Identifying Relative Vehicle Positions via Electronic and Visual Signals

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：114

、訪客IP：3.145.41.253

姓名

張博鈞(Po-Chun Chang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Identifying Relative Vehicle Positions via Electronic and Visual Signals)

相關論文

★ Dynamic Overlay Construction for Mobile Target Detection in Wireless Sensor Networks	★ 車輛導航的簡易繞路策略
★ 使用傳送端電壓改善定位	★ 利用車輛分類建構車載網路上的虛擬骨幹
★ Why Topology-based Broadcast Algorithms Do Not Work Well in Heterogeneous Wireless Networks?	★ 針對移動性目標物的有效率無線感測網路
★ 適用於無線隨意網路中以關節點為基礎的分散式拓樸控制方法	★ A Review of Existing Web Frameworks
★ 將感測網路切割成貪婪區塊的分散式演算法	★ 無線網路上Range-free的距離測量
★ Inferring Floor Plan from Trajectories	★ An Indoor Collaborative Pedestrian Dead Reckoning System
★ Dynamic Content Adjustment In Mobile Ad Hoc Networks	★ 以影像為基礎的定位系統
★ 大範圍無線感測網路下分散式資料壓縮收集演算法	★ 車用WiFi網路中的碰撞分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

對於提供駕駛者注意以及提升駕駛流暢度，辯識鄰近車輛的相對位置的有效性是十分重要的，然而大部分的無線定位技術，無法提供足夠的定位精準度去辨識車輛相對位置而且需要透過昂貴的硬體設備才能達到高精度。為了解決這個問題，在這篇論文中，我們提出EV車輛相對位置辯識。為了有效率的配對無線訊號以及視覺訊號，EV配對演算法可以做到將在車輛樣貌以及它的特定無線識別之間的配對的可能性提升最大限度。為了評估EV車輛相對位置辯識的可行性，我們實作在Raspberry Pi自走車上。實驗結果顯示出，EV車輛相對位置辯識可以達到很優良的精確度而配對演算法有效率而且趨於穩定。

摘要(英)

The availability of relative location of nearby vehicles is critical in providing safety alerts to the drivers and enhancing driving experience. However, most of wireless localization techniques either fail to provide sufficient accuracy to identify the relative vehicle positioning or require expensive hardware to achieve high accuracy. To resolve this issue, in this thesis we propose E-V relative vehicle positioning. To effectively pair electronic and visual signals, the E-V matching algorithm is used, which can maximize the probability of correct pairing between an vehicle′s electronic identity and its visual appearance. To evaluate performance of the E-V relative vehicle positioning, a prototype is built on the Raspberry B+ self-driven car. The conducted experiment results show that E-V relative vehicle positioning system is able to achieve a much better vehicle relative positioning accuracy and the matching result is efficient and stable throughout the experiment.

關鍵字(中)

★ 辯識車輛位置
★ 訊號配對演算法

關鍵字(英)

★ Relative positioning
★ Vehicle detection
★ Vehicle Localization

論文目次

1 Introduction
2 Preliminary
2.1 Raspberry Pi
2.2 OpenCV
2.3 Integrating Electronic and Visual Signals for Accurate Localization
3 Related Work
3.1 Vehicle detection
3.2 Vehicle Localization
3.3 Relative Position Techniques
4 Our Solution
4.1 Model
4.2 Problem Definition
4.3 Design Overview
4.4 E-V Signal Matching
5 Performance Evaluation
5.1 Experimental setup
5.2 Performance metrics
5.3 Experimental results
6 Conclusions
reference

