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

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...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.19638-19645
Main Authors: Bichicchi, Arianna, Belaroussi, Rachid, Simone, Andrea, Vignali, Valeria, Lantieri, Claudio, Li, Xuanpeng
Format: Article
Language:English
Subjects:
Complexity
Data mining
Data models
Deep learning
Driver behavior
Drivers
Engineering Sciences
event detection
Feature extraction
Graphical representations
Image color analysis
Noise reduction
Other
Principal component analysis
road safety
Roads
Vehicles
workload
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2965940