• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.1-11
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• 2020

Recently, autonomous things, which are pieces of equipment or devices that grasp the context of circumstances on their own and perform actions appropriate for the situation in the surrounding environment, are attracting much research interest. This is because autonomous things are expected to be able to interact with humans more naturally, supersede humans in many tasks, and further solve problems by themselves by collaborating with each other without human intervention. This prospect leans heavily on AI as deep learning has delivered astonishing breakthroughs recently and broadened its range of applications. This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1126-1130
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• 2004

This paper describes the design, development, and performance testing of an autonomous ground vehicle that was developed to participate in the DARPA Grand Challenge that was held in March 2004. The authors of this paper are members of Team CIMAR which was one of twenty five teams selected by DARPA to participate in a competition to develop an autonomous vehicle that can navigate from near Los Angeles to near Las Vegas at speeds averaging twenty miles per hour. Most of the event was held on open terrain and trails in a rocky desert environment. This paper describes the overall system design and the performance of the system at the event.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.4
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• pp.121-128
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• 2000

In this paper, the control algorithm fur an autonomous vehicle is studied and applied to an actual 2 wheel-driven vehicle system. In order to control a nonholonomic system, the kinematic model for an autonomous vehicle is constructed by relative velocity relationship about the virtual point at distance from the vehicle's frame. And the optimal controller that based on the kinematic model is operated on purpose to track a reference vehicle's path. The actual system is designed with named 'HYAVI' and the system controller is applied. Because all the results of simulation don't satisfy the driving conditions of HYAVI, a reformed control algorithm that satisfies an actual autonomous vehicle is applied at HYAVI. At the results of actual experiments, the path tracking works very well by the reformed control algorithm. An autonomous vehicle that applied this control algorithm can be easily used for a path generation algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.251-257
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• 2012

Unmanned ground vehicles have important military, reconnaissance, and materials handling application. Many of these applications require the UGVs to move at high speeds through uneven, natural terrain with various compositions and physical parameters. This paper presents a framework for high speed autonomous navigation based on the integrated real time traversability. Specifically, the proposed system performs real-time dynamic simulation and calculate maximum traversing velocity guaranteeing safe motion over rough terrain. The architecture of autonomous navigation is firstly presented for high-speed autonomous navigation. Then, the integrated real time traversability, which is composed of initial velocity profiling step, dynamic analysis step, road classification step and stable velocity profiling step, is introduced. Experimental results are presented that demonstrate the method for a $6{\times}6$ autonomous vehicle moving on flat terrain with bump.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.285-292
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• 2021

Autonomous vehicles are divided into an upper controller that calculates control value through cognitive judgment and a lower controller that appropriately transmits its control value to an actuator. Here, the longitudinal control in a lower controller has a problem as the road slopes due to the property of the Acceleration sensor to output the acceleration as the slope of the device. Therefore, in this paper, a sigmoid function is proposed to determine the slope to compensate for this problem. Through the experiment, Checked performance by comparing the existing table model with the proposed model.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.22-31
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• 2021

In this thesis, we examine the development status of autonomous mobility services using various artificial intelligence algorithms and propose a solution by combining edge and cloud computing to overcome technical difficulties. A fully autonomous vehicle with enhanced safety and ethics can be implemented using the proposed solution. In addition, for the future of 2035, we present a new concept that enables two- and three-dimensional movement via cooperation between ecofriendly, low-noise, and modular fully autonomous vehicles. The zero-error autonomous driving system will safely and conveniently transport people, goods, and services without time and space constraints and contribute to the autonomous mobility services that are free from movement in connection with various mobility.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.3
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• pp.180-187
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• 2014

This paper introduces reliable multicast MAC protocol for cooperative unmanned vehicles. cooperative unmanned vehicles communicate with infrastructure and other unmanned vehicles in order to increase driving safety. They exchange information related to driving and thus it requires real-time and reliable multicast. However, the international vehicular communication standard, IEEE 802.11p WAVE, does not provide a reliable multicast scheme on the MAC layer. To address the problems of reliability, we propose a reliable multicast protocol called WiVCL, which avoids contention and collision. Our evaluation shows that the WiVCL achieves a high degree of reliability and real-time features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.205-206
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• 2018

Recently, interest in unmanned vehicles, autonomous vehicles and connected cars has increased, and autonomous driving capability is also increasing. Depending on the autonomous driving ability, the maximum number of steps is divided into 6 steps. The higher the step, the less the involvement of the person in the running, and the person does not need to be involved at the highest stage. Today's autonomous vehicles have been developed in stages 4 to 5, but solutions are not clearly defined in case of an accident, so only the test run is possible. Such an accident occurring during traveling is almost inevitable, and it is judged who has made a mistake in case of an accident, which increases the punishment for the accident. Although a black box is used to clarify such a part, it is easy to delete a photographed image, and it is difficult to solve an accident such as a hit-and-run. In this paper, we design a driving record system using black chain to solve such an accident.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.10
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• pp.931-940
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• 2017

This paper describes a predictive fault diagnosis algorithm for autonomous vehicles based on a kinematic model that uses a sliding mode observer. To ensure the safety of autonomous vehicles, reliable information about the environment and vehicle dynamic states is required. A predictive algorithm that can interactively diagnose longitudinal environment and vehicle acceleration information is proposed in this paper to evaluate the reliability of sensors. To design the diagnosis algorithm, a longitudinal kinematic model is used based on a sliding mode observer. The reliability of the fault diagnosis algorithm can be ensured because the sliding mode observer utilized can reconstruct the relative acceleration despite faulty signals in the longitudinal environment information. Actual data based performance evaluations are conducted with various fault conditions for a reasonable performance evaluation of the predictive fault diagnosis algorithm presented in this paper. The evaluation results show that the proposed diagnosis algorithm can reasonably diagnose the faults in the longitudinal environment and acceleration information for all fault conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.4
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• pp.28-46
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• 2021

85-90% of the causes of traffic accidents are human factors, and autonomous vehicles with little free running distance can be an alternative to prevent traffic accidents caused by human factors. However, securing safety of autonomous vehicles should be preceded in order to reduce traffic accident damage through the introduction of autonomous vehicles. Therefore, it is necessary to verify whether the vehicle can respond appropriately to changes in the road and traffic environment through repeated and reproduced test runs in an environment similar to the actual road. In this study, K-City's development strategies for upgrading, differentiating, and systematic development were established by comparing and analyzing the current status of domestic and foreign testbeds and business environment analysis. Furthermore, we derive challenge tasks to achieve each strategy.