Vehicle road friction control

Patent metadata

Field	Value
Publication number	US-11142209-B2
Application number	US-201916273623-A
Country	US
Kind code	B2
Filing date	Feb 12, 2019
Priority date	Feb 12, 2019
Publication date	Oct 12, 2021
Grant date	Oct 12, 2021

Abstract

A road friction is determined for a road location based on a traffic speed determined from respective speeds of each of a plurality of vehicles. A vehicle can be operated based on the determined road friction.

First claim

What is claimed is:

1. A method, comprising:
determining a road friction for a road location based on a traffic speed determined from respective speeds of each of a plurality of vehicles;
determining whether the traffic speed is below a threshold traffic speed, and determining the road friction based on the traffic speed only upon determining that the traffic speed is below the threshold traffic speed; and
operating a vehicle based on the road friction.

2. The method of claim 1 , wherein the road friction is determined based on, in addition to the traffic speed, a second datum that is one of whether the location includes a bridge or overpass, a status of daylight, a rain condition, a snow condition, a fog condition, a construction condition, a presence or absence of an emergency vehicle, a presence or absence of debris, or an ambient temperature.

3. The method of claim 2 , further comprising obtaining the second datum from a map.

4. The method of claim 2 , further comprising providing the traffic speed and the second datum for an area that includes the road location, whereby the road friction is provided for the area; wherein the area is defined according to a starting point and an ending point of a segment of a road.

5. The method of claim 1 , wherein the area is included on a planned route for the vehicle.

6. The method of claim 1 , wherein operating the vehicle based on the road friction includes controlling traction, speed, steering, braking, lane keeping, or lane changing.

7. The method of claim 1 , wherein determining the road friction includes obtaining the road friction as output from a machine learning program.

8. The method of claim 7 , wherein the machine learning program is provided in an infrastructure node computer.

9. The method of claim 7 , wherein the machine learning program is provided in a computer in the vehicle.

10. A computer comprising a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to:
determine a road friction for a road location based on a traffic speed determined from respective speeds of each of a plurality of vehicles;
determine whether the traffic speed is below a threshold traffic speed, and determine the road friction based on the traffic speed only upon determining that the traffic speed is below the threshold traffic speed; and
operate a vehicle based on the road friction.

11. The computer of claim 10 , further programmed to determine the road friction based on, in addition to the traffic speed, a second datum that is one of whether the location includes a bridge or overpass, a status of daylight, a rain condition, a snow condition, a fog condition, a construction condition, a presence or absence of an emergency vehicle, a presence or absence of debris, or an ambient temperature.

12. The computer of claim 11 , further programmed to obtain the second datum from a map.

13. The computer of claim 11 , further programmed to provide the traffic speed and the second data for an area that includes the road location, whereby the road friction is provided for the area; wherein the area is defined according to a starting point and an ending point of a segment of a road.

14. The computer of claim 10 , wherein the area is included on a planned route for the vehicle.

15. The computer of claim 10 , wherein operating the vehicle based on the road friction includes controlling traction, speed, steering, braking, lane keeping, or lane changing.

16. The computer of claim 10 , wherein determining the road friction includes obtaining the road friction as output from a machine learning program.

17. The computer of claim 16 , wherein the machine learning program is provided in an infrastructure node computer.

18. The computer of claim 16 , further comprising programming to execute the machine learning program.

19. A computer comprising a processor and a memory, the memory storing instructions executable by the processor such that the computer is programmed to:
determine whether the traffic speed is below a threshold traffic speed, the traffic speed being determined from respective speeds of each of a plurality of vehicles;
upon determining the traffic speed is below the threshold traffic speed, determine a second datum that is one of whether the location includes a bridge or overpass, a status of daylight, a rain condition, a snow condition, a fog condition, a construction condition, a presence or absence of an emergency vehicle, a presence or absence of debris, or an ambient temperature;
determine the road friction based on the second datum; and
operate a vehicle based on the road friction.

20. The computer of claim 19 , further programmed to determine the road friction based on, in addition to the second datum, the traffic speed.

Assignees

• Ford Global Tech Llc

Inventors

• Simmons Kyle
• Tseng Eric Hongtei
• Hafner Michael
• Lakehal-Ayat Mohsen

Classifications

• B60W40/068 Primary

  Road friction coefficient · CPC title

• B60W30/143

  Speed control (B60W30/16 takes precedence) · CPC title

• B60W10/18

  including control of braking systems · CPC title

• G08G1/0116

  from roadside infrastructure, e.g. beacons · CPC title

• G08G1/0112

  from the vehicle, e.g. floating car data [FCD] · CPC title