Abstract
Intelligent transportation systems are advanced applications with aim to provide innovative services relating to road transport management and enable the users to be better informed and make safer and coordinated use of transport networks. A crucial element for the success of these systems is that vehicles can exchange information not only among themselves but with other elements in the road infrastructure through different applications. One of the most important information sources in this kind of systems is sensors. Sensors can be located into vehicles or as part of an infrastructure element, such as bridges or traffic signs. The sensor can provide information related to the weather conditions and the traffic situation, which is useful to improve the driving process. In this paper a multiagent system using ontologies to improve the driving environment is proposed. The system performs different tasks in automatic way to increase the driver safety and comfort using sensor information.
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References
Verdone, R., Dardari, D., Mazzini, G., Conti, A.: Wireless Sensor and Actuator Networks. Elsevier, London, UK (2008)
Hill, J., Culler, D.: A wireless embedded sensor architecture for system level optimization, UC Berkeley Technical Report, University of California: Berkeley, CA, USA (2001)
Rabaey, J., Ammer, J., Da Silva, J.L., Jr., Patel, D.: PicoRadio: ad hoc wireless networking of ubiquitous low-energy sensor/monitor nodes. In: Proceeding of IEEE Computer Society Workshop on VLSI, Orlando, FL, USA, 27–28 April 2000, pp. 9-12
Kahn, J.M., Katz, R.H., Pister, K.S.J.: Next century challenges: mobile networking for “Smart Dust”. In: Proceeding of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, New York, NY, USA, Aug 1999, pp. 271–278
Asada, G., Dong, M., Lin, T.S., Newberg, F., Pottie, G., Kaiser, W.J.: Wireless integrated network sensors: low power systems on a chip. In: Proceeding of the 24th European Solid-State Circuits Conference (ESSCIRC 1998), The Hague, Netherlands, 22–24 Sep 1998, pp. 9–16
Gorender, S., Silva, Í.: An ontology for a fault tolerant traffic information system.In: 22nd International Congress of Mechanical Engineering (COBEM 2013), Ribeirão Preto, SP, Brazil, 3–7 Nov 2013
Pollard, E., Morignot, P., Nashashibi, F.: An ontology-based model to determine the automation level of an automated vehicle for co-driving. In: Fusion pp. 596–603 (2013)
Lee, D., Meier, R.: Primary-context model and ontology: a combined approach for pervasive transportation services. In: First International Workshop on Pervasive Transportation Systems (PerTrans 2007), with the Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 419–424. PerCom 07 (2007)
Hülsen, M., Zöllner, J.M., Weiss, C.: Traffic intersection situation description ontology for advanced driver assistance. In: 2011 IEEE Intelligent Vehicles Symposium (IV) Baden-Baden, Germany, 5–9 June 2011
Bermejo, A.J., Villadangos, J., Astrain, J.J., Cordoba, A.: Ontology based road traffic management. In: Fortino, G et al. (eds) Intelligent Distributed Computing VI, pp. 103–108. SCI 446
Herzog, A.; Jacobi, D.; Buchmann, A.: A3ME-An agent-based middleware approach for mixed mode environments. In: Proceeding of Second International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM 2008), Valencia, Spain, 29 Sep–4 Oct 2008; pp. 191–196
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML, Submission to W3C, May 2004. http://www.w3.org/Submission/SWRL/
Zhao, L., Ichise, R., Mita, S., Sasaki, Y.: Ontologies for Advanced Driver Assistance Systems
Dean, M., Schreiber, G.: OWL Web Ontology Language Reference. http://www.w3.org/TR/2004/REC-owl-ref-20040210/ (2004)
Protégé. http://protege.stanford.edu/
Pellet. http://clarkparsia.com/pellet/
SPARQL. http://sparql.org/
Lieu, H.: Traffic-flow theory. Public Roads (US Dep of Transportation) 62(4)
Fernandez, S., Hadfi, R., Ito, T., Marsa-Maestre, I., Velasco, J. R.: Ontology-based architecture for intelligent transportation systems using a traffic sensor network. Sensors 16(8), 1287 (2016)
Acknowledgements
This work was partially supported by Research and Development on Utilization and Fundamental Technologies for Social Big Data by NICT (National Institute of Information and Communications Technology), and the Fund for Strengthening and Facilitating the National University Reformations by Ministry of Education, Culture, Sports, Science, and Technology, Japan.
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Fernandez, S., Ito, T. (2018). Ontology-Based Architecture to Improve Driving Performance Using Sensor Information for Intelligent Transportation Systems. In: Theeramunkong, T., Skulimowski, A., Yuizono, T., Kunifuji, S. (eds) Recent Advances and Future Prospects in Knowledge, Information and Creativity Support Systems. KICSS 2015. Advances in Intelligent Systems and Computing, vol 685. Springer, Cham. https://doi.org/10.1007/978-3-319-70019-9_20
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DOI: https://doi.org/10.1007/978-3-319-70019-9_20
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