• Title/Summary/Keyword: Driving support

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Development of Embedded Lane Detection Image Processing Algorithm for Car Black Box (차량용 블랙박스를 위한 임베디드 차선감지 영상처리 알고리즘 개발)

  • Yi, Soo-Yeong;Ryu, Ji-Hyoung;Lee, Chang-Goo
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.11 no.8
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    • pp.2942-2950
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    • 2010
  • Car black box helps to investigate the cause of accident by recording time, position and videos as well as shock information. In addition, the car black box need a function to support safe driving for preventing accident. The representative driving support function is a lane departure warning. In order to implement the function, it is necessary to carry out the image processing to detect the lane first. The image processing algorithm requires computational burden to handle so much data and complicated structure of algorithm. This paper describes the efficient image processing algorithm with relatively low amount of computation for car black box embedded platform to detect lanes from the real-time lane image.

Driver Assistance System By the Image Based Behavior Pattern Recognition (영상기반 행동패턴 인식에 의한 운전자 보조시스템)

  • Kim, Sangwon;Kim, Jungkyu
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.12
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    • pp.123-129
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    • 2014
  • In accordance with the development of various convergence devices, cameras are being used in many types of the systems such as security system, driver assistance device and so on, and a lot of people are exposed to these system. Therefore the system should be able to recognize the human behavior and support some useful functions with the information that is obtained from detected human behavior. In this paper we use a machine learning approach based on 2D image and propose the human behavior pattern recognition methods. The proposed methods can provide valuable information to support some useful function to user based on the recognized human behavior. First proposed one is "phone call behavior" recognition. If a camera of the black box, which is focused on driver in a car, recognize phone call pose, it can give a warning to driver for safe driving. The second one is "looking ahead" recognition for driving safety where we propose the decision rule and method to decide whether the driver is looking ahead or not. This paper also shows usefulness of proposed recognition methods with some experiment results in real time.

Electroencephalogram-based Driver Drowsiness Detection System Using AR Coefficients and SVM (AR계수와 SVM을 이용한 뇌파 기반 운전자의 졸음 감지 시스템)

  • Han, Hyungseob;Chong, Uipil
    • Journal of the Korean Institute of Intelligent Systems
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    • v.22 no.6
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    • pp.768-773
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    • 2012
  • One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

A Study on Safety Evaluation Method of LKAS in Actual Road (LKAS의 실도로 안전성 평가방법에 관한 연구)

  • Yoon, PilHwan;Lee, SeonBong
    • Journal of Auto-vehicle Safety Association
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    • v.10 no.4
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    • pp.33-39
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    • 2018
  • Recently, the automobile industry has developed ADAS (Advanced Driver Assistance System) to prevent traffic accidents and reduce driver's driving burden. Among the ADAS, the LKAS (Lane Keeping Assistance System) is a support system for the convenience and safety of the driver, and the main function is to maintain the driving lane of the vehicle. LKAS is a system that uses radar sensor and camera sensor to collect information about the position of the vehicle in the lane and to support keeping the lane through control if necessary. In many countries, LKAS has already been commercialized and the convenience and safety of drivers have been improved. The international LKAS evaluation test procedure is being developed and discussed by standardization committees such as the ISO (International Organization for Standardization) and the Euro NCAP (New Car Assessment Program). In Korean, the LKAS test method is specified in the KNCAP (Korean New Car Assessment Program), but the evaluation method is not defined. Therefore, the LKAS test procedure that meets international standards and is suitable for domestic road environment is necessary. In this paper, development of LKAS test evaluation scenarios that meets international standards and considering domestic road environment, and the formula that can evaluate the result value after control as the relative distance of lane and the front wheel are suggested. And a comparative analysis was conducted to verify the validity of the suggested scenario and formula. The test evaluation was conducted using the vehicle equipped with the LKAS.

