Evaluation and Optimization of Adaptive Cruise Control Policies Via Numerical Simulations

Clement U. Mba, Carlo Novara

Abstract

Adaptive Cruise Control (ACC) makes the driving experience safer and more pleasurable. Several appealing ACC policies have been introduced so far. However, it is difficult in general to understand which is the actual performance that can be guaranteed on a real vehicle. Another relevant issue is that no systematic methods can be found for the optimization of a control policy performance. The first aim of this paper is to compare different ACC policies by means of extensive simulations, considering different realistic road scenarios. This kind of study is important to analyze which policies can be more effective in view of their implementation on real vehicles. The second aim is to develop an optimization method based on a multi-objective Pareto criterion, finalized at designing high-performance policies. The method is tested by means of extensive simulations.

References

  1. B. Howard.(2013). What is Adaptive Cruise Control, and how does it work? http://www.extremetech.com/ extreme/157172-what-is-adaptive-cruise-control-andhow-does-it-work.
  2. P. Shakouri, A. Ordys, and M. Askari. (2012.) Adaptive Cruise Control with stop & go function using the State-dependent Nonlinear Predictive control approach. ISA Transactions 51 Elsevier.
  3. P. Shakouri and A. Ordys. (2014). Nonlinear Model Predictive Control approach in design of Adaptive Cruise Control with automated switching to cruise control. Control Engineering Practice 26, pages 160-177.
  4. L. Xiao and F. Gao. (2010). A Comprehensive Review of the development of Adaptive Cruise Control Systems. Vehicle System Dynamics, pages 1167-1192.
  5. R. Rajamani. (2012). Vehicle Dynamics and Control. Mechanical Engineering Series Springer 2nd ed.
  6. W. Schakel, B. Arem and B. Netten. (2010). Effects of Cooperative Adaptive Cruise Control on Traffic Flow Stability. Proceedings of the 13th IEEE Annual Conference on Intelligent Transportation Systems, pages 759-764.
  7. S. Oncu, N. Wouw and H. Nijmeijer. (2011). Cooperative Adaptive Cruise Control: Tradeoffs between Control and Network Specifications. Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, pages 2051-2056.
  8. D. Swaroop. (1995). String Stability of Interconnected Systems: An Application to Platooning in Automated Highway Systems. PhD dissertation, Dept. Mechanical Eng., Univ. California Berkeley.
  9. D. Yanakiev and I. Kanellakopoulos. (1995). Variable Time Headway for String Stability of Automated HeavyDuty Vehicles. Proceedings of the 34th IEEE Conference on Decision and Control, pages 4077-4081.
  10. D. Yanakiev and I. Kanellakopoulos. (1995). Variable Longitudinal Control of Heavy-Duty Vehicles for Automated Highway Systems. Proceedings of the American Control Conference, pages 3096-3100.
  11. D. Yanakiev and I. Kanellakopoulos. (1998). Nonlinear Spacing Policies for Automated Heavy-Duty Vehicles. IEEE Transactions on Vehicular Technology volume 47, pages 1365-1377.
  12. K. Santhanakrishnan and R. Rajamani. (2003). On Spacing Policies for Highway Vehicle Automation. IEEE Transactions on Intelligent Transportation Systems Volume 4, pages 198-204.
  13. D. Swaroop, J. Hedrick, C. Chien and P Ioannou. (1994). A Comparison of Spacing and Headway Control Laws for Automatically Controlled Vehicles. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, pages 597-625.
  14. L. Chi-Ying and P. Huei. (1999). Optimal Adaptive Cruise Control with Guaranteed String Stability. Vehicle System Dynamics 31, pages 313-330.
  15. D. Swaroop and R. Hundra. (1998) Intelligent Cruise Control System Design based on a Traffic Flow Specification. Vehicle System Dynamics volume 30, pages 319-344.
  16. J. Zhou and H. Peng. (2004). Range Policy of Adaptive Cruise Control Vehicles for Improved Flow and String Stability. Proceedings of the IEEE International Conference on Networking, Sensing and Control, pages 595-600.
  17. J. Zhao, M. Oya and A. Kamel. (2009). A Safety Spacing Policy and its Impact on Highway Traffic Flow. Intelligent Vehicles Symposium, pages 960-965.
  18. J. Wang and R. Rajamani. (2004). Should Adaptive CruiseControl Systems be designed to maintain a Constant Time Gap between Vehicles? IEEE Transactions on Vehicular Technology volume 53, pages 1480-1490.
  19. J. Wang and R. Rajamani. (2002). Adaptive Cruise Control System Design and Its Impact on Highway Traffic Flow. Proceedings of American Control Conference, pages 3690-3695.
  20. J. Zhou and H. Peng. (2005). Range Policy of Adaptive Cruise Control Vehicles for Improved Flow and String Stability. IEEE Transactions on Intelligent Transportation Systems Volume 6, pages 229-237.
  21. B. Brownstein. (1980). Pareto Optimality, External Benefits and Public Goods: A Subjectivist Approach. Journal of Libertarian Studies, pages 93-106.
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Paper Citation


in Harvard Style

Mba C. and Novara C. (2016). Evaluation and Optimization of Adaptive Cruise Control Policies Via Numerical Simulations . In Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-185-4, pages 13-19. DOI: 10.5220/0005621100130019


in Bibtex Style

@conference{vehits16,
author={Clement U. Mba and Carlo Novara},
title={Evaluation and Optimization of Adaptive Cruise Control Policies Via Numerical Simulations},
booktitle={Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2016},
pages={13-19},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005621100130019},
isbn={978-989-758-185-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Evaluation and Optimization of Adaptive Cruise Control Policies Via Numerical Simulations
SN - 978-989-758-185-4
AU - Mba C.
AU - Novara C.
PY - 2016
SP - 13
EP - 19
DO - 10.5220/0005621100130019