Toyota Jidosha Kabushiki Kaisha

Toyota Research Institute, Inc.

Franck Djeumou

Yan Ming Jonathan Goh

System, methods, and other embodiments described herein relate to tire models based on neural-ExpTanh parameterization. In one embodiment, a method includes operating a vehicle with a control framework incorporating an ExpTanh function; calculating prior slip data based on measurements obtained by the control framework; selecting a confidence parameter; using a first predictive model to determine ExpTanh parameters based upon the prior slip data, the measurements, and the confidence parameter; and inputting a slip parameter and the ExpTanh parameters into the ExpTanh function to estimate a tire force.

Systems and methods for neural-EXPTANH learned tire models

System, methods, and other embodiments described herein relate to NODE learned tire models. In one embodiment, a method includes calculating estimated tire forces based on vehicle measurements; solving a second order differential equation in a repetitive manner until an error calculation based on a tire force function and the estimated tire forces reaches a minimum value, by: using a first predictive model to provide one or more inflection points and initial conditions based on the vehicle measurements, using a second and third predictive model to act as, respectively, exponents to a positive and a negative exponential equation based on the one or more inflection points, the initial conditions, and the vehicle measurements, and integrating the exponential equations to obtain the tire force function; and applying the tire force function to new vehicle measurements to estimate current tire forces.

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Palo Alto, CA, United States of America

Franck Djeumou