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Mixed Traffic Control Involving Manually-Controlled and Automatically-Controlled Vehicles in IVHS

  • Su-Nan Huang
  • Steven C. Chan
  • Wei Ren
Chapter
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 18)

Abstract

Traffic congestion is a global problem. Intelligent Vehicle Highway Systems (IVHS) have been proposed to help solve this problem (see [1, 2, 3, 4, 5, 6, 7, 8, 9]). The works of [1, 2, 3] propose a fully automated traffic system in which vehicles are automated and organized in platoons through interactive vehicle-to-vehicle communication. This approach can yield superior control quality and has the best potential for throughput improvement but is difficult to implement due to high costs and heavy infrastructure requirements. The reports in [4, 5, 6, 7] suggest an autonomous intelligent cruise control for the IVHS, i.e., to keep a desired velocity when there is no vehicle in front, and to keep a desired spacing (possibly velocity dependent) when there is a vehicle in front. This approach allows a mixture of automated and non-automated vehicles and requires little vehicle-to-vehicle communication. However, it is not a completely self-contained intelligent controller that can handle all possible scenarios such as sudden lane changes, following a vehicle with rapid acceleration or deceleration, emergency braking, switching between human drivers and automatic controllers, etc. In [8, 9], artificial intelligence is used to design fully autonomous vehicle control system which can operate in mixed traffic. The research in this area is mostly concerned with handling complex traffic scenarios and do not attempt to optimize control performance for tight vehicle spacing and passenger comfort. In addition, the previous results do not provide the performance analysis of mixed traffic control, though they claim their control can be used in an environment of automatically controlled and manually controlled vehicles. It is therefore necessary to design a complete intelligent control system that 1) handles all possible scenarios in mixed traffic, 2) optimizes control performance so that tight vehicle spacing can be achieved with minimum passenger discomfort, 3) evaluates the performance of mixed traffic.

Keywords

Lane Change Optimal Velocity Bernoulli Random Variable Mixed Traffic Human Driver 
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.

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References

  1. [1]
    Varaiya, P. and Shladover, S.E.: Sketch of an IVHS systems architecture, PATH Research Rep., UCB-ITS-PRR-91-3Google Scholar
  2. [2]
    Varaiya, P. Smart cars on smart roads: problems for contral, IEEE Trans.on Automatie Control, 38(1993), 195–207MathSciNetCrossRefGoogle Scholar
  3. [3]
    Huang, S.N. and Ren,W.:Design of vehicle following control systems with actuator delays, Int.J.Systems and Seienee, 28(1997), 145–151zbMATHCrossRefGoogle Scholar
  4. [4]
    Ioannou, P.A.; Chien, C.C.: Autonomous intelligent cruise control. IEEE Transactions on Vehicular Technology, 42(4)(1993), 657–672.CrossRefGoogle Scholar
  5. [5]
    Chien, C.C.; Ioannou, P.: Automatie vehicle-following, Proceedmgs ol lhe 1992 American Control Conlerence,Chicago, 1992. pp. 1748–1752Google Scholar
  6. [6]
    Ren, W. and Green, D.:Continuous platooning: a new evolutionary and operating concept for automated highway systems, Memorandum No.UCB/ERL M94/24Google Scholar
  7. [7]
    Swaroop, D.: String stabilily of interconnected applications to IVHS, Ph.D Disseration, 1994.Google Scholar
  8. [8]
    Forbes, J. Huang, T. Kanazawa, K. and Russell, S.: The BATmobile:Towards a bayesian automated Taxi, Proceedings of Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, 1995.Google Scholar
  9. [9]
    Niehaus, A. and Stengel, R. E.: Probability-based decision making for automated highway driving, IEEE Trans.on Vehicular Technology,43(3)(1992),pp.626–634CrossRefGoogle Scholar
  10. [10]
    Eskafi, F, Khorramabadi, D. and Varaiya, P.: Smartpath: An automated highway system simulator, TECH.REP.PATH Techical Note 94-3Google Scholar
  11. [11]
    Eskafi, F, and Khorramabadi, D.: Smartpath User's Manual. Department of Electrical Engineering and Computer Science and PATH/ITS, UCB-94Google Scholar
  12. [12]
    Shiekholeslam, S.: Control of a class of interconnected nonlinear dynamical systems: the platoon problem, Ph.D.Dissertation, Department, of Electrical Engineering and Computer Sciences, University of California at Berkeley, 1991Google Scholar
  13. [13]
    Bekey, Burnham, G.A.G. and Seo, J.:Control theoretic models of human drivers in car following, Human Factors, 19(4)(1977), 399–413.Google Scholar
  14. [14]
    Gazis, Herman, D.C and Potts, R.B.:Car-following theory of steady state traffic flow, Operations Research, 7(1959), 499–505.MathSciNetCrossRefGoogle Scholar
  15. [15]
    Chandler, R.E., Herman, R., and Montroll, E.W.: Traffic dynamics: Studies in car following, Operations Research, 6(1958), 165–184.MathSciNetCrossRefGoogle Scholar
  16. [16]
    Godbole, D. and Lygeros, J.: Longitudinal control of the lead car of a platoon, UCB Rep., TECH MEMO-93-07, 1993Google Scholar
  17. [17]
    Gerdes, J.C.; Hedrick, J.K.: Brake system requirements for platooning on an automated highway. Proceedings of the 1995 American Control Conference, 1995, pp165–169Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Su-Nan Huang
    • 1
  • Steven C. Chan
    • 1
  • Wei Ren
    • 1
  1. 1.Dept of Electrical Engineering and Computer SciencesUniversity of California at BerkeleyBerkeleyUSA

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