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研究生:洪國鈞
研究生(外文):Gwo-JiunHorng
論文名稱:城市環境下建置車輛社群之智慧型運輸系統
論文名稱(外文):Forming Car Society for Intelligent Transportation System in City Environments
指導教授:鄭憲宗鄭憲宗引用關係
指導教授(外文):Sheng-Tzong Cheng
學位類別:博士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:92
中文關鍵詞:車載隨意網路車對車通訊細胞自動機交通壅塞城市車載網路八方位道路變換
外文關鍵詞:VANETInterest ontologyV2VCA clusteringZOIV2Rtraffic congestionurban vehicular networkEight-directionlane-changingOBU
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本論文提出了一個新穎的方法,用來有效的群聚具有相同興趣的車輛駕駛,以增加興趣群組的生存時間與增加車對車(V2V)環境的訊息通訊量。興趣群組採用了細胞自動機,興趣本體論與Mobicast通訊分法來達成群組內的訊息交換。本方法的關鍵在於提出了使用細胞自動機的方式來將相同興趣車輛駕駛加以群組。我們認為使用興趣本體論結合細胞自動機來分群,可增加興趣群組的生存時間。透過實驗也證明本論文提出的方法的確可以有效的增加興趣群組的生存時間與增加訊息的通訊量。
以興趣組成汽車社群主要重點為如何使有相關,類似興趣的車輛加入興趣群組。在本論文中,我們提出了使車輛加入或者是離開興趣群組的機制,可以使興趣群組不斷的隨著時間一直更新群組成員。
本論文也提出了嶄新的認知細胞自動機方法,可視目前的需求,適應性調整各個分布狀況,以增加系統效能與降低交通壅塞。在認知無線電模式下運作可以有效地增頻道使用率與減少冗餘通道。其優點為於異質通訊介面下可以使用不同的傳輸調變模式.我們假設每一車輛均透過路邊單元(RSU)與交通壅塞計算中心連線。
本論文提出一減少道路壅塞的機制,可使車輛在導航裝置的幫助下,由出發地到目的地,而不會遇到塞車狀況。本機制可以計算車輛即將走的路段,其道路的狀況以推薦駕駛是否繼續行駛。透過道路區段的資訊收集與追蹤,本機制可以處理跨車輛社群,以增進道路資訊交換、提高效能。透過模擬實驗顯示,本機制可以有效的將車輛導引至較不壅塞路段與順利地到達目的地。
本論文提出一個嶄新的八方向導引機制,可以透過收集車輛附近的資訊,用來輔助駕駛做超車,車道變換等較危險的動作。我們假設每一車輛擁有一個安全距離的3x3方格矩陣,這車輛位於3x3方格矩陣的中心位置。而每一車輛皆有車上機(OBU)用來收集運算車輛附近的資訊,透過細胞自動機與3x3方格矩陣的運算可以了解車輛附近或是本身是否處於駕車的安全距離模式下,否則將會提醒駕駛。透過模擬實驗顯示,本機制的確可以有效的使車輛保持安全距離變換車道,大大的降低了發生事故的機率。
This thesis proposes a novel approach to clustering the interests of car drivers, increasing the lifetime of interest groups, and increasing the throughput in vehicle-to-vehicle (V2V) environments. It develops an interest ontology of cellular automata (CA) clustering using zone of interest (ZOI) for mobicast communications in vehicular ad hoc network (VANET) environments. The key to the proposed method is to integrate CA clustering with the ontology of users’ interests. This study argues for the use of both an interest profile (ontology) of drivers and information about vehicles to form a group of VANET-related interests. The current study evaluates the performance of the approach by conducting computer simulations. Simulation results reveal the strengths of the proposed CA clustering algorithm in terms of increased group lifetime and increased ZOI throughput for VANETs.
The car society interest group mechanism is focus on how the vehicle join into a interest group through cell (car) evolution and CA mechanism. In this work, we want to investigate the interest group evolution progress. When interest group and car member evolution at different time stamp, how the car member join or leave the interest group.
This thesis proposes a novel cognitive CA approach that can adapt to immediate requirements, spread to use in cross-area car societies, enhance system performance, and decrease traffic congestion problems. We propose a mechanism that operates in a cognitive radio mode for increasing the channel reuse rate and decreasing the consumption of redundant channels. The advantage is a heterogeneous communication interface available through cognitive mechanisms that can recognize different transmission modulation modes. The receiver will get messages through different transmission modulation modes. In this work, we consider vehicles connecting to traffic-congestion computing centers (TCCCs) by vehicle-to-roadside (V2R) communications under a car society. RSUs serve each segment, and we suppose that every car has a navigation device.
We propose an innovative congestion reducing mechanism that can help vehicles get directions with the help of a navigation device after drivers set the origin location and the destination location. This mechanism can calculate the congestion status of the upcoming segment of road. By tracking roadway segments’ status from a point of origin to a destination, our proposed mechanism can handle cross-area car societies. The current study evaluates this approach’s performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA mechanism in terms of increased lifetime and increased congestion avoidance for urban vehicular networks.
This thesis proposes a novel eight-direction mechanism that can receive location information about nearby vehicles to perform self-analysis for lane-changing activities. In this work, we assume that each vehicle creates 3x3 grids called safety distance fields, and that the central grid is based on the given vehicle’s location. Also assuming one on-board unit (OBU) in each vehicle, this work uses location information of nearby vehicles as input for a CA model that calculates whether or not vehicles in nearby lanes are a safe distance from the vehicle for which the calculations are being performed. The current study evaluates this approach’s performance by conducting computer simulations. Simulation results reveal the strengths of the proposed CA model in terms of increased safety distance and increased collision-avoidance for urban vehicular environments.
摘 要 I
Abstract III
List of Tables ii
List of Figures iii
List of Figures iv
List of Figures iv
Chapter 1. Introduction 1
Chapter 2. Background and Related Work 6
2.1. Using CA to Form Car Society in VANETs 6
2.2. Using CA to Reduce Congestion for Urban Traffic
2.3. Using Eight-Direction Mechanism for Lane-Changing with Safety Distance 14
Chapter 3. Using CA to Form Car Society in VANET 15
3.1. The Car Socity Mechanism 15
3.2. Performance Evaluation 31
Chapter 4. Using CA to Reduce Congestion for Urban Traffic 39
4.1. Urban Traffic System Model 39
4.2. Adaptive Segment Recommendation Mechanism 49
4.3. Simulation Discussion 51
Chapter 5. Using CA Mechanism for Lane-Changing at Safe Distances 65
5.1. Lane-Changing System Model 65
5.2. Simulation Discussion 72
Chapter 6. Conclusions 79
References 81
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