NDLI: Statistical-based approach for driving style recognition using Bayesian probability with kernel density estimation

Content Provider	IET Digital Library
Author	Han, Wei Wang, Wenshuo Li, Xiaohan Xi, Junqiang
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.
Starting Page	22
Ending Page	30
Page Count	9
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0379
Journal	IET Intelligent Transport Systems
Publisher Date	2018-02-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Bayesian Probability Conditional Kernel Density Function Cross-validation Method Data Handling Technique Discriminative Feature Extraction Driver Behaviour Uncertainty Driver Information System Driving Style Classification Driving Style Recognition Eco-driving Estimation Theory Euclidean Distance-based Method Feature Extraction Feature Vector FL Method Full Bayesian Theory Fuzzy Logic Method Intelligent Vehicle Control Low Computational Cost Path-following Behaviour Characterization Pattern Classification Posterior Probability Probability Road Safety Statistical Analysis Statistical-based Approach Statistical-based Recognition Method Statistics Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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