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

Depeng Chen, Zhijun Chen*, Yishi Zhang, Xu Qu, Mingyang Zhang, Chaozhong Wu

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

1 Sitaatiot (Scopus)
9 Lataukset (Pure)

Abstrakti

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.

AlkuperäiskieliEnglanti
Artikkeli6687378
Sivumäärä12
JulkaisuJOURNAL OF ADVANCED TRANSPORTATION
Vuosikerta2021
DOI - pysyväislinkit
TilaJulkaistu - 2 kesäkuuta 2021
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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