臺灣博碩士論文加值系統

在本篇論文中，我們提出一個使用車間 (vehicle-to-vehicle , V2V)通訊並且不需要基礎設施的協同式車道定位(infrastructure-less cooperative lane positioning , ICLP)解決方案，ICLP應用了車載感測網路來定位車輛位於道路上的哪個車道，並且讓裝備影像感測器的車輛不須使用不精準的GPS定位和昂貴的基礎路側設施便可以偵測出自己位於哪個車道。ICLP可以透過影像感測器持續辨識一輛車目前自身所在的車道位置，當有所需要時還可以透過V2V通訊向其他同車道的前方車輛發出車道位置資訊的請求。ICLP讓依靠高定位精準度智慧運輸系統的創新應用，像協同式車道等級防碰撞、動態交通控制和智慧型個人導航成為可能。我們推導出一個用來分析ICLP的預期定位成功率數學模型，此外，我們也實作出一個以Android為基礎的雛型系統來驗證我們解決方案的可行性和優勢，實測結果也顯示出ICLP可以達到很高的定位成功率，並且提供車輛精準的車道位置。

In this paper, we propose an infrastructure-less cooperative lane positioning (ICLP) framework using vehicle-to-vehicle (V2V) communications. The ICLP framework applies vehicular sensor networks to localize lane positions of vehicles on roads. ICLP allows vehicles equipped with image sensors to detect the current located lane without utilizing inaccurate GPS locations and costly roadside infrastructures. Through image sensors, ICLP can keep recognizing the lane position for a vehicle. With V2V communications, ICLP can request lane position information from other vehicles in the same lane as necessary. ICLP makes innovative applications of intelligent transportation systems relying on high positioning accuracy possible, such as lane-level cooperative collision avoidance, dynamic traffic control, and intelligent personal navigation. An analytical model is derived to estimate the expected positioning success ratio of ICLP. In addition, we verify the superiority and feasibility by implementing our framework in an Android-based prototype. Experimental results show that ICLP can achieve high positioning success ratios and provide accurate lane positions for vehicles.

誌謝...............................................i
摘要..............................................ii
Abstract.........................................iii
Contents..........................................iv
List of Figures....................................v
List of Tables...................................vii
Chapter 1 Introduction.............................1
Chapter 2 System Model.............................4
Chapter 3 Cooperative Lane Positioning Framework...6
Chapter 4 Analysis Of Positioning Success Ratio...10
Chapter 5 System Implementation...................14
Chapter 6 Experimentation.........................17
Chapter 7 Conclusion..............................24
References........................................25
Vita..............................................28

