• Journal of Auto-vehicle Safety Association
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• v.12 no.1
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• pp.15-25
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• 2020

This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.

• ETRI Journal
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• v.36 no.6
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• pp.968-978
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• 2014

Autonomous vehicle technology based on information technology and software will lead the automotive industry in the near future. Vehicle localization technology is a core expertise geared toward developing autonomous vehicles and will provide location information for control and decision. This paper proposes an effective vision-based localization technology to be applied to autonomous vehicles. In particular, the proposed technology makes use of numerical maps that are widely used in the field of geographic information systems and that have already been built in advance. Optimum vehicle ego-motion estimation and road marking feature extraction techniques are adopted and then combined by an extended Kalman filter and particle filter to make up the localization technology. The implementation results of this paper show remarkable results; namely, an 18 ms mean processing time and 10 cm location error. In addition, autonomous driving and parking are successfully completed with an unmanned vehicle within a $300m{\times}500m$ space.

• Journal of the HCI Society of Korea
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• v.13 no.2
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• pp.5-20
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• 2018

The fully autonomous vehicle, which has been actively studied in a worldwide before commercialization, is expected to become a living space by securing time and space compared to existing automobile. For this reason, interior design of fully autonomous vehicle has become very important. To enhance passenger's experience and satisfaction in fully autonomous vehicle, it is necessary to design an optimized space that can support in-vehicle activities. For this purpose, efforts to analyze the passenger's in-vehicle activities should be preceded. However, there were limited studies that define space and in-Vehicle activities of fully autonomous vehicle in Korea. The purpose of this study is to suggest the guideline of the interior design of fully autonomous vehicle by analyzing and classifying the scope of activities that the passenger can perform within the vehicle. As a method of the study, literature studies on future concept cars, human lifetime behavior and consumer needs had been conducted. As a result in-vehicle activities could be applied in a fully autonomous vehicle. Four in-vehicle activities 'work', 'home life and personal care', 'relaxation' and 'conversation and hobby' had been derived through the analysis of in-vehicle activities. Based on the results, the interior design of fully autonomous vehicle guideline has been suggested. The study is significant because the result of the study can act as a basic study which considers the activities in the fully autonomous vehicle environment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.274-281
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• 1998

This paper describes a visual control of autonomous vehicle using neural network. Visual control for road-following of autonomous vehicle is based on road image from camera. Road points on image are inputs of controller and vehicle speed and steering angle are outputs of controller using neural network. Simulation study confirmed the visual control of road-following using neural network. For experimental test, autonomous electric vehicle is designed and driving test is realized

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.75-80
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• 2005

In this paper, a 3-dimensional analysis of magnetic road and a position sensing system for an autonomous vehicle system is described. Especially, a new position sensing system, end of the important component of an autonomous vehicle, is proposed. In a magnet based autonomous vehicle system, to sense the vehicle position, the sensor measures the field of magnetic road. The field depends on the sensor position of the vehicle on the magnetic road. As the rotation between the magnetic field and the sensor position is highly complex, it is difficult that the relation is stored in memory. Thus, a neural network is used to learn the mapping from th field to the position. The autonomous vehicle system with the proposed position sensing system is tested in experimental setup.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.20-25
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• 2022

This paper presents a development of simulation environment for validation of autonomous driving (AD) algorithm based on Robot Operating System (ROS). ROS is one of the commonly-used frameworks utilized to control autonomous vehicles. For the evaluation of AD algorithm, a 3D autonomous driving simulator has been developed based on LGSVL. Two additional sensors are implemented in the simulation vehicle. First, Lidar sensor is mounted on the ego vehicle for real-time driving environment perception. Second, GPS sensor is equipped to estimate ego vehicle's position. With the vehicle sensor configuration in the simulation, the AD algorithm can predict the local environment and determine control commands with motion planning. The simulation environment has been evaluated with lane changing and keeping scenarios. The simulation results show that the proposed 3D simulator can successfully imitate the operation of a real-world vehicle.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.39-44
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• 2022

This paper presents a development of throttle and brake controller for autonomous vehicle simulation environment. Most of 3D simulator control autonomous vehicle by throttle and brake command. Therefore additional longitudinal controller is required to calculate pedal input from desired acceleration. The controller consists of two parts, feedback controller and feedforward controller. The feedback controller is designed to compensate error between the actual acceleration and desired acceleration calculated from autonomous driving algorithm. The feedforward controller is designed for fast response and the output is determined by the actual vehicle speed and desired acceleration. To verify the performance of the controller, simulations were conducted for various scenarios, and it was confirmed that the controller can successfully follow the target acceleration.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.14-19
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• 2022

This paper presents motion planning algorithm that yields to intervening side lane vehicles in a congested traffic flow based on vehicle to vehicle (V2V) communication. Autonomous driving in dense traffic situation requires advanced driving performance in terms of vehicle interaction and risk mitigation. One of the most important functions necessary for congested traffic autonomous driving is to predict the lane change intention of the side lane target vehicle. However, implementing this function by using only environmental sensors has limitations. In this study, V2V communication is used to overcome the limitations and determine the intention of cut-in vehicles. Lane change intention of the intervening side lane vehicle is inferred by its longitudinal speed, steering angle, and turn signal light information received by the on-board-unit (OBU). Once the yield decision is made, the subject vehicle decelerates to generate sufficient clearance for the target vehicle to enter. Validation of the algorithm was conducted with actual autonomous test vehicles.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.5
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• pp.495-503
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• 2016

Regulation for the testing and operation of autonomous vehicles on public roadways has been recently developed all over the world. For example, the licensing standards and the evaluation technology for autonomous vehicles have been proposed in California, Nevada and EU. But specific safety evaluation scenarios for autonomous vehicles have not been proposed yet. This paper presents safety evaluation scenarios for extraordinary service permission of autonomous vehicles on highways. A total of five scenarios are selected in consideration of safety priority and real traffic situation. These scenarios are developed based on existing ADAS evaluation and simulation of autonomous vehicle algorithm. Also, Safety evaluation factors are developed based on ISO requirements, other papers and the current traffic regulations. These scenarios are investigated via computer simulation.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.106-113
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• 2021

In this study, The behavior of an autonomous vehicle in an intersection accident situation is predicted. Based on a representative intersection accident situation from actual intersection accident database, simulation was performed by applying the automatic emergency braking algorithm used in the autonomous driving system. Accident reconstruction was performed based on the accident report of the representative accident situation. After applying the autonomous driving system to the accident-related vehicle, the tendency of intersection accidents that may occur in autonomous vehicles was identified and analyzed.