Multivariate Sequence Clustering for Driving Preference Classification Based on Wide-Range Trajectory Data

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Fältnamn	Indikatorer	Metadata
000		02826naa a2200325 4500
001		oai:research.chalmers.se:1eb0755f-9b5c-4cae-8c56-c8608fc26e5a
003		SwePub
008		230722s2023 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://research.chalmers.se/publication/5367122 URI
024	7	a https://doi.org/10.1007/978-981-99-3284-9_52 DOI
040		a (SwePub)cth
041		a engb eng
042		9 SwePub
072	7	a kon2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Wang, Shuli,d 1996u Tongji University4 aut0 (Swepub:cth)shuli
245	1 0	a Multivariate Sequence Clustering for Driving Preference Classification Based on Wide-Range Trajectory Data
264	1	c 2023
520		a Accurate driving preferences classification is a crucial component for autonomous connected vehicles in making more safety and more efficient driving decisions. Most existing studies identify drivers’ driving preferences based on the historical data of the individual vehicle, and the selected variables are limited to the mechanical motion of the vehicle, which seldomly takes the influence of road traffic conditions and surrounding vehicles into account. This study proposes a driving preferences classification method by multivariate sequence clustering algorithm based on wide-range trajectory data. Based on the specific range of road sections, the selected variables for each trajectory are converted from the time domain to the space domain separately, to capture the dynamic changes of the features along the road area. Multivariate time series clustering combining a weighted Dynamic Time Warping (WDTW) and the k-medoids algorithm is used to classify driving preferences into different levels, and a popular internal evaluation metric is employed to determine the optimal cluster result. This study also investigates the heterogeneity of driving behaviors at different driving preference levels. The results show that the proposed method could better recognize drivers’ internal driving preferences.
650	7	a TEKNIK OCH TEKNOLOGIERx Maskinteknikx Farkostteknik0 (SwePub)203032 hsv//swe
650	7	a ENGINEERING AND TECHNOLOGYx Mechanical Engineeringx Vehicle Engineering0 (SwePub)203032 hsv//eng
653		a Wide-range trajectory data
653		a Driving preferences
653		a Multivariate sequences clustering
700	1	a Jia, Ruo,d 1993u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)ruoj
700	1	a Zhang, Lanfangu Tongji University4 aut
710	2	a Tongji Universityb Chalmers tekniska högskola4 org
773	0	t Smart Innovation, Systems and Technologiesg 356, s. 45-54q 356<45-54x 2190-3026x 2190-3018
856	4 8	u https://research.chalmers.se/publication/536712
856	4 8	u https://doi.org/10.1007/978-981-99-3284-9_5

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Av författaren/redakt...: Wang, Shuli, 199 ...; Jia, Ruo, 1993; Zhang, Lanfang

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TEKNIK OCH TEKNOLOGIER: TEKNIK OCH TEKNO ...; och Maskinteknik; och Farkostteknik

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Av lärosätet: Chalmers tekniska högskola

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