• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.123-133
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• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.87-88
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• 2016

본 논문에서는 현재 서비스 중인 지자기를 이용한 위치인식 서비스를 더울 효율적이고 정확하게 이용하기 위해, 스마트폰 카메라 기능을 이용한 이미지 인식 서비스 기술을 융합하여 상호 보완적이고 완성적인 실내 위치인식 서비스 시스템을 제안하고자 한다. 본 시스템은 스마트폰 카메라와 지자기 센서를 이용하여 특정 인프라 구축 없이 데이터 분석만으로 실내 측위 시스템 구현을 목표로 한다.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.495-498
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• 2009

유비쿼터스 시대에서 요구하는 가장 핵심적인 요소기술 즉 인프라이며 이를 이용한다면 다양한 시설물에서의 침입자에 대한 감시, RFID를 이용한 효율적인 인력배치에 응용이 가능하고 또한 온도, 조도, 연기 센서 등을 활용하면 실시간으로 화재 및 테러에 대해 조기에 인지하여 빠르게 대처할 수 있다. 본 논문에서는 USN이 적용되는 대상은 침입탐지를 감시해야 하는 구역, 보안감시 대상이 되는 구역, 위험물 관리와 같은 구역으로 분류하여 구역의 특성마다 센서와 카메라, Tag와 센서(Hybrid Reader)기술 등을 하나의 센서노드로 융합하여 관리대상에 이상유무가 있을 경우 빠르게 대처하거나, Tag와 센서(Hybrid Reader)기술을 이용한 효율적인 인력배치 및 관리할 수 있도록 USN을 구성한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.191-199
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• 2023

In this paper, we propose a method to extract the features of five sensor-only facilities built as infrastructure for autonomous cooperative driving, which are from point cloud data acquired by LiDAR. In the case of image acquisition sensors installed in autonomous vehicles, the acquisition data is inconsistent due to the climatic environment and camera characteristics, so LiDAR sensor was applied to replace them. In addition, high-intensity reflectors were designed and attached to each facility to make it easier to distinguish it from other existing facilities with LiDAR. From the five sensor-only facilities developed and the point cloud data acquired by the data acquisition system, feature points were extracted based on the average reflective intensity of the high-intensity reflective paper attached to the facility, clustered by the DBSCAN method, and changed to two-dimensional coordinates by a projection method. The features of the facility at each distance consist of three-dimensional point coordinates, two-dimensional projected coordinates, and reflection intensity, and will be used as training data for a model for facility recognition to be developed in the future.

• Journal of Platform Technology
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• v.11 no.6
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• pp.106-115
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• 2023

In this paper, we propose a sensor data sharing system for the safe and smooth operation of autonomous vehicles on two-way single lanes in traffic-isolated areas and implement the core module, the situational awareness technology. Two-way single lanes pose challenges for autonomous vehicles, particularly when encountering parked vehicles or oncoming traffic, leading to reversing issues. We introduce a system using infrastructure cameras to detect vehicles' approach, enter, and leave on twoway single lanes in real-time, transmitting this information to autonomous vehicles via V2N communication, thereby expanding the sensing range of the autonomous vehicles. The core part of the proposed system is the situational awareness of the two-way single lane using infrastructure cameras. In this paper, we implement this using object detection and tracking technology. Finally, we validate the implemented situational awareness technology using data collected from actual two-way single lanes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.125-135
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• 2012

This paper proposes the new object detection system with two laser-scanners and a camera for classifying the objects and predicting the location of objects on road street. This detection system could be applied the new C-ITS service such as ADAS(Advanced Driver Assist System) or (semi-)automatic vehicle guidance services using object's types and precise position. This study describes the some examples in other countries and feasibility of object detection system based on a camera and two laser-scanners. This study has developed the heterogenous sensor's fusion method and shows the results of implementation at road environments. As a results, object detection system at roadside infrastructure is a useful method that aims at reliable classification and positioning of road objects, such as a vehicle, a pedestrian, and obstacles in a street. The algorithm of this paper is performed at ideal condition, so it need to implement at various condition such as light brightness and weather condition. This paper should help better object detection and development of new methods at improved C-ITS environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.935-941
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• 2020

People estimation is important to provide IoT services. Most people counting technologies use camera or sensor data. However, the conventional technologies have the disadvantages of invasion of privacy and the need to install extra infrastructure. This paper proposes a method for estimating the number of people using a Wi-Fi AP. We use channel state information of Wi-Fi and analyze that using deep learning technology. It can be achieved by pre-installed Wi-Fi infrastructure that reduce cost for people estimation and privacy infringement. The proposed algorithm uses a k-binding data for pre-processing process and a 1D-CNN learning model. Two APs were installed to analyze the estimation results of six people. The result of the accurate number estimation was 64.8%, but the result of classifying the number of people into classes showed a high result of 84.5%. This algorithm is expected to be applicable to estimate the density of people in a small space.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.240-257
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• 2022

Autonomous vehicles use various local sensors such as camera, radar, and lidar to perceive the surrounding environment. However, it is difficult to predict the movement of vulnerable road users using only local sensors that are subject to limits in cognitive range. This is true especially when these users are blocked from view by obstacles. Hence, this paper developed an algorithm for collision avoidance with VRU using V2X information. The main purpose of this collision avoidance system is to overcome the limitations of the local sensors. The algorithm first evaluates the risk of collision, based on the current driving condition and the V2X information of the VRU. Subsequently, the algorithm takes one of four evasive actions; steering, braking, steering after braking, and braking after steering. A simulation was performed under various conditions. The results of the simulation confirmed that the algorithm could significantly improve the performance of the collision avoidance system while securing vehicle stability during evasive maneuvers.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.588-597
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• 2016

According to an annual industrial accident report from Ministry of Employment of Labor, among the various types of accidents, the number of accidents from construction industry increases every year with the percentage of 27.56% as of 2014. In fact, this number has risen almost 3% over the last four years. Currently, among the industrial accidents, heavy machinery causes most of the tragedy such as collision or narrowness. As reported by the government, most of the time, both heavy machinery drivers and workers were unaware of each other's positions. Nowadays, however when society requires highly complex structures in minimal time, it is inevitable to allow heavy construction equipments running simultaneously in a construction field. In this paper, we have developed Approach Detection System for excavator in order to reduce the increasing number. The imaged based Approach Detection System contains camera, approach detection sensor and Around View Monitor (AVM). This system is also applicable in a small scale construction fields along with other machineries besides excavators since this system does not require additional communication infra such as server.