System and method for determining state of stiffness of tires of vehicle

• US 10,011,284 B2
• Filed: 07/13/2016
• Issued: 07/03/2018
• Est. Priority Date: 07/13/2016
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Berntorp, Karl, Di Cairano, Stefano
• Primary Examiner(s): Mancho, Ronnie
• Application Number:
  US15/209,027
• Publication Number:
  US 20180015931A1
• Time in Patent Office: 720 Days
• Field of Search: 701 70
• CPC Class Codes: B60T 2210/12 Friction
• CPC Class Codes: B60T 2240/00 Monitoring, detecting wheel/tire behaviour; counteracting thereof
• CPC Class Codes: B60T 2240/04 Tire deformation
• CPC Class Codes: B60T 2270/86 Optimizing braking by using ESP vehicle or tire model
• CPC Class Codes: B60T 8/171 Detecting parameters used in the regulation; Measuring values used in the regulation
• CPC Class Codes: B60T 8/172 Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters B60T8/17551 takes precedence
• CPC Class Codes: B60W 2400/00 Indexing codes relating to detected, measured or calculated conditions or factors
• CPC Class Codes: B60W 2720/26 Wheel slip
• CPC Class Codes: B60W 40/12 related to parameters of the vehicle itself , e.g. tyre models

Abstract

A method jointly estimates a state of a vehicle including a velocity and a heading rate of the vehicle and a state of stiffness of tires of the vehicle including at least one parameter defining an interaction of at least one tire of the vehicle with a road on which the vehicle is traveling. The method uses the motion and measurement models that include a combination of deterministic component independent from the state of stiffness and probabilistic components dependent on the state of stiffness. The method represents the state of stiffness with a set of particles. Each particle includes a mean and a variance of the state of stiffness defining a feasible space of the parameters of the state of stiffness. The method updates iteratively the mean and the variance of at least some particles using a difference between an estimated state of stiffness estimated using the motion model of the vehicle including the state of stiffness with parameters sampled on the feasible space of the particle and the measured state of stiffness determined according to the measurement model using measurements of the state of the vehicle. The method outputs a mean and a variance of the state of stiffness determined as a function of the updated mean and the updated variance in at least one particle.

Claims

1. A method for controlling a vehicle by jointly estimating a state of a vehicle and a state of stiffness of tires of the vehicle, wherein the state of the vehicle includes a velocity and a heading rate of the vehicle, and wherein the state of stiffness includes at least one parameter defining an interaction of at least one tire of the vehicle with a road on which the vehicle is traveling, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor carry out at least some steps of the method, comprising:

retrieving from a memory a motion model of the vehicle and a measurement model of the vehicle, wherein the motion model of the vehicle includes a combination of a deterministic component of the motion and a probabilistic component of the motion, wherein the deterministic component of the motion is independent from the state of stiffness and defines the motion of the vehicle as a function of time, wherein the probabilistic component of the motion includes the state of stiffness having an uncertainty and defines disturbance on the motion of the vehicle, wherein the measurement model of the vehicle includes a combination of a deterministic component of the measurement model independent from the state of stiffness and a probabilistic component of the measurement model that includes the state of stiffness;

representing the state of stiffness with a set of particles, each particle includes a mean and a variance of the state of stiffness defining a feasible space of the parameters of the state of stiffness;

updating iteratively the mean and the variance of at least some particles using a difference between an estimated state of stiffness estimated using the motion model of the vehicle including the state of stiffness with parameters sampled on the feasible space of the particle and the measured state of stiffness determined according to the measurement model using measurements of the state of the vehicle;

determining a mean and a variance of the state of stiffness as a function of the updated mean and the updated variance in at least one particle; and

controlling the vehicle using the mean and the variance of the state of stiffness.

2. The method of claim 1, wherein an iteration for updating the particle comprises:

sampling the feasible space of the particle to produce a sampled parameter;

estimating a state of the vehicle at an instant of time using inputs to the vehicle and a model of a motion of the vehicle including the sampled parameter;

measuring the state of the vehicle at the instant of time;

determining the mean of the measured state of stiffness that results in the measured state of the vehicle according to the measurement model;

determining the variance of the measured state of stiffness as a combination of an uncertainty of the measurements and the variance of the state of stiffness in the particle;

updating the mean of the state of stiffness in the particle using the mean of the measured state of stiffness; and

updating the variance of the state of stiffness in the particle using the variance of the measured state of stiffness.

3. The method of claim 2, wherein, for updating the set of particles, the variance of the measured state of stiffness is determined as the combination of the uncertainty of the measurements and a set of variances of the state of stiffness in the set of particles.

4. The method of claim 1, further comprising:

determining a probability of each particle to represent a true state of the stiffness based on a difference between the state estimated using the state of stiffness of the particle and the measured state; and

determining the state of stiffness according to the function that uses a weighted combination of the updated mean and the updated variance in each particle, wherein the weights in the combination are determined by the probability of each particle to represent the true state of the stiffness.

5. The method of claim 4, further comprising:

determining a probability distribution of the state of the vehicle and the state of the stiffness using a probability distribution of the measurement model centered on the measured state; and

determining the probability of each particle to represent the true state of the stiffness according to a placement of the mean in the particle on the probability distribution of the state of the vehicle and the state of the stiffness.

6. The method of claim 1, further comprising:

classifying the surface of the road using the outputted mean and variance of the state of stiffness.

7. The method of claim 1, wherein the classifying comprises:

determining a type of the surface based on a value of the mean of the state of stiffness unless the variance of the state of stiffness covers a value corresponding to a different type of the surface.

8. The method of claim 1, wherein the classifying comprises:

determining a type of the surface based on a value of the mean of the state of stiffness; and

determining a probability of the surface to have the type based on the variance of the state of stiffness.

9. The method of claim 1, wherein the parameter of the state of stiffness includes one or combination of a longitudinal stiffness of the tire, a lateral stiffness of the tire, a friction between the tire and the road.

10. A system for controlling a vehicle by jointly estimating a state of a vehicle and a state of stiffness of tires of the vehicle, wherein the state of the vehicle includes a velocity and a heading rate of the vehicle, and wherein the state of stiffness includes at least one parameter defining an interaction of at least one tire of the vehicle with a road on which the vehicle is traveling, wherein the parameter of the state of stiffness includes one or combination of a longitudinal stiffness of the tire, a lateral stiffness of the tire, a friction between the tire and the road, the system comprising:

a memory to store a motion model of the vehicle and a measurement model of the vehicle, wherein the motion model of the vehicle includes a combination of a deterministic component of the motion and a probabilistic component of the motion, wherein the deterministic component of the motion is independent from the state of stiffness and defines the motion of the vehicle as a function of time, wherein the probabilistic component of the motion includes the state of stiffness having an uncertainty and defines disturbance on the motion of the vehicle, wherein the measurement model of the vehicle includes a combination of a deterministic component of the measurement model independent from the state of stiffness and a probabilistic component of the measurement model that includes the state of stiffness;

at least one sensor to determine measurements of the state of the vehicle;

a processor operatively connected to the memory and to the sensor, wherein the processor is configured to

represent the state of stiffness with a set of particles, each particle includes a mean and a variance of the state of stiffness defining a feasible space of the parameters of the state of stiffness;

update iteratively the mean and the variance of at least some particles using a difference between an estimated state of stiffness estimated using the motion model of the vehicle including the state of stiffness with parameters sampled on the feasible space of the particle and the measured state of stiffness determined according to the measurement model using measurements of the state of the vehicle; and

determine output a mean and a variance of the state of stiffness as a function of the updated mean and the updated variance in at least one particle; and

a controller to control the vehicle based on the mean and the variance of the state of the stiffness of the tires.

11. The system of claim 10, wherein the processor updates the particle iteratively, and wherein for an iteration of updating the particle, the processor is configured to

sample the feasible space of the particle to produce a sampled parameter;

estimate a state of the vehicle at an instant of time using inputs to the vehicle and a model of a motion of the vehicle including the sampled parameter;

acquire from the sensor the measurement of the state of the vehicle at the instant of time;

determine the mean of the measured state of stiffness that results in the measured state of the vehicle according to the measurement model;

determine the variance of the measured state of stiffness as a combination of an uncertainty of the measurements and the variance of the state of stiffness in the particle;

update the mean of the state of stiffness in the particle using the mean of the measured state of stiffness; and

update the variance of the state of stiffness in the particle using the variance of the measured state of stiffness.

12. The system of claim 11, wherein, the processor determines the variance of the measured state of stiffness as the combination of the uncertainty of the measurements and a set of variances of the state of stiffness in the set of particles.

13. The system of claim 10, wherein the processor

determines a probability of each particle to represent a true state of the stiffness based on a difference between the state estimated using the state of stiffness of the particle and the measured state;

and

determines the state of stiffness according to the function that uses a weighted combination of the updated mean and the updated variance in each particle, wherein the weights in the combination are determined by the probability of each particle to represent the true state of the stiffness.

14. The system of claim 13, wherein the processor

determines a probability distribution of the state of the vehicle and the state of the stiffness using a probability distribution of the measurement model centered on the measured state;

and

determines the probability of each particle to represent the true state of the stiffness according to a placement of the mean in the particle on the probability distribution of the state of the vehicle and the state of the stiffness.

15. The system of claim 10, wherein the processor is configured to

classify the surface of the road using the outputted mean and variance of the state of stiffness.

16. The system of claim 15, wherein the processor determines a type of the surface based on a value of the mean of the state of stiffness;

and determines a probability of the surface to have the type based on the variance of the state of stiffness.

17. The system of claim 10, further comprising:

a display for displaying the state of the stiffness of the tires.

18. A non-transitory computer readable memory embodied thereon a program executable by a processor for performing a method for jointly estimating a state of a vehicle and a state of stiffness of tires of the vehicle, wherein the state of the vehicle includes a velocity and a heading rate of the vehicle, and wherein the state of stiffness includes at least one parameter defining an interaction of at least one tire of the vehicle with a road on which the vehicle is traveling, the method comprising:

retrieving a motion model of the vehicle and a measurement model of the vehicle, wherein the motion model of the vehicle includes a combination of a deterministic component of the motion and a probabilistic component of the motion, wherein the deterministic component of the motion is independent from the state of stiffness and defines the motion of the vehicle as a function of time, wherein the probabilistic component of the motion includes the state of stiffness having an uncertainty and defines disturbance on the motion of the vehicle, wherein the measurement model of the vehicle includes a combination of a deterministic component of the measurement model independent from the state of stiffness and a probabilistic component of the measurement model that includes the state of stiffness;

representing the state of stiffness with a set of particles, each particle includes a mean and a variance of the state of stiffness defining a feasible space of the parameters of the state of stiffness;

updating iteratively the mean and the variance of at least some particles using a difference between an estimated state of stiffness estimated using the motion model of the vehicle including the state of stiffness with parameters sampled on the feasible space of the particle and the measured state of stiffness determined according to the measurement model using measurements of the state of the vehicle;

determining a mean and a variance of the state of stiffness as a function of the updated mean and the updated variance in at least one particle; and

controlling the vehicle using the mean and the variance of the state of stiffness.