Multivariate Sequence Clustering for Driving Preference Classification Based on Wide-Range Trajectory Data
Paper in proceeding, 2023

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.

Wide-range trajectory data

Driving preferences

Multivariate sequences clustering

Author

Shuli Wang

Tongji University

Ruo Jia

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Lanfang Zhang

Tongji University

Smart Innovation, Systems and Technologies

2190-3018 (ISSN) 2190-3026 (eISSN)

Vol. 356 45-54
9789819932832 (ISBN)

6th KES International Symposium on Smart Transportation Systems, KES STS 2023
Rome, Italy,

Subject Categories

Vehicle Engineering

DOI

10.1007/978-981-99-3284-9_5

More information

Latest update

10/25/2023