• The Journal of Bigdata
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• v.7 no.1
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• pp.37-47
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• 2022

Autonomous delivery operation data is the key to driving a paradigm shift for last-mile delivery in the Corona era. To bridge the technological gap between domestic autonomous delivery robots and overseas technology-leading countries, large-scale data collection and verification that can be used for artificial intelligence training is required as the top priority. Therefore, overseas technology-leading countries are contributing to verification and technological development by opening AI training data in public data that anyone can use. In this paper, 326 objects were collected to trainn autonomous delivery robots, and artificial intelligence models such as Mask r-CNN and Yolo v3 were trained and verified. In addition, the two models were compared based on comparison and the elements required for future autonomous delivery robot research were considered.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.115-120
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• 2015

Recently, in automotive technology area, intelligent safety systems have been actively accomplished for drivers, passengers, and pedestrians. Also, many researches are focused on development of autonomous vehicles. This paper propose the application of LiDAR sensors, which takes major role in perceiving environment, terrain classification, obstacle data clustering method, and local map building for autonomous driving. Finally, based on these results, planning for lane change path that vehicle tracking possible were created and the reliability of path generation were experimented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.355-360
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• 2017

Recently, with the development of autonomous vehicle technology, the importance of precise road maps is increasing. A precise road map is a digital map with lane information, regulations, safety information, and various road facilities. Conventional precise road maps have been tested and developed based on the mobile mapping system (MMS). But they have not been activated due to high introduction costs. However, in the case of unmanned aerial vehicles (UAVs), the application field is continuously increasing. This study tries to extract information through classification of high-resolution UAV images for autonomous driving. Autonomous vehicle test roads were selected as study sites, and high-resolution orthoimages were produced using UAVs. In addition, the utilization of high-resolution orthoimages has been proposed by effectively extracting data for precise road map construction, such as road lines, guards, and machines through image classification. If additional experimentation and verification are performed, the field of UAV image use will be expanded, providing the data to automobile manufacturers and related public and private organizations, and venture companies will contribute to the development of domestic autonomous vehicle technology.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.97-105
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• 2023

Recently, with the development of artificial intelligence technology, many technologies for user convenience are being developed. Among them, interest in autonomous vehicles is increasing day by day. Currently, many automobile companies are aiming to commercialize autonomous vehicles. In order to lay the foundation for the government's new and reasonable policy establishment to support commercialization, we tried to analyze changes and perceptions of public opinion through news article data. Therefore, in this paper, 35,891 news article data mentioning terms similar to 'autonomous vehicles' over the past three years were collected and network analyzed. As a result of the analysis, major keywords such as 'autonomous driving', 'AI', 'future', 'Hyundai Motor', 'autonomous driving vehicle', 'automobile', 'industrial', and 'electric vehicle' were derived. In addition, the autonomous vehicle industry is developing into a faster and more diverse platform and service industry by converging with various industries such as semiconductor companies and big tech companies as well as automobile companies and is paying attention to the convergence of industries. To continuously confirm changes and perceptions in public opinion, it is necessary to analyze perceptions through continuous analysis of SNS data or technology trends.

• ETRI Journal
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• v.37 no.5
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• pp.1032-1043
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• 2015

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co-Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.1
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• pp.30-36
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• 2015

This paper presents the theoretical development of a complete navigation problem of a nonholonomic mobile robot by using ultrasonic sensors. To solve this problem, a new method to computer a fuzzy perception of the environment is presented, dealing with the uncertainties and imprecision from the sensory system and taking into account nonholonomic constranits of the robot. Fuzzy perception, fuzzy controller are applied, both in the design of each reactive behavior and solving the problem of behavior combination, to implement a fuzzy behavior-based control architecture. The performance of the proposed obstacle avoidance robot controller in order to determine the exact dynamic system modeling system that uncertainty is difficult for nomadic controlled robot direction angle by ultrasonic sensors throughout controlled performance tests. In additionally, this study is an in different ways than the self-driving simulator in the development of ultrasonci sensors and unmanned remote control techniques used by the self-driving robot controlled driving through an unmanned remote controlled unmanned realize the performance of factory antomation.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.153-161
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• 2015

