An IMU Based Turn Prediction System

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：85

、訪客IP：3.139.94.174

姓名

葉芯妤(Hsin-Yu Yeh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(An IMU Based Turn Prediction System)

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)

根據台灣內政部的統計資料，有超過五分之一的死亡車禍是因為駕駛的不注意所導致。在這些死亡車禍中，很多是因為駕駛沒注意前車車況，太晚發現前車因為要轉彎而減速進而導致追撞。為了減少此類交通事故的發生，我們開發了一個轉彎預測系統，本系統能預測即將發生的轉彎事件，以提前通知鄰近車輛，藉此減少交通事故的發生。本系統包括一個車載單元(OBU)及包含數位路線地圖的智慧型手機。我們利用智慧型手機中的感測器來收集車輛的位置訊息，並使用粒子濾波器(Particle filters)來預測車輛未來的位置，然後利用這些位置來計算車輛軌跡的曲率。此外，我們根據位置訊息及數位路線地圖，可以計算出車輛目前所在位置的道路的曲率。藉由比較車輛軌跡曲率和道路曲率的差異以及車輛當時的行駛速度，系統可以判斷車輛是否將要發生轉彎事件。如果受偵測的車輛即將發生轉彎事件，系統便會利用車載單元廣播訊息給鄰近車輛，以警示鄰近車輛避免發生追撞，藉此提升行車安全。實驗結果表示，本系統可以判斷出所有的轉彎事件，且不需要安裝額外的硬體設備，這讓本系統可以容易推廣給大眾。

摘要(英)

Many fatal car accidents are rear-end collisions, i.e., drivers were not aware of the car in the front of them making a turn until it was too late. To help alleviate this issue, we develop a lightweight turn prediction system, which identies the forthcoming turn event at early stage so that the neighboring vehicles can be notied in advance to prevent trac accidents. The system consists of an On-board Unit and a smartphone containing the digital road maps. In the system, the IMU sensors in the smartphone collect position information of the vehicle and predict the future position using particle lters. These positions are then used to compute the curvature of the vehicle trace. In addition, from the position information and digital maps, we can also compute the curvature of the road the vehicle is currently on. By examine the dierence of these two curvatures as well as the speed of the vehicle, the system determines if the vehicle is making a turn at early stage. The experiment results show that the proposed system can identify all the turns perfectly on the road. In addition, the proposed system does not require extra hardware, which makes it possible for inexpensive wide deployment.

關鍵字(中)

★ 偵測車輛轉彎
★ 行車安全

關鍵字(英)

★ IMU
★ Particle filters

論文目次

1 Introduction 1
2 Related Work 6
2.1 Image-based . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Non Image-based . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.1 Beyond IMU Sensors . . . . . . . . . . . . . . . . . . . 7
2.2.2 IMU Sensors . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Preliminary 10
3.1 Particle lters . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Data processing techniques (moving average lters, complementary lters) . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.3 Coordinates transformation . . . . . . . . . . . . . . . . . . . 15
4 System Design 20
4.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2 Internal Calculation . . . . . . . . . . . . . . . . . . . . . . . . 22
4.3 Turn Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5 Experimental Validation 29
5.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . 29
5.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 40
6 Conclusions 49
Reference 51

