A Novel Big-data-based Estimation Method of Side-slip Angles for Autonomous Road Vehicles

Authors: 
Fényes, Dániel & Nemeth, Balazs & Gáspár, Péter
Year: 
2018 (published)
DOI: 
10.5220/0006849504200426
ISSN (or eSSN): 
ISBN: 978-989-758-321-6
Open access: 
Yes
Abstract: 
In the paper a novel side-slip estimation algorithm, which is based on big data approaches, is proposed. The idea of the estimation is based on the availability of a large amount of information of the autonomous vehicles, e.g. yaw-rate, accelerations and steering angles. The significant number of signals are processed through big data approaches to generate a simplified rule for the side-slip estimation using the onboard signals of the vehicles. Thus, a subset selection method for time-domain signals is proposed, by which the attributes are selected based on their relevance. Furthermore, a linear regression using the Ordinary Least Squares (OLS) method is applied to derive a relationship between the attributes and the estimated signal. The efficiency of the estimation is presented through several CarSim simulation examples, while the WEKA data-mining software is used for the OLS method.
SCI: 
No
Kiemelt: 
No
Pdf: 
No
Place of publication: 
https://bit.ly/2RYcoSZ