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Behavior Prediction and Planning for Intelligent Vehicles Based on Multi-vehicles Interaction and Game Awareness

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Cognitive Systems and Signal Processing (ICCSIP 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1006))

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Abstract

In this study, a maneuver prediction and planning framework is proposed on the basis of game theories for complex traffic scenarios. In this framework, the interaction and gaming between multiple vehicles are considered by employing the extensive form game theories, which were extensively researched for sequential gaming problems. Finally, this framework is applied and proved in different lane-change scenarios. The results show that this framework could predict other vehicles’ driving maneuvers and plan maneuvers for ego vehicles by considering interaction and gaming between multiple vehicles, which helps AVs understand the environment better and make the cooperative maneuver planning in complex traffic scenarios.

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References

  1. van Arem, B.: A Strategic approach to intelligent functions in vehicles. In: Eskandarian, A. (ed.) Handbook of Intelligent Vehicles, pp. 17–29. Springer, London (2012). https://doi.org/10.1007/978-0-85729-085-4_2

    Chapter  Google Scholar 

  2. Baines, V., Padget, J.: A situational awareness approach to intelligent vehicle agents. In: Behrisch, M., Weber, M. (eds.) Modeling Mobility with Open Data, pp. 77–103. Lecture Notes in Mobility. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15024-6_6

    Chapter  Google Scholar 

  3. Liu, H.P., Yu, Y.L., Sun, F.C., Gu, J.: Visual-tactile fusion for object recognition. IEEE Trans. Autom. Sci. Eng. 14(2), 996–1008 (2017)

    Article  Google Scholar 

  4. Peters, H.: Game Theory: A Multi-leveled Approach, 2nd edn. Springer, New York (2015)

    Book  Google Scholar 

  5. Gao, H.B., Cheng, B., Wang, J.Q., et al.: Object classification using CNN-based fusion of vision and LIDAR in autonomous vehicle environment. IEEE Trans. Ind. Inf. PP(99), 1 (2018)

    Google Scholar 

  6. Liu, H.P., Sun, F.C., Fang, B.: Robotic room-level localization using multiple sets of sonar measurements. IEEE Trans. Instrum. Meas. 66(1), 2–13 (2017)

    Article  Google Scholar 

  7. Gao, H.B., Zhang, X.Y., Liu, Y.C., et al.: Longitudinal control for Mengshi autonomous vehicle via gauss cloud model. Sustainability 9(12), 2259–2275 (2017)

    Article  Google Scholar 

  8. Liu, H.P., Sun, F.C., Guo, D., et al.: Structured output-associated dictionary learning for haptic understanding. IEEE Trans. Syst. Man Cybern.: Syst. 47(7), 1564–1574 (2017)

    Article  Google Scholar 

  9. Kim, K., Kim, B., Lee, K., Ko, B., Yi, K.: Design of integrated risk management-based dynamic driving control of automated vehicles. IEEE Intell. Transp. Syst. Mag. 9(1), 57–73 (2017)

    Article  Google Scholar 

  10. Xie, G.T., Gao, H.B., Qian, L.J., et al.: Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models. IEEE Trans. Ind. Electron. 56(7), 5999–6008 (2017)

    Article  Google Scholar 

  11. Bahram, M., Hubmann, C., Lawitzky, A., Aeberhard, M., Wollherr, D.: A combined model-and learning-based framework for interaction-aware maneuver prediction. IEEE Trans. Intell. Transp. Syst. 17(6), 1538–1550 (2016)

    Article  Google Scholar 

  12. Gao, H.B., Zhang, X.Y., Zhang, T.L., et al.: Research of intelligent vehicle variable granularity evaluation based on cloud model. Acta Electronica Sinica 44(2), 365–374 (2016)

    MathSciNet  Google Scholar 

  13. Li, K., Wang, X., Xu, Y., Wang, J.: Lane changing intention recognition based on speech recognition models. Transp. Res. C-Emerg. 69, 497–514 (2016)

    Article  Google Scholar 

  14. Huang, J., Tan, H.S.: Vehicle future trajectory prediction with a DGPS/INS-based positioning system. In: American Control Conference, Minneapolis, MN, USA, pp. 5831–5836, June 2006

    Google Scholar 

  15. Sorstedt, J., Svensson, L., Sandblom, F., Hammarstrand, L.: A new vehicle motion model for improved predictions and situation assessment. IEEE Trans. Intell. Transp. Syst. 12(4), 1209–1219 (2011)

    Article  Google Scholar 

  16. Polychronopoulos, A., Tsogas, M., Amditis, A.J., Andreone, L.: Sensor fusion for predicting vehicles’ path for collision avoidance systems. IEEE Trans. Intell. Transp. Syst. 8(3), 549–562 (2007)

