(3.231.29.122) 您好!臺灣時間:2021/02/26 01:08
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:劉員成
研究生(外文):Yuan-Cheng Liu
論文名稱:以機率模型預測都會區路口的駕駛行為
論文名稱(外文):Probabilistic Modeling of Driver Behaviors at Urban Crossroads
指導教授:詹魁元
指導教授(外文):Kuei-Yuan Chan
口試委員:鄭榮和劉霆
口試委員(外文):Jung-Ho ChengTyng Liu
口試日期:2019-07-19
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:機械工程學研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:107
中文關鍵詞:混流無人駕駛機率模型駕駛行為十字路口互動模型避障
DOI:10.6342/NTU201903004
相關次數:
  • 被引用被引用:0
  • 點閱點閱:29
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
無人車與人類駕駛的互動與溝通,將會在不遠的將來成為主要的議題。在這篇論文中,本論文將研究聚焦於最仰賴互動的無交通號誌十字路口。為了研究駕駛是如何進行決策來安全通過路口,本論文首先定義了路口的重要參數,包括了互動車輛的速度與離路口的距離,並定義了一個駕駛決策行為。藉由研究此決策行為,本論文得到了互動車輛煞車讓道的機率,以及駕駛行為的機率模型。為了驗證此模型,本論文在模擬環境中進行測試,並蒐集真人駕駛的數據進行分析。所得到的驗證結果證明,所提出模型的預測準確率與現有方法接近,且具有更廣泛的應用,同時此預測模型也能夠反映出不同駕駛特徵參數的差異。接著,本論文嘗試使用最佳化方法,藉由所蒐集數據進行駕駛行為的特徵參數回推。儘管此參數辨認的準確率尚有改進空間,目前所得到結果證明了所提出模型在此應用上的可行性。同時,為了驗證此模型在實際路口的可行性,本論文蒐集了真實路口的車輛數據並使用所提出模型進行行為預測,所得結果與模擬環境中的結果一致。最後,本論文將所提出模型應用在無人車的決策行為上並與人類駕駛進行互動,初步結果證明無人車的駕駛行為能夠更順暢的與人類駕駛進行互動。
The interactions with human drivers is one of the major challenges for autonomous vehicles in the near future. In this work we consider urban crossroads without signals where driver interactions are indispensable. Crossroad parameters are defined and how drivers passing the crossroad while maintaining a desired speed without collision is studied. A point of action is defined for incoming vehicles from different directions and a probability of yielding for each car is proposed as a function of vehicle speed and the distance-to-intersection for both vehicles. Driver behaviors with these probability models are also proposed. The method is then analyzed and validated by data collected from human drivers in the simulated environments. The result shows comparable prediction accuracy to the state of the art method, where characteristic parameters of drivers are also shown to be critical for the behavior predictions. Afterwards, parameters representing driving styles of drivers are attempted to identify using the optimization approach. In spite of the limited accuracy of parameter identifications, important attributes of the proposed model as well as possible modification are pinpointed. The proposed model is also applied at the urban crossroads to evaluate the applicability in real world. The prediction results are analogous to those acquired in virtual environments. Finally, a procedure is constructed to achieve smoother interactions with human drivers. Preliminary results suggested a human-like computer driver is born while more instances and aspects of evaluations should be accomplished in the future work.
口試委員會審定書 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . vi
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv
LIST OF ABBREVIATIONS . . . . . . . . . . . . . . . . . . . . . . . . . xv
摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii
CHAPTER
I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . 4
II. Background and Literature Review . . . . . . . . . . . . . . . . 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Physics-Based Models . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Driver Behavior Models . . . . . . . . . . . . . . . . . . . . . 9
2.4 Interactive Models . . . . . . . . . . . . . . . . . . . . . . . . 10
2.5 Explicit Driver Behavior Models . . . . . . . . . . . . . . . . 11
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
III. Human Driver Modeling at Crossroads . . . . . . . . . . . . . 13
3.1 Time to Collision and Time for Action . . . . . . . . . . . . . 14
3.1.1 Crossroad Modeling . . . . . . . . . . . . . . . . . . 14
3.1.2 Time to Collision . . . . . . . . . . . . . . . . . . . 16
3.1.3 Time for Action . . . . . . . . . . . . . . . . . . . . 17
3.2 The TFA Distribution . . . . . . . . . . . . . . . . . . . . . 22
3.2.1 TFA Distribution . . . . . . . . . . . . . . . . . . . 22
3.2.2 TFA Probability Density Function . . . . . . . . . 27
3.3 Probability of Yielding . . . . . . . . . . . . . . . . . . . . . 30
3.3.1 TFA Distribution Modeling . . . . . . . . . . . . . 31
3.3.2 The Model for Probability of Yielding Estimation . 37
3.4 Driver Intentions Prediction with Probability of Yielding . . 44
3.4.1 Urban Crossroads in Simulated Environment . . . . 44
3.4.2 Experimental Results in Simulated Environment . . 49
3.4.3 Validation for Experiments in Simulated Environment 57
IV. Model Parameters Identification . . . . . . . . . . . . . . . . . . 61
4.1 Characteristic Parameters . . . . . . . . . . . . . . . . . . . . 61
4.2 Objective Function and Constraints for Optimization . . . . 67
4.3 Optimization Using Simulated Annealing . . . . . . . . . . . 71
V. Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.1 Crash Examinations in the Simulated Crossroad . . . . . . . 81
5.1.1 Crashing Likelihood Using TFA Distributions . . . 82
5.1.2 TTC differences and POYs . . . . . . . . . . . . . . 87
