Trajectory Estimation Autonomous Car using Ensemble Kalman Filter (EnKF)
Q A Fiddina, S Subchan, and H Nurhadi

Institut Teknologi Sepuluh Nopember

Abstract

Autonomous technology is being developed by researchers because this technology trend in manufacture, industry and academic. Autonomous vehicle require tracking trajectory using estimation. Tracking autonomous vehicle is needed methods of estimation. Method estimation most use by researchers is Kalman Filter Method or Modified Kalman Filter Method. In this paper discuss about navigation of autonomous car using Ensemble Kalman Filter (EnKF) Methods. In this paper, autonomous car will be use is intelligent Car or i-Car. i-Car is autonomous car developed by Institut Teknologi Sepuluh Nopember (ITS). Model of autonomous car are two model, there are kinematic and dynamic model. That model will be used for estimation using Ensemble Kalman Filter (EnKF) method. Simulation of result use assume &#8710-t=0.1,T=100, and sum of ensemble N=100. Result of estimation trajectory surge is around 2500 meter and sway is 444.23 meter. Moreover, we can get RMSE for estimation surge is 0.1883 and estimation sway is 0.1026. the value of RMSE is relatively small. So, we can called the estimation is optimum and autonomous car model can be implement Ensemble Kalman Filter (EnKF) method

Keywords: Autonomous Car, Ensemble Kalman Filter, Trajectory

Topic: MATHEMATICS AND SCIENCE EDUCATIONS