• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.05a
• /
• pp.166-166
• /
• 2004

운전자 보조시스템에는 적응순항제어 (adaptive cruise control), 차선변경 (lane change), 충돌경고 (collision warning), 충돌회피 (collision avoidance), 및 자동주차 (automatic parking) 등이 있다. 이런 운전자 보조시스템은 어떤 목적을 가지고 있다. 운전자의 부담을 줄이고 안전을 위하여 차량의 주행방향에 있는 장애물이나 차량을 감지하여 차량간의 안전거리론 유지하고 자동차가 일정 속도를 유지하도록 한다. 운전자 보조시스템의 효율은 센서들로부터 얻어진 정보의 해석에 달려있다.(중략)

• 한국IT서비스학회:학술대회논문집
• /
• 2009.05a
• /
• pp.509-512
• /
• 2009

최근 차량용 IT 기술이 발달함에 따라 네비게이션, 위치추적, 인터넷 접수, 원격 차량 진단, 사고감지, 긴급구난, 교통정보 등을 제공하는 서비스들이 등장하고 있다. 또한 차량의 편의성과 안정성을 추구함과 동시에 친환경 등에 대한 요구도 증가하고 있다. 그리고 최근 차량 상호간 정보의 교환이 더욱 필요해짐에 따라 차량간의 무선 통신 기능이 중요해지고 있으며 차량 내의 네트워크 기술에 대한 연구도 필요하다. 현재 차량 내 네트워크로는 CAN, LIN, MOST등의 유선으로 된 버스 시스템을 중심으로 한 차량 제어 시스템과 멀티미디어 시스템으로 크게 구분할 수 있다. 그러나 자동차 내에 장치 배선이 복잡해짐에 따라 차량의 무게 증가, 고장율의 증가, 연비 저하 등으로 이어지고 있다. 이러한 문제를 보완하기 위해 차량 내에 무선 센서 네트워크 시스템과의 통합 개발이 요구되고 있다. 본 논문에서는 빠르게 발전하고 있는 차량 내 네트워크에 대한 기술개발 동향을 분석하고, 유무선 통합형 차량 내 네트워크 응용 서비스들을 제시하고자 한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.6
• /
• pp.13-22
• /
• 2009

Recently, DSRC technology is used in the various fields such as parking system, BIS, ETC, etc. This paper suggests a traffic information system using this DSRC technology. The traffic information processing based on point detection using existing vehicle detection equipment is the system in which a collection and a service are operated separately while the traffic information system based on the link detection using DSRC is able to collect and provide the traffic information through the communication between RSE and OBU. The speed of a traffic congestion is high on the process converted from a point passing speed to a link average speed because the vehicle detection equipment makes the link traffic information into the point information. When the condition of traffic is deteriorated, traffic speed of the vehicle detection equipment becomes higher than DSRC. Especially, in this system, deflection by data of the traffic speed of the traffic information system is much decreased, and the unexpected condition detection and traffic condition are provided promptly.

• Journal of Korean Society of Transportation
• /
• v.26 no.4
• /
• pp.265-273
• /
• 2008

Video Image Processing (VIP) for traffic surveillance has been used not only to gather traffic information, but also to detect traffic conflicts and incident conditions. This paper presents a system development of gathering traffic information and conflict detection based on automatic calculation of pixel length within the detection zone on a Video Detection System (VDS). This algorithm improves the accuracy of traffic information using the automatic detailed line segmentsin the detection zone. This system also can be applied for all types of intersections. The experiments have been conducted with CCTV images, installed at a Bundang intersection, and verified through comparison with a commercial VDS product.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.8
• /
• pp.1026-1031
• /
• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.493-497
• /
• 2015

This study was started with concern about problem of increasing physical and personal injury caused by traffic accidents, despite of technological advances in transportation. As the vehicles, which is currently produced, informs the driver only detecting the proximity of an object by the front and rear sensor, this study implemented the color detection algorithm, the circular shape recognition algorithm, and the distance recognition algorithm and built the accident prevention beyond accident perception, which commends to avoid the object or to stop the robot, if object was detected by algorithms. For the simulation, we made the Arduino vehicle robot equipped with compact wireless communication camera and confirmed that the robot successfully avoids an object or stops itself in simulated driving.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.7 no.4
• /
• pp.49-60
• /
• 2008

The current VIPS are not effective in safety point of view, because they are originally developed for mimicking loop detectors. Therefore, it is important to identify vehicle trajectories in real time, because recognizing vehicle movements over a detection zone enables to identify which situations are hazardous, and what causes them to be hazardous. In order to improve limited safety functions of the current VIPS, this research has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, incident detection and conflict as well as traffic information via tracking image detectors. This system is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of various traffic situations. Experiments were conducted for measuring the cases of incident detection and abnormal vehicle trajectory with rapid lane change.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.9 no.1
• /
• pp.9-18
• /
• 2010

Our country has so large mountain area that the tunnel construction is inevitable and the need of incident detection that provides safe management of tunnels is increasing. In this paper, we suggest a tunnel incident detection system using computer vision techniques, which can detect the incidents in a tunnel and provides the information to the tunnel administrative office in order to help safe tunnel operation. The suggested system enhances the processing speed by using simple processing algorithm such as image subtraction, and ensures the accuracy of the system by focused on the incident detection itself rather than its classification. The system is also cost effective because the video data from 4 cameras can be simultaneously analyzed in a single PC-based system. Our system can be easily extended because the PC-based analyzer can be increased according to the number of cameras in a tunnel. Also our web-based structure is useful to connect the other remotely located tunnel incident systems to obtain interoperability between tunnels. Through the experiments the system has successfully detected the incidents in real time including dropped luggage, stoped car, traffic congestion, man walker or bicycle, smoke or fire, reverse driving, etc.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.2
• /
• pp.128-133
• /
• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.

• Proceedings of the Korean Institute of Industrial Safety Conference
• /
• 1998.05a
• /
• pp.241-244
• /
• 1998

최근에 이르러 자동차 화재는 일반화재 보다 높은 연평균 16.5%의 증가율을 나타내며, 전체 화재 건수의 17%로 주택 및 아파트 다음으로 화재 발생 건수가 많다. 자동차 화재는 엔진과열, 전기장치의 불량, 전자 제어화에 따른 각종 감지기의 연결상태 및 기능 불량, 교통사고로 인한 엔진의 파괴 및 연료 누출등이 원인이 되고 있으며, 주로 엔진룸에서 화재가 발생하여 차량 내부로 전파되는 특징이 있다. 따라서 화재 발생 초기에 화재를 감지하여 운전자에게 경보를 발하고, 화재를 진압할 수 있는 자동차 화재 감지시스템의 개발이 중요해 지고 있다. (중략)