Predicting Car States Through Learned Models Of Vehicle Dynamics And User Behaviours
2015 IEEE Intelligent Vehicles Symposium (IV)（2015）
摘要
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.
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关键词
car states prediction,learned models,vehicle dynamics,user behaviours,smart assistance systems,prediction methodology,vehicle information,user information,Gaussian processes,high speed car racing,car position,car speed,root mean square error
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