The implementation of anticipating driving styles in adaptive cruise control systems promises to considerably reduce fuel consumption of vehicles. As drivers have to accept the optimised driving styles of such systems, which implement longitudinally automated driving, the optimisation results should not deviate strongly from the average driving behaviour. This work presents an approach to the optimisation of the vehicle's longitudinal dynamics, which is based on a predicted average driving profile. The proposed approach ensures that the optimisation results meet the expectations of drivers by directly accounting for driver's preferences on weighting up travel time against fuel consumption relative to the average driving profile. Based on human decision finding, rational and intuitive planning decisions are modelled in a cost function and represent optimisation constraints. The approach generally includes information from vehicle-to-vehicle and vehicle-to-infrastructure communication (V2X), which is an extension to the state-of-the-art. This study describes the optimisation approach and presents a method to determine suitable optimisation parameters in order to consider driver's preferences. The optimisation approach is applied in a simulated test drive and improvements in fuel economy are analysed. Finally, the authors sketch a reference system architecture to prove the feasibility of the presented approach.

References

1. 1)
  - 10. Roth, M., Radke, T., Lederer, M.: ‘Porsche InnoDrive – an innovative approach for the future of driving’. 20th Aachen Colloquium Automobile and Engine Technology, Aachen, Germany, October 2011.
2. 2)
  - 6. Scania Group Press Release: ‘Scania Ecocruise’. [Online] Available at: http://www.scania.com, accessed July 2013.
3. 3)
  - 5. Hellström, E.: ‘Look-ahead control of heavy trucks utilizing road topography’. Dissertation, Linköping University, Sweden, 2007.
4. 4)
  - 26. Dornieden, B., Themann, P., Zlocki, A., Junge, L.: ‘Energy efficient longitudinal vehicle control based on analysis of driving situations’. 20th Aachen Colloquium Automobile and Engine Technology, Aachen, Germany, October 2011, pp. 1491–1511.
5. 5)
  - 21. Themann, P., Eckstein, L.: ‘Modular approach to energy efficient driver assistance incorporating driver acceptance’. IEEE Intelligent Vehicles Symp., Alcalá de Henares, Spain, June 2012.
6. 6)
  - 18. Themann, P., Uhrner, T., Müller-Werth, T., et al: ‘ecoSituational model’. Deliverable D 2.9 of the eCoMove project, 2012. Published at: http://www.ecomove-project.eu.
7. 7)
  - 24. Sniedovich, M.: ‘Dijkstra's algorithm revisited: the dynamic programming connexion’, Control Cybern., 2006, (35), p. 599.
8. 8)
  - 7. Daimler press release: ‘Predictive powertrain control’. [Online] Available at: www.daimler.com, accessed at 04.02.2013.
9. 9)
  - 14. Kamal, M., Mukai, M., Murata, J., Kawabe, T.: ‘Ecological driver assistance system using model-based anticipation of vehicle–road–traffic information’, IET Intell. Transp. Syst., 2010, 4, (4), pp. 244–251 (doi: 10.1049/iet-its.2009.0127).
10. 10)
  - 16. Mensing, F., Trigui, R., Bideaux, E.: ‘Vehicle trajectory optimization for application in ECO-driving’. IEEE Vehicle Power and Propulsion Conference, Chicago, USA, 6–9 September 2011, pp. 1–6.
11. 11)
  - 15. Kamal, M., Mukai, M., Murata, J., Kawabe, T.: ‘Model predictive control of vehicles on urban roads for improved fuel economy’, IEEE Trans. Control Syst. Technol., 2013, 21, (3), pp. 831–841 (doi: 10.1109/TCST.2012.2198478).
12. 12)
  - E.W. Dijkstra . A note on two problems in connexion with graphs. Numerische mathematik , 269 - 271
13. 13)
  - 8. Banerjee, A., Würthner, M., Tudosie, C.: ‘Prevision GPS – Die Schaltstrategie für das Getriebesystem TRAXON’. ATZ Automobiltechnische Zeitschrift, June 2013, pp. 493–496.
14. 14)
  - 25. Bellman, R.E.: ‘Dynamic programming’ (Princeton University Press, Princeton, 1957).
15. 15)
  - 22. Evans, J.B.: ‘Dual-processing accounts of reasoning, judgment, and social cognition’, Ann. Rev. Psychol., 2008, 59, (1), pp. 255–278 (doi: 10.1146/annurev.psych.59.103006.093629).
16. 16)
  - 13. Jiménez, F., López-Covarrubias, J., Cabrera, W., Aparicio, F.: ‘Real-time speed profile calculation for fuel saving considering unforeseen situations and travel time’, IET Intell. Transp. Syst., 2013, 7, (1), pp. 10–19 (doi: 10.1049/iet-its.2011.0132).
17. 17)
  - 12. Gausemeier, S., Jäker, K.-P., Trächtler, A.: ‘Multi-objective optimization of a vehicle velocity profile by means of dynamic programming’. Sixth IFAC Symp. Advances in Automotive Control – AAC, Schwabing, Germany, July 2010.
18. 18)
  - 1. European Parliament and Council: Regulation (EC) No 443/2009, 23 April 2009 and ‘Regulation (EC) No 397/2013’. 30 April 2013.
19. 19)
  - J.N. Barkenbus . Eco-driving: an overlooked climate change initiative. Energy Policy , 762 - 769
20. 20)
  - 17. Mensing, F., Trigui, R., Bideaux, E.: ‘Vehicle trajectory optimization for hybrid vehicles taking into account battery state-of-charge’. IEEE Vehicle Power and Propulsion Conf., Seoul, Korea, 9–12 October 2012.
21. 21)
  - 9. Dornieden, B., Junge, L., Pascheka, P.: ‘Anticipatory energy-efficient longitudinal vehicle control’. ATZ worldwide eMagazines Edition, March 2012.
22. 22)
  - 20. Themann, P., Eckstein, L.: ‘Prediction of driver behaviour by situational models in a cooperative environment to optimize energy efficiency’. Ninth ITS European Congress 2013, Dublin, Ireland, June 2013.
23. 23)
  - 3. Wahlberg, A.E.: ‘Long-term effects of training in economical driving: fuel consumption, accidents, driver acceleration behavior and technical feedback’, Int. J. Ind. Ergonom., 2007, 37, (4), pp. 333–343 (doi: 10.1016/j.ergon.2006.12.003).
24. 24)
  - 19. Themann, P., Uhrner, T., Kuck, D., Müller, M., Klubal, M.: ‘ecoDriving support based on cooperative prediction models’. ITS World Congress, Vienna, Austria, October 2012, paper EU-00374.
25. 25)
  - 4. Rowsome, F.: ‘What it's like to drive an auto-pilot car’, Popular Sci., 1958, 172, (4), pp. 105–107.
26. 26)
  - 11. Markschläger, P., Wahl, H.-G., Weberbauer, F., Lederer, M.: ‘Assistance system for higher fuel efficiency’. ATZ Worldwide eMagazines Edition, November 2012.

Optimisation of energy efficiency based on average driving behaviour and driver's preferences for automated driving

References

Related content