Predicting car states through learned models of vehicle dynamics and user behaviours

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Title: Predicting car states through learned models of vehicle dynamics and user behaviours
Author(s): Georgiou, T
Demiris, Y
Item Type: Conference Paper
Abstract: © 2015 IEEE.The ability to predict forthcoming car states is crucial for the development of smart assistance systems. Forthcoming car states do not only depend on vehicle dynamics but also on user behaviour. In this paper, we describe a novel prediction methodology by combining information from both sources - vehicle and user - using Gaussian Processes. We then apply this method in the context of high speed car racing. Results show that the forthcoming position and speed of the car can be predicted with low Root Mean Square Error through the trained model.
Publication Date: 1-Jul-2015
Date of Acceptance: 1-Mar-2015
URI: http://hdl.handle.net/10044/1/26623
DOI: http://dx.doi.org/10.1109/IVS.2015.7225852
Publisher: IEEE
Start Page: 1240
End Page: 1245
Copyright Statement: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Conference Name: Intelligent Vehicles Symposium (IV)
Publication Status: Published
Start Date: 2015-06-28
Finish Date: 2015-07-01
Conference Place: Seoul, South Korea
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering



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