Predicting car states through learned models of vehicle dynamics and user behaviours
File(s)iv_georgiou.pdf (2.72 MB)
Accepted version
Author(s)
Georgiou, T
Demiris, Y
Type
Conference Paper
Abstract
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.
Date Issued
2015-07-01
Date Acceptance
2015-03-01
Citation
2015, pp.1240-1245
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.
Source
Intelligent Vehicles Symposium (IV)
Publication Status
Published
Start Date
2015-06-28
Finish Date
2015-07-01
Coverage Spatial
Seoul, South Korea