Skip to main content
SpringerLink
Log in

Using Agent Based Modeling to Frame Autonomous Vehicle Navigation as Complex Systems

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1 (FTC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 358))

Included in the following conference series:

  • 1149 Accesses

Abstract

Highly mobile populations can quickly overwhelm an existing urban infrastructure as large numbers of people move into the city. In this paper, we frame autonomous vehicle (AV) simulation by implementing an agent-based model. Our work includes ideas of incorporating cultural differences which emerge during driving; for example, simulating the case when the AVs move through different countries. Our models are decentralized model based on our thinking that this will provide more robust and resilience AVs simulation studies and, in our case leads us to a new concept based on tokens emerges as a novel approach for future AVs. Procedural content generation (PCG) and agent-based modelling concepts are proposed and implemented, to frame AVs simulation as a complex system.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Schmidt-Daffy, M.: Fear and anxiety while driving: differential impact of task demands, speed and motivation. Transport. Res. Part F: Traffic Psychol. Behav. 16, 14–28 (2012)

    Article  Google Scholar 

  2. Mudrak, G.: Modeling Aggression & Bullying: A Complex Systems Approach, Master Thesis UCCS (2013)

    Google Scholar 

  3. Mudrak, G., Semwal, S.: Modeling aggression and bullying: a complex systems approach. In: Annual Review of Cyber Therapy and Telemedicine, pp. 187–191. IOS Press, Amsterdam (2015)

    Google Scholar 

  4. Mudrak, G., Semwal, S.: Group aggression and bullying through complex systems agent based modeling. Annu. Rev. Cyberther. Telemed. 14, 189–194 (2016)

    Google Scholar 

  5. Ozkan, T., Lajunen, T., Tzamalouka, G., Warner, H.W.: Cross-cultural comparison of drivers’ tendency to commit different aberrant driving behaviors. Transport. Res. Part F: Traffic Psychol. Behav. 14(5), 390–399 (2011)

    Article  Google Scholar 

  6. Kelly, G., McCabe, H., Whelan, G.: Roll your own city. In: DIMEA 2008 Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts, pp. 534–535 (2008)

    Google Scholar 

  7. Smith, G.: Understanding procedural content generation: a design-centric analysis of the role of PCG in games. In: CHI 2014 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 917–926 (2014)

    Google Scholar 

  8. Karmeshu, P.K., Sikdar, P.K.: On population growth of cities in a region: a stochastic nonlinear model. Environ. Plan. A: Econ. Space 14(5), 585–590 (1982)

    Article  Google Scholar 

  9. Muller, P., Parish, Y.: Procedural modeling of cities. In: SIGGRAPH 2001 Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 301–308 (2001)

    Google Scholar 

  10. Yu, X., Bacui, G., Green, M., Sun, J.: Template-based generation of road networks for virtual city modeling. In: VRST 2002 Proceedings of the ACM Symposium on Virtual Reality Software and Technology, pp. 33–40 (2002)

    Google Scholar 

  11. Lindenmayer, A.: Mathematical models for cellular interactions in development, Parts I and II. Theor. Biol. 280–315 (1968)

    Google Scholar 

  12. Prusinkiewicz, P.: A look at the visual modeling of plants using L-systems. Agron. EDP Sci. 19(3–4), 211–214 (1999)

    Google Scholar 

  13. Kelly, G.: Citygen: An interactive system for procedural city generation. In: GDTW 2007: The 5th Annual International Conference in Computer Game Design and Technology (2007)

    Google Scholar 

  14. Prusinkiewicz, P.: A look at the visual modeling of plants using L-systems. In: German Conference on Bioinformatics, pp. 11–29 (2005)

    Google Scholar 

  15. Morkel, C., Bangay, S., Glass, K.: Duplicating road patterns in south African informal settlements using procedural techniques. In: AFRIGRAPH 2006 Proceedings of the 4th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa, pp. 161–169 (2006)

    Google Scholar 

  16. Tutenel, T., Bidarra, R., Benes, B., Smelik, R.: A survey on procedural modelling for virtual worlds. Comp. Graph. Forum 33(6), 31–50 (2014)

