• 한국기후변화학회지
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• 제7권1호
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• pp.19-29
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• 2016

In this study, the emission intensity calculation method of GHGs was developed by considering the characteristics of the models and time series. The telematics device was installed on the car (OBD-II) to collect information on the operation conditions from each sample vehicle of public authorities. Based on emission intensity of GHGs, it presented a methodology of quantitative comparison of GHGs emission by vehicles. Collected driving information of vehicle was used for operating characteristics analysis of the target vehicle, and it was confirmed different operating characteristics through comparison of the results and previous study. GHGs emission intensity were analyzed considering characteristics of vehicle type by passenger car, van, cargo, and considering characteristics of the time series by summer, winter, and intermediate. From the analysis result, it was calculated GHGs emission intensity based on mileage ($g\;CO_2\;eq./km$) and operating time ($g\;CO_2\;eq./sec$).

• 한국기후변화학회지
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• 제6권2호
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• pp.143-158
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• 2015

This study developed the emission intensity estimation method of GHGs by considering the characteristics of the models and time series. The telematics device was installed on the vehicle (OBD-II) to collect information on the operation conditions from each sample vehicle of public authorities. As a result of comparing the mileage distance and fuel consumption, the matching degree is analyzed very high, showed a ${\pm}1{\sim}4%$ error for each vehicle. By comparing driving record diary of vehicles managed by public authorities, this study presents the method that can be used to verify driving information in order to derive the GHGs emission intensity.

• International Journal of Automotive Technology
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• 제8권4호
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• pp.513-520
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• 2007

In this paper, we propose an approach to identify the driving environment for intelligent highway vehicles by means of image processing and computer vision techniques. The proposed approach mainly consists of two consecutive computational steps. The first step is the lane marking detection, which is used to identify the location of the host vehicle and road geometry. In this step, related standard image processing techniques are adapted for lane-related information. In the second step, by using the output from the first step, a four-stage algorithm for vehicle detection is proposed to provide information on the relative position and speed between the host vehicle and each preceding vehicle. The proposed approach has been validated in several real-world scenarios. Herein, experimental results indicate low false alarm and low false dismissal and have demonstrated the robustness of the proposed detection approach.

• 대한원격탐사학회지
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• 제23권5호
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• pp.365-373
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• 2007

Car navigation system is a key application in geographic information system and telematics. A recent trend of car navigation system is using real video captured by camera equipped on the vehicle, because video has more representation power about real world than conventional map. In this paper, we suggest a visual car navigation system that visually represents route guidance. It can improve drivers' understanding about real world by capturing real-time video and displaying navigation information overlaid directly on the video. The system integrates real-time data acquisition, conventional route finding and guidance, computer vision, and augmented reality display. We also designed visual navigation controller, which controls other modules and dynamically determines visual representation methods of navigation information according to current location and driving circumstances. We briefly show implementation of the system.

• Journal of information and communication convergence engineering
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• 제15권3호
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• Journal of Information Processing Systems
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• 제20권3호
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• pp.296-306
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• 2024

Traditional ecological driving (Eco-Driving) evaluations often rely on mathematical models that predominantly offer subjective insights, which limits their application in real-world scenarios. This study develops a robust, data-driven Eco-Driving evaluation model by integrating dynamic and distributed multi-source data, including vehicle performance, road conditions, and the driving environment. The model employs a combination weighting method alongside K-means clustering to facilitate a nuanced comparative analysis of Eco-Driving behaviors across vehicles with identical energy consumption profiles. Extensive data validation confirms that the proposed model is capable of assessing Eco-Driving practices across diverse vehicles, roads, and environmental conditions, thereby ensuring more objective, comprehensive, and equitable results.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.508-511
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• 2007

Car navigation system is one of the most important applications in telematics. A newest trend of car navigation system is using real video captured by camera equipped on the vehicle, because video can overcome the semantic gap between map and real world. In this paper, we suggest a visual car navigation system that visually represents navigation information or route guidance. It can improve drivers' understanding about real world by capturing real-time video and displaying navigation information overlaid on it. Main services of the visual car navigation system are graphical turn guidance and lane change guidance. We suggest the system architecture that implements the services by integrating conventional route finding and guidance, computer vision functions, and augmented reality display functions. What we designed as a core part of the system is visual navigation controller, which controls other modules and dynamically determines visual representation methods of navigation information according to a determination rule based on current location and driving circumstances. We briefly show the implementation of system.

