• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• The Journal of Korea Robotics Society
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• v.5 no.2
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• pp.120-126
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• 2010

This paper presents to study the path tracking method of AGV(autonomous guided vehicle) which has a laser guidance system. An existing automatic guided vehicles(AGVs) which were able to drive on wired line only had a automatic guidance system. However, the automatic guidance systems that those used had the high cost of installation and maintenance, and the difficulty of system change according to variation of working environment. To solve such problems, we make the laser guidance system which is consisted of a laser navigation and gyro, encoder. That is robust against noise, and flexible according to working environment through sensor fusion. The laser guidance system can do a perfect autonomous driving. However, the commercialization of perfect autonomous driving system is difficult, because the perfect autonomous driving system must recognize the whole environment of working space. Hence, this paper studied the path tracking of AGV using laser guidance system without wired line. The path tracking method is consisted of virtual path generation method and driving control method. To experiment, we use the fork-type AGV which is made by ourselves, and do a path tracking experiments repeatedly on same experimental environment. In result, we verified that proposed system is efficient and stable for actual fork-type AGV.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• ETRI Journal
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• v.41 no.4
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• pp.506-514
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• 2019

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three-lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.216-223
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• 2023

As the technology and level of autonomous vehicles advance and they drive in more diverse road environments, an intuitive and efficient interaction system is needed to resolve and respond to the situations the vehicle faces. The development of driving technology from the perspective of autonomous driving has the ultimate goal of responding to situations involving humans or more. In particular, in complex road environments where mutual concessions must be made, the role of a system that can respond flexibly through efficient communication methods to understand each other's situation between vehicles or between pedestrians and vehicles is important. In order to resolve the status of the vehicle or the situation being faced, the provision and method of information must be intuitive and the efficient operation of an autonomous vehicle through interaction with intention is required. In this paper, we explain the vehicle structure and functions that can display information about the situation in which the autonomous vehicle driving in a living lab can drive stably and efficiently in a diverse and complex environment.

• 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.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.1001-1014
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• 2018

Purpose: Autonomous driving cars, which are often represent the new convergence product, have been researched since the early years of 1900 but their safety assurance policies are yet to be implemented for real world practices. The primary purpose of this paper is to propose a modular concept based on which a safety assurance system can be designed and implemented for operating autonomous driving cars. Methods: We combine a set of key attributes of CE mark (European Assurance standard), E-Mark (Automobile safety assurance system), and A-SPICE (Automobile software assurance standard) into a modular approach. Results: Autonomous vehicles are emphasizing software safety, but there is no integrated safety certification standard for products and software. As such, there is complexity in the product and software safety certification process during the development phase. Using the concept of module, we were able to come up with an integrated safety certification system of product and software for practical uses in the future. Conclusion: Through the modular concept, both international and domestic standards policy stakeholders are expected to consider a new structure that can help the autonomous driving industries expedite their commercialization for the technology advanced market in the era of Industry 4.0.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.311-317
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• 2024

Large-scale three-wheeled cleaning robots are utilized to clean large spaces such as warehouses and manufacturing plants where significant floor contamination occurs. Although there are autonomous cleaning robots, user-operated cleaning robots are often preferred because they are easy to repair and inexpensive. Therefore, workers have to spend extra time on cleaning, which reduces work efficiency. In this paper, we propose an autonomous driving system designed to automate the operation while maintaining the structure of existing cleaning robots. The contributions of this paper are as follows: 1) Hardware modules that control the driving and steering components. 2) A LiDAR-based autonomous driving system and path point generation system considering the mechanical characteristics of the cleaning robot. 3) The proposed system is implemented on an actual cleaning robot and driving tests are performed. As a result, when path planning is performed to cover the cleaning area, the average RMSE for each straight path is 0.0802 m, which is smaller than the minimum cleaning overlap of 0.3 m that occurs during the straight cleaning of the robot. This shows that the proposed system effectively covers the entire cleaning area.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.35-41
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• 2024

In order to verify autonomous driving scenarios and safety, a lot of driving and accident data is needed, so various organizations are conducting classification and analysis of traffic accident types. In this study, it was determined that accident recording devices such as EDR (Event Data Recorder) and DSSAD (Data Storage System for Automated Driving) would become an objective standard for analyzing the causes of autonomous vehicle accidents, and traffic accidents that occurred from 2015 to 2020 were analyzed. Using the database system of IGLAD (Initiative for the Global Harmonization of Accident Data), approximately 360 accident data of EDR-equipped vehicles were classified and their characteristics were analyzed by comparing them with accident types of ADAS (Advanced Driver Assistance System)-equipped vehicles. It will be used to develop autonomous vehicle accident investigation guidelines in the future.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.223-230
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• 2021

Currently, self-driving vehicles are on the verge of commercialization after testing. However, even though autonomous vehicles have not been fully commercialized, 81 accidents have occurred, and the driving method of vehicles to avoid accidents relies heavily on LiDAR. In order for the currently commercialized 3-level autonomous vehicle to develop into a 4-level autonomous vehicle, more information must be collected than previously collected information. Therefore, this paper proposes a Driving Situation Judgment System (DSJS) that accurately calculates the crisis situation the vehicle is in by useing the roughness of the road and the state of the passengers of surrounding vehicles including road information and weather information collected from existing autonomous vehicles. As a result of DSJS's PDM experiment, PDM was able to classify passengers 15.52% more accurately on average than the existing vehicle's passenger recognition system. This study can be a basic research to achieve the 4th level autonomous vehicle by collecting more various types than the data collected by the existing 3rd level autonomous vehicle.