The vehicle sideslip angle is an important state for vehicle dynamic control, which needs to be estimated as it could not be obtained directly by the vehicle. To improve the estimation accuracy of the sideslip angle based on the intelligent vehicle platform, this study proposes a novel vehicle sideslip angle estimation algorithm with the fusion of dynamic model and vision information. Firstly, to further improve the model accuracy of the vehicle during lateral acceleration conditions, a vehicle dynamic model is established considering the acceleration error compensation with the assistance of attitude information. In addition, based on the lane line information obtained from the equipped camera in intelligent vehicles, a visual geometric model is established. Owing to the measurement delay and low sampling frequency of the camera, a multi-rate sideslip angle observer with delay compensation is designed to coordinate with the inter-frequency signal of the vehicle chassis. Finally, the effectiveness of the algorithm is verified by the slalom test.
Vision-aided intelligent vehicle sideslip angle estimation based on a dynamic model
IET Intelligent Transport Systems ; 14 , 10 ; 1183-1189
2020-10-01
7 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
vehicle dynamics , road vehicles , intelligent vehicle platform , estimation accuracy , multirate sideslip angle observer , computer vision , novel vehicle sideslip angle estimation algorithm , steering systems , observers , vehicle dynamic model , vision-aided intelligent vehicle sideslip angle estimation , vehicle dynamic control , vision information , cameras , model accuracy , intelligent vehicles , visual geometric model , traffic engineering computing , vehicle chassis , control engineering computing
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