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Differential-Game-Based Driver Assistance System for Fuel-Optimal Driving

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Frontiers of Dynamic Games

Part of the book series: Static & Dynamic Game Theory: Foundations & Applications ((SDGTFA))

Abstract

Increasing the fuel-efficiency is a current and essential question for all major car manufacturers. Supporting these efforts, the paper presents a shared control driver assistance system that may help the driver to apply a fuel-efficient driving strategy. For the proposed system, both driver and assistance system can apply forces to the acceleration pedal enabling a close cooperation between the two partners. The interaction between driver and such kind of assistance system can be described by means of a differential game. By solving this differential game, the assistance system calculates optimal control outputs. For realization, the assistance system is required to solve different game theoretic problems that are presented in this paper. The assistance system was implemented on a real time system, integrated in a driving simulator and validated in a driving study. The results indicate that the proposed system is able to save in average about 10% fuel in a highway scenario.

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References

  1. Abbink, D.A.: Neuromuscular analysis of haptic gas pedal feedback during car following. Doctoral Thesis, Delft University of Technology (2006)

    Google Scholar 

  2. Automation from driver assistance systems to automated driving. Tech. rep., Verband der Automobilindustrie (2015)

    Google Scholar 

  3. Bainbridge, L.: Ironies of automation. Automatica 19, 775–779 (1983)

    Article  Google Scholar 

  4. Borrelli, F., Bemporad, A., Morari, M.: Predictive control for linear and hybrid systems (2014). http://control.ee.ethz.ch/stdavid/BBMbookCambridgenewstyle.pdf

  5. Braun, D., Ortega, P., Wolpert, D.: Nash equilibria in multi-agent motor interactions. PLoS Comput. Biol. 5(8) (2009). https://doi.org/10.1371/journal.pcbi.1000468

    Article  MathSciNet  Google Scholar 

  6. Chow, C., Jacobson, D.: Studies of human locomotion via optimal programming. Math. Biosci. 10(34), 239–306 (1971)

    Article  Google Scholar 

  7. Engwerda, J.: LQ Dynamic Optimization and Differential Games. Wiley, Chichester (2005)

    Google Scholar 

  8. Flad, M.: Kooperative regelungskonzepte auf basis der spieltheorie und deren anwendung auf fahrerassistenzsysteme. Ph.D. Thesis, Karlsruher Institut fr Technologie (KIT) (2016). https://doi.org/10.5445/IR/1000062759

  9. Flad, M., Trautmann, C., Diehm, G., Hohmann, S.: Experimental validation of a driver steering model based on switching of driver specific primitives. In: 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 214–220 (2013). https://doi.org/10.1109/SMC.2013.43

  10. Flad, M., Otten, J., Schwab, S., Hohmann, S.: Necessary and sufficient conditions for the design of cooperative shared control. In: IEEE International Conference on Systems, Man, and Cybernetics (2014)

    Google Scholar 

  11. Flad, M., Otten, J., Schwab, S., Hohmann, S.: Steering driver assistance system: a systematic cooperative shared control design approach. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 3585–3592 (2014)

    Google Scholar 

  12. Flad, M., Rothfuss, S., Diehm, G., Hohmann, S.: Active brake pedal feedback simulator based on electric drive. SAE Int. J. Passeng. Cars Electron. Electr. Syst. 7(1), 189–200 (2014)

    Article  Google Scholar 

  13. Flemisch, F., Kelsch, J., Lper, C., Schieben, A., Schindler, J., Heesen, M.: Cooperative control and active interfaces for vehicle assistance and automation. In: FISITA World automotive Congress, Munich (2008)

    Google Scholar 

  14. Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Boston (1989)

    Google Scholar 

  15. Gote, C., Flad, M., Hohmann, S.: Driver characterization and driver specific trajectory planning: an inverse optimal control approach. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3014–3021 (2014). https://doi.org/10.1109/SMC.2014.6974389

  16. Hatze, H.: The complete optimization of a human motion. Math. Biosci. 28(12), 99–135 (1976)

    Article  MathSciNet  Google Scholar 

  17. Hayashi, Y.: Study on acceleration and deceleration maneuver guidance for driver by gas pedal reaction force control. In: 13th International IEEE Annual Conference on Intelligent Transportation Systems (2010)

