In this section
@inproceedings{Georgiou:2015:10.1109/IVS.2015.7225852,
author = {Georgiou, T and Demiris, Y},
doi = {10.1109/IVS.2015.7225852},
pages = {1240--1245},
publisher = {IEEE},
title = {Predicting car states through learned models of vehicle dynamics and user behaviours},
url = {http://dx.doi.org/10.1109/IVS.2015.7225852},
year = {2015}
}
TY - CPAPER
AB - 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.
AU - Georgiou,T
AU - Demiris,Y
DO - 10.1109/IVS.2015.7225852
EP - 1245
PB - IEEE
PY - 2015///
SP - 1240
TI - Predicting car states through learned models of vehicle dynamics and user behaviours
UR - http://dx.doi.org/10.1109/IVS.2015.7225852
UR - http://hdl.handle.net/10044/1/26623
ER -
