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 -