Smoothing GPS Data for Vehicle Collision Avoidance

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姓名

阮源翔(Yuan-Hsiang Juan) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Smoothing GPS Data for Vehicle Collision Avoidance)

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摘要(中)

近年來，隨著交通事故發生的數量日漸增加，如何及早預防交通事故的發生漸漸變成一個相當重要的議題。本論文提出了一套車輛路徑預測的演算法，此演算法計算出車輛的未來軌跡後，並利用預測的軌跡來偵測車輛在未來一段時間內是否有可能會與其他車輛發生碰撞。我們所提出的方法是利用高精準度的定位系統來蒐集車輛資訊。由於蒐集的部分資料會有數值起伏較大的情況，所以我們利用三種不同的平滑資料方法來處理資料並增加預測結果的準確性。在實驗中，我們蒐集三個資料集來評估演算法的效能。實驗結果也顯示此演算法有效地降低預測的車輛位置與實際的車輛位置之間的誤差距離。

摘要(英)

As the number of traffic accidents increases in recent years, how to prevent traffic accidents becomes more and more important. In this thesis, we propose a vehicle trajectory prediction algorithm which can be used to detect future collisions in advance. The proposed algorithm uses the high precision GPS built in the ITRI WAVE/DSRC Communications Onboard Unit to collect the vehicle information. Because part of the collected data appear to fluctuate, three different smoothing procedures are used to process the data to improve the accuracy of the prediction result. In the experiments, three datasets are collected to evaluate the performance of the prediction algorithm. The experiment results show that the proposed algorithm significantly reduces the deviation between the predicted position of vehicle and the ground truth.

關鍵字(中)

★ 平滑方法
★ 車輛路徑預測
★ 車輛安全
★ 預防碰撞

關鍵字(英)

論文目次

1 Introduction 1
2 Related Work 5
2.1 Non Image-based Approach.......................... 5
2.1.1 GPS................................... 5
2.1.2 Digital Maps.............................. 7
2.1.3 Other Sensors.............................. 7
2.1.4 CAMP.................................. 8
2.2 Image-based................................... 11
3 Preliminary 13
3.1 Moving Average................................. 13
3.2 Exponential Smoothing............................. 14
3.3 Holt′s Exponential Smoothing......................... 15
4 Design 18
4.1 Data Collection................................. 18
4.2 Internal Calculation............................... 19
4.3 Smoothing Procedure.............................. 20
4.3.1 Moving Average............................. 20
4.3.2 Exponential Smoothing......................... 21
4.3.3 Holt′s Exponential Smoothing..................... 24
4.4 Future Location Calculation.......................... 26
4.4.1 The Basic Idea............................. 26
4.4.2 Case Discussion............................. 28
iii
5 Performance 30
5.1 Data Collection................................. 30
5.2 Experiment Result............................... 31
6 Conclusions 46
Reference 47

