Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation. Issue 1 (23rd March 2018)

• Record Type: Journal Article
• Authors: Han, Wei; Wang, Wenshuo; Li, Xiaohan; Xi, Junqiang
• Is Part Of: IET intelligent transport systems. Volume 13:Issue 1(2019)
• Page Start: 22
• Page End: 30
• Publication Date: 2018-03-23
• Subjects: statistical analysis -- feature extraction -- probability -- Bayes methods -- estimation theory -- road safety -- driver information systems -- pattern classification; statistical‐based approach -- driving style recognition -- Bayesian probability -- eco‐driving -- road safety -- intelligent vehicle control -- statistical‐based recognition method -- driver behaviour uncertainty -- discriminative feature extraction -- conditional kernel density function -- path‐following behaviour characterization -- posterior probability -- full Bayesian theory -- Euclidean distance‐based method -- low computational cost -- feature vector -- driving style classification -- cross‐validation method -- fuzzy logic method -- FL method; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2017.0379
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Driving style recognition plays a crucial role in eco‐driving, road safety, and intelligent vehicle control. This study proposes a statistical‐based recognition method to deal with driver behaviour uncertainty in driving style recognition. First, the authors extract discriminative features using the conditional kernel density function to characterise path‐following behaviour. Meanwhile, the posterior probability of each selected feature is computed based on the full Bayesian theory. Second, they develop an efficient Euclidean distance‐based method to recognise the path‐following style for new input datasets at a low computational cost. By comparing the Euclidean distance of each pair of elements in the feature vector, then they classify driving styles into seven levels from normal to aggressive. Finally, they employ a cross‐validation method to evaluate the utility of their proposed approach by comparing with a fuzzy logic (FL) method. The experiment results show that the proposed statistical‐based recognition method integrating with the kernel density is more efficient and robust than the FL method.