• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.343-348
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• 2007

Development of autonomous vehicle system that use magnetic position meter research of intelligence transportation system is progressed worldwide active by fast increase of vehicles. Among them, research about autonomous of vehicles occupies field. And autonomous of vehicles is element that path recognition is basic. Existent magnetic base autonomous system analyzes three-dimensional data of magnet marker to 3 axises magnetic sensor and recognized route. But because using Magnetic Wire and Magnetic Position Meter in treatise that see, measure side lateral error and propose system that driving. And system that compare with system of autonomous vehicles and propose wishes to verify by hardware of that specification and simple algorithm through an experiment that autonomous is available.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.485-496
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• 2021

This study explores the direct relationship between challenge and hindrance demands affecting autonomous extrinsic motivation and sales performance. In addition, we examine the mediating role of autonomous extrinsic motivation in the relationship between challenge demands, hindrance demands, and sales performance. This study explores the direct relationship between challenge and hindrance demands affecting autonomous extrinsic motivation and sales performance. In addition, we examine the mediating role of autonomous extrinsic motivation in the relationship between challenge demands, hindrance demands, and sales performance. This study proceeded in two phases comprising preliminary and prime research. First, preliminary quantitative research was conducted through face-to-face interviews with 125 observations to analyze the reliability of the scale and exploratory factor analysis to evaluate the measurements. The data collected from 431 real estate market employees shows that both challenge and hindrance demands positively and negatively affect sales performance through autonomous extrinsic motivation. Furthermore, challenge demands and hindrance demands affect positive and negative sales performance through autonomous extrinsic motivation, respectively. This study suggests that business organizations should design job demands to ensure that challenging work is suitable for employees' job positions. Thus, they will contribute to motivation and help employees achieve job performance.

• ETRI Journal
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• v.43 no.4
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• pp.603-616
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• 2021

Over the last few years, autonomous vehicles have progressed very rapidly. The odometry technique that estimates displacement from consecutive sensor inputs is an essential technique for autonomous driving. In this article, we propose a fast, robust, and accurate odometry technique. The proposed technique is light detection and ranging (LiDAR)-based direct odometry, which uses a spherical range image (SRI) that projects a three-dimensional point cloud onto a two-dimensional spherical image plane. Direct odometry is developed in a vision-based method, and a fast execution speed can be expected. However, applying LiDAR data is difficult because of the sparsity. To solve this problem, we propose an SRI generation method and mathematical analysis, two key point sampling methods using SRI to increase precision and robustness, and a fast optimization method. The proposed technique was tested with the KITTI dataset and real environments. Evaluation results yielded a translation error of 0.69%, a rotation error of 0.0031°/m in the KITTI training dataset, and an execution time of 17 ms. The results demonstrated high precision comparable with state-of-the-art and remarkably higher speed than conventional techniques.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.49-58
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• 2022

With the recent start of the 4th Industrial Revolution, markets related to autonomous driving are rapidly developing. In order to understand the rapidly developed technology trend of autonomous driving technology, we would like to investigate the characteristics and differences of level 0 to level 5 of autonomous driving. The overall configuration, recognition technology, and auxiliary technologies of autonomous vehicles are analyzed, and through this, the structure and algorithm of autonomous driving technology are identified. In addition, by manufacturing a simulated autonomous RC car using an ultrasonic sensor and a camera, the necessity of recognition technology and auxiliary technology is identified.

• Journal of Drive and Control
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• v.19 no.4
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• ETRI Journal
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• v.45 no.1
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• pp.75-92
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• 2023

Level 3 autonomous vehicles require conditional autonomous driving in which autonomous and manual driving are alternately performed; whether the driver can resume manual driving within a limited time should be examined. This study investigates whether the demographics and subjective driving tendencies of drivers affect the take-over performance. We measured and analyzed the reengagement and stabilization time after a take-over request from the autonomous driving system to manual driving using a vehicle simulator that supports the driver's take-over mechanism. We discovered that the driver's reengagement and stabilization time correlated with the speeding and wild driving tendency as well as driving workload questionnaires. To verify the efficiency of subjective questionnaire information, we tested whether the driver with slow or fast reengagement and stabilization time can be detected based on machine learning techniques and obtained results. We expect to apply these results to training programs for autonomous vehicles' users and personalized human-vehicle interfaces for future autonomous vehicles.

• Journal of Korea Technology Innovation Society
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• v.21 no.2
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• pp.715-737
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• 2018

The introduction of autonomous vehicles will bring about not only changes in existing automotive ecosystem but also widespread changes in our lives, society, economy, and culture. Social acceptance is one of important influencing factors for the commercialization of autonomous vehicles. The purpose of this study analyzes influencing factors in the acceptance of autonomous vehicles in terms of consumers. Autonomous vehicles in this study were defined as PAV (Partial Autonomous Vehicles) and FAV (Full Autonomous Vehicles) by drivers' intervention or not. The survey was conducted over 20 years old including not only drivers but also non-drivers. The results showed that the factors affecting acceptance of PAV and FAV were different. Factors directly related to drivers influenced PAV acceptance while external environmental factors influenced FAV acceptance. This study is proved that is should need different strategies between PAV and FAV for increasing those acceptance

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.70-86
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• 2022

An autonomous demand-responsive bus with mobility-on-demand service is an innovative transport compensating for the disadvantages of an autonomous bus and a demand-responsive bus with mobility-on-demand service. However, less attention has been paid to the quantitative impact assessment of the autonomous demand-responsive bus due to the technological complexity of the autonomous demand-responsive bus. This study simulates autonomous demand-responsive bus trips by reinforcement learning on a microscopic traffic simulation to quantify the impact of the autonomous demand-responsive bus. The Chungju campus of the Korea National University of Transportation is selected as a testbed. Simulation results show that the introduction of the autonomous demand-responsive bus can reduce the wait time of passengers, average control delay, and increase the traffic speed compared to the results with fixed route bus service. This study contributes to the quantitative evaluation of the autonomous demand-responsive bus.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.258-272
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• 2022

The technology implementation method was diversified into an 'autonomous cooperative driving' method to overcome the limitations of a stand-alone autonomous vehicle with vehicle sensor-based autonomous driving. The autonomous cooperative driving method involves exchanging information between roadside infrastructure and autonomous vehicles. In this process, the concept of dynamic information (LDM), a target of cooperation, was established. But, evaluation methods and standards for dynamic information have not been established. Therefore, this study, a dynamic information evaluation method based on information on pedestrians within the moving objects. In addition, autonomous cooperative driving was demonstrated, and dynamic information was also verified through the evaluation method. The significance of this study is that it established the dynamic information evaluation methodology for autonomous cooperative driving for the first time. Based on this, this study is expected to contribute to the application of safe autonomous cooperative driving technology to the field.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.12
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• pp.587-594
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• 2016

Autonomous A.I. vehicles are seemingly soon ready for our life. One of the critical problems with autonomous vehicles is how one could assign responsibility for accidents to them. We can envisage that autonomous vehicles may confront an ethical dilemma. Then a question arises of how we are able to assign legal responsibility to autonomous vehicles. In this paper, I first introduce what the ethical dilemma of autonomous vehicles is about. Second, I show how we could be able to assign legal responsibility for autonomous vehicles. Legal probability is the received criteria for causal responsibility most of the legal theorists consider. But it remains vague. I articulate the concept of legal probability in terms of the probabilitstic theory of individual level causality while considering how one can assign causal responsibility for autonomous vehicles. My theory of causal responsibility may help one to assign legal responsibility not just for autonomous vehicles but also for people.