• 한국시뮬레이션학회논문지
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• 제18권4호
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• pp.9-20
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• 2009

자동차 내비게이션 시스템은 경로탐색 및 길안내 등의 기능을 제공하는 대표적인 운전자 지원 시스템의 하나로서 그 사용성이 크게 증가하고 있다. 대부분의 자동차 내비게이션 시스템은 2차원 지도에 기반하고 있는데 이는 기본적으로 지도가 지니는 함축화된 정보 표현 패러다임에 기반하고 있음으로 인해 실세계 정보로의 변환에 인지적 부담이 작용하게 된다. 최근에 개념적으로 대두되고 있는 새로운 내비게이션의 형태는 자동차에 장착된 카메라로부터 실시간으로 취득되거나 자동차 전면 유리창에 투영되는 실제 영상위에 내비게이션 정보를 그래픽으로 표현하는 증강현실의 모습을 취하고 있다. 차량 내에서의 정보 제공 장치로서 내비게이션 시스템은 그것이 함축화된 그래픽이든 아니면 실사 영상이든 궁극적으로 운전자의 운전부하를 줄이고 빠른 직관력을 제공함으로써 주의분산을 최소화 시킴이 중요하다 할 수 있다. 본 논문에서는 증강현실 내비게이션 시스템인 RACE(Reality-Augmented Car-navigation Environment)를 설계 및 구현하고 그것이 실제적으로 운전자의 운전 수행과 인지 및 주의 분산에 어떠한 영향을 미치는지, 그리고 증강현실 내비게이션이 기존의 2차원 지도 기반 내비게이션에 비해 갖는 효용성이 무엇인지에 대하여 운전자 행동에 기반한 인지심리학적 실험을 통하여 분석한다. 이러한 작업들을 바탕으로 정보처리 관점에서 운전자의 운전 수행과 인지적 수행을 위한 최적의 내비게이션 정보 제공 방법은 무엇인지 고찰해본다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권8호
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• pp.3025-3047
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• 2021

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive Ethernet technology. The proposed system provides design and optimization algorithms for automotive networking technology related to AVB (Audio Video Bridge) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machine to Machine (RLMM) plays an outstanding role in reducing the latency among devices. RLMM's approach to real-world experimental cases indicates a reduction in latency of around 41.2%. The setup optimized for the automotive network environment is expected to significantly reduce the time in the development and design process. The results obtained in the study of image transmission latency are trustworthy because average values were collected over a long period of time. It is necessary to analyze a latency between multimedia devices within limited time which will be of considerable benefit to the industry. Furthermore, the proposed reliable camera and video streaming through optimized AVB device settings would provide a high level of support in the real-time comprehension and analysis of images with AI (Artificial Intelligence) algorithms in autonomous driving.

• 한국통신학회논문지
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• 제42권4호
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• pp.892-898
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• 2017

본 논문은 차량 내 AVN(Audio Video Navigation)에서 차량정보 분석과 제스처 인식이 가능한 소프트웨어 구조를 설계하고 구현 방법을 서술한다. 설계된 소프트웨어는 차량정보 분석을 위해 CAN(Controller Area Network) 통신 데이터 분석 모듈을 구현하여 차량의 주행 상태를 분석했다. AVN 소프트웨어는 분석된 정보를 웨어러블 디바이스의 제스처 정보와 융합토록 했다. 도출된 융합정보는 운전자의 명령 수행 단계로 매칭하고 서비스를 지원하는데 사용됐다. 설계된 AVN 소프트웨어는 기성 제품과 유사한 환경의 HW 플랫폼 상에 구현되어 차량 주행 상황과 동일하게 모사된 상황에서의 차량정보분석, 제스처 인식 수행 등의 기능을 지원함을 확인했다.