Work

Driving Style Tendency Quantification Method Based on Short-Term Lane Change Feature Extraction

Year: 2025

Type: article

Abstract: To enhance road safety and optimize intelligent driving systems, this study introduces the concept of “driving style tendency” to characterize short-term driver behavior, particularly lane-changing patterns. A multidimensional framework is established to analyze driving roles and behaviors, utilizing a Hidden Semi-Markov Model and Hierarchical Dirichlet Process for the unsupervised segmentation of driving trajectory data into behavioral primitives. By systematically analyzing driver behaviors in leading and following scenarios, characteristic thresholds are derived through distribution fitting, enabling the development of a non-parametric Bayesian-based scoring method for driving style tendency. The K-means clustering algorithm is employed to transform primitive segments into quantifiable semantic information, facilitating the interpretation of driver behavior preferences. This research contributes to improved collision risk prediction in complex traffic environments, supports the design of personalized driving assistance systems, and provides valuable insights for autonomous driving technology development.

Source: Sustainability

DOI: 10.3390/su17083563

Authors: Yanfeng Jia, Zhi Zhang, Xiantong Li, Xiufeng Chen, Dayi Qu

Institutions: Qingdao University of Technology, China Academy of Transportation Sciences

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896