Open AccessVehicle path tracking Based on Strong Tracking Cubature Kalman Filter While Encountering An Emergency Collision Avoidance
Author(s) -
Weihong Bi,
Wei Wang,
Shaoyi Bei,
Jianwei Ben,
Bo Li
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/787/1/012002
Subject(s) - control theory (sociology) , kalman filter , robustness (evolution) , extended kalman filter , vehicle tracking system , computer science , collision avoidance , path (computing) , tracking (education) , collision , engineering , artificial intelligence , control (management) , psychology , pedagogy , biochemistry , chemistry , computer security , gene , programming language
Considering the high speed emergency avoidance situations, the vehicle path tracking was studied in this paper. The three degrees of freedom nonlinear vehicle model and the cubature Kalman filter are applied to tracking vehicle emergency avoidance path. For the degradation of adaptive tracking performance in the vehicle emergency avoidance, an improved strong tracking cubature Kalman filter algorithm is proposed by introducing the fading factor into filtering process which is learnt from strong tracking filter. The algorithm has a simple implementation, high estimation accuracy and good robustness. Different target paths are selected and the cubature Kalman filter is applied to simulate path tracking in the vehicle emergency avoidance. The simulation results show that the proposed path tracking method can control the vehicle tracking the ideal collision avoidance path rapidly without collisions.