參考文獻

[1] Iwcu 4.4 obu speciﬁcation. https://www.itri.org.tw/chi/Content/MsgPic01/Contents.aspx?SiteID=1&MmmID=620624054325020775&MSid=620634640463241437.
[2] The statistics of road traﬃc accidents from national police agency of ministry of the interior. http//stat.motc.gov.tw/mocdb/stmain.jsp?sys=100&funid=b3303.
[3] 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. Simon Fraser University, 1986.
[4] Phil Blunsom. Hidden markov models. 2004.
[5] Derek Caveney. Hierarchical software architectures and vehicular path prediction for cooperative driving system. In IEEE/ITS, 2008.
[6] Derek Caveney. Vehicular path prediction for cooperative driving applications through digital map and dynamic vehicle model fusion. In IEEE/70th Vehicular Technology Conference Fall, 2009.
[7] Eddie C. L. Chan and George Baciu. 2012.
[8] Clif Droke. Moving Averages Simpliﬁed. Marketplace Books, 2001.
[9] Ayman El-Fatatry. Inertial measurement units imu. 2004.
[10] Ahmed El-Rabbany. 2002.
[11] Ahmed-Zaid F., Bai F., Bai S., Basnayake C., Bellur B., Brovold S., Brown G., Caminiti L., Cunningham D., Elzein H., Hong K., Ivan J., Jiang D., Kenney J., Krishnan H., Lovell J., Maile M., Masselink D., McGlohon E., Mudalige P., Popovic Z., Rai V., Stinnett J., Tellis L., Tirey K., and VanSickle S. Vehicle safety communi cations applications (vsc-a). In Intelligent Transportation System (ITS), volume 1, September 2011.
[12] Jianchen Gao. Gps/ins/g sensors/yaw rate sensor/wheel speed sensors integrated vehicular positioning system. 2006.
[13] Xinli Geng, Huawei Liang, Hao Xu, Biao Yu, and Maofei Zhu. Human-driver speed proﬁle modeling for autonomous vehicles velocity strategy on curvy paths. In IEEE/ Intelligent Vehicles Symposium (IV), 2016.
[14] Mohinder S. Grewal. International Encyclopedia of Statistical Science. 2011.
[15] Simon Haykin. 2002.
[16] Jihua Huang and Han-Shue Tan. Vehicle future trajectory prediction with a dgps/ins based positioning system. In ACC, 2006.
[17] Rob Hyndman, Anne Koehler, Keith Ord, and Ralph Snyder. Forecasting with Exponential Smoothing. Springer Berlin Heidelberg, 2008.
[18] ITRI. Crash avoidance metrics partnership. In Intelligent Transportation System(ITS), 2011.
[19] Kun Jiang, Alessandro Victorino, and Ali Charara. Estimation and prediction of vehicle dynamics states based on fusion of openstreetmap and vehicle dynamics models. In IEEE/Intelligent Vehicles Symposium (IV), 2016.
[20] Kalekar and Prajakta S. Time series forecasting using holt-winters exponential smoothing. December 2004.
[21] Billy Kihei, John A. Copeland, and Yusun Chang. Predicting car collisions using rssi. In IEEE/Global Communications Conference (GLOBECOM), 2015.
[22] Philipp Kohler, Christian Connette, and Alexander Verl. Vehicle tracking using ultrasonic sensors and joined particle weighting. In IEEE/International Conference on Robotics and Automation(ICRA), May 2013.
[23] Arun Kumar and Sima Noghanian. Wireless channel test-bed for dsrc applications using usrp software deﬁned radio. In Antennas and Propagation Society International Symposium (APSURSI), 2013 IEEE, pages 2105–2106. IEEE, 2013.
[24] Chiu-Feng Lin and A. Galip Ulsoy. Vehicle dynamics and external disturbance estimation for future vehicle path prediction. In American Control Conference, volume 1, 1995.
[25] Leonardo Martinez, Mark Paulik, Mohan Krishnan, and Eyad Zeino. Map-based lane identiﬁcation and prediction for autonomous vehicles. In IEEE International Conference on Electro/Information Technology, 2014.
[26] Tyler Mitchell. 2005.
[27] Yuanfu Mo, Dexin Yu, Jun Song, Kun Zheng, and Yajuan Guo. Vehicle position updating strategy based on kalman ﬁlter prediction in vanet environment. In Discrete Dynamics in Nature and Society, 2016.
[28] Narin Persad-Maharaj, Sean J. Barbeau, Miguel A. Labrador, Philip L. Winters, Rafael Prez, and Nevine Labib Georggi. Real-time travel path prediction using gps enabled mobile phones. In Intelligent Transportation Systems, 2008.
[29] Zhen-Wei Zhu Ping-Min Hsu. Car trajectory prediction in image processing and control manners. 2016.
[30] ALI TAUSEEF REZA, T ANIL KUMAR, and T SIVAKUMAR. Position prediction based multicast routing (ppmr) using kalman ﬁlter over vanet. In IEEE/ International Conference on Engineering and Technology (ICETECH), 2016.
[31] Jochen Schiller and Agnes Voisard. 2004.
[32] Julian Schlechtriemen, Gabi Breuel Andreas Wedel, Joerg Hillenbrand, and Klaus Dieter Kuhnert. A lane change detection approach using feature ranking with maximized predictive power. In IEEE/Intelligent Vehicles Symposium, 2014.
[33] Stuart B Smithline. Collision avoidance system, March 31 1998. US Patent 5,734,336.
[34] Thomas Streubel and Karl Heinz Hoﬀmann. Prediction of driver intended path at intersections. In IEEE/ Intelligent Vehicles Symposium, 2014.
[35] S. H. Tsang, P. S. Hall E. G. Hoare, and N. J. Clarke. Automotive radar image processing to predict vehicle trajectory. In Communications, IEEE Transactions on, 1999.
[36] B. Wikipedians. Derivatives. PediaPress.
[37] YoungJoon Yoo, Sangdoo Yun Kimin Yun, Hawook Jeong JongHee Hong, and Jin Young Choi. Visual path prediction in complex scenes with crowded moving objects. In IEEE/Computer Vision and Pattern Recognition, 2016.

指導教授

孫敏德(Min-Te Sun)

審核日期

2017-6-28

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