Situational Awareness in Intelligent Vehicles


Cooperative intelligent vehicle systems constitute a promising way to improving traffic throughput, safety and comfort, and thus are the focus of intensive research and development. The vehicles implement more and more complex onboard functionalities, which interact with each other and with their surroundings, including other vehicles and roadside information infrastructure. In order for a functionality “do the right thing” it should have a sufficiently complete and certain interpretation of the surrounding world (i.e., relevant part of the road infrastructure, the surrounding vehicles, the ego-vehicle itself, etc.). Due to the ever-existing limitations of the sensing, the complexity of the data interpretation and the inherent uncertainty of the world “out there”, creating this representation poses major challenges and has far reaching consequences concerning how onboard functionalities should be built. The situational awareness term covers an overarching research field, which addresses this understanding process from different angles. It attempts the conceptualization of the problem domain, it relates sensory data processing and data fusion with the understanding process, and it investigates the role of humans in the related processes and even gives architectural guidelines for system design.

First a brief overview is given about the established models for situational awareness emphasizing the specialties of the intelligent vehicle systems. Then the representation problem is covered in details because the representation has strong influence both on the sensing, data interpretation, control and architectural aspect. Finally the control and architectural aspects are covered addressing the design for dependability.


Data Fusion Situational Awareness Intelligent Transportation System World Model Control Command 
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. Albus JS (2000) 4-D/RCS reference model architecture for unmanned ground vehicles. Proceedings of the ICRA ’00, (IEEE international conference on robotics and automation, 2000), San Francisco, CA, USA, 4:3260–3265Google Scholar
  2. Botts M et al (2007) OGC sensor web enablement: overview and high level architecture (OGC 07–165), Open Geospatial Consortium white paper. Accessed 28 Dec 2007
  3. Caveney D (2010) Cooperative vehicular safety applications. Control Syst IEEE 30(4):38–53MathSciNetCrossRefGoogle Scholar
  4. Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Factors 37(1):32–64CrossRefGoogle Scholar
  5. Endsley MR (2000) Theoretical underpinning of situational awareness: a critical review. In: Endsley MR, Garland DJ (eds) Situation awareness: analysis and measurement. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  6. Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23CrossRefGoogle Scholar
  7. Halle S, Laumonier J, Chaib-Draa B (2004) A decentralized approach to collaborative driving coordination. Proceedings of the 7th international IEEE conference on intelligent transportation systems, Washington, DC, USA, pp. 453–458, 3–6 Oct 2004Google Scholar
  8. Horowitz R, Varaiya P (2000) Control design of an automated highway system. Proc IEEE 88(7):913–925CrossRefGoogle Scholar
  9. Hurdus JG, Hong DW (2008) Behavioral programming with hierarchy and parallelism in the DARPA urban challenge and robocup. Proceedings of the IEEE international conference on multisensor fusion and integration for intelligent systems, Seoul, South Korea, pp. 503–509, 20–22 Aug 2008Google Scholar
  10. Kester LJHM (2008) Designing networked adaptive interactive hybrid systems. Proceedings of the IEEE international conference on multisensor fusion and integration for intelligent systems, Seoul, South Korea, pp. 516–521, 20–22 Aug 2008Google Scholar
  11. Li L et al (2005a) IVS 05: new developments and research trends for intelligent vehicles. Intell Syst 20(4):10–14CrossRefGoogle Scholar
  12. Li L, Wang F-Y, Kim H (2005) Cooperative driving and lane changing at blind crossings. In: Proceedings of the IEEE intelligent vehicles symposium, IEEE, Las Vegas, NV, USA, pp. 435–440Google Scholar
  13. Lygeros J, Godbole DN, Sastry S (1996) Multiagent hybrid system design using game theory and optimal control. In: Proceedings of the IEEE conference on decision and control, Kobe, 11–13 Dec 1996, pp. 1190–1195Google Scholar
  14. Lygeros J, Godbole DN, Sastry S (1998) Verified hybrid controllers for automated vehicles. IEEE Trans Autom Control 43(4):522–539MathSciNetMATHCrossRefGoogle Scholar
  15. Matheus CJ, Baclawski K, Kokar MM (2003) Derivation of ontological relations using formal methods in a situation awareness scenario. Proceedings of SPIE conference on multi-sensor, multi-source information fusion, Orlando, Cairns, Queensland, Australia, April 2003, pp. 298–309. Accessed Oct 2011Google Scholar
  16. Matheus CJ, Kokar M, Baclawski K (2003) A core ontology for situation awareness. Proceedings of the sixth international conference on information fusion 2003 1:545–552Google Scholar
  17. Michaud F, Lepage P, Frenette P, Letourneau D, Gaubert N (2006) Coordinated maneuvering of automated vehicles in platoons. IEEE Trans Intell Transp Syst 7(4):437–447CrossRefGoogle Scholar
  18. Papp Z, Brown C, Bartels C (2008) World modeling for cooperative intelligent vehicles. Prceedings of the IEEE intelligent vehicles symposium 2008, Eindhoven, The Netherlands, 4–6 June 2008, pp. 1050–1055Google Scholar
  19. Polychronopoulos A, Amditis A (2006) Revisiting JDL model for automotive safety applications: the PF2 functional model. 2006 9th international conference on Information fusion, Florence, Italy, 10–13 July 2006, pp. 1–7Google Scholar
  20. Schlenoff C, Washington R, Barbera T, Manteuffel C(2005) A standard intelligent system ontology. Proceedings of the unmanned ground vehicle technology VII conference (2005 SPIE defense and security symposium), Kissimmee, 28 Mar–1 Apr 2005Google Scholar
  21. Sheth A, Henson C, Sahoo SS (2008) Semantic sensor web. Internet Comput IEEE 12(4):78–83CrossRefGoogle Scholar
  22. Steinberg AN, Bowman CL, White FE (1999) Revisions to the JDL data fusion model. Proceedings of the SPIE in sensor fusion. Architectures, algorithms, and applications, Orlando, FL, USA, vol. 3719Google Scholar
  23. Urmson C, Baker C, Dolan J, Rybski P, Salesky B, Whittaker W, Ferguson D, Darms M (2008) Autonomous driving in traffic: boss and the urban challenge. AI Mag 30(2):17–28Google Scholar
  24. van Arem B, van Driel CJG, Visser R (2006) The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans Intell Transp Syst 7(4):429–436CrossRefGoogle Scholar
  25. Varaiya P (1993) Smart cars on smart roads: problems of control. IEEE Trans Autom Control 38(2):195–207MathSciNetCrossRefGoogle Scholar
  26. Zott C,Yuen SY, Brown CL, Bartels C, Papp Z, Netten B (2008) Safespot local dynamic maps – Context-dependent view generation of a platform’s state & environment. Proceedings of the15th world congress on intelligent transport systems, New York City, USAGoogle Scholar

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© Springer-Verlag London Ltd. 2012

Authors and Affiliations

  1. 1.TNO Technical SciencesThe HagueNetherlands

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