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Calibration and validation of a tire–snow interaction model

Jonah H. Lee
Journal of Terramechanics, Volume 50, Issues 5–6, October–December 2013, Pages 289-302, ISSN 0022-4898,
Abstract: We address the challenge of the validation of models for a vehicle interacting with a natural snowy terrain by applying a rigorous statistical framework. Gaussian process-based stochastic metamodels were used to fit noisy test data in drawbar pull and traction as a function of slip, and to transform the deterministic physically-based tire–snow interaction model into a stochastic one. Important parameters such as the mechanical properties of snow, the coefficient of friction between the tire and snow, and the depth of snow were obtained using a Gaussian maximum likelihood method. The uncertainties of parameters, and prediction using calibrated parameters for front and rear wheels were quantified and assessed using interval-based local and global validation metrics between models and test data. Overall agreement between models and test data is good.
Keywords: Snow; Drucker–Prager; Drawbar pull; Traction; Validation metrics; Calibration; Bayesian; Metamodel; Stochastic; Gaussian process