System Identification of Vehicle Dynamics Using Recurrent Neural Networks

초록

This paper presents the data-driven modeling based on recurrent neural network (RNN) for the system identification of vehicle dynamics. For the accurate control of an autonomous vehicle, a sophisticated model of the vehicle dynamics is required. Data-driven modeling can achieve such a model only with the data obtained from the target. A simulation result by using the data from a driving simulator is included to test the feasibility of the RNN for the system identification of a ground vehicle.