A state observer, robust to road surface conditions, is designed to estimate the longitudinal speed (and slip) which is essential for controls and safety-critical decision making in autonomous driving. The novel approach estimates slip at each wheel, and can be integrated with the existing visual-inertial navigation systems. The wheel-level observer, which uses proprioceptive sensor data, fuses vehicle kinematic states, tire internal states, and the wheel dynamics to estimate the speed at each tire, without any information of the road surface friction or global navigation satellite systems (GNSS). Then, a wheel-vehicle dynamical model, which augments estimates at each tire with the vehicle dynamics, is developed to design an integrated slip-aware framework for speed estimation. The stability of the augmented error dynamics is studied and the mean square estimation error is proved to be uniformly bounded. Experimental tests have been conducted to validate the proposed framework in pure- and combined-slip driving scenarios on various surface friction conditions. As confirmed by several road experiments, the designed observer provides consistent and accurate speed (and slip) estimates at each tire for high-slip scenarios, which are essential for safe navigation, motion planning, and path following in automated driving systems.
Proprioceptive Observer Design for Speed Estimation in Automated Driving Systems
05.06.2022
1688523 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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