Skip to main content
SpringerLink
Log in

Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

This paper describes a multi-agent coordination mechanism applied to intersection simulation situations. In a goal of urban traffic simulation, we must consider the dynamic interactions between autonomous vehicles. The field of multi-agent systems provides us some studies for such systems, in particular on the coordination mechanisms. Conflicts between vehicles (i.e. agents) are very frequent in such applications, and they may cause deadlocks, particularly at intersections such as crossroads. Our approach is based on the solving of two player games/decision matrices which characterize three basic situations. An aggregation method generalizes to n-player games for complex crossroads. The objective of this approach consists in searching basic two-player matrices for solving n-agent problems. To explain the principle, we describe our approach for a particular case of crossroad with three agents. Finally, the obtained results have been examined via a tool of road traffic simulation, ARCHISIM. We assume also that the global traffic replicates the behavior of agents in different situations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Adam E, Mandiau R (2005) Roles and hierarchy in multi-agent organizations In: Pechoucek M, Petta P, Varga LZ (eds) Multi-agent systems and applications IV, 4th international central and Eastern European conference on multi-agent systems: CEEMAS 2005, Budapest, Hungary, September 2005. Lecture notes in artificial intelligence, vol 3690. Springer, Berlin, pp 539–542.

    Google Scholar 

  2. AIMSUM2 V3.3 (1999) User’s manual, Getram transport simulation systems

  3. Aknine S, Pinson S, Shakun MF (2000) New coalition formation methods for multi-agent coordination. In: Group decision and negotiation, Glasgow, Scotland, July 2000

  4. Bazzan ALC (2005) A distributed approach for coordination of traffic signal agents. J Auton Agents Multi-Agent Syst 10(2):131–164

    Article  Google Scholar 

  5. Castelfranchi C (1998) Modeling social action for AI agents. Artif Intell 103:157–182

    Article  MATH  Google Scholar 

  6. Chaib-Draa B, Moulin B, Mandiau R, Millot P (1992) Trends in distributed artificial intelligence. Artif Intell Rev 6:35–66

    Article  Google Scholar 

  7. Champion A, Espié S, Mandiau R, Kolski C (2001) Multi-agent road traffic simulation: the coordination issue. In: Giambiasi N, Frydman C (eds) Proceedings of the 13th European simulation symposium, London, October 2001, pp 903–908

  8. Champion A (2003) Mécanisme de coordination multi-agent à base de jeux: application à la simulation comportementale de trafic routier en situation de carrefour. PhD thesis, University of Valenciennes, France, December 2003 (in French)

  9. Chan PL, Teply S (1991) Simulation of multi-lane stop-controlled T-intersections by Knosimo in Canada. In: Brilon W (ed) Intersections without traffic signals II. Springer, Berlin

    Google Scholar 

  10. Doniec A, Espié S, Mandiau R, Piechowiak S (2005) Dealing with multi-agent coordination by anticipation: application to the traffic simulation at junctions. In: Gleizes MP, Kaminka GA, Nowé A, Ossowski S, Tuyls K, Verbeeck K (eds) Proceedings of the 3rd European workshop on multi-agent systems, Brussels, Belgium, December 2005

  11. Dressner K, Stone P (2004) Multi-agent traffic management: a reservation based intersection control mechanism. In: Proceedings of the 3rd international conference on autonomous agents and multiagent systems: AAMAS 04, New York, July 2004, pp 530–537

  12. El Fallah Segrouchni A, Demgirmenciyan-Cartault I, Marc F (2004) Modelling, control and validation of multi-agents plans in highly dynamic context. In: Proceedings of the 3rd international conference on autonomous agents and multi-agent systems: AAMAS’04, New York, July 2004, pp 44–51

  13. Espié S, Saad F, Schnetzler B (1994) Microscopic traffic simulation and driver behavior modeling: the ARCHISIM project. In: Proceedings of the strategic highway research program and traffic safety on two continents, Lille, France

  14. Espié S (1999) Vehicle driven simulator versus traffic-driven simulator: the INRETS approach. In: Proceedings of driving simulation conference (DSC’99), Paris, France

  15. Ferber J (1999) Multi-agent systems: an introduction to distributed artificial intelligence. Addison–Wesley, Reading

    Google Scholar 

  16. Findler N, Stapp J (1992) Distributed approach control of street traffic signals. J Transp Eng 118(1):99–110

    Article  Google Scholar 

  17. Genesereth MR, Ginsberg ML, Rosenschein JS (1986) Cooperation without communication. In: Proceedings of the 5th American association for AI

  18. Georgeff MP (1983) Communication and interaction in multi-agent planning. In: Proceedings of the 3rd national conference on artificial intelligence (AAAI’83), pp 125–129, August 1983

  19. Jennings N (1996) Coordination techniques for distributed artificial intelligence. In: Foundation of distributed artificial intelligence. Wiley, New York, pp 187–210

    Google Scholar 

  20. Jennings N, Sycara K, Wooldridge M (1998) A roadmap of agent research and development. J Agents Multi-Agent Syst 1(7):7–38

    Article  Google Scholar 

  21. Leutzbach W (1998) Introduction to the theory of traffic flow. Springer, Berlin

    Google Scholar 

  22. Mandiau R, Grislin-Le Strugeon E, Agimont G (1999) Study of the influence of the organizational structure on the efficiency of a multi-agent system. Netw Inf Syst J 2:153–179

    Google Scholar 

  23. Mandiau R, Grislin-Le Strugeon E, Peninou A (2002) Organisation et applications des SMA. Hermès, Paris (in French)

    Google Scholar 

  24. Ossowski S (1998) Coordination in agent artificial agent societies: social structures and its implications for autonomous solving agents. In: Lecture notes of artificial intelligence, vol 1535. Springer, Berlin

  25. Prentice JW (1974) The evasive action decision in an intersection accident: a game theory approach. J Saf Res 6(4):146–149

    MathSciNet  Google Scholar 

  26. Reece DA, Shafer SA (1993) A computational model of driving for autonomous vehicles. Transp Res A 27(1):23–50

    Google Scholar 

  27. Rodriguez S, Hilaire V, Koukam A (2003) Towards a methodological framework for holonic multi-agent systems. In: Proceedings of the 4th international workshop of engineering societies in the agents, Imperial College London, UK, 29–31, October 2003

  28. SciLab (2006) http://www-rocq.inria.fr/scilab

  29. Shoham Y, Tennenholz M (1995) On social laws for artificial agents societies: off-line design. Artif Intell 1–2(73):231–252

    Article  Google Scholar 

  30. PTV Planung Transport Verkhehr AG (2003) VISSIM 3.70 User Manual, Karlsruhe, Germany

  31. Yokoo M (2001) Distributed constraint satisfaction: foundations of cooperation in multi-agent systems. Springer, Berlin

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LAMIH UMR CNRS 8530, Bat. Jonas 1, Université de Valenciennes, 59313, Valenciennes, France

    René Mandiau, Alexis Champion & Christophe Kolski

  2. INRETS Arcueil, 2 Avenue du Général Malleret-Joinville, 94114, Arcueil, France

    Alexis Champion, Jean-Michel Auberlet & Stéphane Espié

Authors
  1. René Mandiau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexis Champion
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Michel Auberlet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stéphane Espié
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christophe Kolski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to René Mandiau.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mandiau, R., Champion, A., Auberlet, JM. et al. Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation. Appl Intell 28, 121–138 (2008). https://doi.org/10.1007/s10489-007-0045-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-007-0045-3

Keywords

Search

Navigation