial Revolution era has come up and autonomous vehicle gets a huge attention for its commercialization as well as development. To this end, many countries such as US, UK, Germany are looking into laws and policies related to autonomous vehicle making a new law system, laws, policies or at least modifying the existing ones. Korea is also facing commercialization and development of autonomous vehicle yet it's law system, laws and policies are far beyond comparing to those of advanced countries. This paper details current law system comparison of several countries providing differences and characteristics for the purpose of success of auto drive vehicle industry. On top of that we suggest a new law system, laws and policies and then provide directions as steps for mature implementation. In addition, we discuss how the new laws and policies can bring out successful commercialization as well as industrial success of autonomous vehicle at the points of consumers, vehicle makers, insurance companies, and government.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.75-81
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• 2022

Autonomous vehicles, which can dramatically solve the lack of parking spaces, are making great progress through deep reinforcement learning. Activation functions are used for deep reinforcement learning, and various activation functions have been proposed, but their performance deviations were large depending on the application environment. Therefore, finding the optimal activation function depending on the environment is important for effective learning. This paper analyzes 12 functions mainly used in reinforcement learning to compare and evaluate which activation function is most effective when autonomous vehicles use deep reinforcement learning to learn parking. To this end, a performance evaluation environment was established, and the average reward of each activation function was compared with the success rate, episode length, and vehicle speed. As a result, the highest reward was the case of using GELU, and the ELU was the lowest. The reward difference between the two activation functions was 35.2%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.4
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• pp.519-526
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• 2022

Basically, autonomous vehicles drive using road and traffic information collected by various sensors. However, it is known that there is a limitation to realizing fully autonomous driving with only such technologies and information. In recent, various efforts are being made to overcome the limitations of sensor-based autonomous driving, and efforts are also underway to utilize more specific and accurate road and traffic information, called local dynamic map (LDM). However, LDM-related data standards and specifications have not yet been sufficiently verified, and research on the spatiotemporal scope of LDM during autonomous driving is extremely limited. Based on this background, the purpose of this study is to identify these limitations through an analysis of previous LDM-related studies and to present a framework for calculating the spatiotemporal activation section of LDM-based road and traffic information.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.433-437
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• 1999

본 연구는 도로표면의 기복 또는 굴곡의 변화가 자동차 탑승자에게 미치는 자율신경계의 반응을 관찰하고자 하였다. 아스팔트, 시멘트, 비포장도로를 각각 30 km/h의 속력으로 정속 주행하면서 ECG, GSR, Skin Temperature 의 생리변화를 3분간 측정하였고, 주행 후에는 각각의 도로 주행시 느꼈던 감성의 변화를 주관적 평가지로 검정하였다. 건강한 5명의 지원자가 이 실험에 참여하였고 도로표면의 기복의 변화로부터 유발되는 감성에만 집중하도록 요구하여 다른 간섭효과로부터 유발되는 감성의 변화를 최소화하도록 하였다. 정차에 비해 각 도로 주행시 피험자는 아스팔트, 시멘트, 비포장도로 순서로 불쾌도와 긴장도가 증가하였다고 주관적 평가를 하였다. 또한 아스팔트, 시멘트, 비포장도로 순서로 평균 R-R 간격이 점차 감소하였고, GSR의 진폭은 증가하였으며, 피부온도는 감소하였다. 본 연구로부터 도로표면의 기복의 정도가 증가할수록 교감신경계가 활성화된다는 사실을 관찰할 수 있었고 이러한 결과는 주관적 평가결과와도 일치하였다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2016.05a
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• pp.335-355
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• 2016

• Science of Emotion and Sensibility
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• v.22 no.4
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• pp.15-30
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• 2019

The reduction of traffic accidents is a primary potential benefit of autonomous vehicles (AVs). However, the prevalence of AVs also arouses a key question: to what extent should a human wrest control back from AVs? Specifically, in an unavoidable situation of emergency, should an AV be able to decide between the safety of its own passengers and endangered pedestrians? Should AV programming include well-accepted decision rules about actionsto take in hypothetical situations? The current study (N = 103) examined individual/situational variables that could perform critical decision-making roles in AV related traffic accidents. The individual variable of attitudes toward AVs was assessed using the Self-driving Car Acceptance Scale. To investigate situational influences on decisional processes, the study's participants were assigned to one of two groups: the achievement value was activated in one group and the benevolence value was triggered in the other through the use of a sentence completion task. Thereafter, participants were required to indicate who should be protected from injury: the passengers of the concerned AV, or endangered pedestrians. Participants were also asked to record the extent to which they intended to buy an AV programmed to decide in favor of the greater good according to Utilitarian principles. The results suggested that participants in the "achievement value: driver perspective" groupexpressed the lowest willingness to sacrifice themselves to save several pedestrians in an unavoidable traffic accident. This group of participants was also the most reluctant to buy an AV programmed with utilitarian rules, even though there were significant positive relationships between members' acceptance of AVs and their expressed intention to purchase one. These findings highlight the role of the decisional processes involved in the "achievement value" pertaining to AVs. The paper finally records the limitations of the present study and suggests directions for future research.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.91-112
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• 2019

In accordance with the advent of the 5th generation (5G) communication technology, we are having a change in various communication services which converge with high technologies related to the 4th Industrial Revolution. To utilize the upcoming 5G technology effectively and practically, we analyzed the technologies which have the most potential in convergence under the introduction of 5G technology and as a result, it is a autonomous vehicle that we'll discuss the core technologies of the 4th Industrial Revolution, which can lead to service activation by being combined with 5G technology. In addition, we developed an industry-service classification of 5G-based autonomous vehicle, we provided a basis for supporting a new business and its new business model converged with 5G communication technology. Furthermore, we will create a linkage matrix with the industry-service classification system of a new autonomous vehicles. This matrix will service as a guideline for industry-service development where autonomous vehicles can be utilized actively in the next generation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.219-222
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• 2008

Recently, parking problems for an autonomous vehicle have attracted a great deal of attention and have been examined in many papers in the literature. In this paper we design a fuzzy logic based parking system at the slant parking space which is a important part for designing a autonomous parking system. We first design an optimal parking path for the slant space and present the simulation results of the fuzzy logic based parking system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.117-122
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• 2021

Autonomous driving allows cars to drive without people and is being studied very actively thanks to the recent development of artificial intelligence technology. Among artificial intelligence technologies, deep reinforcement learning is used most effectively. Deep reinforcement learning requires us to build a neural network using an appropriate activation function. So far, many activation functions have been suggested, but different performances have been shown depending on the field of application. This paper compares and evaluates the performance of which activation function is effective when using deep reinforcement learning to learn autonomous driving on highways. To this end, the performance metrics to be used in the evaluation were defined and the values of the metrics according to each activation function were compared in graphs. As a result, when Mish was used, the reward was higher on average than other activation functions, and the difference from the activation function with the lowest reward was 9.8%.