Detection of Driver Styles in Lane Changes using Wavelet Transform

Autor: Yunus Emre Avcı, Adem Tuncer
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Universal Computer Science, Vol 30, Iss 5, Pp 590-602 (2024)
Druh dokumentu: article
ISSN: 0948-6968
DOI: 10.3897/jucs.108073
Popis: Lane change detection is crucial for intelligent transportation systems, as it affects traffic flow on both macroscopic and microscopic levels. Lane change models are widely used in traffic and transportation studies, making it important to understand the factors that affect drivers’ lane changing behavior. In this context, we proposed a novel model for detecting lane changes by applying wavelet transform to high-resolution data from unmanned aerial vehicles. The model was trained and tested using empirical lane changing data from pNEUMA. Firstly, the azimuth angle was calculated on WGS-84 coordinates of each vehicle found in the specified road segment. Next, a multi-level wavelet transform was applied to the azimuth series using mother wavelets such as Haar, Daubechies, and Symlet for each vehicle. Machine learning method was applied to extracted features to detect lane changing. Additionally, the lane changing style of drivers was classified as sudden or normal using the same model. The results indicate that the proposed data-driven model is able to accurately detect lane changes and the type of lane change.
Databáze: Directory of Open Access Journals