In the present study, as part of the technology development (Quality of Life Technology, QoLT) to improve the socio-economic status of people with disabilities as an extension of these studies, we propose the development of the smart wheelchair system and navigation technology for stable and safe driving in various environments. For the disabled and the elderly make driving easy and convenient with manual/autonomous driving condition, we firstly develop the user-oriented smart wheelchair system with optimized sensors for environment recognition, and then we propose a navigation framework of a hierarchical structure to ensure real-time response, as well as driving stability when traveling to various environmental changes, and to enable a more efficient operation. From the result of several independent experiments, we ensure efficiency and safety of smart wheelchair and its navigation system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4518-4540
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• 2019

With the development of IT convergence technology, autonomous driving has gradually developed; however, the vehicle is still operated by the driver, who should always be in good health - but sometimes, this is not the case. It is especially dangerous to drive when drowsy, and unable to fully concentrate on driving, such as when taking certain medicines, or through fatigue. Drowsy driving is at least eight times more dangerous than normal driving, and as dangerous as drunk driving. Previous research has looked at technology to detect drowsiness, in order to wake up drivers when necessary, or to safely stop the vehicle. Furthermore, many studies have been conducted to find out when drowsiness occurs. However, it is more desirable for the driver to take sufficient rest during a break, in order to be able to continue to focus and drive. In other words, it is important to maintain a normal state before drowsiness. In this study, we introduce a sound source to increase driver concentration and prevent drowsiness, another that can improve the quality of sleep, and a system that produces these sound sources. The proposed system has a noise reduction effect of about 15 dB. We have confirmed that the proposed sound induces an EEG of the desired form.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.163-181
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• 2023

Improving the detection performance of facilities corresponding to the sensors of autonomous vehicles helps driving safety. In the road and transportation field, research is being conducted to improve the detection performance of sensors by road infrastructure or facilities. As part of this on the development of autonomous driving support infrastructure, the shape of traffic cones and drums to ensure sufficient LiDAR detection performance even rainy conditions and maintain the line-of-sight guidance function in construction zones improvement effect. The principle was to increase reflection performance and ensure no significant difference in shape from existing facilities. Traffic cones were manufactured in square pyramid shapes instead of cones, and drums were manufactured in hexagonal and octagonal pillar shapes instead of cylinders. LiDAR detection data for the facility was confirmed on a clear day and with 20 mm/h and 40 mm/h rainfall. The detection performance of the square pyramid-shaped traffic cone and octagonal column-shaped drum was to the existing facility. On the other hand, deviations occurred due to repeated measurements, and significance could not be confirmed through statistical analysis. By reflecting these results, future studies will seek a form in which data can be obtained uniformly despite the diversity of measurement environments.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1266-1272
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• 2022

With the recent development of computer vision technology, image processing-based mechanical devices are being developed to realize autonomous driving. The camera-taken images of image processing-based machines are invisible due to scattering and absorption of light in foggy conditions. This lowers the object recognition rate and causes malfunction. The safety of the technology is very important because the malfunction of autonomous driving leads to human casualties. In order to increase the stability of the technology, it is necessary to apply an efficient haze removal algorithm to the camera. In the conventional haze removal method, since the haze removal operation is performed regardless of the haze concentration of the input image, excessive haze is removed and the quality of the resulting image is deteriorated. In this paper, we propose an automatic haze removal method that removes haze according to the haze density of the input image by applying Ngo's Haziness Degree Evaluator (HDE) to Kim's haze removal algorithm using Hazy Particle Map. The proposed haze removal method removes the haze according to the haze concentration of the input image, thereby preventing the quality degradation of the input image that does not require haze removal and solving the problem of excessive haze removal. The superiority of the proposed haze removal method is verified through qualitative and quantitative evaluation.