• Journal of the HCI Society of Korea
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• v.13 no.2
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• pp.5-20
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• 2018

The fully autonomous vehicle, which has been actively studied in a worldwide before commercialization, is expected to become a living space by securing time and space compared to existing automobile. For this reason, interior design of fully autonomous vehicle has become very important. To enhance passenger's experience and satisfaction in fully autonomous vehicle, it is necessary to design an optimized space that can support in-vehicle activities. For this purpose, efforts to analyze the passenger's in-vehicle activities should be preceded. However, there were limited studies that define space and in-Vehicle activities of fully autonomous vehicle in Korea. The purpose of this study is to suggest the guideline of the interior design of fully autonomous vehicle by analyzing and classifying the scope of activities that the passenger can perform within the vehicle. As a method of the study, literature studies on future concept cars, human lifetime behavior and consumer needs had been conducted. As a result in-vehicle activities could be applied in a fully autonomous vehicle. Four in-vehicle activities 'work', 'home life and personal care', 'relaxation' and 'conversation and hobby' had been derived through the analysis of in-vehicle activities. Based on the results, the interior design of fully autonomous vehicle guideline has been suggested. The study is significant because the result of the study can act as a basic study which considers the activities in the fully autonomous vehicle environment.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.17-34
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• 2018

The era of fully autonomous vehicles which can travel to their destination completely, is expected to arrive in the near future. The automobile industry will face a huge change in the near future. It makes the industry in the midst of a major turning point, since the automobile was born in the late 1800s. It is expected that the fully autonomous vehicle will be defined as a part of a living space beyond the concept of transportation. However, the existing research on the interior design of the fully autonomous vehicle is insufficient. The purpose of this study is to propose a design method of interior space through analyzing in-vehicle activities, which is a fundamental design element that can satisfy user's emotional experience suitable the concept of living space. For this purpose, the consumer's consciousness about the in-vehicle activity of fully autonomous vehicle was investigated. As a result, domestic consumers perceived the needs of in-vehicle activities related to resting, listening and watching activities such as 'sleeping and resting', 'watching TV and movie', 'listening to music' among 'self - centered' activities. Based on the results of the investigation, this study will suggest a design method of interior design of the fully autonomous vehicle. This study is meaningful, because it is a leading research that suggests new ways of designing interior space by analyzing consumer's needs in a quantitative method.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.93-102
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• 2023

Data processing through the cloud causes many problems, such as latency and increased communication costs in the communication process. Therefore, many researchers study edge computing in the IoT, and autonomous driving is a representative application. In indoor self-driving, unlike outdoor, GPS and traffic information cannot be used, so the surrounding environment must be recognized using sensors. An efficient autonomous driving system is required because it is a mobile environment with resource constraints. This paper proposes a machine-learning method using neural networks for autonomous driving in an indoor environment. The neural network model predicts the most appropriate driving command for the current location based on the distance data measured by the LiDAR sensor. We designed six learning models to evaluate according to the number of input data of the proposed neural networks. In addition, we made an autonomous vehicle based on Raspberry Pi for driving and learning and an indoor driving track produced for collecting data and evaluation. Finally, we compared six neural network models in terms of accuracy, response time, and battery consumption, and the effect of the number of input data on performance was confirmed.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1298-1299
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• 2015

