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Ontology-Based Context Awareness for Driving Assistance Systems

Alexandre Armand 1, 2 David Filliat 1, 3 Javier Ibañez-Guzman 2
3 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : Within a vehicle driving space, different entities such as vehicles and vulnerable road users are in constant interaction. That governs their behaviour. Whilst smart sensors provide information about the state of the perceived objects, considering the spatio-temporal relationships between them with respect to the subject vehicle remains a challenge. This paper proposes to fill this gap by using contextual information to infer how perceived entities are expected to behave, and thus what are the consequences of these behaviours on the subject vehicle. For this purpose, an ontology is formulated about the vehicle, perceived entities and context (map information) to provide a conceptual description of all road entities with their interaction. It allows for inferences of knowledge about the situation of the subject vehicle with respect to the environment in which it is navigating. The framework is applied to the navigation of a vehicle as it approaches road intersections, to demonstrate its applicability. Results from the real-time imple- mentation on a vehicle operating under controlled conditions are included. They show that the proposed ontology allows for a coherent understanding of the interactions between the perceived entities and contextual data. Further, it can be used to improve the situation awareness of an ADAS (Advanced Driving Assistance System), by determining which entities are the most relevant for the subject vehicle navigation.
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Submitted on : Wednesday, June 25, 2014 - 2:09:23 PM
Last modification on : Thursday, January 21, 2021 - 9:26:01 AM
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  • HAL Id : hal-01012078, version 1



Alexandre Armand, David Filliat, Javier Ibañez-Guzman. Ontology-Based Context Awareness for Driving Assistance Systems. IEEE Intelligent Vehicles Symposium (IV), Jun 2014, Dearborn, United States. pp.1-6. ⟨hal-01012078⟩



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