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Journal of Zhejiang University SCIENCE A

, Volume 11, Issue 3, pp 191–201 | Cite as

Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following

  • Li-hua Luo
  • Hong Liu
  • Ping Li
  • Hui Wang
Article

Abstract

For automated vehicles, comfortable driving will improve passengers’ satisfaction. Reducing fuel consumption brings economic profits for car owners, decreases the impact on the environment and increases energy sustainability. In addition to comfort and fuel-economy, automated vehicles also have the basic requirements of safety and car-following. For this purpose, an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework. In the proposed ACC algorithm, safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy. The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments. The results show that not only are safety and car-following objectives satisfied, but also driving comfort and fuel-economy are improved significantly.

Key words

Adaptive cruise control (ACC) Multi-objectives Comfort Fuel-economy Model predictive control (MPC) 

CLC number

U463 TP273 

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References

  1. Bageshwar, V.L., Garrard, W.L., Rajamani, R., 2004. Model predictive control of transitional maneuvers for adaptive cruise control vehicles. IEEE Transactions on Vehicular Technology, 53(5):1573–1585. [doi:10.1109/TVT.2004.833625]CrossRefGoogle Scholar
  2. Barth, M., Scora, G., Younglove, T., 2004. Modal emissions model for heavy-duty diesel vehicles. Transportation Research Record, 1880(1):10–20. [doi:10.3141/1880-02]CrossRefGoogle Scholar
  3. Bemporad, A., Morari, M., Dua, V., Pistikopoulos, E.N., 2002. The explicit linear quadratic regulator for constrained systems. Automatica, 38(1):3–20. [doi:10.1016/S0005-1098(01)00174-1]zbMATHCrossRefMathSciNetGoogle Scholar
  4. Bose, A., Ioannou, P., 2001. Analysis of Traffic Flow with Mixed Manual and Intelligent Cruise Control (ICC) Vehicles: Theory and Experiments. California PATH Report, UCB-ITS-PRR-2001-13.Google Scholar
  5. Boyd, S., Vandenberghe, L., 2004. Convex Optimization. Cambridge University, New York.zbMATHGoogle Scholar
  6. Crew, H., 2008. The Principles of Mechanics. BiblioBazaar, p.43.Google Scholar
  7. Hiraoka, T., Kunimatsu, T., Nishihara, O., Kumamoto, H., 2005. Modeling of Driver Following Behavior Based on Minimum-jerk Theory. 12th World Congress on ITS, San Francisco, p.3416–3427.Google Scholar
  8. Holzmann, H., Halfmann, C., Germann, S., Wurtenberger, M., Isermann, R., 1997. Longitudinal and lateral control and supervision of autonomous intelligent vehicles. Control Engineering Practice, 5(11):1599–1605. [doi:10.1016/S0967-0661(97)10015-6]CrossRefGoogle Scholar
  9. Ioannou, P.A., Stefanovic, M., 2005. Evaluation of ACC vehicles in mixed traffic: Lane change effects and sensitivity analysis. IEEE Transactions on Intelligent Transportation Systems, 6(1):79–89. [doi:10.1109/TITS.2005.844226]CrossRefGoogle Scholar
  10. Ioannou, P.A., Xu, Z., Eckert, S., Clemons, D., Sieja, T., 1993. Intelligent Cruise Control: Theory and Experiment. Proceedings of the 32nd IEEE Conference on Decision and Control, p.1885–1890. [doi:10.1109/CDC.1993.325521]Google Scholar
  11. James, W.J., Neil, D.L., Steve, M., Scott, O., Brain, C.T., 2008. Use of Advanced In-vehicle Technology by Young and Older Early Adopters, Survey Results on Adaptive Cruise Control Systems. Report No. DOT HS 810 917. National Highway Traffic Safety Administration.Google Scholar
  12. Jiang, R., Wu, Q.S., 2006. The adaptive cruise control vehicles in the cellular automata model. Physics Letters A, 359(2):99–102. [doi:10.1016/j.physleta.2006.06.015]zbMATHCrossRefMathSciNetGoogle Scholar
  13. Liang, C.Y., Peng, H., 1999. Optimal adaptive cruise control with guaranteed string stability. Vehicle System Dynamics, 32(4–5):313–330. [doi:10.1076/vesd.32.4.313.2083]CrossRefGoogle Scholar
  14. Martinez, J.J., Canudas-De-Wit, C., 2007. A safe longitudinal control for adaptive cruise control and stop-and-go scenarios. IEEE Transactions on Control Systems Technology, 15(2):246–258. [doi:10.1109/TCST.2006.886432]CrossRefGoogle Scholar
  15. Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M., 2000. Constrained model predictive control: Stability and optimality. Automatica, 36(6):789–814. [doi:10.1016/S0005-1098(99)00214-9]zbMATHCrossRefMathSciNetGoogle Scholar
  16. Naranjo, J.E., Gonzalez, C., Garcia, R., de Pedro, T., 2007. Cooperative throttle and brake fuzzy control for ACC+Stop&Go maneuvers. IEEE Transactions on Vehicular Technology, 56(4):1623–1630. [doi:10.1109/TVT.2007.897632]CrossRefGoogle Scholar
  17. 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. American Control Conference, p.224–229. [doi:10.1109/ACC.2008.4586495]Google Scholar
  18. Pasquier, M., Quek, C., Toh, M., 2001. Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles. Neural Networks, 14(8):1099–1112. [doi:10.1016/S0893-6080(01)00048-X]CrossRefGoogle Scholar
  19. Rajamani, R., Zhu, C., 2002. Semi-autonomous adaptive cruise control systems. IEEE Transactions on Vehicular Technology, 51(5):1186–1192. [doi:10.1109/TVT.2002.800617]CrossRefGoogle Scholar
  20. Scora, G., Barth, M., 2006. User’s Guide: Comprehensive Modal Emissions Model (CMEM), Version 3.01. Available from http://pah.cert.ucr.edu/cmem/docs/CMEM_User_Guide_v3.01d.pdf [Accessed on June 20, 2009]
  21. Sprott, J.C., 1997 Some simple chaotic jerk functions. American Journal of Physics, 65(6):536–543. [doi:10.1119/1.18585]CrossRefGoogle Scholar
  22. Vahidi, A., Eskandarian, A., 2003. Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Transactions on Intelligent Transportation Systems, 4(3):143–153. [doi:10.1109/TITS.2003.821292]CrossRefGoogle Scholar
  23. Yi, K.S., Chung, J.T., 2001. Nonlinear brake control for vehicle CW/CA systems. IEEE/ASME Transactions on Mechatronics, 6(1):17–25. [doi:10.1109/3516.914387]CrossRefGoogle Scholar
  24. Zhang, J., Ioannou, P.A., 2006. Longitudinal control of heavy trucks in mixed traffic: environmental and fuel economy considerations. IEEE Transactions on Intelligent Transportation Systems, 7(1):92–104. [doi:10.1109/TITS.2006.869597]CrossRefGoogle Scholar
  25. Zhou, J., Peng, H., 2005. Range policy of adaptive cruise control vehicles for improved flow stability and string stability. IEEE Transactions on Intelligent Transportation Systems, 6(2):229–237. [doi:10.1109/TITS.2005.848359]CrossRefMathSciNetGoogle Scholar

Copyright information

© Zhejiang University and Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina

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