參考文獻

[1] J.C. Knight. Safety critical systems: challenges and directions. In Software Engineer-
ing, 2002. ICSE 2002. Proceedings of the 24rd International Conference on, pages
547{550, May 2002.
[2] Patrick Moriarty and Damon Honnery. The prospects for global green car mobility.
Journal of Cleaner Production, 16(16):1717{1726, 2008.
[3] M. Gadelrab, A. Kalam, and Y. Deswarte. Manipulation of network traffic traces for
security evaluation. In Advanced Information Networking and Applications Work-
shops, 2009. WAINA ′09. International Conference on, pages 1124{1129, May 2009.
[4] FAQ : How accurate is GPS? http://www.gps-basics.com/faq/q0116.shtml, 2015.
[5] Valeo Raytheon Systems http://www.valeo.com., 2015.
[6] Dong Li, Tarun Bansal, Zhixue Lu, and Prasun Sinha. Marvel: Multiple antenna
based relative vehicle localizer. In Proceedings of the 18th Annual International
Conference on Mobile Computing and Networking, Mobicom ′12, pages 245{256,
New York, NY, USA, 2012. ACM.
[7] R. Parker and S. Valaee. Vehicular node localization using received-signal-strength
indicator. Vehicular Technology, IEEE Transactions on, 56(6):3371{3380, Nov 2007.
[8] Alper Yilmaz, Omar Javed, and Mubarak Shah. Object tracking: A survey, 2006.
[9] Raspberry Pi https://www.raspberrypi.org/, 2015.
[10] Raspbian http://www.raspbian.org/, 2015.
[11] Raspbberry Pi faq http://www.raspbian.org/raspbianfaq, 2015.
[12] Aruino https://www.arduino.cc/, 2015.
[13] Itseez. Opencv http://opencv.org/.
[14] Open source computer vision library:http://sourceforge.net/projects/opencvlibrary/les/, 2015.
[15] OpenCV Developer Site: http://code.opencv.org/, 2015.
[16] Boying Zhang, Jin Teng, Junda Zhu, Xinfeng Li, Dong Xuan, and Yuan F. Zheng. Evloc: Integrating electronic and visual signals for accurate localization. In In Proc.
of ACM MobiHoc, 2012.
[17] Sang Jin Park, Tae Yong Kim, Sung Min Kang, and Kyung Heon Koo. A novel signal
processing technique for vehicle detection radar. In Microwave Symposium Digest,
2003 IEEE MTT-S International, volume 1, pages 607{610 vol.1, June 2003.
[18] Chieh-Chih Wang, Chuck Thorpe, and Arne Suppe. Ladar-based detection and
tracking of moving objects from a ground vehicle at high speeds. In IEEE Intelligent
Vehicles Symposium (IV2003), June 2003.
[19] R. Chellappa, Gang Qian, and Qinfen Zheng. Vehicle detection and tracking using acoustic and video sensors. In Acoustics, Speech, and Signal Processing, 2004.
Proceedings. (ICASSP ′04). IEEE International Conference on, volume 3, pages iii, 793-6 vol.3, May 2004.
[20] Jamal Saboune, Mehdi Arezoomand, Luc Martel, and Robert Laganiere. A visual
blindspot monitoring system for safe lane changes. In Proceedings of the 16th International Conference on Image Analysis and Processing - Volume Part II, ICIAP′11,
pages 1{10, Berlin, Heidelberg, 2011. Springer-Verlag.
[21] F. Heimes and H.-H. Nagel. Towards active machine-vision-based driver assistance for urban areas. Int. J. Comput. Vision, 50(1):5{34, October 2002.
[22] Shih-Shinh Huang and Li-Chen Fu. Driver assistance system for lane detection and
vehicle recognition with night vision. In Intelligent Robots and Systems, 2005. (IROS
2005). 2005 IEEE/RSJ International Conference on, pages 3530{3535, Aug 2005.
[23] Hsu-Yung Cheng, Bor-Shenn Jeng, Pei-Ting Tseng, and Kuo-Chin Fan. Lane detection with moving vehicles in the traffic scenes. Intelligent Transportation Systems,
IEEE Transactions on, 7(4):571{582, Dec 2006.
[24] V. N. Padmanabhan P. Mohan and R. Ramjee. Nericell. Using mobile smartphones
for rich monitoring of road and traffic conditions. In Proc. of ACM EMNETS, 2008.
[25] Jie Yang, Simon Sidhom, Gayathri Chandrasekaran, Tam Vu, Hongbo Liu, Nicolae Cecan, Yingying Chen, Marco Gruteser, and Richard P. Martin. Detecting driver phone use leveraging car speakers. In Proceedings of the 17th Annual International
Conference on Mobile Computing and Networking, MobiCom ′11, pages 97{108, New York, NY, USA, 2011. ACM.
[26] Tareq Alhmiedat, Anas Abu Taleb, and Mohammad Bsoul. A study on threats detection and tracking systems for military applications using wsns.
[27] Radiolocation https://en.wikipedia.org/wiki/radiolocation., 2015.
[28] J.J. Caery and G.L. Stuber. Overview of radiolocation in cdma cellular systems.
Communications Magazine, IEEE, 36(4):38{45, Apr 1998.
[29] Srdjan Capkun, Maher Hamdi, and Jean-Pierre Hubaux. Gps-free positioning in
mobile ad hoc networks. In Cluster Computing, pages 3481{3490, 2001.
[30] Ananth Rao, Sylvia Ratnasamy, Christos Papadimitriou, Scott Shenker, and Ion Stoica. Geographic routing without location information. In Proceedings of the 9th
Annual International Conference on Mobile Computing and Networking, MobiCom
′03, pages 96{108, New York, NY, USA, 2003. ACM.
[31] Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan. The cricket
location-support system. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, MobiCom ′00, pages 32{43, New York,
NY, USA, 2000. ACM.
[32] N. Bulusu, J. Heidemann, and D. Estrin. Gps-less low-cost outdoor localization for
very small devices. Personal Communications, IEEE, 7(5):28{34, Oct 2000.
[33] Chris Savarese and Jan M. Rabaey. Locationing in distributed ad-hoc wireless sensor
networks. In in ICASSP, pages 2037{2040, 2001.
[34] Andreas Savvides, Chih-Chieh Han, and Mani B. Strivastava. Dynamic fine-grained
localization in ad-hoc networks of sensors. In Proceedings of the 7th Annual International Conference on Mobile Computing and Networking, MobiCom ′01, pages
166{179, New York, NY, USA, 2001. ACM.
[35] S. Capkun, M. Hamdi, and J.-P. Hubaux. Gps-free positioning in mobile ad-hoc
networks. In System Sciences, 2001. Proceedings of the 34th Annual Hawaii Inter-
national Conference on, pages 10 pp.{, Jan 2001.
[36] R. Iyengar and B. Sikdar. Scalable and distributed gps free positioning for sensor
networks. In Communications, 2003. ICC ′03. IEEE International Conference on,
volume 1, pages 338{342 vol.1, May 2003.
[37] Ocial U.S. Government information about the Global Positioning System (GPS)
and related topics http://www.gps.gov/, 2015.
[38] Mike Harwood. CompTIA Network+ N10-004 Exam Cram. Que Publishing Company, 3rd edition, 2009.
[39] Erin-Ee-Lin Lau andWan-Young Chung. Enhanced rssi-based real-time user location
tracking system for indoor and outdoor environments. In Proceedings of the 2007
International Conference on Convergence Information Technology, ICCIT ′07, pages
1213{1218, Washington, DC, USA, 2007. IEEE Computer Society.
[40] Giovanni Maria Farinella, Sebastiano Battiato, and Roberto Cipolla. Advanced Topics in Computer Vision. Springer Publishing Company, Incorporated, 2013.
[41] Quick Response code https://en.wikipedia.org/wiki/qrcode, 2015.

指導教授

孫敏德(Min-Te Sun)

審核日期

2016-1-27

推文