• 한국ITS학회:학술대회논문집
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• 2005.11a
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• pp.141-148
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• 2005

• Journal of the Korean Society for Precision Engineering
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• v.18 no.5
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• pp.42-49
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• 2001

In This paper, we present a collaborative method for material delivery using a distributed vehicle agents system. Generally used AGV(Autonomous Guided Vehicle) systems in FA(Factory Automation) require extraordinary facilities like guidepaths and landmarks and have numerous limitations for application in different environments. Moreover in the case of controlling multi vehicles, the necessity for developing corporation abilities like loading and unloading materials between vehicles including different types is increasing nowadays for automation of material flow. Thus to compensate and improve the functions of AGV, it is important to endow vehicles with the intelligence to recognize environments and goods and to determine the goal point to approach. In this study we propose an interaction method between hetero-type vehicles and adaptive fuzzy logic controllers for sensor-based path planning methods and material identifying methods which recognizes color. For the purpose of carrying materials to the goal, simple color sensor is used instead of intricate vision system to search for material and recognize its color in order to determine the goal point to transfer it to. The technique for the proposed method will be demonstrated by experiment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.4
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• pp.399-404
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• 2011

Overload vehicles have demoralizing influence upon the social overhead capital, economics of nation, traffic flow and road safe as various components. Accordingly, this study established a ubiquitous sensor network system to develop an intelligent regulation system to monitor overloaded vehicles in motion. and Unlike WIM, after detecting the axle of driving vehicles by measuring deformation of roads, this system calculates the weights of vehicles by using signals from the strain sensors installed under the road and an analysis method. Also the study conducted an simulation test for vehicle load analysis using genetic algorithm. and tested wireless sensor for USN system.

• Advances in nano research
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• v.13 no.6
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• pp.587-597
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• 2022

In the present study, a novel optimization method in formation control of multi -system vehicles based on the trajectory of the nearest neighbor trajectory is presented. In this regard, the state equations of each vehicle and multisystem is derived and the optimization scheme based on minimizing the differences between actual positions and desired positions of the vehicles are conducted. This formation control is a position-based decentralized model. The trajectory of the nearest neighbor are optimized based on the current position and state of the vehicle. This approach aids the whole multi-agent system to be optimized on their trajectory. Furthermore, to overcome the cumulative errors and maintain stability in the network a semi-centralized scheme is designed for the purpose of checking vehicle position to its predefined trajectory. The model is implemented in Matlab software and the results for different initial state and different trajectory definition are presented. In addition, to avoid collision avoidance and maintain the distances between vehicles agents at a predefined desired distances. In this regard, a neural fuzzy network is defined to be utilized in conjunction with the control system to avoid collision between vehicles. The outcome reveals that the model has acceptable stability and accuracy.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.7-12
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• 2022

Safety and security are the topmost priority in every environment. With the aid of Artificial Intelligence (AI), many objects are becoming more intelligent, conscious, and curious of their surroundings. The recent scientific breakthroughs in autonomous vehicular designs and development; powered by AI, network of sensors and the rapid increase of Internet of Things (IoTs) could be utilized in maintaining safety and security in our environments. AI based on deep learning architectures and models, such as Deep Neural Networks (DNNs), is being applied worldwide in the automotive design fields like computer vision, natural language processing, sensor fusion, object recognition and autonomous driving projects. These features are well known for their identification, detective and tracking abilities. With the embedment of sensors, cameras, GPS, RADAR, LIDAR, and on-board computers in many of these autonomous vehicles being developed, these vehicles can properly map their positions and proximity to everything around them. In this paper, we explored in detail several ways in which these enormous features embedded in these autonomous vehicles, such as the network of sensors fusion, computer vision and natural image processing, natural language processing, and activity aware capabilities of these automobiles, could be tapped and utilized in safeguarding our lives and environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.12-20
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• 2008

Recent wireless communication technologies are envisioned as an innovative alternative to solve transportation problems. On ad hoc networks, as a wireless communication technology, nodes can communicate data without any infrastructure. In particular, vehicular ad hoc networks (VANETs), a specific ad hoc network applied to vehicles, enable vehicles equipped with a communication device to form decentralized traffic information systems in which vehicles share traffic information they experienced. This study investigated traffic information dissemination in a VANET-based traffic information system. For this study, an integrated transportation and communications simulation framework was developed, and experiments were conducted with real highway networks and traffic demands. The results showed that it took 3 minutes in the low traffic density situations (10 vehicle/lane.km) and 43 seconds in the high traffic density condition (40 vehicle/lane.km) to deliver traffic information of 5km away with 10% market penetration rate. In uncongested traffic conditions, information seems to be disseminated via equipped vehicles in the opposite direction. In congested traffic conditions, the sufficient availability of equipped vehicles traveling in the same direction reduces the chance to use vehicles in the opposing direction even though it is still possible.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.284-298
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• 2022

It is necessary to acquire information on traffic safety facilities installed on the roadways specifically for the operation of autonomous vehicles. The purpose of this study is to prepare a Korean version of an integrated message-set design as a way to provide to autonomous vehicles standardized information on traffic safety facilities. In this study, necessary facilities are classified according to four criteria (no legal basis; not providing information to autonomous vehicles; providing duplicate information; not standardized, and too difficult to generalize) based on information that must be provided to operate autonomous vehicles. The priority of information delivery (gross negligence followed by behavior change) was classified according to the importance of the information to be provided during autonomous driving, and the form was defined for the classification code in the information delivered. Finally, the information location and delivery method of traffic facilities for compliance with SAE J2735 were identified. This study is meaningful in that it provides a plan for roadway operations by suggesting a method for providing information to autonomously driven vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.93-104
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• 2023

Currently, the permanent road traffic volume surveys under Road Act are conducted using a intrusive Automatic Vehicle Classification (AVC) equipments to classify 12 categories of vehicles. However, intrusive AVC equipment inevitably have friction with vehicles, and physical damage to sensors due to cracks in roads, plastic deformation, and road construction decreases the operation rate. As a result, accuracy and reliability in actual operation are deteriorated, and maintenance costs are also increasing. With the recent development of ITS technology, research to replace the intrusive AVC equipment is being conducted. However multiple equipments or self-built DB operations were required to classify 12 categories of vehicles. Therefore, this study attempted to prepare a method for classifying 12 categories of vehicles using vehicle specification information of the Vehicle Management Information System(VMIS), which is collected and managed in accordance with Motor Vehicle Management Act. In the future, it is expected to be used to upgrade and diversify road traffic statistics using vehicle specifications such as the introduction of a road traffic survey system using Automatic Number Plate Recognition(ANPR) and classification of eco-friendly vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.311-326
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• 2023

This paper proposes a plan to improve the installation and operation of traffic safety facilities using a microscopic simulation by confirming the difference in the perception method and range of autonomous vehicles and human vehicles. In this study, the existing 『Traffic Safety Sign Installation·Management Guidelines』 was reviewed, and safety signs among traffic safety facilities were classified according to changes in vehicle behavior. Subsequently, for the classified facilities, the installation location of the traffic sign was changed through simulation experiments, and the optimal location was inferred to suggest an improvement plan. This study confirmed how traffic safety facilities installed based on the visibility of human drivers affect road efficiency and safety in mixed traffic flow with autonomous vehicles and human-controlled vehicles. The optimal location derived through this study is meaningful because it can be used as the basis for revising the guidelines on the installation and management of traffic safety facilities.