Graduate School and Research Center in Digital Sciences

A driving context ontology for making sense of cross-domain driving data

Klotz, Benjamin; Troncy, Raphaël; Wilms, Daniel; Bonnet, Christian

2018 BMW Summer School "Intelligent Cars on Digital Roads - Frontiers in Machine Intelligence", 29 July-3 August 2018, Raitenhaslach, Germany

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.

Document Bibtex

Title:A driving context ontology for making sense of cross-domain driving data
Type:Poster / Demo
Language:English
City:Raitenhaslach
Country:GERMANY
Date:
Department:Communication systems
Eurecom ref:5848
Copyright: © 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 :
Bibtex: @poster / demo{EURECOM+5848, year = {2018}, title = {{A} driving context ontology for making sense of cross-domain driving data}, author = {{K}lotz, {B}enjamin and {T}roncy, {R}apha{\"e}l and {W}ilms, {D}aniel and {B}onnet, {C}hristian}, number = {EURECOM+5848}, month = {07}, institution = {Eurecom} address = {{R}aitenhaslach, {GERMANY}}, url = {http://www.eurecom.fr/publication/5848} }
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