• International Journal of Automotive Technology
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• v.7 no.2
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• pp.227-235
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• 2006

In this paper a robust image processing method with modified Otsu algorithm to recognize the road lane for a real-time controlled autonomous vehicle is presented. The main objective of a proposed method is to drive an autonomous vehicle safely irrespective of road image qualities. For the steering of real-time controlled autonomous vehicle, a detection area is predefined by lane segment, with previously obtained frame data, and the edges are detected on the basis of a lane width. For stable as well as psudo-robust autonomous driving with "good", "shady" or even "bad" road profiles, the variable threshold with modified Otsu algorithm in the image histogram, is utilized to obtain a binary image from each frame. Also Hough transform is utilized to extract the lane segment. Whether the image is "good", "shady" or "bad", always robust and reliable edges are obtained from the algorithms applied in this paper in a real-time basis. For verifying the adaptability of the proposed algorithm, a miniature vehicle with a camera is constructed and tested with various road conditions. Also, various highway road images are analyzed with proposed algorithm to prove its usefulness.

• 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.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.6
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• pp.803-812
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• 2017

This paper proposes a local path planning method for stable autonomous driving in rough terrain. There are various path planning techniques such as candidate paths, star algorithm, and Rapidly-exploring Random Tree algorithms. However, such existing path planning has limitations to reflecting the stability of unmanned ground vehicles. This paper suggest a path planning algorithm that considering the stability of unmanned ground vehicles. The algorithm is based on the genetic algorithm and assumes to have probability based obstacle map and elevation map. The simulation result show that the proposed algorithm can be used for real-time local path planning in rough terrain.

• Tunnel and Underground Space
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• v.30 no.1
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• pp.76-86
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• 2020

In this study, a small autonomous driving robot was developed for underground mines using the Light Detection and Ranging (LiDAR) sensor. The developed robot measures the distances to the left and right wall surfaces using the LiDAR sensor, and automatically controls its steering to drive along the centerline of mine tunnel. A field experiment was conducted in an underground amethyst mine to test the driving performance of developed robot. During five repeated driving tests, the robot showed stable driving performance overall. There were no collision accidents with the wall of mine tunnel.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.

• 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 Propulsion Engineers Conference
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• 2008.03a
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• pp.419-426
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• 2008

As pulse detonation engine(PDE) does not need compression mechanisms such as compressors because self-sustained detonation waves are able to compress propellant gases by their incident shock waves, the PDE can have a simple straight-tube structure. In this study, we propose an autonomous driving valve system of the PDE, which fill premixed gases into the PDE tubes at high frequency with high mass flow rate. The proposed valve is composed of only three parts: a piston, a cylinder, and a spring. This valve system can produce intermittent flow at high mass flow rate, and also can keep stable reciprocal motion by using the propellant-gas enthalpy. When the cylinder content product is assumed to be constant, experimental results of the mass flow rate were approximately equal to the calculation model. We confirmed the autonomous driving valve performance by experiments, and concluded that this extremely simple valve with no electrical power and controller can be used as the PDE propellant supply system.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.3-12
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• 2020

The purpose of this work is to suggest the optimal color temperature, which induces a sense of comfort for autonomous vehicle users through the analysis of biosignal using electroencephalography (EEG) and photoplethysmography (PPG). To achieve this purpose, we applied lighting with a color temperature of 3000 K, 4000 K, 5000 K, and 6000 K to the autonomous driving environment. We experimented in a laboratory equipped with a graphic driving simulator. The experimental procedure is as follows: 1) stabilization (5 min). 2) Uchida-Kraepelin test (3 min). 3) Automatic driving + lighting (3 min). This procedure was repeated four times under different color temperatures. We performed frequency analysis on a collected time-series data and calculated the power value for each frequency band through power spectrum analysis. In the case of EEG, we analyzed α- and β-waves, which are indicators of stability and arousal, respectively. In the case of PPG, we analyzed the sympathetic nervous system activity. To reduce deviations between the subjects, we normalized the data before analysis. The result of the first analysis revealed that α-wave increased only at 5000 K, while the β-wave increased at almost all color temperatures. In addition, in the case of PPG, sympathetic nervous system activity (SNSA) increased under driving conditions. The result of the second analysis revealed that the difference between β-wave and SNSA is insignificant. In conclusion, the increase in α-waves showed that EEG was most stable at 5000 K. The results of this study can be applied to the upcoming autonomous driving era to induce high driver satisfaction. Furthermore, this approach could eventually lead to the acceptance of autonomous vehicles by suggesting a positive effect of autonomous driving.

• Journal of the Korean Society of Industry Convergence
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• v.17 no.4
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• pp.217-226
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• 2014

Since the world has changed to a society of 21st century high-tech industries, the modern people have become reluctant to work in a difficult and dirty environment. Therefore, unmanned technologies through robots are being demanded. Now days, effects such as voice, control, obstacle avoidance are being suggested, and especially, voice recognition technique that enables convenient interaction between human and machines is very important. In this study, in order to conduct study on the stable motion control of the robot system that has mobile-manipulator structure and is voice command-based, kinetic interpretation and dynamic modeling of two-armed manipulator and three-wheel mobile robot were conducted. In addition, autonomous driving of three-wheel mobile robot and motion control system of two-armed manipulator were designed, and combined robot control through voice command was conducted. For the performance experiment method, driving control and simulation mock experiment of manipulator that has two-armed structure was conducted, and for experiment of combined robot motion control which is voice command-based, through driving control, motion control of two-armed manipulator, and combined control based on voice command, experiment on stable motion control of voice command-based robot system that has mobile-manipulator structure was verified.