• 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.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.26-38
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• 2022

This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).

• International Journal of Automotive Technology
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• v.5 no.2
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• pp.135-144
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• 2004

In this paper, the model-matching control in the longitudinal autonomous driving system is investigated by vehicle dynamics simulation, which contains nonlinear subcomponents and simplified subcomponents. The design of the robust model-matching controller is performed by the characteristics of the 2 degrees of freedom controller, which is composed of the feedforward compensator and the feedback compensator. It makes the characteristics of tractive and brake force to be equivalent to the specific transfer function, which is suggested as the reference model. Mathematical models of vehicle dynamic analysis including the model-matching control are constructed for computer simulation. Then, simple examples on open-loop simulation without any controller and closed loop simulation with the model-matching controller are applied to check the validity of the robust controller. As the practical example, the autonomous driving system in the longitudinal direction is adopted. It is proved that the model-matching control is effective and adequate to the disturbances and the perturbations, which are shown in the responses of the change of a vehicle mass and a road gradient.

• ETRI Journal
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• v.41 no.4
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• pp.506-514
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• 2019

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three-lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.40-49
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• 2022

Remote-controlled and autonomous driving based on artificial intelligence are key elements required for unmanned combat vehicles. The required capability of such an unmanned combat vehicle should be expressed in reasonable required operational capability(ROC). To this end, in this paper, the requirements of an unmanned combat vehicle operated under a manned-unmanned teaming were analyzed. The functional requirements are remote operation and control, communication, sensor-based situational awareness, field environment recognition, autonomous return, vehicle tracking, collision prevention, fault diagnosis, and simultaneous localization and mapping. Remote-controlled and autonomous driving of unmanned combat vehicles could be achieved through the combination of these functional requirements. It is expected that the requirement analysis results presented in this study will be utilized to satisfy the military operational concept and provide reasonable technical indicators in the system development stage.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.129-143
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• 2021

This paper proposes actuator fault detection and adaptive fault-tolerant control algorithms using performance index and human-like learning for longitudinal autonomous vehicles. Conventional longitudinal controller for autonomous driving consists of supervisory, upper level and lower level controllers. In this paper, feedback control law and PID control algorithm have been used for upper level and lower level controllers, respectively. For actuator fault-tolerant control, adaptive rule has been designed using the gradient descent method with estimated coefficients. In order to adjust the control parameter used for determination of adaptation gain, human-like learning algorithm has been designed based on perceptron learning method using control errors and control parameter. It is designed that the learning algorithm determines current control parameter by saving it in memory and updating based on the cost function-based gradient descent method. Based on the updated control parameter, the longitudinal acceleration has been computed adaptively using feedback law for actuator fault-tolerant control. The finite window-based performance index has been designed for detection and evaluation of actuator performance degradation using control error.

• 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.

• Journal of Auto-vehicle Safety Association
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• v.10 no.1
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• pp.38-44
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• 2018

This paper presents an analysis on driving safety in lane change situation based on road driving data. Autonomous driving is a global trend in vehicle industry. LKAS technologies are already applied in commercial vehicle and researches about lane change maneuver have been actively studied. In autonomous vehicle, not only safety control issue but also imitating human driving maneuver is important. Driving data analysis in lane change situation has been usually dealt with ego vehicle information such as longitudinal acceleration, yaw rate, and steering angle. For this reason, developing safety index according to surrounding vehicle information based on human driving data is needed. In this research, driving data is collected from perception module using LIDAR, radar and RT-GPS sensors. By analyzing human driving pattern in lane change maneuver, safety index that considers both ego vehicle and surrounding vehicle state by using relative velocity and longitudinal clearance has been designed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.608-613
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• 2002

In this paper we developed the MPC sensor for steering control and steering control of the AGVDS(Autonomous Guided Vehicle Driving System) for Durability test. Among durability tests, the accelerated durability test has been widely used to evaluate the durability of vehicle structure and chassis parts in a short period of time on the designed road that has severe surface conditions. However it increased the drivers fatigue mainly caused by the severe driving conditions. The driver's difficulty to maintain the constant speed and control the steering wheel reduces the reliability of test results. In addition to the general detecting sensor for steering control was restricted by surrounding condition. So we need to develop steering control sensor was robust in the bad driving condition. In this paper we developed steering control sensor using magnetic induction which is robust in the bad driving condition and implemented the AGVDS.

• Journal of Drive and Control
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• v.20 no.4
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• pp.80-91
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• 2023

The purpose of this study is to suggest the direction for the development of intelligent agricultural machinery technology in the Republic of Korea. For this purpose, intelligent technology of agricultural machinery was divided into autonomous agricultural machinery and tractor-implement intelligent communication technology. Then, a survey and analysis of a previous study of the Republic of Korea and foreign countries were conducted. GNSS-based autonomous driving technology is still widely used worldwide, and recently, as research on camera and LiDAR-based autonomous driving is actively progressing, autonomous driving technology is becoming more advanced. ISOBUS-based technology is being developed worldwide for intelligent control of tractor-attached implements, and major global agricultural machinery manufacturers are actively applying it to their products. However, although some ISOBUS technologies are being researched in the Republic of Korea, there are no cases of application on agricultural machinery yet. Therefore, to be globally competitive in the agricultural machinery manufacturing industry, there is an urgent need to advance autonomous driving technology and commercialize agricultural machinery using ISOBUS technology.