• 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.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• 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.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1505-1515
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• 2016

The age of autonomous vehicles has come according to development of high performance sensing and artificial intelligence technologies. And importance of the vehicle's surrounding environment sensing and observation is increasing accordingly because of its stability and control efficiency. In this paper we propose an integrated platform for efficient networking, analysis and monitoring of multiple sensing data on the vehicle that are equiped with various automotive sensors such as GPS, weather radar, automotive radar, temperature and humidity sensors. From simulation results, we could see that the proposed platform could perform realtime analysis and monitoring of various sensing data that were observed from the vehicle sensors. And we expect that our system can support drivers or autonomous vehicles to recognize optimally various sudden or danger driving environments on the road.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.205-213
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• 2017

This paper is a study on the method to derive the functional level required for autonomous unmanned ground vehicle, one of the defense robots. Conventional weapon systems are not significantly affected by the operating environment, while defense robots exhibit different performance depending on the operating environment, even if they are on the same platform. If the performance of defense robot is different depending on operational environment, results of mission performance will be vary significantly. Therefore, it is necessary to clarify the level of function required by the military in order to research and develop most optimal defense robots. In this thesis, we propose a method to derive the required function level of unmanned ground vehicles, focusing on autonomous driving, one of the most vital functions of defense robots. Our results showed that the autonomous driving function depending intervention levels and evaluated functional sensitivity for autonomous driving of the unmanned vehicle using climate and topography as variables.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• Journal of Platform Technology
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• v.10 no.3
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• pp.60-68
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• 2022

As AI-technology develops, interest in the safety of autonomous driving is increasing. Recently, autonomous vehicles have been increasing, but efforts to solve side effects have been sluggish. In particular, night autonomous vehicles have more problems. This is because the probability of accidents is higher in the night driving environment than in the day environment. There are more factors to consider for self-driving at night. Among these factors, reflection of light or reflected light of lighting may be a fundamental cause of night accidents. Therefore, this study proposes method to reduce accidents and improve safety by reducing reflected light generated by the headlights of opposite vehicles or various surrounding light that appear as an important problem in night autonomous vehicles. Therefore, first, in an image obtained by a sensor of a night autonomous vehicle, illumination reflected light is extracted using reflected light characteristic information, and a color of each pixel using a reflection coefficient is found to reduce a special area generated by geometric characteristics. In addition, we find a new area using only the brightness component of the specular area, define it as Illuminated Reflection Light (IRL), and finally present a method to reduce it. Although the illumination reflection light could not be completely reduce, generally satisfactory results could be obtained. Therefore, it is believed that the proposed study can reduce casualties by solving the problems of night autonomous driving and improving safety.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.123-128
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• 2022

This paper proposes a mobile robot platform for education that can experiment with various autonomous driving algorithms such as obstacle avoidance and path planning. The platform consists of a robot module and a remote controller module, both of which are based on the Arduino Nano 33 IoT embedded board. The robot module is designed as a differential drive type using two encoder motors, and the speed of the motor is controlled using PID control. In the case of the remote controller module, a command to control the robot platform is received with a 2-axis joystick input, and an elliptical grid mapping technique is used to convert the joystick input into a linear and angular velocity command of the robot. WiFi and Zigbee are used for communication between the robot module and the remote controller module. The proposed robot platform was tested by measuring and comparing the linear velocity and angular velocity of the actual robot according to the linear velocity and angular velocity commands of the robot generated by the input of the joystick.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.177-193
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• 2022

In the near future, it is expected that the use of autonomous mobility-on-demand services will increase. Considering its complicated service components, including vehicle convenience, driving and matching speed, and platform convenience, the priorities of them will need to be determined for a successful establishment. In this context, this study examined the importance rank of each service component through an online survey of potential users of autonomous mobility-on-demand services. As a result of the AHP (Analytic Hierarchy Process) with respect to the upper-level components, driving and matching speed component is selected as most important, followed by platform convenience and vehicle convenience. Mean rank analysis with respect to lower-level components showed that the in-vehicle congestion level of vehicle convenience, waiting time of driving and matching speed, and pre-booking availability of platform convenience each ranked first. Additional analysis regarding each group was conducted to establish a group-specific strategy. As a result, it would be better to focus on a vehicle than a mobile platform when designing services for the region with a high proportion of the older. Moreover, it is recommended to speed up the driving and matching speeds more than the current public transport, alleviate in-vehicle congestion, and enable the users to book the schedule in advance.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.198-208
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• 2022

In this paper, an autonomous driving system is implemented for the Segye AI Robot Race Competition that multiple vehicles drive simultaneously. By utilizing the ERP42-racing platform, RTK-GPS, and LiDAR sensors provided in the competition, we propose an autonomous driving system that can drive safely and quickly in a road environment with multiple vehicles. This system consists of a recognition, judgement, and control parts. In the recognition stage, vehicle localization and obstacle detection through waypoint-based LiDAR ROI were performed. In the judgement stage, target velocity setting and obstacle avoidance judgement are determined in consideration of the straight/curved section and the distance between the vehicle and the neighboring vehicle. In the control stage, adaptive cruise longitudinal velocity control based on safe distance and lateral velocity control based on pure-pursuit are performed. To overcome the limited experimental environment, simulation and partial actual experiments were conducted together to develop and verify the proposed algorithms. After that, we participated in the Segye AI Robot Race Competition and performed autonomous driving racing with verified algorithms.