Learning-based driving events classification

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 learning-b...

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Bibliographic Details
Published in16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) pp. 1778 - 1783
Main Authors D'Agostino, Claire, Saidi, Alexandre, Scouarnec, Gilles, Chen, Liming
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2013
Subjects
Acceleration
Context
Decision trees
Fuels
Logistics
Roads
Vehicles
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Summary: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 learning-based 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.
ISSN:2153-0009
2153-0017
DOI:10.1109/ITSC.2013.6728486