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    题名: 先進駕駛輔助系統中的多樣警示資訊融合模式;The Fusion Model of Multiple Warning Data for Advanced Driver Assistance System
    作者: 施承斈;Shi,Cheng-xue
    贡献者: 資訊工程學系
    关键词: 駕駛輔助;車輛安全;模糊系統;資訊融合;ADAS;fuzzy;fuzzy system;data fusion;FCW;CSW
    日期: 2015-07-30
    上传时间: 2015-09-23 14:45:55 (UTC+8)
    出版者: 國立中央大學
    摘要: 有鑑於交通事故的頻繁,車輛安全系統的研發日益蓬勃。現今駕駛對於車輛的需求不再只是傳統的硬體規格,面對行車環境潛在的危險,提供偵測、預警的車輛安全系統需求與日俱增。然而,現行車輛安全系統著重在單一的功能的安全警示,面對複雜行車狀況,缺乏綜觀全局的考量。因此,本研究致力將多種警示資訊整合,提出一個以模糊推論為基礎的多樣警示資訊融合模式,提供駕駛面對複雜行車環境潛在危險的單一警示,作為採取相對反應的依歸。
    本資訊融合模式整合包含車道偏離警示 (LDW)、前車碰撞警示 (FCW)、路口警示、路口警示、及彎曲道路車速 (CSW) 警示。藉由相機的影像輸入及GPS與電子地圖的配合,涵蓋近處與遠方的安全資訊。GPS配合電子地圖提供了當前道路的速限以及路口資訊,提醒駕駛要留心與斟酌減速。彎曲道路車速警示系統根據GPS與電子地圖所傳回之前方道路資訊,計算出前方100公尺至250公尺路段的道路曲率,並判斷當前車速是否超過安全車速而提出警示。最後,將車道偏離距離、前車距離、碰撞時間、彎路曲率、與車速等警示資料輸入至一模糊推論系統,以人類經驗知識建立一模糊規則庫,依據當前各項警示資料,查詢一系列的模糊規則及所選用的模糊推論引擎推論出結論。最後經由解模糊化界面得到一明確的結論值,此為當前的警示程度值。
    本研究藉由多個街道場景實驗驗證了,我們系統可以成功地將前述FCW、LDW警示系統結合,接著透過GPS結合電子地圖將前方道路曲率、車速限制、及路口資訊彙整進來;再將這五種遠近警示輸入至模糊推論系統,以單點模糊的模糊化界面加上最大-最小模糊推論引擎,比對模糊規則庫中的44條規則,考量所有的行車情境,最終將推論而得的模糊結論利用面積中心法解模糊,得到單一危險數值,達成對駕駛提出單一警示的目的。
    ;Due to the frequent traffic accidents, the development of vehicle safety systems becomes more and more important. Nowadays, the demands for vehicles are not only convenience and comfortability but also the needs of vehicle safety which can detect dangerous states and issue warnings for drivers. Currently, the vehicle safety systems are separately developed one after one; each system has its own detection and warning modules. If a driver uses more than one system in his vehicle, the multiple warning signals maybe confuse and interfere the driver. On the other hand, the sensor-based detection systems can only detect the short-distance dangerous states; the long-distance detection functions were seldom considered. Thus, in this study, we propose a long-distance detection and warning system and then propose a fuzzy-based fusion model to combine the multiple warning data from all considered short-distance and long-distance detection and warning systems to provide the drivers only a single significant warning signal.
    The proposed fusion model integrates Land departure Warning (LDW), Forward Collision Warning (FCW), Curve Speed Warning (CSW), speeding warning, and crossroad warning systems. GPS and electronic map provide speed limit on current road and the information of forward crossroad to warn drivers of the danger. CSW reminds the forward road information based on GPS and electronic map. CSW system computes the curvature radius of 100 to 250 meters road ahead and issue warning if current speed is higher than the critical speed in such radius.
    Based on the experiments of several street sceneries, it is certified that the proposed system can successfully integrates FCW and LDW with CSW, speed warning and crossroad warning via GPS and electronic map. Then these five warning data are feed into the proposed fuzzy inference system and fuzzified into fuzzy singleton. We choose max-min inference engine and check the 44 rules in the rule base to conclude all driving situations. Eventually, we use center of area defuzzifier to defuzzify the final conclusion membership function to obtain a single warning. The experimental results show that the proposed system is reasonable and useful for practical ADAS applications.
    显示于类别:[資訊工程研究所] 博碩士論文

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