• The Journal of the Korea Contents Association
• /
• v.11 no.11
• /
• pp.42-48
• /
• 2011

This paper presents a bridge road surface frost prediction and monitoring system. The node sensing hardware comprises microprocessor, temperature sensors, humidity sensors and Zigbee wireless communication. A software interface is implemented the control center to monitor and acquire the temperature and humidity data of bridge road surface. A bridge road surface frost occurs when the bridge deck temperature drops below the dew point and the freezing point. Measurement data was used for prediction of road surface frost occurrences. The actual alert is performed at least 30 minutes in advance the road surface frost. The road surface frost occurrences data are sent to nearby drivers for traffic accidents prevention purposes.

• Proceedings of the Korea Association of Information Systems Conference
• /
• 2000.05a
• /
• pp.195-199
• /
• 2000

본 논문에서는 인터넷 상의 영상에 기반한 교통 감시 시스템을 구현한다. 영상 기반의 교통 감시 시스템은 루프 감지기 등의 센서를 이용한 방법에 비해 비용과 설치, 유지, 보수면에서의 장점으로 인하여 많이 연구되고 있다. 제안한 시스템은 인터넷상에서 FPA(Field Processing Agent)와 TSM(Traffic Surveillance Manager)으로 구성되며, FPA는 TSM에게 도로 영상과 차량의 속도, 도로 점유율과 같은 교통 정보를 제공한다. 차량의 평균 속도와 도로 점유율은, 도로색 영상과 연속된 입력 영상간의 샘플링 지점의 색상 차이변화를 이용하여 추출한다. 제안한 방법은 근사적인 교통정보를 추출해 주며, 입력 영상 전체에 대한 처리 과정 없이 제한된 영역만을 처리하기 때문에, 실시간 감시 시스템을 구축하는데 용이하다.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.3
• /
• pp.1098-1104
• /
• 2011

This paper presents the development of the surface profile sensing system (SPSS) and its application to analysis of road surface. The SPSS which can robustly reconstruct the road input profiles from the intermixed data with the vehicle's dynamic motion, is implemented using the multi-sensor system with the optimally shaped transfer function. The performance of this system is evaluated by a series of experimental works in the devised simulator. And a real car test equipped with the proposed system is performed in the proving ground over both deterministic and random road surfaces. Finally, a feasibility of the system is investigated considering the road model.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.36C no.8
• /
• pp.62-69
• /
• 1999

This paper presents a vision-based real-time traffic surveillance system. Current research in machine vision applied to traffic is due to its potential for more powerful process, its flexibility and its lower cost. Our traffic surveillance system consists of FPA and TSM on the Internet. The FPA provides image data, vehicle speed and vehicle density for the TSM. Traffic parameters, the vehicle speed and density, are extracted over many frames using differences between the sampling points of a background image and those of incoming frames. The FPA works by just processing groups of pixels without any understanding of the image. Although this system has an accuracy limitation, it can be used in applications requiring an approximate vehicle density and vehicle speed in real-time.

• Journal of Practical Engineering Education
• /
• v.14 no.3
• /
• pp.593-600
• /
• 2022

Various sensors such as lidar, radar, and camera are fused and used in autonomous vehicles. Rider and radar sensors are difficult to popularize because they are expensive equipment. In order to popularize autonomous vehicles, research that can replace expensive equipment is continuously being conducted. In this paper, we use a single camera that is inexpensive and can be easily mounted. We propose a method for detecting the wheels and adjacent lanes of a front-side vehicle of a driving vehicle and estimating distances. Our proposed method detects lanes and wheels from frame images after frame extraction via input images. In addition, the distance is measured and compared with the actual distance measured in the actual road environment. The distance could be calculated relatively accurately within the error range of ± 3 cm. Through this, it is expected that the camera can be used as an alternative means when the cost of autonomous vehicles is reduced or when the lidar or radar sensor fails.