English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109952/140887 (78%)
Visitors : 46370328      Online Users : 348
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137162


    Title: 以車載網路支援安全與舒適導向之跟車系統的效能研究
    Performance Study On VANET-Enabled Safety and Comfort-Oriented Car Following System
    Authors: 李秉諺
    Li, Bing-Yan
    Contributors: 張宏慶
    Jang, Hung-Chin
    李秉諺
    Li, Bing-Yan
    Keywords: 車載網路
    跟車系統
    VANET
    Car Following System
    Date: 2021
    Issue Date: 2021-09-02 18:16:39 (UTC+8)
    Abstract: 截至2020年12月底,全國汽車數量相較於十年前,增加約1,140,155輛,即增加約16.16%,且仍持續上升,汽車數量增加,伴隨而來的就是交通壅塞以及交通事故的機率都大大提升。為了能夠舒緩交通流量以及降低交通事故發生的機率,於是各大車廠積極發展先進駕駛輔助系統(Advanced Driver Assistance Systems, ADAS),其中自適應巡航控制系統(Adaptive Cruise Control System, ACC)算是近年來比較廣為人知且逐漸變成消費者在購買車輛時的基本要求。除了提供駕駛者較便利及輕鬆的駕駛體驗,大幅降低了駕駛者的疲勞程度,對於安全性也有一定程度的提升。但這些系統主要的功能還是在於「輔助」,實際在操控車輛的各種行為還是在於駕駛者本身。近年來偶爾會發生此系統因為其他因素造成沒有保持安全距離或是沒有採取緊急煞車而發生交通事故的案例。本研究參考其他相關跟車模型(Car-Following Model),將加速度與減速度控制在一個平滑的區間,使加減速度緩慢增加與減少,不會每次都以最大加減速度加速或煞車,以提升駕駛者與乘坐者的舒適度。同時,將最小安全跟車距離加大,提升跟車時的安全性,並比較僅透過雷達偵測執行的跟車系統與透過以車載網路(Vehicular Ad Hoc Network, VANET)支援的跟車系統,利用SUMO及OMNeT++等工具模擬行車動態。實驗結果顯示,透過以車載網路支援的跟車系統的反應時間比僅透過雷達偵測要來得短,且更加即時。此現象對於跟隨在越後面的車輛效益越明顯,大大提升了行車安全性。最後我們比較整個跟車系統的執行過程,僅透過雷達偵測功能之總花費時間約為198.2秒,透過車載網路功能之總花費時間約為177.3秒,後者比前者省了約10.54%的時間,兼具舒適與安全又有效率。
    Compared with ten years ago, the number of cars in Taiwan has increased by about 1,171,285, or about 20.64%, and keeps rising. The increase in the number of cars is accompanied by a significant increase in the incidence of traffic congestion and traffic accidents. In order to ease the flow of traffic and reduce the incidence of traffic accidents, many car manufacturers have actively developed Advanced Driver Assistance Systems (ADAS). Adaptive Cruise Control System (ACC) has become widely known and gradually becomes an essential requirement for consumers when buying vehicles in recent years. In addition to providing drivers with a more convenient and relaxing driving experience, it greatly reduces drivers` fatigue and enhances safety. While the systems are only designed to “assist” drivers, drivers are the ones who practically control the vehicles. In recent years, traffic accidents have occasionally resulted from not maintaining a safe distance or not taking emergency braking in the systems. This study refers to other related car-following models to increase safety and improve comfort during operation, uses SUMO and OMNeT++ to simulate driving dynamics, and adds Vehicular Ad Hoc Network (VANET) to achieve vehicle information exchange, and finally compare the performance evaluation with or without VANET. Simulation results show that running the car-following system through VANET is more efficient than simply using the radar or the camera in front of the vehicle to detect and operate the system. The entire driving process saves about 10.54% of the time.
    Reference: [1] “Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey,“ NHTSA, 2018.
    [2] 施聰平, 林信賢, “先進駕駛輔助系統(ADAS)法規趨勢,“ 財團法人車輛研究測試中心, 2015.
