Systems and methods for detecting a road surface condition

Patent metadata

Field	Value
Publication number	US-2025178609-A1
Application number	US-202418901558-A
Country	US
Kind code	A1
Filing date	Sep 30, 2024
Priority date	Dec 4, 2023
Publication date	Jun 5, 2025
Grant date	—

Abstract

Disclosed are various embodiments for predicting a road surface condition based at least in part on tire resistive forces and vehicle data obtained from the controller area network (CAN) bus of a vehicle. In response to determining that the vehicle is in a steady state, tractive forces can be estimated using the vehicle data. The tractive forces and the vehicle speed can be inputted into a trained surface condition detection model. The surface condition detection model is trained to predict a probability that a surface is wet or dry based at least in part on the tractive forces. The probability that a surface is wet or dry can be provided to other vehicle systems for adjustment of vehicle operations.

First claim

Therefore, the following is claimed:

1 . A method for determining a road surface condition, comprising:
obtaining, by at least one computing device, vehicle data collected from a vehicle controller area network (CAN) bus in communication with one or more vehicle systems of a vehicle traveling along a surface; determining, by the at least one computing device, that the vehicle is operating in a steady state based at least in part on the vehicle data; and in response to determining that the vehicle is operating in the steady state:
estimating, by the at least one computing device, a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data;
applying, by the at least one computing device, the tractive force and a vehicle speed of the vehicle to a surface condition detection model; and
determining, by the at least one computing device, a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model.

2 . The method of claim 1 , wherein the vehicle data comprises an engine torque, an engine revolutions per minute (RPM), the vehicle speed, a wheel speed, and a vehicle acceleration.

3 . The method of claim 2 , wherein determining that the vehicle is operating in the steady state further comprises:
determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.

4 . The method of claim 1 , wherein the surface condition detection model comprises a trained classifier.

5 . The method of claim 1 , further comprising:
determining a total tractive force; determining a road grade force; and determining an aerodynamic drag force, and wherein the tractive force or the net resistive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.

6 . The method of claim 1 , further comprising transmitting the probability to at least one vehicle system of the one or more vehicle systems, the at least one vehicle system being configured to adjust an operation of the vehicle based at least in part on the probability.

7 . The method of claim 1 , wherein the at least one computing system is located within the vehicle or is located remote from the vehicle.

8 . A road surface detection system, comprising:
a computing device comprising a processor and a memory; and machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
obtain vehicle data collected from a vehicle controller area network (CAN) bus in communication with one or more vehicle systems of a vehicle traveling along a surface;
determine that the vehicle is operating in a steady state based at least in part on the vehicle data; and
in response to determining that the vehicle is operating in the steady state:
estimate a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data;
apply the tractive force and a vehicle speed of the vehicle to a surface condition detection model; and
determine a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model.

9 . The system of claim 8 , wherein the vehicle data comprises an engine torque, an engine revolutions per minute (RPM), the vehicle speed, a wheel speed, and a vehicle acceleration.

10 . The system of claim 9 , wherein determining that the vehicle is operating in the steady state further comprises:
determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.

11 . The system of claim 8 , wherein the surface condition detection model comprises a trained classifier.

12 . The system of claim 8 , wherein the machine-readable instructions further cause the computing device to at least:
determine a total tractive force; determine a road grade force; and determine an aerodynamic drag force, wherein the tractive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.

13 . The system of claim 8 , wherein the machine-readable instructions further cause the computing device to at least transmit the probability to at least one vehicle system of the one or more vehicle system, the at least one vehicle system being configured to adjust an operation of the vehicle based at least in part on the probability.

14 . The system of claim 8 , wherein the at least one computing system is located within the vehicle or is located remote from the vehicle.

15 . A non-transitory, computer-readable medium, comprising machine-readable instructions that, when executed by a processor of a computing device, cause the computing device to at least:
obtain vehicle data collected from a vehicle controller area network (CAN) bus in communication with one or more vehicle systems of a vehicle traveling along a surface; determine that the vehicle is operating in a steady state based at least in part on the vehicle data; and in response to determining that the vehicle is operating in the steady state:
estimate a tractive force associated with at least one tire of the vehicle and the surface based at least in part on the vehicle data;
apply the tractive force and a vehicle speed of the vehicle to a surface condition detection model; and
determine a probability that the surface is wet or dry based at least in part on an output of the surface condition detection model.

16 . The non-transitory, computer-readable medium of claim 15 , wherein the vehicle data comprises an engine torque, an engine revolutions per minute (RPM), the vehicle speed, a wheel speed, and a vehicle acceleration.

17 . The non-transitory, computer-readable medium of claim 16 , wherein determining that the vehicle is operating in the steady state further comprises:
determining that the engine RPM exceeds an RPM threshold; determining that the vehicle speed exceeds a speed threshold; and determining that the vehicle acceleration exceeds an acceleration threshold.

18 . The non-transitory, computer-readable medium of claim 15 , wherein the surface condition detection model comprises a trained classifier.

19 . The non-transitory, computer-readable medium of claim 15 , wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least: transmit the probability to at least one vehicle system of the one or more vehicle system, the at least one vehicle system being configured to adjust an operation of the vehicle based at least in part on the probability.

20 . The non-transitory, computer-readable medium of claim 15 , wherein the machine-readable instructions, when executed by the processor, further cause the computing device to at least:
determine a total tractive force; determine a road grade force; and determine an aerodynamic drag force, wherein the tractive force is estimated by subtracting the road grade force and the aerodynamic drag force from the total tractive force.

Assignees

• Goodyear Tire & Rubber

Inventors

• Singh Kanwar Bharat
• Balaga Sanjay Raghav

Classifications

• B60W2520/12

  Lateral speed · CPC title

• B60W2510/0657

  Engine torque · CPC title

• B60W2530/16

  Driving resistance · CPC title

• B60W40/1005

  Driving resistance · CPC title

• B60W2520/30

  Wheel torque · CPC title