Learning-based driving events classification

Claire D'Agostino 1 Alexandre Saidi 1 Gilles Scouarnec Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Drivers typically depict different behavior with respect to various driving events. The modeling of their behavior enables an accurate estimation of fuel consumption during the truck design process and is also helpful for ADAS in order to give relevant advices. In this paper, we propose a learningbased approach to the automatic recognition of driving events, e.g., roundabouts or stops, which impact the driver behavior. We first synthesize and categorize meaningful driving events and then study a set of features potentially sensitive to the driver behavior. These features were experimented on real truck driver data using two machine-learning techniques, i.e., decision tree and linear logic regression, to evaluate their relevance and ability to recognize driving events.
Type de document :
Communication dans un congrès
ITSC 2013, Oct 2013, The Hague, Netherlands. pp.1778-1783, 2013, 〈10.1109/ITSC.2013.6728486〉
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Soumis le : mercredi 29 juin 2016 - 15:52:18
Dernière modification le : mercredi 31 octobre 2018 - 12:24:25




Claire D'Agostino, Alexandre Saidi, Gilles Scouarnec, Liming Chen. Learning-based driving events classification. ITSC 2013, Oct 2013, The Hague, Netherlands. pp.1778-1783, 2013, 〈10.1109/ITSC.2013.6728486〉. 〈hal-01339305〉



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