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Nonlinear Moving Horizon Estimation for combined state and friction coefficient estimation in autonomous driving

Abstract : Real-time autonomous driving requires a precise knowledge of the state and the ground parameters, especially in dangerous situations. In this paper, an accurate yet computationally efficient nonlinear multi-body vehicle model is developed, featuring a detailed Pacejka tire model, and a Moving Horizon Estimation (MHE) scheme is formulated. To meet the real-time requirements, an efficient algorithm based on the Real Time Iteration (RTI) scheme for the Direct Multiple Shooting method is exported through automatic C code generation. The exported plain C-code is tailored to the model dynamics, resulting in computation times in the range of a few milliseconds. In addition to state estimates, MHE provides friction coefficient estimates, allowing the controller to adapt to varying road conditions. Simulation results from an obstacle avoidance scenario on a low friction road are presented.
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https://hal.inria.fr/hal-01068542
Contributor : Estelle Bouzat <>
Submitted on : Thursday, September 25, 2014 - 5:47:25 PM
Last modification on : Thursday, February 21, 2019 - 10:31:46 AM

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  • HAL Id : hal-01068542, version 1

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Mario Zanon, Janick V. Frasch, Moritz Diehl. Nonlinear Moving Horizon Estimation for combined state and friction coefficient estimation in autonomous driving. European Control Conference (ECC), 2013, Zurich, Switzerland. pp.4130 - 4135. ⟨hal-01068542⟩

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