    Google Scholar 

  18. Hjaelmdahl, M., Almqvist, S., Varhelyi, A.: Speed regulation by in car active accelerator pedal: effects on speed and speed distribution. IATSS Res. 26(2), 60–66 (2002)

    Article  Google Scholar 

  19. Jagacinski, R., Flach, J.: Control Theory for Humans: Quantitative Approaches to Modeling Performance. Erlbaum, Mahwah (2009)

    Google Scholar 

  20. Johnson, M., Aghasadeghi, N., Bretl, T.: Inverse optimal control for deterministic continuous-time nonlinear systems. In: 52nd IEEE Conference on Decision and Control, pp. 2906–2913 (2013). https://doi.org/10.1109/CDC.2013.6760325

  21. Ludwig, J., Gote, C., Flad, M., Hohmann, S.: Cooperative dynamic vehicle control allocation using time-variant differential games. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 117–122 (2017). https://doi.org/10.1109/SMC.2017.8122588

  22. MacAdam, C.: Understanding and modeling the human driver. Veh. Syst. Dyn. 40(1–3), 101–134 (2003)

    Article  Google Scholar 

  23. Mombaur, K., Truong, A., Laumond, J.P.: From human to humanoid locomotion an inverse optimal control approach. Auton. Robot. 28(3), 369–383 (2010). https://doi.org/10.1007/s10514-009-9170-7

    Article  Google Scholar 

  24. Mosbach, S., Flad, M., Hohmann, S.: Cooperative longitudinal driver assistance system based on shared control. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1776–1781 (2017). https://doi.org/10.1109/SMC.2017.8122873

  25. Mulder, M.: Haptic Gas Pedal Feedback for Active Car-Following Support. Delft University of Technology, Doktorarbeit (2007)

    Google Scholar 

  26. Mulder, M., Abbink, D., van Paassen, M., Mulder, M.: Design of a haptic gas pedal for active car-following support. IEEE Trans. Intell. Transp. Syst. 12(1), 268–279 (2011)

    Article  Google Scholar 

  27. Na, X., Cole, D.J.: Linear quadratic game and non-cooperative predictive methods for potential application to modelling driverafs interactive steering control. Veh. Syst. Dyn. 51(2), 165–198 (2013)

    Article  Google Scholar 

  28. Na, X., Cole, D.J.: Game theoretic modelling of a human drivers steering interaction with vehicle active steering collision avoidance system. IEEE Trans. Hum.-Mach. Syst. 45(1), 25–38 (2015)

    Article  Google Scholar 

  29. Nash, J.: Non-cooperative games. Ann. Math. 2, 286–295 (1951)

    Article  MathSciNet  Google Scholar 

  30. Nelson, W.: Physical principles for economies of skilled movements. Biol. Cybern. 46(2), 135–147 (1983)

    Article  Google Scholar 

  31. Nocedal, J., Wright, S.: Numerical Optimization, 2nd edn. Springer, New York (2006)

    MATH  Google Scholar 

  32. Petermeijer, S., Abbink, D., Mulder, M., deWinter, J.: The effect of haptic support systems on driver performance: a literature survey. IEEE Trans. Haptic 8(4), 467–479 (2015)

    Article  Google Scholar 

  33. Rasmussen, J.: Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models. IEEE Trans. Syst. Man Cybern. 13(3), 257–266 (1983)

    Article  Google Scholar 

  34. Society of Automotive Engineers (SAE): Stepwise coastdown methodology for measuring tire rolling resistance SAE J 2452 (2008)

    Google Scholar 

  35. Tamaddoni, S., Ahmadian, M., Taheri, S.: Optimal vehicle stability control design based on preview game theory concept. In: American Control Conference (ACC), pp. 5249–5254 (2011)

    Google Scholar 

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Correspondence to Michael Flad .

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Appendix

Appendix

See Table 2.3.

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Flad, M. (2019). Differential-Game-Based Driver Assistance System for Fuel-Optimal Driving. In: Petrosyan, L., Mazalov, V., Zenkevich, N. (eds) Frontiers of Dynamic Games. Static & Dynamic Game Theory: Foundations & Applications. Birkhäuser, Cham. https://doi.org/10.1007/978-3-030-23699-1_2

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