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.
Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation
IET Intelligent Transport Systems ; 13 , 1 ; 22-30
01.01.2019
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
statistical analysis , intelligent vehicle control , driver information systems , Bayesian probability , discriminative feature extraction , road safety , driving style recognition , statistical‐based approach , posterior probability , Euclidean distance‐based method , statistical‐based recognition method , full Bayesian theory , eco‐driving , pattern classification , cross‐validation method , feature vector , estimation theory , low computational cost , probability , Bayes methods , driving style classification , conditional kernel density function , path‐following behaviour characterization , feature extraction , FL method , fuzzy logic method , driver behaviour uncertainty
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