    [3] Bram, Geenen, Ali, Nasseri, & Adriaan, Schiphorst,“ 2020 Autonomous Vehicle Technology Report, “ Wevolver, 2020.
    [4] 交通部公路總局統計查詢網, “機動車輛登記數,“ 2021. [Online]. Available: https://stat.thb.gov.tw/hb01/webMain.aspx?sys=100&funid=11100
    [5] Yang Yu, Rui Jiang, and Xiaobo Qu, “A Modified Full Velocity Difference Model With Acceleration and Deceleration Confinement: Calibrations, Validations, and Scenario Analyses,“ IEEE Intelligent Transportation Systems Magazine ( Volume: 13, Issue: 2, Summer 2021).
    [6] Gor Hakobyan, and Bin Yang, “High-Performance Automotive Radar A Review of Signal Processing Algorithms and Modulation Schemes,“ IEEE Signal Processing Magazine ( Volume: 36, Issue: 5, Sept. 2019).
    [7] Rasheed Hussain, Safdar H. Bouk, Nadeem Javaid, Adil M. Khan, and Jooyoung Lee, “Realization of VANET-Based Cloud Services through Named Data Networking,“ IEEE Communications Magazine ( Volume: 56, Issue: 8, August 2018).
    [8] Xiyan Chen, Jian Yang, Chunjie Zhai, Jiedong Lou, and Chenggang Yan, “Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario,“ IEEE Access ( Volume: 9, 2021).
    [9] Fang Zong, Meng Wang, Ming Tang, Xiying Li, and Meng Zeng, “An Improved Intelligent Driver Model Considering the Information of Multiple Front and Rear Vehicles,“ IEEE Access ( Volume: 9, 2021).
    [10] Zuping Cao, Lili Lu, Chen Chen, and Xu Chen, “Modeling and Simulating Urban Traffic Flow Mixed With Regular and Connected Vehicles,“ IEEE Access ( Volume: 9, 2021).
    [11] Lucas de Paula Veronese, Fernando Auat-Cheein, Filipe Mutz, Thiago Oliveira-Santos, José E. Guivant, Edilson de Aguiar, Claudine Badue, and Alberto Ferreira De Souza, “Evaluating the Limits of a LiDAR for an Autonomous Driving Localization,“ IEEE Transactions on Intelligent Transportation Systems ( Volume: 22, Issue: 3, March 2021).
    [12] Yanjun Shi, Tao Li, and Qiaomei Han, “An Adaptive Car-Following Strategy for Vehicle Platooning Control,“ IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2021.
    [13] Ghulam Hyder, Bhawani Shankar Chowdhry, Khuhed Memon, and Aisha Ahmed, “The Smart Automobile (SAM): An Application Based on Drowsiness Detection, Alcohol Detection, Vital Sign Monitoring and Lane based Auto Drive to avoid Accidents,“ Global Conference on Wireless and Optical Technologies (GCWOT), 2020.
    [14] Muhamad Aliff Alias, Siti Noraini Sulaiman, Iza Sazanita Isa, Rozan Boudville, and Zainal Hisham Che Soh, “Detection of Sudden Pedestrian Crossing For Driving Assistance Systems,“ 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE), 2020.
    [15] Sai Krishna Chada, Ankith Purbai, Daniel Görges, Achim Ebert, and Roman Teutsch, “Ecological Adaptive Cruise Control for Urban Environments using SPaT Information,“ IEEE Vehicle Power and Propulsion Conference (VPPC), 2020.
    [16] Mehmet Fatih Ozkan, and Yao Ma, “Modeling Driver Behavior in Car-Following Interactions With Automated and Human-Driven Vehicles and Energy Efficiency Evaluation,“ IEEE Access ( Volume: 9, 2021).
    [17] Yuan Lin, John McPhee, and Nasser L. Azad, “Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control,“ IEEE Access ( Volume: 9, 2021).
    [18] M.Treiber and A.Kesting, “Traffic Flow Dynamics,“ Chapter 11 Car-Following Models based on Driving Strategies, ISBN 978-3-642-32460-4, 2013.
