• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.1
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• pp.99-108
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• 2014

Recently, the interest in autonomous vehicle which is an aggregate of the automotive control technology is increasing. In particular, researches on the self-localization technology that is directly connected with stable driving of autonomous vehicle have been performed. Various dead reckoning technologies which are solutions for resolving the limitation of GPS have been introduced. However, the conventional dead reckoning technologies have two disadvantages to apply on the autonomous vehicle. First one is that the expensive sensors must be equipped additionally. The other one is that the accuracy of self-localization decreases caused by wheel slip when the vehicle's motion changed rapidly. Based on this background, in this paper, the wheel speed sensor which is equipped on most of vehicles was used and the dead reckoning algorithm considering wheel slip ratio was developed for autonomous vehicle. Finally, in order to evaluate the performance of developed algorithm, the various simulation were conducted and the results were compared with the conventional algorithm.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.146-154
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• 2011

This article describes the Autonomous Vehicle #1 (A1), which won the 2010 Autonomous Vehicle Competition (AVC) organized by Hyundai Kia automotive group. The A1 was developed for high speed and stable driving without human intervention. The autonomous system of A1 was developed based on in-vehicle networks, electronic control units, and embedded software. Novel environment perception and navigation algorithm were evaluated and validated through the AVC. In this paper, we presented the system and software architecture of A1.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.151-200
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• 2018

At the Davos Forum in 2016, the Fourth Industrial Revolution, a reference to cloud Schwab, is dramatically changing our lives, and at its height, self-driving cars are emerging as the talk of the day. But there are still many hurdles to overcome before the nation can successfully introduce and establish self-driving cars. In particular, it is necessary to change the paradigm of the legal system centered on human beings to one that includes artificial intelligence. The stable operation of the self-driving car era requires drastic changes to the people-centric legislation system. That is, it is necessary to collect information on the total number of drivers of self-driving cars (what is available), general vehicles on general roads, civil and criminal liability issues in the event of traffic accidents, and collection of insurance problems concerning autonomous driving vehicles. In this study, a separate bill was proposed to address the various legal issues arising from the operation of self-driving cars from a legislative perspective by considering the domestic laws related to road transport, the current state of legislation on foreign soil and legal issues related to self-driving cars.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.7-14
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• 2018

The purpose of this study is to analyze the effect of commuter's departure time on transportation system in future traffic system operated autonomous vehicle using agent based model. Various scenarios have been set up, such as when all passenger choose a similar departure time, or if the passenger chooses a different departure time. Also, this study tried to analyze the effect of road capacity. It was found that although many of the scenarios had been completed in a stable manner, many commuters were significantly coordinated at the desired departure time. In particular, in the case of a reduction in road capacity or in certain scenarios, it has been shown that, despite excessive schedule adjustments, many passengers are unable to commute before 9 o'clock. As a result, it is suggested that traffic management and pricing policies are different from current ones in the era of autonomous car operation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.912-918
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• 2016

This paper presents various strategies for humanoid vehicle driving and egress tasks. For driving, a tele-operating system that controls a robot based on a human operator's commands is built. In addition, an autonomous assistant module is developed for the operator. Normal position control can result in severe damage to robots when they egress from vehicles. To prevent this problem, another approach that mixes various joint control techniques is adopted in this study. Additionally, a footplate is newly designed and attached to the vehicle floor for the ground landing phase of the egress task. The attached plate enables the robot to step down onto the ground in a safe manner. For stable locomotion, a balance controller is designed for the humanoid. For the design of the controller, the robot is modeled using an inverted pendulum that consists of a spring and a damper. Then, a state feedback controller (with pole placement and a state observer) is built based on the simplified model. Many approaches that are presented in this paper were successfully applied to a full-sized humanoid, DRC-HUBO+, in the DARPA Robotics Challenge Finals, which were held in the United States in 2015.

• Journal of Power System Engineering
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• v.16 no.1
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• pp.84-90
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• 2012

In unmanned vehicles' navigation, the shapes of obstacles are generally irregular and complex. The motion of vehicles based on the range sensor system such as ultrasonic sensors or laser sensors can be unstable due to the irregular shape of the obstacles. In this case, to generate stable trajectory of unmanned vehicles equipped with range sensors, we need an approach that can simplify an obstacle's irregular shape information. In this paper, we propose the trajectory generation algorithm that an vehicle can stably navigate in the environments where irregular shaped obstacles are scattered. The proposed method is verified through the analysis of vehicle's trail and direction data acquired by simulations and implementations.

• Journal of Drive and Control
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• v.17 no.4
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• pp.72-79
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• 2020

To ensure the safety of Advanced Driver Assistance Systems (ADAS) or autonomous vehicles, it is important to recognize the vehicle position, and specifically, the increased accuracy of the lateral position of the vehicle is required. In recent years, the quality of GPS signals has improved a lot and the price has decreased significantly, but extreme urban environments such as tunnels still pose a critical challenge. In this study, we proposed stable and precise lane recognition and tracking methods to solve these two issues via fusion of AVM images and vehicle sensor data using an extended Kalman filter. In addition, the vehicle's lateral position recognition and the abnormal state of RTK GPS were determined using this approach. The proposed method was validated via actual vehicle experiments in urban areas reporting multipath and signal disconnections.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.178-188
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• 2020

This paper proposes a lane recognition CNN network using split-attention network as a backbone to extract feature. Split-attention is a method of assigning weight to each channel of a feature map in the CNN feature extraction process; it can reliably extract the features of an image during the rapidly changing driving environment of a vehicle. The proposed deep neural networks in this paper were trained and evaluated using the Tusimple data set. The change in performance according to the number of layers of the backbone network was compared and analyzed. A result comparable to the latest research was obtained with an accuracy of up to 96.26, and FN showed the best result. Therefore, even in the driving environment of an actual vehicle, stable lane recognition is possible without misrecognition using the model proposed in this study.

• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.157-163
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• 2011

In this paper, configuration control for the Horizontal Maintenance and driving of the mobile inverted pendulum robot has been studied using ARS(Attitude Refrence System). The inverted pendulum technique is getting attention and there have been many researches on the seg-way since the US. Using its 2 freedom, a mobile inverted pendulum robot can move in various modes and Our robot performs goal reaching ARS. Mobile inverted pendulum robot fall down to the forward or reverse direction to converge to the stable point. Kalman Filter is normally used for the algorithm and numerous research is progressing at the moment. To calculate the attitude in ARS using 2 axis gyro(roll, pitch) and 3 axis accelerometers (x, y, z). In this paper we present a two wheel robot system for an autonomous mobile robot. This paper realized the robot control method which is much simpler but able to get desired performance by using the IMU and PID control.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.394-399
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• 2010

This paper presents to study the velocity control method of AGV for heavy material transport. Generally, in the industries, fork-type AGV using path tracking requires high stop-precision with performing operations for 20 hours. To obtain the high stop-precision of AGV for heavy material transport, AGV requires driving technic during low speed. Hence, we use encoder with keeping the speed of AGV and study the velocity control method to improve for the stop-precision of AGV. To experiment the proposed the velocity control method, we performed the experiments engaging the pallet located 4m in front of the AGV. In the experimental result, the maximum error of stop-precision was less than 18.64mm, and we verified that the proposed method is able to control stable.