Authors:
Dániel Fényes
;
Balázs Németh
and
Péter Gáspár
Affiliation:
Institute for Computer Science and Control, Hungarian Academy of Sciences, Kende u. 13-17, H-1111 Budapest and Hungary
Keyword(s):
Side-slip Estimation, Regression Analysis, Big Data, Kalman Filtering.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Machine Learning in Control Applications
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
Vehicle Control Applications
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.