    [19] Wikipedia, “Gipps` model,“ 2020. [Online]. Available: https://en.wikipedia.org/wiki/Gipps`_model
    [20] Krauss, S., “Microscopic Modeling of Traffic Flow: Investigation of Collision Free Vehicle Dynamics,“ PhD Thesis, University of Cologne, 1997.
    [21] Jie SONG, Yi WU, Zhexin XU, and Xiao LIN, “Research on Car-Following Model Based on SUMO,“ The 7th IEEE/International Conference on Advanced Infocomm Technology, 2015.
    [22] Kallirroi N. Porfyri, Evangelos Mintsis and Evangelos Mitsakis, “Assessment of ACC and CACC systems using SUMO,“ EPiC Series in Engineering, Volume 2, 2018, Pages 82-93.
    [23] Gao Zhenhai,Wang Jun, Hu Hongyu, Yan Wei, Wang Dazhi, Wang Lin, “Multi-argument Control Mode Switching Strategy for Adaptive Cruise Control System,“ Procedia Engineering, 2016, Pages 581-589.
    [24] Wikipedia, “Vehicular ad hoc network,“ 2020. [Online]. Available: https://en.wikipedia.org/wiki/Vehicular_ad_hoc_network
    [25] 曾蕙如, “結合車間通訊之先進駕駛輔助系統應用,“ 工業技術研究院資訊與通訊研究所, 2016.
    [26] 曾蕙如, “設計/板材力克耗損 5G毫米波前端模組開發挑戰多,“ 台灣資通產業標準協會, 技術專欄-035, 2020.
    [27] 黃威陞, “智慧時代來臨 車聯網技術的選擇,“ 財團法人車輛研究測試中心, [Online]. 2020. Available: https://www.artc.org.tw/chinese/03_service/03_02detail.aspx?pid=13371
    [28] Wikipedia, “Lidar,“ 2020. [Online]. Available: https://en.wikipedia.org/wiki/Lidar
    [29] Edntaiwan, “讓自動駕駛車輛安全上路的電子技術, “2020. [Online]. Available: https://www.edntaiwan.com/20180903NT31-Autonomous-vehicles-The-electronics-road-to-making-them-safe/
    [30] Bosch, “Mid-range radar sensor (MRR) for front and rear applications,“ 2020. [Online]. Available: https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-cars-and-light-commercial-vehicles/driver-assistance-systems/lane-change-assist/mid-range-radar-sensor-mrrrear/
    [31] WWW.Cvlibs.net, “The KITTI Vision Benchmark Suite,“ 2020. [Online]. Available: http://www.cvlibs.net/datasets/kitti/
    [32] Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wießner, “Microscopic Traffic Simulation using SUMO,“ IEEE Intelligent Transportation Systems Conference (ITSC), 2018.
    [33] Wikipedia, “Simulation of Urban MObility,“ 2020. [Online]. Available: https://en.wikipedia.org/wiki/Simulation_of_Urban_MObility
    [34] WWW.Eclipse.org, “About SUMO,“ 2020. [Online]. Available: https://www.eclipse.org/sumo/about/
    [35] OMNeT++, “OMNeT++,“ 2020. [Online]. Available: https://omnetpp.org/
    [36] Wikipedia, “OMNeT++,“ 2020. [Online]. Available: https://en.wikipedia.org/wiki/OMNeT%2B%2B
    [37] 潘儀聰, 陳協慶, “勞工安全設施規則振動暴露方法與ISO 2631規範比較研究, “ 勞動部勞動及職業安全衛生研究所, 2009.
    [38] 許鉅秉, 吳熙仁, 蔡孟釗, “自動公路系統發生事件下自動駕駛車輛於鄰近混合車道跟車邏輯之研究, “ 交通部運輸研究所運輸計劃季刊, 2016.
    [39] ISO 15622:2018, Intelligent transport systems — Adaptive cruise control systems — Performance requirements and test procedures.
    Description: 碩士
    國立政治大學
    資訊科學系碩士在職專班
    105971020
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105971020
    Data Type: thesis
    DOI: 10.6814/NCCU202101409
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

    Files in This Item:

    File Description SizeFormat
    102001.pdf3708KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback