• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.29-39
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• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.321-322
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• 2022

본 논문에서는 ROS(Robot Operating System) 기반으로 한 로봇(Robot)에 레이저 거리 센서(LiDAR)를 설치하여 SLAM(Simultaneous Localization And Mapping) 기법으로 지도 정보를 습득 및 저장하고, 이를 기반으로 맵핑된 환경과 환경 내 장애물을 회피하여 안전하고 정확하게 이동할 수 있도록 하였다. ROS는 하드웨어 추상화, 장치 드라이버, 시각화 도구, 패키지 관리 등 로봇 애플리케이션을 개발할 수 있도록 라이브러리와 도구를 제공한다. 또한 로봇 동작에 사용되는 프로세스 간 TCP-IP 통신을 통해 연동할 수 있도록 한다[1]. Ubuntu 18.04 버전의 OS에 ROS Melodic 버전을 설치해서 앱으로 선택된 목적지로 이동하는 물류 이송 로봇을 구현하였다.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.327-335
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• 2021

In this paper, we propose an embedded robot system based on "ROS2 on Yocto" that can support various robots. We developed a lightweight OS based on the Yocto Project as a next-generation robot platform targeting cloud robotics. Yocto Project was adopted for portability and scalability in both software and hardware, and ROS2 was adopted and optimized considering a low specification embedded hardware system. We developed SLAM, navigation, path planning, and motion for the proposed robot system validation. For verification of software packages, we applied it to home cleaning robot and indoor delivery robot that were already commercialized by LG Electronics and verified they can do autonomous driving, obstacle recognition, and avoidance driving. Memory usage and network I/O have been improved by applying the binary launch method based on shell and mmap application as opposed to the conventional Python method. Finally, we verified the possibility of mass production and commercialization of the proposed system through performance evaluation from CPU and memory perspective.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• ETRI Journal
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• v.36 no.6
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• pp.968-978
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• 2014

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a $300m{\times}500m$ space.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.219-230
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• 2023

With the development of autonomous driving technology, as the use of software in vehicles increases, the complexity of the system increases and the difficulty of development increases. Developments that meet ISO 26262 must be carried out to reduce the malfunctions that may occur in vehicles where the system is becoming more complex. ISO 26262 for the functional safety of the vehicle industry proposes to consider functional safety from the design stage to all stages of development. Specifically at the software level, the requirements to be complied with during development and the requirements to be complied with during verification are defined. However, it is not clearly expressed about specific design methods or development methods, and it is necessary to supplement development guidelines. The importance of analysis and verification of requirements is increasing due to the development of technology and the increase of system complexity. The vehicle industry must carry out developments that meet functional safety requirements while carrying out various development activities. We propose a process that reflects the perspective of system engineering to meet the smooth application and developmentrequirements of ISO 26262. In addition, the safety analysis/verification FMEA processforthe safety of the proposed ISO 26262 function was conducted based on the FCAS (Forward Collision Avoidance Assist System) function applied to autonomous vehicles and the results were confirmed. In addition, the safety analysis/verification FMEA process for the safety of the proposed ISO 26262 function was conducted based on the FCAS (Forward Collision Avoidance Assist System) function applied to the advanced driver assistance system and the results were confirmed.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.413-420
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• 2005

In this paper, we apply software agents, which use fuzzy logic and make autonomous decisions according to state transitions, to car driving environment. We carry out an experiment on artificial intelligent car driving in terms of real-time reactive agents. Inference techniques for constructing real-time reactive agents consider the settings with max-product inference, n-fuzzy rules, and n-associatives ($A_l,\;B_l),\;{\ldots}(A_n,\;B_n$). Then we perform defuzzification processes, extract a central value, and work out inference processes.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.90-98
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• 2020

Modern vehicles are evolving from manual to automatic driving. As the ratio of electrical equipment and software increases inside the vehicle, vehicles that support autonomous driving are becoming another open computer system that can communicate with the outside. The safety of the vehicle means the safety of both the passenger and the non-passenger. It is not clear whether the safety problem of ultimate autonomous vehicles can be solved by the current solution of computer systems related to fault tolerance and security. Autonomous vehicles should not be dangerous to people after they are released to the market, so it is necessary to proactively diagnose all the risks that can be predicted with current technology. This paper examines the current developments of autonomous vehicles and analyzes their dangers that threaten driving safety, as well as their countermeasures.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.787-793
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• 2012

This paper introduces the autonomous vehicle Pharos, which participated in the 2010 Autonomous Vehicle Competition organized by Hyundai-Kia motors. PHAROS was developed for high-speed on/off-road unmanned driving avoiding diverse patterns of obstacles. For the high speed traveling up to 60 km/h, long range terrain perception, real-time path planning and high speed vehicle motion control algorithms are developed. This paper describes the major hardware and software components of our vehicle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3955-3971
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• 2020

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.