US 12,252,139 B2
Systems and methods for neural ordinary differential equation learned tire models
Yan Ming Jonathan Goh, Palo Alto, CA (US); and Franck Djeumou, Palo Alto, CA (US)
Assigned to Toyota Research Institute, Inc., Los Altos, CA (US); and Toyota Jidosha Kabushiki Kaisha, Toyota (JP)
Filed by Toyota Research Institute, Inc., Los Altos, CA (US)
Filed on Mar. 13, 2023, as Appl. No. 18/182,494.
Prior Publication US 2024/0308531 A1, Sep. 19, 2024
Int. Cl. B60W 40/101 (2012.01); B60W 40/12 (2012.01); B60W 50/00 (2006.01); G06F 7/64 (2006.01); G06N 5/022 (2023.01)
CPC B60W 50/0097 (2013.01) [B60W 40/101 (2013.01); G06F 7/64 (2013.01); G06N 5/022 (2013.01); B60W 2530/201 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving estimated tire forces and 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
wherein the tire force function when applied to further vehicle measurements provides a tire force estimate.