[1] L.-W. Chen, Y.-H. Peng, and Y.-C. Tseng. An infra-structure-less framework for preventing rear-end collisions by vehicular sensor networks. IEEE Communications Letters, 15(3):358-360, Mar. 2011.
[2] L.-W. Chen, P. Sharma, and Y.-C. Tseng. Dynamic Traffic Control with Fairness and Throughput Op-timization Using Vehicular Communications. IEEE Journal on Selected Areas in Communications, 31(9):504-512, Sept. 2013.
[3] D. Betaille and R. Toledo-Moreo. Creating Enhanced Maps for Lane-Level Vehicle Navigation. IEEE Transactions on Intelligent Transportation Systems, 11(4):786-798, Dec. 2010.
[4] N. M. Drawil and O. Basir. Intervehi-cle-communication-assisted localization. IEEE Transactions on Intelligent Transportation Systems, 11(3):678-690, Sept. 2010.
[5] K. Liu, H. B. Lim, E. Frazzoli, H. Ji, and V.C.S. Lee. Improving Positioning Accuracy Using GPS Pseu-dorange Measurements for Cooperative Vehicular Localization. IEEE Transactions on Vehicular Technology, 63(6):2544-2556, July 2014.
[6] W. Cheng, S. Wang, X. Cheng. Virtual track: applica-tions and challenges of the RFID system on roads. IEEE Network, 28(1):42-47, Jan.-Feb. 2014.
[7] E.-K. Lee, Y. M. Yoo, C. G. Park, M. Kim, and Mario Gerla. Installation and Evaluation of RFID Readers on Moving Vehicles. In Proc. of ACM International Workshop on Vehicular Internetworking (VANET), 2009.
[8] M. Collotta, G. Pau, V.M. Salerno, and G. Scata. Wireless Sensor Networks to Improve Road Moni-toring (Chapter 15). Wireless Sensor Networks - Technology and Applications, July, 2012.
[9] K. Kwong, R. Kavaler, R. Rajagopal, and P. Varaiya. Real-Time Measurement of Link Vehicle Count and Travel Time in a Road Network. IEEE Transactions on Intelligent Transportation Systems, 11(4):814-825, Dec. 2010.
[10] N. Alam, AT. Balaie, and AG. Dempster. A DSRC-Based Traffic Flow Monitoring and Lane Detection System. In IEEE 73rd Vehicular Tech-nology Conference (VTC-Spring), pages 1-5, May 2011.
[11] N. Alam, AT. Balaei, and AG. Dempster. An In-stantaneous Lane-Level Positioning Using DSRC Carrier Frequency Offset. IEEE Transactions on In-telligent Transportation Systems, 13(4):1566-1575, Dec. 2012.
[12] N. Alam and AG. Dempster. Cooperative Positioning for Vehicular Networks: Facts and Future. IEEE Transactions on Intelligent Transportation Systems, 14(4):1708-1717, Dec. 2013.
[13] S. Mazuelas, F. A. Lago, D. Gonzalez, A. Bahillo, J. Blas, P. Fernandez, R. M. Lorenzo, and E. J. Abril. Dynamic estimation of optimum path loss model in a RSS positioning system. In Proc. of IEEE PLANS, 2008.
[14] D. Zhou and T. H. Lai. An accurate and scalable clock synchronization protocol for IEEE 802.11-based multihop ad hoc networks. IEEE Transactions on Parallel and Distributed System, 18(12):1797-1808, Dec. 2007.
[15] R. K. Satzoda, and M. M. Trivedi. Drive Analysis Using Vehicle Dynamics and Vision-Based Lane Semantics. IEEE Transactions on Intelligent Trans-portation Systems, 16(1): 9-18, Feb. 2015.
[16] Y. Wanga, E. K. Teoha, and D. Shenb. Lane detection and tracking using B-Snake. ELSEVIER Image and Vision Computing, 22(4):269-280, Apr. 2004.
[17] C. Guo, S. Mita, and D. McAllester. Lane Detection and Tracking in Challenging Environments Based on a Weighted Graph and Integrated Cues. IEEE Inter-national Conference on Intelligent Robots and Sys-tems, Oct, 2010.
[18] G. Liu, F. Worgotter, and I. Markelic. Stochastic Lane Shape Estimation Using Local Image De-scriptors. IEEE Transactions on Intelligent Trans-portation Systems, 14(1):13-21, Mar. 2013.
[19] L.-W. Chen, Y.-F. Ho, C.-C. Chang, and Y.-C. Tseng. A Video-Based Metropolitan Positioning System with Centimeter-Grade Localization for VANETs. IEEE International Conferenceon Pervasive Computing and Communications (PerCom), Mar. 2015. (Recip-ient of Best Demo Award)
[20] C. Campolo, A. Molinaro, A. V. Vinel, Y. Zhang. Modeling Event-Driven Safety Messages Delivery in IEEE 802.11p/WAVE Vehicular Networks. IEEE Communications Letters, 17(12):2392-2395, Dec. 2013.
[21] LUXGEN’s Advanced Technologies. http://www.luxgen-motor.com/technology/technology-experience.asp.
[22] Y. Park and H. Kim. Application-level Frequency Control of Periodic Safety Message in the IEEE WAVE. IEEE Transactions on Vehicular Technology, 61(4):1854-1862, May 2012.
[23] IEEE P1609.4/D6.0. Draft Standard for Wireless Access in Vehicular Environments (WAVE) - Mul-ti-channel Operation. Mar. 2010.
[24] L.-W. Chen, Y.-C. Tseng, and K.-Z. Syue. Surveil-lance on-the-road: Vehicular tracking and reporting by V2V communications. ELSEVIER Computer Networks, 67(4):154-164, July 2014.
[25] R. Toledo-Moreo, D. Betaille, and F. Peyret. Lane-Level Integrity Pro-vision for Navigation and Map Matching With GNSS, Dead Reckoning, and Enhanced Maps. IEEE Transactions on Intelligent Transportation Systems, 11(1):100-112, Mar. 2010.
[26] Open Source Computer Vision (OpenCV). http://opencv.org
[27] ITRI WAVE Communication Unit. http://www.itri.org.tw.
[28] IEEE P1609.3/D5.0. Draft Standard for Wireless Access in Vehicular Environments (WAVE) - Net-working Services. Mar. 2010.