• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.235-245
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• 2007

UUV(Unmanned Underwater Vehicle) should possess an intelligent control software performing intellectual faculties such as cognition, decision and action which are parts of domain expert's ability, because unmanned underwater robot navigates in the hazardous environment where human being can not access directly. In this paper, we suggest a RVC intelligent system architecture which is generally available for unmanned vehicle and develope an autonomous navigation system for UUV, which consists of collision avoidance system, path planning system, and collision-risk computation system. We present an obstacle avoidance algorithm using fuzzy relational products for the collision avoidance system, which guarantees the safety and optimality in view of traversing path. Also, we present a new path-planning algorithm using poly-line for the path planning system. In order to verify the performance of suggested autonomous navigation system, we develop a simulation system, which consists of environment manager, object, and 3-D viewer.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.86-99
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• 2021

This research proposes the Accident Prevention System to prevent collision accident that can occur due to blind spots such as crossway or school zone using V2I communication. Vision sensor and LiDAR sensor located in the infrastructure of crossway somewhere like that recognize objects and warn vehicles at risk of accidents to prevent accidents in advance. Using deep learning-based YOLOv4 to recognize the object entering the intersection and using the Manhattan Distance value with LiDAR sensors to calculate the expected collision time and the weight of braking distance and secure safe distance. V2I communication used ROS (Robot Operating System) communication to prevent accidents in advance by conveying various information to the vehicle, including class, distance, and speed of entry objects, in addition to collision warning.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.536-542
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• 1994

This paper introduces a vehicle-following-system in which a moving vehicle recognizes the front vehicle's tail-light color and luminance, while maintaining a certain distance and avoiding collision. Using color images rather than using gray-scale images makes it easier to detect the objective color and eliminates the need of a thresholding. The Methods used are RGB to HSV conversion and global region growing method. This paper contributes to the basic study of Color-Vision, and can be extended to color inspection systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.5
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• pp.126-141
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• 2019

One of the Cooperative Intelligent Transportation System(C-ITS) priority services is collision prevention support service. Several studies have considered V2I2V communication-based collision prevention support services using Artificial Neural Networks(ANN). However, such services still show some issues due to a low penetration of C-ITS devices and large delay, particularly when loading massive traffic data into the server in the C-ITS center. This study proposes the Artificial Neural Network-based Collision Warning Service(ACWS), which allows upstream vehicle to update pre-determined weights involved in the ANN by using real-time sectional traffic information. This research evaluates the proposed service with respect to various penetration rates and delays. The evaluation result shows the performance of the ACWS increases as the penetration rate of the C-ITS devices in the vehicles increases or the delay decreases. Furthermore, it reveals a better performance is observed in more advanced ANN model-based ACWS for any given set of conditions.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.609-620
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• 2016

Recently, there are active studies on a forward collision warning system to prevent the accidents and improve convenience of drivers. For collision evasion, the vehicle detection system is required. In general, existing learning-based vehicle detection methods use the entire appearance of the vehicles from rear-view images, so that each vehicle types should be learned separately since they have distinct rear-view appearance regarding the types. To overcome such shortcoming, we learn Haar-like features from the lower part of the vehicles which contain tail lights to detect vehicles leveraging the fact that the lower part is consistent regardless of vehicle types. As a verification procedure, we detect tail lights to distinguish actual vehicles and non-vehicles. If candidates are too small to detect the tail lights, we use HOG(Histogram Of Gradient) feature and SVM(Support Vector Machine) classifier to reduce false alarms. The proposed forward vehicle detection method shows accuracy of 95% even in the complicated images with many buildings by the road, regardless of vehicle types.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.487-490
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• 2003

In this research, we will devise an obstacle avoidance algorithm for a previously unmanned vehicle. Whole systems consist mainly of the vehicle system and the control system. The two systems are separated; this system can communicate with the vehicle system and the control system through wireless RF (Radio Frequency) modules. These modules use wireless communication. And the vehicle system is operated on PIC Micro Controller. Obstacle avoidance method for unmanned vehicle is based on the Virtual Force Field (VFF) method. An obstacle exerts repulsive forces and the lane center point applies an attractive force to the unmanned vehicle. A resultant force vector, comprising of the sum of a target directed attractive force and repulsive forces from an obstacle, is calculated for a given unmanned vehicle position. With resultant force acting on the unmanned vehicle, the vehicle's new driving direction is calculated, the vehicle makes steering adjustments, and this algorithm is repeated.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.3
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• pp.252-259
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• 2011

This paper is focused on dynamic modeling and control system design as well as vision based collision avoidance for multi-rotor unmanned aerial vehicles (UAVs). Multi-rotor UAVs are defined as rotary-winged UAVs with multiple rotors. These multi-rotor UAVs can be utilized in various military situations such as surveillance and reconnaissance. They can also be used for obtaining visual information from steep terrains or disaster sites. In this paper, a quad-rotor model is introduced as well as its control system, which is designed based on a proportional-integral-derivative controller and vision-based collision avoidance control system. Additionally, in order for a UAV to navigate safely in areas such as buildings and offices with a number of obstacles, there must be a collision avoidance algorithm installed in the UAV's hardware, which should include the detection of obstacles, avoidance maneuvering, etc. In this paper, the optical flow method, one of the vision-based collision avoidance techniques, is introduced, and multi-rotor UAV's collision avoidance simulations are described in various virtual environments in order to demonstrate its avoidance performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1671-1677
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• 2018

In this research, the collision detection system based on insect's visual interneuron has been designed. The lobula giant movement detector (LGMD) corresponds to the movement value that increases in direct collision process. If the collision is detected by the LGMD only, it could generate a crash warning even in a non-collision situation, resulting in a lot of false alarms. Directionally sensitive movement detectors (DSMD) are directionally sensitive algorithm based on the elementary movement detectors (EMD) in four directions (up, down, left, and right). In this paper, we propose an efficient VLSI architecture for a realtime collision detection system that is robust to the surrounding environment while improving accuracy. The proposed architecture is synthesized with Dongbu Hightech 110nm standard cell library and shows 333MHz of maximum operating frequency and requires 8400 gates with about 16.5KB of internal memories.

• Journal of the Korea Concrete Institute
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• v.26 no.3
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• pp.393-400
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• 2014

Structures are often subject to vehicle collision which can be accidental or terrorist attack. Previous research shows that the damage in major columns may result in progressive collapse of a whole building. This study investigates the performance of a steel column standing on a reinforced concrete footing subjected to a vehicle collision. The size and the axial load of the steel column are determined based on the assumption that it is the first story corner column in a typical three-story building with six meter span length. The finite element model of a eight-ton single unit truck provided by the NCAC (National Crash Analysis Center) is used in the numerical analysis. The finite element analysis is performed using the LS-DYNA, and the results show that the behavior of the column subjected to car impact depends largely on the column-foundation connection detail.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5558-5565
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• 2012

Roadside barriers are facility for preventing cars from out of path, and 7 classes of barrier are prescribed in criteria of road grades and speed limit, etc. However, overload and overspeed are increased according to improvement of vehicle performance, and falling over frequently occur in vehicle accident related in barriers. Therefore, enhancement of the existing design criteria of roadside barriers is demanded. In this research, vehicle crash simulation was carried out, and the condition for fracture of roadside barriers and vehicle overturn was evaluated in order to verify the defence performance of the barriers, which are SB5 steel barrier and SB6 concrete barrier adapted mainly to highway.