A model for ontology-based scene description for context-aware driver assistance systems

Driving assistance systems (DAS) offer support in potentially dangerous situations, especially for unexperienced drivers. Co-operative systems improve their performance by sharing information with each other. One key-enabler for describing and exchanging context between intelligent vehicles, which use it for reasoning about their environment, is a common context-model. In this paper, we briefly discuss the influence of the driving context on decision-making and present an OWL-based context-model for abstract scene representation of driving scenarios. We further outline the integration of scene-descriptions with a logic-based reasoning system, based on a set of transformation rules.

[1]  A.D. Lattner,et al.  A knowledge-based approach to behavior decision in intelligent vehicles , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[2]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[3]  John A. Michon,et al.  Generic intelligent drive support , 1993 .

[4]  Zhaohui Wu,et al.  ScudWare: A Semantic and Adaptive Middleware Platform for Smart Vehicle Space , 2007, IEEE Transactions on Intelligent Transportation Systems.

[5]  Yoshiaki Shirai,et al.  An active vision system for real-time traffic sign recognition , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[6]  Ian Horrocks.  OWL Rules, OK? , 2005, Rule Languages for Interoperability.

[7]  A. Shashua,et al.  Pedestrian detection for driving assistance systems: single-frame classification and system level performance , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[8]  Harry Chen,et al.  An ontology for context-aware pervasive computing environments , 2003, The Knowledge Engineering Review.

[9]  J A Michan.  Generic intelligent driver support: a comprehensive report on GIDS , 1993 .

[10]  Andry Rakotonirainy,et al.  Context-Aware Driving Behaviour Model , 2005 .

[11]  Dariu Gavrila,et al.  Pedestrian Detection from a Moving Vehicle , 2000, ECCV.

[12]  Jadwiga Indulska,et al.  A software engineering framework for context-aware pervasive computing , 2004, Second IEEE Annual Conference on Pervasive Computing and Communications, 2004. Proceedings of the.

[13]  Christof Simons.  CMP: A UML Context Modeling Profile for Mobile Distributed Systems , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[14]  Claudia Linnhoff-Popien,et al.  CoOL: A Context Ontology Language to Enable Contextual Interoperability , 2003, DAIS.

[15]  Quan Z. Sheng,et al.  ContextUML: a UML-based modeling language for model-driven development of context-aware Web services , 2005, International Conference on Mobile Business (ICMB'05).

[16]  Gudrun Klinker,et al.  Ontology-Based Pervasive Spatial Knowledge for Car Driver Assistance , 2007, Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07).

[17]  A. Miene,et al.  Dynamic-preserving qualitative motion description for intelligent vehicles , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[18]  Claudia Linnhoff-Popien,et al.  A Context Modeling Survey , 2004 .

[19]  Ian Horrocks,et al.  A proposal for an owl rules language , 2004, WWW '04.

[20]  Reto Krummenacher,et al.  Ontology-Based Context Modeling , 2007 .

[21]  Qiang Ji,et al.  Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance , 2002, Real Time Imaging.

[22]  Josef F. Krems,et al.  Situation Awareness beim Autofahren als Verstehensprozess , 2006, MMI Interakt..

[23]  Chris Swan,et al.  Position Paper for W3C Workshop on Rule Languages for Interoperability , 2005, Rule Languages for Interoperability.

[24]  Mohan M. Trivedi,et al.  Holistic Sensing and Active Displays for Intelligent Driver Support Systems , 2007, Computer.