    Article  Google Scholar 

  17. McCabe, H., Kelly, G.: A survey of procedural techniques for city generation. ITB J. 7(2), 5 (2006)

    Google Scholar 

  18. Likhachev, M., Stentz, A., Ferguson, D.: A guide to heuristic-based path planning. School of Computer Science, Carnegie Mellon University (2005)

    Google Scholar 

  19. Church, R.L., Zeng, W.: Finding the shortest paths on real road networks: the case for A*. Int. J. Geogr. Inf. Sci. 23(4), 531–543 (2009)

    Article  Google Scholar 

  20. ResearchGate. https://www.researchgate.net/figure/A-search-algorithm-Pseudocode-of-the-A-search-algorithm-operating-with-open-and-closed_fig8_232085273

  21. Domingos, P.: The master algorithm: how the quest for the ultimate learning machine will remake our world, 1st edn. Basic Books (2015)

    Google Scholar 

  22. Langer, G.: ABC News Poll: Traffic in the United States. ABC News (2005)

    Google Scholar 

  23. Dollard, M., McLinton, S.: Work stress and driving anger in Japan. Accid. Anal. Prev. 42(1), 174–181 (2010)

    Article  Google Scholar 

  24. Tractinsky, N., Inbar, O.: Make a trip an experience: sharing in-car information with passengers. In: CHI EA 2011 CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pp. 1243–1248 (2011)

    Google Scholar 

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

    Article  Google Scholar 

  26. Wu, G., Boriboonsomsin, K., Barth, M., Jin, Q.: Multi-agent intersection management for connected vehicles using an optimal scheduling approach. In: ICCVE International Conference on Connected Vehicles and Expo, pp. 185–190 (2012)

    Google Scholar 

  27. NetLogo. (2019). https://ccl.northwestern.edu/netlogo/index.shtml

  28. Mudrak, G., Semwal, S.: AgentCity: an agent-based modeling approach to city planning and population dynamics. In: International Conference on Collaboration Technologies and Systems, CTS, pp. 91–96 (2012)

    Google Scholar 

  29. Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.P.: Agent based modelling and simulation tools: a review of the state-of-art software. Comp. Sci. Rev. 24, 13–33 (2017)

    Article  Google Scholar 

  30. Kravari, K., Bassiliades, N.: A survey of agent platforms. J. Artif. Soc. Soc. Simulat. 18(1) (2015). http://jasss.soc.surrey.ac.uk/18/1/11.html

  31. Zhu, Y., Ferreira, J., Jr.: Synthetic population generation at disaggregated spatial scales for land use and transportation microsimulation. Transp. Res. Rec. 2429(1), 168–177 (2014)

    Article  Google Scholar 

  32. Auld, J., Mohammadian, A.: Efficient methodology for generating synthetic populations with multiple control levels. Transp. Res. Rec. 2175(1), 138–147 (2010)

    Article  Google Scholar 

  33. Imai, H., Asano, T.: Efficient algorithms for geometric graph search problems. SIAM J. Comput. 15(2), 478–494 (2006)

    Article  MathSciNet  Google Scholar 

  34. Zhou, R., Hansen, E.A.: Sparse-memory graph search. Int. Joint Conf. Artif. Intell. 9, 1259–1268 (2003)

    Google Scholar 

  35. Filippis, L.D., Guglieri, G.: Advanced graph search algorithms for path planning of flight vehicles. In: Recent Advances in Aircraft Technology, Copyright 2012. InTech (2012)

    Google Scholar 

  36. Hansen, E.A., Zilberstein, S.: LAO*: a heuristic search algorithm that finds solutions with loops. Artif. Intell. 129(1), 35–62 (2001)

    Article  MathSciNet  Google Scholar 

  37. Dechter, R., Pearl, J.: Generalized best-first search strategies and the optimality of A*. J. ACM 32, 505–536 (1985)

    Article  MathSciNet  Google Scholar 

  38. Alshawi, I.S., Yan, L., Pan, W., Luo, B.: Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm. In: IET Conference on Wireless Sensor Systems (2012)

