A Situational Awareness Approach to Intelligent Vehicle Agents

  • Vincent BainesEmail author
  • Julian Padget
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)


As an increasing number of technological developments are made in the field of autonomous vehicles, the question of what intelligent system(s) will be placed around these vehicles both for the pursuit of individual goals and conformance to regulations as part of a wider collective of vehicles becomes pertinent, especially in the context of a mixed environment of autonomous and human controlled vehicles. The requirement to conform both to the law and with social conventions, in unpredictable circumstances, poses the problem of how to encode such knowledge. This paper adopts a Situational Awareness approach to agent knowledge, from low level perceptions, through to high level projection of future events, and explores a number of traffic scenarios where agents adopt different plans based on expected future states. A variant on such reactions is also presented, where the use of institutional governance frameworks is adopted to enforce certain behaviour, offering a ‘late binding’ mechanism for socially complex situations.


Resource Description Framework Vehicle Speed Situational Awareness Traffic Light Intelligent Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Markoff J (2010) Google cars drive themselves, in traffic. Accessed 8 Oct 2011
  2. 2.
    Nikki Gordon-Bloomfield (2013) Nissan takes Japanese PM on autonomous LEAF test drive. Accessed 19 Jan 2014
  3. 3.
    Bergenhem C, Huang Q, Benmimoun A, Robinson T (2010) Challenges of platooning on public motorways. In: 17th world congress on intelligent transport systemsGoogle Scholar
  4. 4.
    Naylor N (2014) U.S. Department of Transportation Announces Decision to Move Forward with Vehicle-to-Vehicle Communication Technology for Light Vehicles. Accessed Feb 2014
  5. 5.
    Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors: J Hum Factors Ergon Soc 37(1):32–64CrossRefGoogle Scholar
  6. 6.
    Bordini RH, Hübner JF, Wooldridge M (2007) Programming multi-agent systems in AgentSpeak using Jason. Wiley, HobokenCrossRefzbMATHGoogle Scholar
  7. 7.
    Krajzewicz D, Erdmann J, Behrisch M, Bieker L (2012) Recent development and applications of SUMO—Simulation of Urban MObility. Int J Adv Syst Meas 5(3 and 4):128–138Google Scholar
  8. 8.
    Cliffe O, De Vos M, Padget J (2006) Answer set programming for representing and reasoning about virtual institutions. In: Inoue K, Satoh K, Toni F (eds) CLIMA VII. Lecture notes in computer science, vol 4371, pp 60–79. SpringerGoogle Scholar
  9. 9.
    UK Highways Agency (2014) Traffic flow database system. Accessed 26 Jan 2014
  10. 10.
    Bratman ME, Israel DJ, Pollack ME (1988) Plans and resource-bounded practical reasoning. Comput Intell 4:349–355CrossRefGoogle Scholar
  11. 11.
    Lee JH, Baines V, Padget J (2012) Decoupling cognitive agents and virtual environments. In: Dignum F, Brom C, Hindriks KV, Beer MD, Richards D (eds) CAVE. Lecture notes in computer science, vol 7764, pp 17–36. SpringerGoogle Scholar
  12. 12.
    Baines V, Padget J (2012) Communication and metrics in agent convoy organization. In: 7th international workshop on agents in traffic and transportation (ATT 2012 at AAMAS 2012), pp 69–77, June 2012Google Scholar
  13. 13.
    Hourizi R (1999) Awareness beyond mode error. Ph.D. thesis, University of BathGoogle Scholar
  14. 14.
    Freie Universitat Berlin (2011) Autonomous car navigates the streets of Berlin. Accessed 8 Oct 2011
  15. 15.
    Volkswagen (2013) Audi in the simTD large-scale test study: the “traffic light info online” project. Accessed 19 Jan 2014
  16. 16.
    Kim KT (2012) STVC: secure traffic-light to vehicle communication. In: ICUMT, pp 96–104. IEEEGoogle Scholar
  17. 17.
    Balke T (2011) Towards the governance of open distributed systems: a case study in wireless mobile grids. Ph.D. thesis, University of Bayreuth, September 2011Google Scholar
  18. 18.
    Cliffe O, De Vos M, Padget JA (2007) Specifying and reasoning about multiple institutions. In: Coordination, organizations, institutions, and norms in agent systems. Lecture notes in artificial intelligence, SpringerGoogle Scholar
  19. 19.
    XMPP Standards Foundation (2014) The XMPP standards foundation homepage., 20130129, no date
  20. 20.
    Stout L, Murphy MA, Goasguen S (2009) Kestrel: an XMPP-based framework for many task computing applications. In: Proceedings of the 2nd workshop on many-task computing on grids and supercomputers, MTAGS’09, pp 11:1–11:6. New York, NY, USA. ACMGoogle Scholar
  21. 21.
    Wagener J, Spjuth O, Willighagen E, Wikberg J (2009) XMPP for cloud computing in bioinformatics supporting discovery and invocation of asynchronous web services. BMC Bioinform 10(1):279CrossRefGoogle Scholar
  22. 22.
    Lee JH, Li T, Padget J (2013) Towards polite virtual agents using social reasoning techniques. Comput Anim Virtual Worlds 24(3–4):335–343CrossRefGoogle Scholar
  23. 23.
    Alechina N, Dastani M, Logan B (2012) Programming norm-aware agents. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems, AAMAS’12, vol 2, pp 1057–1064. Richland, SC, International Foundation for Autonomous Agents and Multiagent SystemsGoogle Scholar
  24. 24.
    Lee JH, Padget J, Logan B, Alechina N, Dybalova D (2014) Run-time norm compliance in BDI agents. In: International conference on autonomous agents and multi-agent systems, AAMAS’14, Paris, France, May 2014. IFAAMAS (to appear)Google Scholar
  25. 25.
    Tobias Knerr (2013) Merging elevation raster data and OpenStreetMap vectors for 3D rendering. Master’s thesis, University of Passau, May 2013Google Scholar
  26. 26.
    Claypool KT, Claypool M (2007) On frame rate and player performance in first person shooter games. Multimedia Syst 13(1):3–17CrossRefGoogle Scholar
  27. 27.
    Smith E (2013) JVM-Serializers. Accessed 2 Feb 2014
  28. 28.
    National Research Council (U.S.) (1998) Transportation Research Board. Committee for Guidance on Setting and Enforcing Speed Limits. Managing speed: review of current practice for setting and enforcing speed limits. Number no. 254 in managing speed. Transportation Research Board, National Research Council, National Academy PressGoogle Scholar
  29. 29.
    Coleman JA, Paniati JF, Cotton RD, Parker MR Jr, Covey R, Pena HE Jr, Graham D, Robinson ML, MaClauley J, Taylor WC et al (1996) Fhwa study tour for speed management and enforcement technology. US Department of Transportation, Washington DCGoogle Scholar
  30. 30.
    Papageorgiou M, Kosmatopoulos E, Papamichail I (2008) Effects of variable speed limits on motorway traffic flow. Transp Res Rec 2047(1):37–48Google Scholar
  31. 31.
    Tafti MF (2008) An investigation on the approaches and methods used for variable speed limit control. In: 15th world congress on intelligent transport systems and ITS America’s 2008 annual meetingGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.University of BathBathUK

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