Citation

Joševski M, Katriniok A, Riek A, Abel D. IFAC-PapersOnLine 2017; 50(1): 987-993.

Copyright

(Copyright © 2017, International Federation of Automatic Control, Publisher Elsevier Publishing)

DOI

10.1016/j.ifacol.2017.08.204

PMID

unavailable

Abstract

Highly automated driving is one of the major visions in automotive research. Low speed autonomy like e.g. highly automated parking is an essential intermediate step towards that goal. While lateral control for automated parking is already successfully implemented in series production, major disturbances such as curbs still challenge longitudinal control. This paper proposes a disturbance estimator that is capable of estimating external resistance forces which originate from the road and impair longitudinal vehicle control in low speed maneuvers. Particularly, we will focus on curbs that can be treated as a road resistance force. The underlying nonlinear model is derived from an extended single-track model. The model accounts for lateral front tire forces which have a major impact on longitudinal vehicle dynamics at large steering angles. In a simulation study, we evaluate the estimator performance in five reverse driving on-curb parking scenarios and demonstrate that road resistance can be estimated appropriately.

Language: en

Keywords

autonomous vehicles; disturbance rejection; extended Kalman filters; State estimation