    Article  Google Scholar 

  17. Hou, Y., Edara, P., Sun, C.: Modeling mandatory lane changing using Bayes classifier and decision trees. IEEE Trans. Intell. Transp. Syst. 15(2), 647–655 (2014)

    Article  Google Scholar 

  18. Ding, C., Wang, W., Wang, X., Baumann, M.: A neural network model for driver’s lane-changing trajectory prediction in urban traffic flow. Math. Probl. Eng. 2013, 8 p. Article ID 967358 (2013)

    Google Scholar 

  19. Peng, J., Guo, Y., Fu, R., Yuan, W., Wang, C.: Multi-parameter prediction of drivers’ lane-changing behaviour with neural network model. Appl. Ergon. 50, 207–217 (2015)

    Article  Google Scholar 

  20. Gadepally, V., Krishnamurthy, A., Ă–zgĂĽner, Ăś.: A framework for estimating long term driver behavior. J. Adv. Transport. 2017, 11 p. Article ID. 3080859 (2017)

    Google Scholar 

  21. Liu, P., Kurt, A.: Trajectory prediction of a lane changing vehicle based on driver behavior estimation and classification. In: 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, pp. 942–947 (2014)

    Google Scholar 

  22. Li, F., Wang, W., Feng, G., Guo, W.: Driving intention inference based on dynamic Bayesian networks. In: Wen, Z., Li, T. (eds.) Practical Applications of Intelligent Systems. AISC, vol. 279, pp. 1109–1119. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54927-4_106

    Chapter  Google Scholar 

  23. Gindele, T., Brechtel, S., Dillmann, R.: Learning driver behavior models from traffic observations for decision making and planning. IEEE Intell. Transp. Syst. Mag. 7(1), 69–79 (2015)

    Article  Google Scholar 

  24. Talebpour, A., Mahmassani, H.S., Hamdar, S.H.: Modeling lane-changing behavior in a connected environment: a game theory approach. Transport. Res. C-Emerg. 59, 216–232 (2015)

    Article  Google Scholar 

  25. Liu, H.X., Xin, W., Adam, Z., Ban, J.: A game theoretical approach for modelling merging and yielding behaviour at freeway on-ramp sections, pp. 197–211. Elsevier, London (2007)

    Google Scholar 

  26. Meng, F., Su, J., Liu, C., Chen, W.H.: Dynamic decision making in lane change: game theory with receding horizon. In: 2016 UKACC 11th International Conference on Control, Belfast, pp. 1–6 (2016)

    Google Scholar 

  27. Gratner, A., Annell, S.: Probabilistic collision estimation system for autonomous vehicles: evaluated in intersection scenarios using a velocity planning controller. M.S. thesis, Industrial Engineering and Management, KTH, Stockholm, Sweden (2016)

    Google Scholar 

  28. Bahram, M., Lawitzky, A., Friedrichs, J., Aeberhard, M., Wollherr, D.: A game-theoretic approach to replanning-aware interactive scene prediction and planning. IEEE Trans. Veh. Technol. 65(6), 3981–3992 (2016)

    Article  Google Scholar 

  29. Schreier, M., Willert, V., Adamy, J.: An integrated approach to maneuver-based trajectory prediction and criticality assessment in arbitrary road environments. IEEE Trans. Intell. Transp. Syst. 17(10), 2751–2766 (2016)

    Article  Google Scholar 

  30. Gao, H.B., Zhang, X.Y., Liu, Y.C., et al.: Cloud model approach for lateral control of intelligent vehicle systems. Sci. Program. 2016(2), 1–12 (2016)

    Google Scholar 

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Acknowledgments

This work was supported by China Postdoctoral Science Foundation Special Funded Projects under Grant No. 2018T110095, Project funded by China Postdoctoral Science Foundation under Grant No. 2017M620765, National Key Research and Development Program of China under Grant No. 2017YFB0102603, and Junior Fellowships for Advanced Innovation Think-tank Program of China Association for Science and Technology under Grant No. DXB-ZKQN-2017-035.

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Correspondence to Guotao Xie .

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Gao, H., Xie, G., Wang, K., Liu, Y., Li, D. (2019). Behavior Prediction and Planning for Intelligent Vehicles Based on Multi-vehicles Interaction and Game Awareness. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2018. Communications in Computer and Information Science, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-7986-4_39

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  • DOI: https://doi.org/10.1007/978-981-13-7986-4_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7985-7

  • Online ISBN: 978-981-13-7986-4

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