In this study, an improved deep learning model is proposed to explore the complex interactions between the road environment and driver’s behaviour throughout the generation of a graphical representation. The proposed model consists of an unsupervised Denoising Stacked Autoencoder (SDAE) able to provide output layers in RGB colors. The dataset comes from an experimental driving test where kinematic measures were tracked with an in-vehicle GPS device. The graphical outcomes reveal the method ability to efficiently detect patterns of simple driving behaviors, as well as the road environment complexity and some events encountered along the path.

Analysis of Road-User Interaction by Extraction of Driver Behavior Features Using Deep Learning

Bichicchi, Arianna;Simone, Andrea;Vignali, Valeria;Lantieri, Claudio;
2020

Abstract

In this study, an improved deep learning model is proposed to explore the complex interactions between the road environment and driver’s behaviour throughout the generation of a graphical representation. The proposed model consists of an unsupervised Denoising Stacked Autoencoder (SDAE) able to provide output layers in RGB colors. The dataset comes from an experimental driving test where kinematic measures were tracked with an in-vehicle GPS device. The graphical outcomes reveal the method ability to efficiently detect patterns of simple driving behaviors, as well as the road environment complexity and some events encountered along the path.
IEEE ACCESS
Bichicchi, Arianna; Belaroussi, Rachid; Simone, Andrea; Vignali, Valeria; Lantieri, Claudio; Li, Xuanpeng
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/723219
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