Predictive Cooperative Adaptive Cruise Control: Fuel Consumption Benefits and Implementability

  • Dominik Lang
  • Thomas Stanger
  • Roman Schmied
  • Luigi del Re
Chapter
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 455)

Abstract

Impressive improvements of efficiency and safety of vehicles have been achieved over the last decade, but increasing traffic density and drivers’ age accentuate the need of further improvements. The contributions summarized in this chapter argue that a substantial additional fuel benefit can be achieved by extending the well introduced Adaptive Cruise Control in a predictive sense, e.g. taking into account a predicted behavior of other traffic components. This chapter starts by discussing results on the potential benefits in the ideal case (full information, no limits on computing power) and then examines how much of the potential benefits is retained if approximate solutions are used to cope with a realistic situation, with limited information and computing power. Two setups are considered: vehicles exchanging a small set of simple data over a V2V link and the case of mixed traffic, in which some vehicles will not provide any information, but the information must be obtained by a probabilistic estimator. The outcome of these considerations is that the approach is able to provide—statistically—a substantial fuel consumption benefit without affecting negatively the driveability or the driver comfort like other methods, e.g. platooning, would.

Keywords

Model Predictive Control Speed Profile Continuous Variable Transmission Adaptive Cruise Control Prediction Horizon 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Vagg C, Brace CJ, Hari D, Akehurst S, Poxon J, Ash L (2013) Development and field trial of a driver assistance system to encourage eco-driving in light commercial vehicle fleets. IEEE Trans Intel Transport Syst 14(2):796–805CrossRefGoogle Scholar
  2. 2.
    Rupp J, King A (2010) Autonomous driving: a practical roadmap. SAE technical paper 2010-01-2335Google Scholar
  3. 3.
    Li S, Li K, Rajamani R, Wang J (2011) Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans Control Syst Technol 19(3):556–566CrossRefGoogle Scholar
  4. 4.
    Lou L, Liu H, Li P, Wang H (2010) Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following. J Zhejiang Univ Sci A 11(3):191–201CrossRefGoogle Scholar
  5. 5.
    Naus G, Ploeg J, van de Molengraft R, Steinbuch M (2008) Explicit MPC design and performance-based tuning of an adaptive cruise control stop-&-go. In: IEEE on intelligent vehicles symposium 2008, pp 434–439Google Scholar
  6. 6.
    Naus G, van den Bleek R, Ploeg J, Scheepers B, van de Molengraft R, Steinbuch M (2008) Explicit MPC design and performance evaluation of an ACC stop-&-go. In: American control conference 2008, pp 224–229Google Scholar
  7. 7.
    Bu F, Tan HS, Jihua H (2010) Design and field testing of a cooperative adaptive cruise control system. In: American control conference (ACC 2010), pp 4616–4621Google Scholar
  8. 8.
    Naus G, Vugts R, Ploeg J, van de Molengraft R, Steinbuch M (2010) Cooperative adaptive cruise control, design and experiments. In: American control conference (ACC 2010), pp 6145–6150Google Scholar
  9. 9.
    Naus GJL, Vugts RPA, Ploeg J, van de Molengraft MJG, Steinbuch M (2010) String-stable CACC design and experimental validation: a frequency-domain approach. IEEE Trans Veh Technol 59(9):4268–4279CrossRefGoogle Scholar
  10. 10.
    Ploeg J, Scheepers BTM, van Nunen E, van de Wouw N, Nijmeijer H (2011) Design and experimental evaluation of cooperative adaptive cruise control. In: 14th international IEEE conference on intelligent transportation systems (ITSC 2011), pp 260–265Google Scholar
  11. 11.
    McDonough K, Kolmanovsky I, Filev D, Yanakiev D, Szwabowski S, Michelini J, Abou-Nasr M (2011) Modeling of vehicle driving conditions using transition probability models. In: IEEE international conference on control applications (CCA 2011), pp 544–549Google Scholar
  12. 12.
    McDonough K, Kolmanovsky I, Filev D, Yanakiev D, Szwabowski S, Michelini J (2012) Stochastic dynamic programming control policies for fuel efficient in-traffic driving. In: American control conference 2012, pp 3986–3991Google Scholar
  13. 13.
    McDonough K, Kolmanovsky I, Filev D, Yanakiev D, Szwabowski S, Michelini J (2013) Stochastic dynamic programming control policies for fuel efficient vehicle following. In: American control conference (ACC 2013)Google Scholar
  14. 14.
    Ericson C, Westerberg B, Egnell R (2005) Transient emission predictions with quasi stationary models. SAE technical paper 2005-01-3852Google Scholar
  15. 15.
    Brackstone M, McDonald M (1999) Car-following: a historical review. Transp Res Part F: Traffic Psychol Behav 2(4):181–196CrossRefGoogle Scholar
  16. 16.
    Marzbanrad J, Karimian N (2011) Space control law design in adaptive cruise control vehicles using model predictive control. J Automobile Eng 225:870–884Google Scholar
  17. 17.
    Lang D, Stanger T, del Re L (2013) Opportunities on fuel economy utilizing v2v based drive systems. SAE technical paper 2013-01-0985Google Scholar
  18. 18.
    Camacho EF, Bordons C (2004) Model predictive control. Springer, LondonMATHGoogle Scholar
  19. 19.
    Maciejowski JM (2002) Predictive control: with constraints. Prentice Hall, Upper Saddle RiverGoogle Scholar
  20. 20.
    Stanger T, del Re L (2013) A model predictive cooperative adaptive cruise control approach. In: American control conference (ACC 2013)Google Scholar
  21. 21.
    del Re L, Allgöwer F, Glielmo L, Guardiola C, Kolmanovsky I (eds) (2010) Automotive model predictive control. Springer, LondonMATHGoogle Scholar
  22. 22.
    Lang D, Stanger T, del Re L (2013) Fuel efficient quasi optimal adaptive cruise control by control identification. In: Control Applications (CCA), 2013 IEEE international conference, pp 229, 234. doi: 10.1109/CCA.2013.6662763

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dominik Lang
    • 1
  • Thomas Stanger
    • 1
  • Roman Schmied
    • 1
  • Luigi del Re
    • 1
  1. 1.Institute for Design and Control of Mechatronical Systems Johannes Kepler University LinzLinzAustria

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