Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-06-02
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
Series
JOURNAL OF ADVANCED TRANSPORTATION, Volume 2021
Abstract
In recent years, the automated driving system has been known to be one of the most popular research topics of artificial intelligence (AI) and intelligent transportation system (ITS). The journey experience on automated vehicles and the intelligent automated driving system could be improved by individualization driving understanding. Although previous studies have proposed methods for driving styles understanding, the individualization driving classification has not been addressed thoroughly. Therefore, in this study, a supervised method is proposed to understand driving behavioral structure and the latent driving styles by incorporating the prior knowledge. Firstly, a novel method is established for driving behavioral encoding and raw driving data mining. Then, the Labeled Latent Dirichlet Allocation (LLDA) is proposed to understand the latent driving styles from individual driving with driving behaviors. Finally, the Safety Pilot Model Deployment (SPMD) data are used to validate the performance of the proposed model. Experimental results show that the proposed model uncovers latent driving styles effectively and shows good agreement to real situations, which provides theoretical guidance on driving behavior recognition for better individual experience on automated driving vehicles.
Description
Publisher Copyright: © 2021 Depeng Chen et al.
Keywords
Other note
Citation
Chen , D , Chen , Z , Zhang , Y , Qu , X , Zhang , M & Wu , C 2021 , ' Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model ' , JOURNAL OF ADVANCED TRANSPORTATION , vol. 2021 , 6687378 . https://doi.org/10.1155/2021/6687378