Autonomous Vehicles Coordination Through Voting-Based Decision-Making

  • Miguel TeixeiraEmail author
  • Pedro M. d’OreyEmail author
  • Zafeiris Kokkinogenis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11327)


This paper proposes the application of computational social choice mechanisms to establish cooperative behavior within traffic scenarios involving autonomous vehicles. The main aim is to understand the suitability of commonly used voting rules as a potential mechanism for collective decision making in platoon applications considering unreliable communications. To realistically assess the system performance, we designed an integrated simulation platform composed of an agent-based platform, a microscopic traffic and a vehicular network models. Results show the viability of these simple voting mechanism to maintain high satisfaction among platoon members, which that can lead to stable formations and consequently better traffic conditions. However, additional mechanisms might need to be considered for larger platoon formations to timely guarantee consensus between voters.


Platooning Voting mechanisms Computational social choice Connected Automated Vehicle Collective decision-making 


  1. 1.
    Amoozadeh, M., Deng, H., Chuah, C.N., Zhang, H.M., Ghosal, D.: Platoon management with cooperative adaptive cruise control enabled by vanet. Veh. Commun. 2(2), 110–123 (2015)Google Scholar
  2. 2.
    Arrow, K., Sen, A., Suzumura, K.: Handbook of Social Choice and Welfare, vol. 1. Elsevier, Amsterdam (2002)zbMATHGoogle Scholar
  3. 3.
    Aschermann, M., Kraus, P., Müller, J.P.: LightJason: a BDI framework inspired by Jason. In: Criado Pacheco, N., Carrascosa, C., Osman, N., Julián Inglada, V. (eds.) EUMAS/AT -2016. LNCS (LNAI), vol. 10207, pp. 58–66. Springer, Cham (2017). Scholar
  4. 4.
    Asplund, M., Lövhall, J., Villani, E.: Specification, implementation and verification of dynamic group membership for vehicle coordination. In: IEEE Pacific Rim International Symposium on Dependable Computing, pp. 321–328. IEEE (2017)Google Scholar
  5. 5.
    Batista, A., Coutinho, L.R.: A multiagent system for combining green wave and adaptive control in a dynamic way. In: IEEE Conference on Intelligent Transportation Systems, pp. 2439–2444. IEEE (2013)Google Scholar
  6. 6.
    Boban, M., d’Orey, P.M.: Exploring the practical limits of cooperative awareness in vehicular communications. IEEE Trans. Veh. Technol. 65(6), 3904–3916 (2016)CrossRefGoogle Scholar
  7. 7.
    Brandt, F., Saile, C., Stricker, C.: Voting with ties: strong impossibilities via sat solving. In: International Conference on Autonomous Agents and Multiagent Systems (2018)Google Scholar
  8. 8.
    Dennisen, S.L., Müller, J.P.: Agent-based voting architecture for traffic applications. In: Müller, J.P., Ketter, W., Kaminka, G., Wagner, G., Bulling, N. (eds.) MATES 2015. LNCS (LNAI), vol. 9433, pp. 200–217. Springer, Cham (2015). Scholar
  9. 9.
    Dennisen, S.L., Müller, J.P.: Iterative committee elections for collective decision-making in a ride-sharing application. In: International Workshop on Agents in Traffic and Transportation (ATT) (2016)Google Scholar
  10. 10.
    Ferreira, M., d’Orey, P.: On the impact of virtual traffic lights on carbon emissions mitigation. IEEE Trans. Intell. Transp. Syst. 13, 284–295 (2012)CrossRefGoogle Scholar
  11. 11.
    Gholibeigi, M., Heijenk, G., Moltchanov, D., Koucheryavy, Y.: Analysis of a receiver-based reliable broadcast approach for vehicular networks. Ad Hoc Netw. 37, 63–75 (2016)CrossRefGoogle Scholar
  12. 12.
    Hollnagel, E., Nåbo, A., Lau, I.V.: A systemic model for driver-in-control. In: International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp. 86–91 (2004)Google Scholar
  13. 13.
    Jia, D., Lu, K., Wang, J., Zhang, X., Shen, X.: A survey on platoon-based vehicular cyber-physical systems. IEEE Commun. Surv. Tutor. 18(1), 263–284 (2016)CrossRefGoogle Scholar
  14. 14.
    Jia, D., Ngoduy, D.: Platoon based cooperative driving model with consideration of realistic inter-vehicle communication. Transp. Res. Part C Emerg. Technol. 68, 245–264 (2016)CrossRefGoogle Scholar
  15. 15.
    Khan, M.A., Boloni, L.: Convoy driving through ad-hoc coalition formation. In: IEEE Real Time and Embedded Technology and Applications Symposium, pp. 98–105. IEEE (2005)Google Scholar
  16. 16.
    Krajzewicz, D., Erdmann, J., Behrisch, M., Bieker, L.: Recent development and applications of SUMO - Simulation of Urban MObility. Int. J. Adv. Syst. Meas. 5, 128–138 (2012)Google Scholar
  17. 17.
    Rewald, H., Stursberg, O.: Cooperation of autonomous vehicles using a hierarchy of auction-based and model-predictive control. In: 2016 IEEE Intelligent Vehicles Symposium (IV), pp. 1078–1084. IEEE (2016)Google Scholar
  18. 18.
    Rios-Torres, J., Malikopoulos, A.A.: A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Trans. Intell. Transp. Syst. 18(5), 1066–1077 (2017)CrossRefGoogle Scholar
  19. 19.
    Sanderson, D., Pitt, J.: Institutionalised consensus in vehicular networks: executable specification and empirical validation. In: IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops, pp. 71–76. IEEE (2012)Google Scholar
  20. 20.
    Santini, S., Salvi, A., Valente, A.S., Pescapé, A., Segata, M., Cigno, R.L.: A consensus-based approach for platooning with intervehicular communications and its validation in realistic scenarios. IEEE Trans. Veh. Technol. 66(3), 1985–1999 (2017)CrossRefGoogle Scholar
  21. 21.
    Segata, M., Bloessl, B., Joerer, S., Dressler, F., Cigno, R.L.: Supporting platooning maneuvers through IVC: an initial protocol analysis for the join maneuver. In: Annual Conference on Wireless On-demand Network Systems and Services (WONS), pp. 130–137. IEEE (2014)Google Scholar
  22. 22.
    Segata, M., Dressler, F., Cigno, R.L.: Jerk beaconing: a dynamic approach to platooning. In: IEEE Vehicular Networking Conference, pp. 135–142 (2015)Google Scholar
  23. 23.
    Sommer, C., German, R., Dressler, F.: Bidirectionally coupled network and road traffic simulation for improved IVC analysis. IEEE Trans. Mob. Comput. 10(1), 3–15 (2011)CrossRefGoogle Scholar
  24. 24.
    Ziegler, J., Bender, P., Schreiber, M., et al.: Making bertha drive–an autonomous journey on a historic route. IEEE Intell. Transp. Syst. Mag. 6(2), 8–20 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidade do PortoPortoPortugal
  2. 2.Instituto de TelecomunicaçõesPortoPortugal

Personalised recommendations