@ARTICLE{ iet:/content/journals/10.1049/iet-its.2017.0379, author = {Wei Han}, author = {Wenshuo Wang}, author = {Xiaohan Li}, author = {Junqiang Xi}, keywords = {path-following behaviour characterization;FL method;intelligent vehicle control;Euclidean distance-based method;conditional kernel density function;driver behaviour uncertainty;discriminative feature extraction;road safety;posterior probability;driving style recognition;Bayesian probability;feature vector;low computational cost;statistical-based recognition method;eco-driving;cross-validation method;fuzzy logic method;full Bayesian theory;statistical-based approach;driving style classification;}, ISSN = {1751-956X}, language = {English}, 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.}, title = {Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation}, journal = {IET Intelligent Transport Systems}, issue = {1}, volume = {13}, year = {2019}, month = {January}, pages = {22-30(8)}, publisher ={Institution of Engineering and Technology}, copyright = {© The Institution of Engineering and Technology}, url = {https://digital-library.theiet.org/;jsessionid=2f8v93v40n1u0.x-iet-live-01content/journals/10.1049/iet-its.2017.0379} }