We propose a car signal ontology named VSSo that provides a formal definition of the numerous sensors embedded in car regardless of the vehicle model and brand, re-using the work made by the GENIVI alliance with the Vehicle Signal Specification (VSS). We observe that recent progress in machine learning enables to predict a number of useful information using the car signals and environmental factors such as the emotion of the driver or the detection of dangerous situation on the road. However, there is a lack of a central modeling pattern for describing the dynamic situation of a vehicle, its driver and passengers, moving in an evolving environment. We propose a driving context ontology relying on a patterns composed of events and states to glue together automotive-related vocabularies.
A driving context ontology for making sense of cross-domain driving data
2018 BMW Summer School "Intelligent Cars on Digital Roads - Frontiers in Machine Intelligence", 29 July-3 August 2018, Raitenhaslach, Germany
Poster / Demo
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in 2018 BMW Summer School "Intelligent Cars on Digital Roads - Frontiers in Machine Intelligence", 29 July-3 August 2018, Raitenhaslach, Germany and is available at :
PERMALINK : https://www.eurecom.fr/publication/5848