이동 로봇의 자기 위치 인식 방법으로 GPS를 많이 이용하지만 건물 내부공간에서는 위성신호 수신 장애가 있기 때문에 GPS 사용이 어렵다. 이에 대한 대안으로 다양한 형태의 실내 측위 기술에 관한 연구가 진행되어왔다. 최근에는 WiFi를 이용한 방법이 일부 상용화 되고 있으나 정밀도가 3~5m라는 한계가 있으며, LED 조명을 이용한 방법은 실용화 단계에 이르지는 못했지만 많은 연구가 진행되고 있다. 당 연구실에서도 LED조명을 기반으로 한 실내위치 인식 시스템을 개발하였으며, 지난 연구에서는 이를 이용한 이동로봇의 자율주행을 연구하였다. 본 연구에서는 지난 연구에 덧붙여 두개의 수신부를 이용하여 로봇의 방향인식오류 개선 및 이동 로봇의 자율주행을 보여주고자 한다. 제시된 시스템은 이동로봇, 조명제어장치 그리고 컴퓨터로 구성된다. 이동로봇은 상용화된 마이크로 마우스에 탑재된 조명신호 수신장치를 통하여 자신의 위치와 방향을 감지하며, 컴퓨터와의 Wi-Fi 통신으로 자신의 위치를 컴퓨터에 전송하거나 위치 명령을 수신한다. 컴퓨터에서는 수신 받은 이동로봇의 위치를 실시간으로 화면에 표시하며, 이동로봇에 전달할 위치명령을 사용자가 입력하는 기능을 제공한다. 사용자가 이동경로를 설정한 후 이동로봇으로 명령을 보내면 로봇은 자신의 위치와 목적지를 비교하며 자율주행을 하게 된다. 실험을 통하여 확인한 결과 지난연구의 방향인식의 문제점이 해결되어 제시된 시스템으로 실내공간에서도 이동로봇의 자율주행이 원만히 이루어짐을 확인하였다.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.13-24
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• 2021

In level 5 (mind-off) of autonomous driving, the autonomous vehicle passengers are expected to have various activities such as face-to-face meetings, working, relaxing, and watching movies. In particular, various changes in the interior space of the vehicle are expected. Moreover, according to the survey conducted by the American Automobile Association, 73% of the respondents reported that they were afraid to board autonomous vehicles. In level 5 of autonomous driving, the subject of safety was expected to be transferred to autonomous vehicles; thus, research should be conducted from the user's perspective. Recently, various studies have been conducted to secure the safety of fully autonomous vehicles. However, there are limited studies addressing the psychological safety of actual passengers. Therefore, this study conducted a questionnaire based on the AHP technique. Consequently, the automobile safety system's priority for securing passengers' psychological safety according to each type of indoor behavior was derived, and the interior space for securing the psychological stability of passengers was suggested based on the obtained results. This study offers a new direction for interior space design, satisfying the psychological safety of passengers. This study is important because it advocates that the interior environment of fully autonomous driving cars is expected to be designed to secure the user's psychological safety.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.8
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• pp.936-942
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• 2019

Autonomous driving of drone indoor must move along a narrow path and overcome other factors such as lighting, topographic characteristics, obstacles. In addition, it is difficult to operate the drone in the hallway because of insufficient texture and the lack of its diversity comparing with the complicated environment. In this paper, we study an autonomous drone navigation using Convolution Neural Network(CNN) in indoor environment. The proposed method receives an image from the front camera of the drone and then steers the drone by predicting the next path based on the image. As a result of a total of 38 autonomous drone navigation tests, it was confirmed that a drone was successfully navigating in the indoor environment by the proposed method without hitting the walls or doors in the hallway.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.77-82
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• 2022

The effect of indoor air pollution on the human body is greater and more dangerous than outdoor air pollution. In general, a person stays indoors for a long time, and in a closed room, pollutants are continuously accumulated and the polluted air is better delivered to the lungs. Especially in the case of young children, it is very sensitive to indoor air and it is fatal. In addition, methods to reduce indoor air pollution, which cannot be ventilated with more frequent indoor activities and continuously increasing external fine dust due to Covid 19, are becoming more important. In order to improve the problems of the existing autonomous driving air purifying robot, this paper divided the map and Upper Confidence bounds applied to Trees(UCT) based algorithm to solve the problem of the autonomous driving robot not sterilizing a specific area or staying in one space continuously, and the problem of children who are vulnerable to indoor air pollution. We propose a space-adaptive autonomous driving air purifying robot for a green smart school that can be improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.349-350
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• 2019

The autonomous driving drones experimented in the existing indoor corridor environment was a way to give the steering command to the drones by the neural network operation of the notebook due to the limitation of the operation performance of the drones. In this paper, to overcome these limitations, we have studied autonomous driving in indoor corridor environment using NVIDIA Jetson TX2 board.