參考文獻

[1] National Police Agency of Ministry of the Interior. Road Trac
accidents. http://stat.motc.gov.tw/mocdb/stmain.jsp?sys=100&
funid=b3303. [Online; accessed 12-January-2017].
[2] Department of Statistics of Ministry of the Interior. A Survey of Traffic Police Enforcement from Jan to Oct in 2015. http://www.moi.gov.tw/stat/news_content.aspx?sn=10073, 2015. [Online; accessed 12-January-2017].
[3] C. Cseh. Architecture of the dedicated short-range communications (DSRC) protocol. In Vehicular Technology Conference, 1998. VTC 98. 48th IEEE, May 1998.
[4] L. Reger. Securely connected vehicles - what it takes to make self-driving cars a reality. In 2016 21th IEEE European Test Symposium (ETS), May 2016.
[5] N. Kaempchen, B. Schiele, and K. Dietmayer. Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios. IEEE Transactions on Intelligent Transportation Systems, December 2009.
[6] I. Ghazi, I. U. Haq, M. R. Maqbool, and S. Saud. GPS based autonomous vehicle navigation and control system. In 2016 13th International Bhurban Conference on Applied Sciences and Technology (IBCAST), January 2016.
[7] S. Canale, A. D. Giorgio, F. Lisi, M. Panli, L. R. Celsi, V. Suraci, and F. D. Priscoli. A future internet oriented user centric extended intelligent transportation system. In 2016 24th Mediterranean Conference on Control and Automation (MED), June 2016.
[8] MOTC R.O.C Taiwan Area National Freeway Bureau. Intelligent
Transportation System Introduction. https://www.freeway.gov.tw/
Publish.aspx?cnid=1556, October 2007. [Online; accessed 14-January-2017].
[9] Ontario Trac Manual Committee. Advanced Trac Management Systems. 2007.
[10] J. Noonan and O. Shearer. Intelligent transportation systems eld operational test cross-cutting study: Advance traveler information systems. September 1998.
[11] M. El Koursi, C.-Y. Chan, and W.-B. Zhang. Preliminary hazard analyses: a case study of advanced vehicle control and safety systems. In Systems, Man, and Cybernetics, 1999. IEEE SMC ′99 Conference Proceedings. 1999 IEEE International Conference on, October 1999.
[12] A. Brown, M. Foy, and M. Zirker. Advanced public transportation systems: The state of the art, update 2000. December 2000.
[13] R. W. Hall and C. Intihar. Commercial vehicle operations: Government interfaces and intelligent transportation systems. In California PATH Research Report UCB-ITS-PRR-97-12, March 1997.
[14] A. Au and G. Chan. Automatic signaling system for vehicles, February 2013. US Patent 8,378,805.
[15] J. Schlechtriemen, A. Wedel, J. Hillenbrand, G. Breuel, and K. D. Kuhnert. A lane change detection approach using feature ranking with maximized predictive power. In 2014 IEEE Intelligent Vehicles Symposium Proceedings, June 2014.
[16] D. S. Breed. Vehicular anticipatory sensor system, April 2014.
[17] P. Khler, C. Connette, and A. Verl. Vehicle tracking using ultrasonic sensors amp; joined particle weighting. In Robotics and Automation (ICRA), 2013 IEEE International Conference on, May 2013.
[18] W.W. Su. Vehicular turning indicator, February 2006. US Patent
7,002,458.
[19] N. Akhtar, K. Pandey, and S. Gupta. Mobile application for safe driving. In Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on, April 2014.
[20] D. A. Johnson and M. M. Trivedi. Driving style recognition using a smartphone as a sensor platform. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), October 2011.
[21] W. Yao, H. Zhao, F. Davoine, and H. Zha. Learning lane change trajectories from on-road driving data. In 2012 IEEE Intelligent Vehicles Symposium (IV), June 2012.
[22] J. Liu, B. Cai, J. Wang, and W. Shangguan. Gnss/ins-based vehicle lane-change estimation using imm and lane-level road map. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), October 2013.
[23] R. Toledo-Moreo and M. A. Zamora-Izquierdo. Imm-based lane-change prediction in highways with low-cost gps/ins. IEEE Transactions on Intelligent Transportation Systems, March 2009.
[24] G.A. Ehlers. System and method for providing information to an operator of an emergency response vehicle, February 2007. US Patent 7,174,153.
[25] N. J. Gordon, D. J. Salmond, and A. F. M. Smith. Novel approach to nonlinear/non-gaussian bayesian state estimation. IEE Proceedings F -Radar and Signal Processing, April 1993.
[26] M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle lters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, February 2002.
[27] H. Ageson. A Comparison of Simple Moving Average Forecasting Algorithms Applied Using the Focus Forecasting Technique with a Traditional Application of Double Exponential Smoothing Forecasting Algorithm. 1986.
[28] W. T. Higgins. A comparison of complementary and kalman ltering. IEEE Transactions on Aerospace and Electronic Systems, May 1975.
[29] Automotive Research & Testing Center. Throttle and brake pedal control. https://www.artc.org.tw/chinese/03_service/03_02detail.aspx?pid=2226, 2012. [Online; accessed 9-January-2017].
[30] Taiwan police college. Conversion Table of vehicle braking distance. https://www.tpa.edu.tw/department/dep03/4e0b8f095c085340,
2006. [Online; accessed 9-January-2017].
[31] IWCU 4.2 OBU Specication. http://www.hhnet.com.tw/products_
show2.asp?pid=192. [Online; accessed 11-January-2017].

指導教授

孫敏德

審核日期

2017-1-19

推文