Learning-based driving events classification

• Published in: 16th 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
• Language: English
• Published: IEEE 01.10.2013
• Subjects: Acceleration; Context; Decision trees; Fuels; Logistics; Roads; Vehicles
• DOI: 10.1109/ITSC.2013.6728486
• ISSN: 2153-0009

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.