Skip to main content
Log in

A robustness approach to the distributed management of traffic intersections

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Nowadays, the development of autonomous vehicles has emerged as an approach to considerably improve the traffic management in urban zones. Thanks to automation in vehicles as well as in other sectors, the probability of errors, typically due to repetitive tasks, has been drastically reduced. Therefore, technological aids in current driving systems are aimed to avoid or reduce human errors like imprudences or distractions. According to this, it is possible to tackle complex scenarios such as the automation of the vehicles traffic at intersections, as this is one of the points with the highest probability of accidents. In this sense, the coordination of autonomous vehicles at intersections is a trending topic. In the last few years, several approaches have been proposed using centralized solutions. However, centralized systems for traffic coordination have a limited fault-tolerance. This paper proposes a distributed coordination management system for intersections of autonomous vehicles through the employment of some well-defined rules to be followed by vehicles. To validate our proposal, we have developed different experiments in order to compare our proposal with other centralized approaches. Furthermore, we have incorporated the management of communication faults during the execution in our proposal. This improvement has also been tested in front of centralized or semi-centralized solutions. The introduction of failures in the communication process demonstrates the sensitivity of the system to possible disturbances, providing a satisfactory coordination of vehicles during the intersection. As final result, our proposal is kept with a suitable flow of autonomous vehicles still with a high communication fails rate.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://news.voyage.auto/an-introduction-to-lidar-the-key-self-driving-car-sensor-a7e405590cff.

  2. The LAIE’s model is an extension of the LAI model, which introduces conflict ways but maintaining the same dynamic model.

References

  • Ahn H, Colombo A, Del Vecchio D (2014) Supervisory control for intersection collision avoidance in the presence of uncontrolled vehicles. In: American control conference (ACC). IEEE, pp 67–873

  • Ahn H et al (2016) Robust supervisors for intersection collision avoidance in the presence of uncontrolled vehicles (arXiv preprint). arXiv:1603.03916

  • Bagloee SA et al (2016) Autonomous vehicles: challenges, opportunities, and future implications for transportation policies. J Mod Transp 24(4):284–303

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Bazzan ALC, Klügl F (2014) A review on agent-based technology for traffic and transportation. Knowl Eng Rev 29(3):375–403

    Article  Google Scholar 

  • Cools S-B, Gershenson C, D’Hooghe B (2013) Self-organizing traffic lights: a realistic simulation. In: Advances in applied self-organizing systems. Springer, Berlin, pp 45–55

  • De Oliveira D, Bazzan ALC, Lesser V (2005) Using cooperative mediation to coordinate traffic lights: a case study. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems. ACM, pp 463–470

  • Dresner K, Stone P (2005) Multiagent traffic management: an improved intersection control mechanism. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems. ACM, pp 471–477

  • Dresner K, Stone P (2006) Traffic intersections of the future. In: Proceedings of the national conference on artificial intelligence, vol. 21. 2, Menlo Park. AAAI Press, Cambridge; MIT Press, London, p 1593

  • Dresner K, Stone P (2008) A multiagent approach to autonomous intersection management. J Artif Intell Res 31:591–656

    Article  Google Scholar 

  • Gershenson C (2004) Self-organizing traffic lights (arXiv preprint). arXiv:nlin/0411066

  • Gershenson C (2007) Design and control of self-organizing systems. CopIt Arxives

  • Gershenson C, Rosenblueth DA (2012) Self-organizing traffic lights at multiple-street intersections. Complexity 17(4):23–39

    Article  MathSciNet  Google Scholar 

  • Gonzalez CL et al (2018) Distributed management of traffic intersections. In: International symposium on ambient intelligence. Springer, Berlin, pp 56–64

  • Gregor D et al (2016) A methodology for structured ontology construction applied to intelligent transportation systems. Comput Stand Interfaces 47:108–119

    Article  Google Scholar 

  • Grünewald M, Rust C, Witkowski U (2006) Using mini robots for prototyping intersection management of vehicles. In: Proceedings of the 3rd international symposium on autonomous minirobots for research and edutainment (AMiRE 2005). Springer, pp 287–292

  • Guo D et al (2003) A study on the framework of urban traffic control system. In: Proceedings of intelligent transportation systems, vol 1. IEEE, pp 842–846

  • Ioannou P (2013) Automated highway systems. Springer Science and Business Media, New York

    Google Scholar 

  • Kaplan J (2018) Digital Trends-Cars. https://www.digitaltrends.com/cars/every-company-developingself- driving-car-tech-ces-2018/. Accessed 22 Feb 2019

  • Knight W (2013) Intelligent machines. https://www.technologyreview.com/s/520431/driverlesscars- are-further-away-than-you-think/. Accessed 22 Feb 2019

  • Kosonen I (2003) Multi-agent fuzzy signal control based on real-time simulation. Transp Res Part C Emerg Technol 11(5):389–403

    Article  Google Scholar 

  • Koźlak J et al (2008) Anti-crisis management of city traffic using agent-based approach. J Univ Comput Sci 14(14):2359–2380

    Google Scholar 

  • Lárraga ME, Alvarez-Icaza L (2010) Cellular automaton model for traffic flow based on safe driving policies and human reactions. In: Physica A: statistical mechanics and its applications 389.23, pp. 5425–5438. ISSN: 0378-4371

  • Li P, Alvarez L, Horowitz R (1997) AHS safe control laws for platoon leaders. In: IEEE transactions on control systems technology 5.6, pp 614– 628. ISSN: 1063-6536

  • Rasouli A, Tsotsos JK (2019) Autonomous vehicles that interact with pedestrians: a survey of theory and practice. In: IEEE transactions on intelligent transportation systems

  • Roozemond DA (2001) Using intelligent agents for pro-active, real-time urban intersection control. Eur J Oper Res 131(2):293–301

    Article  Google Scholar 

  • Rothenbücher D et al (2016) Ghost driver: a field study investigating the interaction between pedestrians and driverless vehicles. In: 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, pp 795–802

  • Wang F-Y (2005) Agent-based control for networked traffic management systems. IEEE Intell Syst 20(5):92–96

    Article  Google Scholar 

  • Wu J, Abbas-Turki A, El Moudni A (2012) Cooperative driving: an ant colony system for autonomous intersection management. Appl Intell 37(2):207–222

    Article  Google Scholar 

  • Zangenehpour S, Miranda-Moreno LF, Saunier N (2015) Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: methodology and application. Transp Res Part C Emerg Technol 56:161–176

    Article  Google Scholar 

  • Zapotecatl JL (2014) QtTrafficLights. https://github.com/Zapotecatl/Traffic-Light. Accessed 22 Feb 2019

  • Zapotecatl JL, Rosenblueth DA, Gershenson C (2017) Deliberative self-organizing traffic lights with elementary cellular automata. In: Complexity 2017

  • Zubillaga D et al (2014) Measuring the complexity of self-organizing traffic lights. Entropy 16(5):2384–2407

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vicente Julian.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

González, C.L., Zapotecatl, J.L., Gershenson, C. et al. A robustness approach to the distributed management of traffic intersections. J Ambient Intell Human Comput 11, 4501–4512 (2020). https://doi.org/10.1007/s12652-019-01424-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-019-01424-w

Keywords

Navigation