5.2 POY Estimation at Urban Crossroads . . . . . . . . . . . . . 91
5.3 Procedures for Autonomous Vehicle . . . . . . . . . . . . . . 96
VI. Conclusions and Future Works . . . . . . . . . . . . . . . . . . . 101
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . 102
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
[1] S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, et al. Stanley: The robot that won the darpa grand challenge. Journal of field Robotics, 23(9):661–692, 2006.
[2] P. Bansal and K. M. Kockelman. Forecasting americans’long-term adoption of connected and autonomous vehicle technologies. Transportation Research Part A: Policy and Practice, 95:49 – 63, 2017.
[3] US Department of Transportation. Preparing for the future of transportation: Automated vehicles 3.0 (av 3.0). Technical report, sep 2018. Accessed: 2019-06-23.
[4] Todd Litman. Autonomous vehicle implementation predictions: Implications for transport planning. Technical report, Victoria Transp. Policy Inst., Victoria, BC, Canada, 2015.
[5] California DMV. Report of traffic collision involving an autonomous vehicle (ol316), 2019.
[6] B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions on Intelligent Vehicles, 1(1):33–55, March 2016.
[7] S. Lefèvre, D. Vasquez, and C. Laugier. A survey on motion prediction and risk assessment for intelligent vehicles. ROBOMECH Journal, 1(1):1–14, 2014.
[8] S. Ammoun and F. Nashashibi. Real time trajectory prediction for collision risk estimation between vehicles. In 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, pages 417–422, Aug 2009.
[9] P. Fiorini and Z. Shiller. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 17(7):760–772, 1998.
[10] W. Zhan, C. Liu, C. Chan, and M. Tomizuka. A non-conservatively defensive strategy for urban autonomous driving. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 459–464, Nov 2016.
[11] M. Ruf, J. Ziehn, B. Rosenhahn, J. Beyerer, D. Willersinn, and H. Gotzig. Situation prediction and reaction control (sparc). In B. Färber, K. Dietmayer, K. Bengler, M. Maurer, Ch. Stiller, and H. Winner, editors, 9. Workshop Fahrerassistenzsysteme (FAS 2014), pages 55–66, Walting im Altmühltal, Germany, March 2014.
[12] S. Lefèvre, C. Laugier, and J. Ibañez-Guzmán. Evaluating risk at road intersections by detecting conflicting intentions. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 4841–4846, Oct 2012.
[13] Ismail Dagli, Michael Brost, and Gabi Breuel. Action recognition and prediction for driver assistance systems using dynamic belief networks. In Jaime G. Carbonell, Jörg Siekmann, Ryszard Kowalczyk, Jörg P. Müller, Huaglory Tianfield, and Rainer Unland, editors, Agent Technologies, Infrastructures, Tools, and Applications for E-Services, pages 179–194, Berlin, Heidelberg, 2003. Springer Berlin Heidelberg.
[14] T. Gindele, S. Brechtel, and R. Dillmann. Learning context sensitive behavior models from observations for predicting traffic situations. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pages 1764–1771, Oct 2013.
[15] A. Foka and P. Trahanias. Probabilistic autonomous robot navigation in dynamic environments with human motion prediction. International Journal of Social Robotics, 2(1):79–94, Mar 2010.
[16] C. Hubmann, J. Schulz, M. Becker, D. Althoff, and C. Stiller. Automated driving in uncertain environments: Planning with interaction and uncertain maneuver prediction. IEEE Transactions on Intelligent Vehicles, 3(1):5–17, March 2018.
[17] M. Liebner, M. Baumann, F. Klanner, and C. Stiller. Driver intent inference at urban intersections using the intelligent driver model. In 2012 IEEE Intelligent Vehicles Symposium, pages 1162–1167, June 2012.
[18] R. Graf, H. Deusch, F. Seeliger, M. Fritzsche, and K. Dietmayer. A learning concept for behavior prediction at intersections. In 2014 IEEE Intelligent Vehicles Symposium Proceedings, pages 939–945, June 2014.
[19] A. Kemeny and F. Panerai. Evaluating perception in driving simulation experiments. Trends in Cognitive Sciences, 7(1):31 – 37, 2003.
[20] J.K. Caird and P.A. Hancock. The perception of arrival time for different oncoming vehicles at an intersection. Ecological Psychology, 6(2):83–109, 1994.
[21] J. Hayward. Near miss determination through use of a scale of danger. Report TTSC-7115, 1972.
[22] J. Tresilian. Visually timed action: time-out for ‘tau’? Trends in Cognitive Sciences, 3(8):301 – 310, 1999.
[23] V. Cavallo and M Laurent. Visual information and skill level in time-to-collision estimation. Perception, 17(5):623–632, 1988. PMID: 3249670.
[24] W. Winsum and W. Brouwer. Time headway in car following and operational performance during unexpected braking. Perceptual and Motor Skills, 84(3_suppl):1247–1257, 1997. PMID: 9229443.
[25] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671–680, 1983.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關論文
 
無相關期刊
 
無相關點閱論文
 
系統版面圖檔 系統版面圖檔