Classification of 3D Road Objects Using Machine Learning (머신러닝을 이용한 3차원 도로객체의 분류)

  • Hong, Song Pyo;Kim, Eui Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.36 no.6
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    • pp.535-544
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    • 2018
  • Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

Lane Map-based Vehicle Localization for Robust Lateral Control of an Automated Vehicle (자율주행 차량의 강건한 횡 방향 제어를 위한 차선 지도 기반 차량 위치추정)

  • Kim, Dongwook;Jung, Taeyoung;Yi, Kyong-Su
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.2
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    • pp.108-114
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    • 2015
  • Automated driving systems require a high level of performance regarding environmental perception, especially in urban environments. Today's on-board sensors such as radars or cameras do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support these systems. An accurate digital map is used as a powerful additional sensor. In this paper, we propose a new approach for vehicle localization using a lane map and a single-layer LiDAR. The maps are created beforehand using a highly accurate DGPS and a single-layer LiDAR. A pose estimation of the vehicle was derived from an iterative closest point (ICP) match of LiDAR's intensity data to the lane map, and the estimated pose was used as an observation inside a Kalmanfilter framework. The achieved accuracy of the proposed localization algorithm is evaluated with a highly accurate DGPS to investigate the performance with respect to lateral vehicle control.

An Event-Driven Failure Analysis System for Real-Time Prognosis (실시간 고장 예방을 위한 이벤트 기반 결함원인분석 시스템)

  • Lee, Yang Ji;Kim, Duck Young;Hwang, Min Soon;Cheong, Young Soo
    • Korean Journal of Computational Design and Engineering
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    • v.18 no.4
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    • pp.250-257
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    • 2013
  • This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

V2X Technology Trends for Next-Generation Mobility

  • Kim, Young-Hak
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.7-13
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    • 2020
  • We describes V2X technology, a connectivity-based recognition technology that is attracting attention as a key technology for implementing autonomous driving technology, and autonomous communication modules that implement ADAS technology, a sensor-based recognition technology. It also explains the trends in V2X technology standardization centered on IEEE 802.11p, which is a WAVE technology standard based on Wi-Fi/DSRC. Finally, we will discuss the market growth trend of V2X communication modules in the United States, the leading V2X technology module, and the development of technology development trends of major domestic and international companies that are leading the global technology market related to V2X communication modules. V2X and ADAS technologies will be the biggest influence on automotive purchasing decisions. In recent years, V2I mandates have been promoted beyond V2V, mainly in developed countries such as the United States. The related industry needs to focus on the development of information transmission network technology that can support high frequency high efficiency(transmission rate) and sophisticated positioning accuracy beyond conventional vehicle communication.

System for Detecting Driver's Drowsiness Robust Variations of External Illumination (외부조명 변화에 강인한 운전자 졸음 감지 시스템)

  • Choi, WonWoong;Pan, Sung Bum;Shin, Ju Hyun
    • Journal of Korea Multimedia Society
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    • v.19 no.6
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    • pp.1024-1033
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    • 2016
  • In this study, a system is proposed for analyzing whether driver's eyes are open or closed on the basis of images to determine driver's drowsiness. The proposed system converts eye areas detected by a camera to a color space area to effectively detect eyes in a dark situation, for example, tunnels, and a bright situation due to a backlight. In addition, the system used a thickness distribution of a detected eye area as a feature value to analyze whether eyes are open or closed through the Support Vector Machine(SVM), representing 90.09% of accuracy. In the experiment for the images of driver wearing glasses, 83.83% of accuracy was obtained. In addition, in a comparative experiment with the existing PCA method by using Eigen-eye and Pupil Measuring System the detection rate is shown improved. After the experiment, driver's drowsiness was identified accurately by using the method of summing up the state of driver's eyes open and closes over time and the method of detecting driver's eyes that continue to be closed to examine drowsy driving.

Design optimization of intelligent service robot suspension system using dynamic model (동역학 모델을 활용한 서비스용 지능형 로봇의 현가시스템 설계 최적화)

  • Choi, Seong-Hoon;Park, Tae-Won;Lee, Soo-Ho;Jung, Sung-Pil
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.565-570
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    • 2008
  • Recently, the intelligent service robot is applied for the purpose of guiding the building or providing information to the visitors of the public institution. The intelligent robot which is on development has a sensor to recognize its location at the bottom of it. Four wheels which are arranged in the form of a lozenge support the weight of the components and structures of the robot. The operating environment of this robot is restricted at the uneven place because the driving part and internal structure is designed in one united body. The impact from the ground is transferred to the internal equipments and structures of the robot. This continuous impact can cause the unusual state of the precise components and weaken the connection between each structural part. In this paper, a suspension system which can be applied to the intelligent robot is designed. The dynamic model of the robot is created, and the driving characteristics of the actual robot and the robot with suspension are compared. The road condition which the robot can operate is expanded by the application of the suspension system. Additionally, the suspension system is optimized to reduce the impact to the robot components.

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