• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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• pp.21-27
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• 2023

This paper presents a simulation tool for developing and evaluating automated driving systems' lane change algorithm in urban congested traffic. The behavior of surrounding vehicles was modeled based on driver driving data measured in urban congested traffic. Surrounding vehicles are divided into aggressive vehicles and non-aggressive vehicles. The degree of aggressiveness is determined according to the lateral position to initiate interaction with the vehicle in the next lane. In addition, the desired velocity and desired time gap of each vehicle are all randomly assigned. The simulation was conducted by reflecting the cognitive limitations and control performance of the autonomous vehicle. It was possible to confirm the change in the lane change performance according to the variation of the lane change decision algorithm.

• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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• pp.6-12
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• 2023

For the commercialization and standardization of autonomous vehicles, demand for rigorous safety criteria has been increased over the world. In Korea, the number of extraordinary service permission for automated vehicles has risen since Hyundai Motor Company got its initial license in March 2016. Nevertheless, licensing standards and evaluation factors are still insufficient for operating on public roadways. To assure driving safety, it is significant to verify whether or not the vehicle's decision is similar to human driving. This paper validates the safety of the autonomous vehicle by drawing scenario-based comparisons between manual driving and autonomous driving. In consideration of real traffic situations and safety priority, seven scenarios were chosen and classified into basic and advanced scenarios. All scenarios and safety factors are constructed based on existing ADAS requirements and investigated via a computer simulation and actual experiment. The input data was collected by an experimental vehicle test on the SNU FMTC test track located at Siheung. Then the offline simulation was conducted to verify the output was appropriate and comparable to the manual driving data.

• Journal of Sensor Science and Technology
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• v.10 no.5
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• pp.329-336
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• 2001

This paper describes the steering control and geomagnetism cancellation for an autonomous vehicle using an MR sensor. The magneto-resistive (MR) sensor obtains the vector summation of the magnetic fields from embedded magnets and the Earth. The vehicle is controlled by the magnetic fields from embedded magnets. So, geomagnetism is the disturbance in the steering control system. In this paper, we propose a new method of the sensor arrangement in order to remove the geomagnetism and vehicle body interference. The proposed method uses two MR sensors located in a level plane and the steering controller has been developed. The controller has three input variables ($dB_x$, $dB_y$, $dB_z$) using the measured magnetic field difference, and an output variable (the steering angle). A simulation program was developed to acquire the data to teach the neural network, in order to test the ability of a neural network to learn the steering control process. Also, the computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. From the simulation and field test, good result was obtained and we confirmed the robustness of the neural network controller in a real autonomous vehicle.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_2
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• pp.359-370
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• 2023

For a vehicle to follow a reference path accurately, its position must be estimated accurately and reliably. In this paper, we propose a lateral control algorithm for autonomous navigation of a vehicle via USAT(Ultrasonic Satellite System), which is an absolute position measurement system using an ultrasonic wave and INS(Inertial Navigation System) integration. In order to estimate the vehicle's parameters, a J-turn test is used. And the autonomous navigation performances of proposed lateral control algorithm and validity of proposed lateral control algorithm are verified and evaluated by simulation and experiments.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.60-69
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• 2022

Recently, the importance of simulation validation in a virtual environment for autonomous driving system validation is increasing. At the same time, interest in the advancement of the virtual driving environment is also increasing. To develop autonomous driving technology, a simulation environment similar to the real-world environment is needed. For this reason, not only the road model is configured in the virtual driving environment, but also the driving environment configuration that includes the surrounding environments -traffic, object, etc- is necessary. In this article, FMTC, which is a test bed for autonomous vehicles, is implemented in a virtual environment and advanced to form a virtual driving environment similar to that of real FMTC. In addition, the similarity of the virtual driving environment is verified through comparative analysis with the real FMTC.

• 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.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.22-26
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• 2003

This paper presents a genetic algorithm based autonomic movement algorithm for an autonomous underwater vehicle(AUV) and verified it to simulation. Also, developed program that can do simulation on two dimension and three dimension in seabed environment. The presented algorithm is applicable to a escape from the recursive search and a development of obstacle avoidance system.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.1
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• pp.1-6
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• 2012

In this paper, an adaptive fuzzy control system is proposed for the speed control of the ASV (Autonomous Surface Vehicle) with nonaffine nonlinear system dynamics. We consider the turning speed of the screw propeller to be the control input instead of thrust so that we do not have to know the exact function between turning speed and thrust. But in this case, the ASV becomes a nonaffine nonlinear system because thrust is a nonlinear function of the turning speed. To solve this problem, we propose a Takagi-Sugeno fuzzy-model-based control system and simulation studies are performed. Simulation results show the effectiveness of the proposed control scheme.