System and method for bayesian inference in the characterization and prediction of vehicle tire wear

Patent metadata

Field	Value
Publication number	US-2022016938-A1
Application number	US-202117449394-A
Country	US
Kind code	A1
Filing date	Sep 29, 2021
Priority date	Apr 1, 2019
Publication date	Jan 20, 2022
Grant date	—

Abstract

A system and method are provided for Bayesian updating of distributions of factors that affect tire wear. Information is accumulated in data storage regarding probability distributions corresponding to each of a respective plurality of tire wear factors. Vehicle data comprising movement data and location data collected in association with a vehicle is transmitted from the vehicle to a centralized (e.g., cloud) computing device or network. At least one observation corresponding to one or more of the plurality of factors is generated based on the transmitted vehicle data. A Bayesian estimation is then generated of a tire wear status at a given time for at least one tire associated with the vehicle, based at least on the at least one generated observation and the stored information regarding probability distributions. The predictions accordingly carry a measure of uncertainty, and Bayesian inference can be used to update distributions based on the observations.

First claim

What is claimed is:

1 . A method for estimating a tire wear status, the method comprising:
accumulating in data storage information regarding probability distributions corresponding to each of a respective plurality of tire wear factors; transmitting vehicle data collected in association with a vehicle from the vehicle to an external computing device or network; generating at least one observation corresponding to one or more of the plurality of factors based on the transmitted vehicle data; and providing a Bayesian estimation of a tire wear status at a given time for at least one tire associated with the vehicle, based at least on the at least one generated observation and the stored information regarding probability distributions.

2 . The method of claim 1 , further comprising storing information regarding updated probability distributions corresponding to a respective plurality of factors contributing to tire wear for the at least one tire associated with the vehicle, based at least on the generated at least one observation.

3 . The method of claim 2 , further comprising predicting a tire wear status at one or more future times for the at least one tire associated with the vehicle.

4 . The method of claim 3 , further comprising predicting a replacement time for the at least one tire associated with the vehicle, based on a current tire wear status or the predicted tire wear status as compared with tire wear thresholds associated with the at least one tire associated with the vehicle.

5 . The method of claim 1 , wherein the information regarding the plurality of probability distributions reflects an array of time-series relationships.

6 . The method of claim 1 , further comprising receiving one or more tire wear input values from a user via a user interface associated with the remote server and generating at least one observation for one or more of the plurality of factors based on the one or more tire wear input values.

7 . The method of claim 1 , further comprising receiving one or more tire wear input values generated by one or more sensors mounted in or on a respective tire of the at least one tire and generating at least one observation for one or more of the plurality of factors based on the one or more tire wear input values.

8 . The method of claim 1 , further comprising receiving one or more tire wear input values generated by a sensor external to the vehicle and generating at least one observation for one or more of the plurality of factors based on the one or more tire wear input values.

9 . The method of claim 8 , wherein at least one of the tire wear input values generated by the sensor external to the vehicle comprises a tread depth measurement.

10 . The method of claim 1 , further comprising generating an estimated tire wear status with a baseline value and a range corresponding to a confidence level for the estimation.

11 . A system for estimating a tire wear status, comprising:
a data storage device or network having stored thereon information regarding probability distributions corresponding to each of a respective plurality of tire wear factors; for each of a plurality of vehicles, distributed computing nodes linked to one or more vehicle-mounted sensors respectively configured to collect vehicle data; a centralized computing device or network comprising computer readable media having instructions residing thereon and executable by one or more processors, the centralized computing device or network configured to
receive the vehicle data and the tire data collected in association with a particular vehicle,
generate at least one observation corresponding to one or more of the plurality of factors based on the transmitted vehicle data, and
provide a Bayesian estimation of a tire wear status at a given time for at least one tire associated with the particular vehicle, based at least on the at least one generated observation and the stored information regarding probability distributions.

12 . The system of claim 11 , wherein the centralized computing device or network is configured to store in the data storage network information regarding updated probability distributions corresponding to a respective plurality of factors contributing to tire wear for the at least one tire associated with the vehicle, based at least on the generated at least one observation.

13 . The system of claim 12 , wherein the centralized computing device or network is further configured to predict a tire wear status at one or more future times for the at least one tire associated with the vehicle.

14 . The system of claim 13 , wherein the centralized computing device or network is configured to predict a replacement time for the at least one tire associated with the vehicle, based on a current tire wear status or the predicted tire wear status as compared with tire wear thresholds associated with the at least one tire associated with the vehicle.

15 . The system of claim 11 , wherein the information regarding the plurality of probability distributions reflects an array of time-series relationships.

16 . The system of claim 11 , wherein the centralized computing device or network is further configured to receive one or more tire wear input values from a user via a user interface, and to generate at least one observation for one or more of the plurality of factors based on the one or more tire wear input values.

17 . The system of claim 11 , wherein the centralized computing device or network is further configured to receive one or more tire wear input values generated by one or more sensors mounted in or on a respective tire of the at least one tire and generate at least one observation for one or more of the plurality of factors based on the one or more tire wear input values.

18 . The system of claim 11 , wherein the centralized computing device or network is further configured to receive one or more tire wear input values generated by a sensor external to the vehicle and generating at least one observation for one or more of the plurality of factors based on the one or more tire wear input values.

19 . The system of claim 18 , wherein at least one of the tire wear input values generated by the sensor external to the vehicle comprises a tread depth measurement.

20 . The system of claim 11 , wherein the centralized computing device or network is further configured to generate an estimated tire wear status with a baseline value and a range corresponding to a confidence level for the estimation.

Assignees

• Bridgestone Americas Tire Operations Llc

Inventors

• Doraiswamy Srikrishna
• Wei Terence E
• Barr Jason R
• Dorfi Hans R

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• H04L2012/40273

  the transportation system being a vehicle · CPC title

• H04L2012/40215

  Controller Area Network CAN · CPC title

• H04L12/40

  Bus networks · CPC title

• G07C5/0808

  Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005) · CPC title