• 한국빅데이터학회지
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• 제2권2호
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• pp.129-140
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• 2017

낮은 시정, 강우, 강풍, 고온 등 기상 상태는 운전 능력, 차량 성능(예: 마찰, 안정성, 조작력), 노면 마찰력, 도로 인프라, 추돌 위험, 교통 흐름 및 도로 관리자 생산성 등에 영향을 미친다. 최근에는 CCTV, 도로 센서, 차량 센서 등 다양한 도로 기상 빅데이터 소스들이 개발되면서 이러한 기상 관련 문제들 해결에 적용되고 있다. 본 연구는 이러한 도로 기상 빅데이터 소스들의 유형과 특징을 정의하고 국내외 실증 사례들을 통해 도로 기상 빅데이터 유형별로 관련 문제들 해결에 활용하는 전략에 대해 제시하고자 한다.

• 자동차안전학회지
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• 제3권2호
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• pp.28-33
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• 2011

This paper presents driving path estimation algorithm for adaptive cruise control system and advanced emergency braking system using multi-sensor fusion. Through data collection, yaw rate filtering based road curvature and vision sensor road curvature characteristics are analyzed. Yaw rate filtering based road curvature and vision sensor road curvature are fused into the one curvature by weighting factor which are considering characteristics of each curvature data. The proposed driving path estimation algorithm has been investigated via simulation performed on a vehicle package Carsim and Matlab/Simulink. It has been shown via simulation that the proposed driving path estimation algorithm improves primary target detection rate.

• 한국지리정보학회지
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• 제11권3호
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• pp.68-79
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• 2008

한국건설기술연구원(KICT)에서는 도로의 안전성 분석을 위해서 도로의 각종 정보를 이동하는 차량을 이용하여 신속하게 취득하고, 이를 토대로 도로의 결함구간을 분석할 수 있는 도로 안전성 조사 분석 차량(RoSSAV, Road Safety Survey and Analysis Vehicle)을 개발하였다. 본 연구를 통해 도로의 안전성에 의심이 되는 지역에 대해서 3차원 도로 모델링을 통한 도로 선형 결함 알고리즘을 개발하였으며, 이를 위해서는 신속하고 정확한 데이터가 수집되어야 한다. 차량에 회전식 레이저 스캐너, GPS(Global Positioning System), INS(Inertial Navigation System), CCD(Charge-Coupled Device) 카메라 그리고 DMI(Distance Measuring Instrument) 등 여러 센서를 장착하여 데이터를 취득하였다. 마지막으로 이들 데이터를 통합하여 3차원 도로 기하(도로 중심선, 도로 경계선), 도로 시설물과 사면을 추출하였다.

• 한국도로학회논문집
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• 제16권5호
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• pp.91-98
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• 2014

PURPOSES : This study is to develop a automate road mapping system using ARASEO(Automated Road Analysis and Safety Evaluation TOol) for road management. METHODS : The road survey van named ARASEO(Automated Road Analysis and Safety Evaluation TOol) was used to generate highway drawings for Korea National Road number 37 automatically. In order to generate the highway drawings for purpose of road management, it is required to acquired the information for highway alignment, road width and road facilities such as safety barrier and road sign. Therefore the survey van acquired and analyzed the road width, median and guardrail data using rear side laser sensor of ARASEO and recognized the traffic control sign and chevron sign using foreside camera images. Also the highway alignment which is the basic information for highway drawing can be analyzed by acquisition the every 1m positional and attitude data using GPU and IMU sensor and developed algorithm. Finally, in this research the CAD based drawing software was developed to draw highway drawing using the analysis result from ARASEO. RESULTS : This study showed the comparison result of the surveyed road width and drawing data. To make the drawing of the road, we made the Autocad ARX program witch run in CAD menu interface. CONCLUSIONS : Using this program we can create the road center line, every 500m horizontal and vertical ground plan drawing automatically.

• 센서학회지
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• 제26권1호
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• pp.1-6
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• 2017

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

• 대한임베디드공학회논문지
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• 제11권5호
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• pp.313-323
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• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.297-310
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• 2022

Road facilities such as CCTV poles have potential risk of collision accidents with a car. A collision detection algorithm installed in the facility allows the collision accident to be known remotely. Most collision detection algorithms are operated by simply focusing on whether a collision have occurred, because these methods are used to measure only acceleration data from a 3-axis sensor to detect collision. However, it is difficult to detect other detailed information such as malfunction of the sensor, collision direction and collision strength, because it is not known without witness the accident. Therefore, we proposed enhanced detection algorithm to get the collision direction, and the collision strength from the tilt of the facility after accident using a 9-axis sensor in this paper. In order to confirm the performance of the algorithm, an accuracy evaluation experiment was conducted according to the data measurement cycle and the invocation cycle to an detection algorithm. As a result, the proposed enhanced algorithm confirmed 100% accuracy for 50 weak collisions and 50 strong collisions at the 9-axis data measurement cycle of 10ms and the invocation cycle of 1,000ms. In conclusion, the algorithm proposed is expected to provide more reliable and detailed information than existing algorithm.

• 통합자연과학논문집
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• 제10권4호
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• pp.232-239
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• 2017

The researcher set up multiple sensor units on the road slope such as national highway and highway where there is a possibility of loss, and using the acceleration sensor built into the sensor unit the researcher will sense whether the inclination of the road slope occur in real time, and Based on the sensed data, the researcher tries to implement a system that detects collapse of road slope and dangerous situation. In the experiment of measuring the error between the actual measurement time and the judgment time of the monitoring system when judging the warning of the sensor and falling rock detection by using the acceleration sensor, the error between measurement time and the judgment time at the sensor warning was 0.34 seconds on average, and an error between measurement time and judgment time at falling rock detection was 0.21 seconds on average. The error is relatively small, the accuracy is high, and thus the change of the slope can be clearly judged.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.79-82
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• 2005

Recently the necessity of road data is still being increased in industrial society, so there are many repairing and new constructions of roads at many areas. According to the development of government, city and region, the update and acquisition of road data for GIS (Geographical Information System) is very necessary. In this study, the fusion method with range data(3D Ground Coordinate System Data) and Intensity data in stand alone LiDAR data is used for road extraction and then digital image processing method is applicable. Up to date Intensity data of LiDAR is being studied. This study shows the possibility method for road extraction using Intensity data. Intensity and Range data are acquired at the same time. Therefore LiDAR does not have problems of multi-sensor data fusion method. Also the advantage of intensity data is already geocoded, same scale of real world and can make ortho-photo. Lastly, analysis of quantitative and quality is showed with extracted road image which compare with I: 1,000 digital map.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권9호
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• pp.526-532
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• 2003

This paper describes a recognition method of a road direction for an autonomous vehicle based on magnetic sensors. Using the sensors mounted on a vehicle and the magnetic markers embedded along the center of road, the autonomous vehicle can recognize a road direction and control a steering angle. Using the front lateral deviation of a vehicle and the rear one, the road direction is calculated. The analysis of magnetic field, the acquisition technique of training data, the training method of neural network and the computer simulation are presented. According to the computer simulation, the proposed method is simulated, and its performance is verified. Also, the experimental test is confirmed its reliability.