    Google Scholar 

  39. Rana, K., Zaveri, M.: A-star algorithm for energy efficient routing in wireless sensor network. In: Wyld, D.C., Wozniak, M., Chaki, N., Meghanathan, N., Nagamalai, D. (eds) Trends in Network and Communications. WeST 2011, NeCoM 2011, WiMoN 2011. Communications in Computer and Information Science, vol. 197. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22543-7

  40. Ahuja, R.K., Ergun, O., Orlin, J.B., Punnen, A.P.: A survey of very large-scale neighborhood search techniques. Discret. Appl. Math. 123(1–3), 75–102 (2002)

    Article  MathSciNet  Google Scholar 

  41. Chen, S., Chen, R., Wang, G., Gao, J., Sangaiah, A.K.: An adaptive large neighborhood search heuristic for dynamic vehicle routing problems. Comput. Electr. Eng. 67, 596–607 (2018)

    Article  Google Scholar 

  42. Schyns, M.: An ant colony system for responsive dynamic vehicle routing. Eur. J. Oper. Res. 245(3), 704–718 (2015)

    Article  MathSciNet  Google Scholar 

  43. Pillac, V., Gueret, C., Medaglia, A.L.: An event-driven optimization framework for dynamic vehicle routing. Decis. Supp. Syst. 54(1), 414–423 (2012)

    Article  Google Scholar 

  44. Fayazi, S.A., Vahidi, A.: Mixed-integer linear programming for optimal scheduling of autonomous vehicle intersection crossing. IEEE Trans. Intell. Vehicles 3(3), 287–299 (2018)

    Article  Google Scholar 

  45. Carlino, D., Boyles, S.D., Stone, P.: Auction-based autonomous intersection management. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), The Hague, pp. 529–534 (2013)

    Google Scholar 

  46. Kamali, M., Dennis, L.A., McAree, O., Fisher, M., Veres, S.M.: Formal verification of autonomous vehicle platooning. Sci. Comput. Prog. 148(15), 88–106 (2017)

    Article  Google Scholar 

  47. Choi, Y., Lim, K., Kim, J.: Lane change and path planning of autonomous vehicles using GIS. In: 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) October, 2015, pp. 163–166 (2015)

    Google Scholar 

  48. Martin, L.A., Thornton, S.: City-wide trial shows how road use charges can reduce traffic jams. The Conversation, November 2017, [Online] (2017). https://theconversation.com/city-wide-trial-shows-how-road-use-charges-can-reduce-traffic-jams-86324

  49. Ettema, D., Knockaert, J., Verhoef, E.: Using incentives as traffic management tool: empirical results of the ‘peak avoidance’ experiment. Int. J. Transp. Res. 2(1), 39–51 (2010)

    Google Scholar 

  50. Pareto Optimality. Wikipedia (2020). https://en.wikipedia.org/wiki/Pareto_efficiency

  51. Kearney, A., De Young, R.: Changing commuter travel behavior: employer-initiated stragegies. J. Environ. Syst. 24(4), 373–393 (1996)

    Article  Google Scholar 

  52. Barrett, B.: Security News this Week: An Unprecedented Cyberattack Hit US Power Utilities. Wired (2019). https://www.wired.com/story/power-grid-cyberattack-facebook-phone-numbers-security-news

  53. Chester, D.: How and Why Power Grid Cyberattacks are Becoming Terrorists’ Go-To. Digital Utility Group (2020). https://energycentral.com/c/iu/how-and-why-power-grid-cyberattacks-are-becoming-terrorists-go

  54. Fu, S., Wan, Y.: Communicating in remote areas or disaster situations using unmanned aerial vehicles. In: Homeland Defense & Security Information Analysis Center (2019). https://www.hdiac.org/wp-content/uploads/2019/10/v2i4-communicating-remote-areas.pdf

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Colorado at Colorado Springs, Colorado Springs, CO, 80918, USA

    George Mudrak & Sudhanshu Kumar Semwal

Authors
  1. George Mudrak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sudhanshu Kumar Semwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudhanshu Kumar Semwal .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mudrak, G., Semwal, S.K. (2022). Using Agent Based Modeling to Frame Autonomous Vehicle Navigation as Complex Systems. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_12

Download citation

Publish with us

Policies and ethics

Search

Navigation