Skip to main content

Collaborative Decision Making for Lane-Free Autonomous Driving in the Presence of Uncertainty

  • 165 Accesses

Part of the Lecture Notes in Computer Science book series (LNAI,volume 13442)

Abstract

The recently introduced lane-free traffic paradigm removes the restrictions of the traffic lanes, so that autonomous vehicles can move anywhere laterally across the road’s width. Previous research in this domain has employed the celebrated max-plus message-passing algorithm in order to allow the coordination of all (connected and autonomous) vehicles in the environment. However, when allowing for the realistic perspective that there exist vehicles that are unable or unwilling to communicate with others, the uncertainty introduced renders the aforementioned coordination approach ineffective. To combat this, in this paper we adjust the Max-plus algorithm accordingly so that agents using max-plus for coordination can also observe and take into consideration independent agents via emulated messages. We put forward different methods to form these messages—namely the Maximax, Maximin, Hurwicz, Minimax Regret and Laplace decision-making criteria. Finally, we provide a thorough evaluation of our approach, including a detailed comparison of all criteria used for message-forming.

Keywords

  • Max-plus algorithm
  • Uncertainty
  • Lane-free traffic

The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ERC Grant Agreement n. [833915], project TrafficFluid.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-20614-6_10
  • Chapter length: 17 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-20614-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. Ahmadzadeh, H., Masehian, E.: Fuzzy coordination graphs and their application in multi-robot coordination under uncertainty. In: 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), pp. 345–350. IEEE (2014)

    Google Scholar 

  2. Albrecht, S.V., Stone, P.: Autonomous agents modelling other agents: a comprehensive survey and open problems. Artif. Intell. 258, 66–95 (2018)

    CrossRef  MathSciNet  MATH  Google Scholar 

  3. Aldea, C., Olariu, C.: Selecting the optimal software solution under conditions of uncertainty. Procedia Soc. Behav. Sci. 109, 333–337 (2014)

    CrossRef  Google Scholar 

  4. Guestrin, C., Koller, D., Parr, R.: Multiagent planning with factored MDPS. In: Advances in Neural Information Processing Systems, vol. 14. MIT Press (2001)

    Google Scholar 

  5. Hurwicz, L.: Some specification problems and applications to econometric models. Econometrica 19(3), 343–344 (1951)

    Google Scholar 

  6. Karafyllis, I., Theodosis, D., Papageorgiou, M.: Two-dimensional cruise control of autonomous vehicles on lane-free roads. In: 60th IEEE Conference on Decision and Control, pp. 2683–2689. CDC (2021)

    Google Scholar 

  7. Kok, J.R., Vlassis, N.: Collaborative multiagent reinforcement learning by payoff propagation. J. Mach. Learn. Res. 7, 1789–1828 (2006)

    MathSciNet  MATH  Google Scholar 

  8. Larbani, M.: Non cooperative fuzzy games in normal form: a survey. Fuzzy Sets Syst. 160(22), 3184–3210 (2009)

    CrossRef  MathSciNet  MATH  Google Scholar 

  9. Li, M., Yang, W., Cai, Z., Yang, S., Wang, J.: Integrating decision sharing with prediction in decentralized planning for multi-agent coordination under uncertainty. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 450–456. IJCAI (2019)

    Google Scholar 

  10. Mulla, A.K., Joshi, A., Chavan, R., Chakraborty, D., Manjunath, D.: A microscopic model for lane-less traffic. IEEE Trans. Control Netw. Syst. 6(1), 415–428 (2019)

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. Papageorgiou, M., Mountakis, K.S., Karafyllis, I., Papamichail, I., Wang, Y.: Lane-free artificial-fluid concept for vehicular traffic. Proc. IEEE 109(2), 114–121 (2021)

    CrossRef  Google Scholar 

  12. Pérez-Galarce, F., Álvarez Miranda, E., Candia-Vejar, A., Toth, P.: On exact solutions for the minmax regret spanning tree problem. Comput. Oper. Res. 47, 114–122 (2014)

    Google Scholar 

  13. Troullinos, D., Chalkiadakis, G., Papamichail, I., Papageorgiou, M.: Collaborative multiagent decision making for lane-free autonomous driving. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS, pp. 1335–1343 (2021)

    Google Scholar 

  14. Troullinos, D., Chalkiadakis, G., Samoladas, V., Papageorgiou, M.: Max-sum with quadtrees for decentralized coordination in continuous domains. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 518–526. International Joint Conferences on Artificial Intelligence Organization (2022)

    Google Scholar 

  15. Wald, A.: Statistical decision functions which minimize the maximum risk. Ann. Math. 46(2), 265–280 (1945)

    CrossRef  MathSciNet  MATH  Google Scholar 

  16. Yanumula, V.K., Typaldos, P., Troullinos, D., Malekzadeh, M., Papamichail, I., Papageorgiou, M.: Optimal path planning for connected and automated vehicles in lane-free traffic. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 3545–3552 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pavlos Geronymakis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Geronymakis, P., Troullinos, D., Chalkiadakis, G., Papageorgiou, M. (2022). Collaborative Decision Making for Lane-Free Autonomous Driving in the Presence of Uncertainty. In: Baumeister, D., Rothe, J. (eds) Multi-Agent Systems. EUMAS 2022. Lecture Notes in Computer Science(), vol 13442. Springer, Cham. https://doi.org/10.1007/978-3-031-20614-6_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20614-6_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20613-9

  • Online ISBN: 978-3-031-20614-6

  • eBook Packages: Computer ScienceComputer Science (R0)