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2020 | OriginalPaper | Buchkapitel

Lateral Control Design for Autonomous Vehicles Using a Big Data-Based Approach

verfasst von: Dániel Fényes, Balázs Németh, Péter Gáspár

Erschienen in: Advances in Dynamics of Vehicles on Roads and Tracks

Verlag: Springer International Publishing

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Abstract

In the paper an improved Model Predictive Control (MPC) design is presented for autonomous vehicles. The improvement of the control design is based on big data analysis of the lateral vehicle dynamics. In the big data analysis, the decision tree algorithm, C4.5 is used to determine the stable regions of the vehicle. Moreover, C4.5 is extended with the MetaCost algorithm, which is able to weight the percentages of certain misclassifications. In this way, the safe motion of the vehicle can be guaranteed. The results of the big data analysis are states-sets, which are used as constraints in the MPC control design.
Vorheriges Kapitel Flow Field and Neural Network Guided Steering Control for Rigid Autonomous Vehicles
Nächstes Kapitel Search-Based Motion Planning for Performance Autonomous Driving
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Metadaten
Titel
Lateral Control Design for Autonomous Vehicles Using a Big Data-Based Approach
verfasst von
Dániel Fényes
Balázs Németh
Péter Gáspár
Copyright-Jahr
2020
Buch
Advances in Dynamics of Vehicles on Roads and Tracks

Print ISBN: 978-3-030-38076-2

Electronic ISBN: 978-3-030-38077-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-38077-9_133

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