Methods and systems for estimating road surface friction coefficient using self aligning torque

Patent metadata

Field	Value
Publication number	US-10272919-B2
Application number	US-201414510862-A
Country	US
Kind code	B2
Filing date	Oct 9, 2014
Priority date	Oct 9, 2014
Publication date	Apr 30, 2019
Grant date	Apr 30, 2019

Abstract

Methods and systems for determining road surface information in a vehicle. In one embodiment, the method includes: determining at least one condition assessment value based on steering data; determining a feature set to include at least one of self-aligning torque (SAT), slip angle, SAT variance, steering rate, and lateral acceleration based on the condition assessment value; processing steering data obtained during a steering maneuver and associated with the feature set using a pattern classification technique; and determining a surface type based on the processing.

First claim

What is claimed is:

1. A method for determining road surface information in a vehicle, comprising:
receiving, by a processor, slip angle data, self-aligning torque data, lateral acceleration data, steering angle data, and steering rate data;
selecting, by the processor, a feature set from a plurality of feature sets based on an evaluation of the slip angle data, the self-aligning torque data, the lateral acceleration data, the steering angle data, and the steering rate data;
wherein a first feature set of the plurality of feature sets includes self-aligning torque (SAT) and slip angle; wherein a second feature set of the plurality of feature sets includes SAT, SAT variance and slip angle; wherein a third feature set of the plurality of feature sets includes lateral acceleration, steering rate and slip angle; and wherein a fourth feature set of the plurality of feature sets includes lateral acceleration;
processing, by the processor, steering data obtained during a steering maneuver and associated with the selected feature set using a pattern classification technique;
determining, by the processor, a surface type based on the processing; and
outputting a signal indicating the surface type for use in controlling the vehicle,
wherein the pattern classification technique compares real-time data and pre-stored data associated with the selected feature set using at least one of a linear discriminant analysis and a support vector machine analysis.

2. The method of claim 1 , wherein the evaluation is of a steering mode that is associated with the steering rate data.

3. The method of claim 1 , wherein the evaluation is of a SAT mode that is associated with a linearity of the SAT data.

4. The method of claim 1 , further comprising determining a steering maneuver type based on the steering angle data, and wherein the evaluation and the processing the steering data is based on the steering maneuver type.

5. The method of claim 4 , wherein the steering maneuver type is at least one of a steering out maneuver and a non-steering out maneuver.

6. The method of claim 5 , wherein the determining the surface type is based on a default value instead of the selected feature set, when the steering maneuver type is determined to be the non-steering out maneuver.

7. The method of claim 1 , further comprising:
determining a slip angle to be greater than a threshold based on the slip angle data determining a SAT mode to be linear based on the SAT data and the lateral acceleration data; determining the steering mode to be normal steering based on the steering rate data; and
wherein the selecting the feature set comprises selecting the first feature set based on the determination of the slip angle being greater than the threshold, the SAT mode being linear, and the steering mode being normal.

8. The method of claim 1 , further comprising:
determining a slip angle to be within a range based on the slip angle data determining a SAT mode to be linear based on the SAT data, and the lateral acceleration data; and determining the steering mode to be normal steering based on the steering rate data; and
wherein the selecting the feature set comprises selecting the second feature set based on the slip angle being within the range, the SAT mode being linear, and the steering mode being normal steering.

9. The method of claim 1 , further comprising:
determining a slip angle to be greater than a threshold based on the slip angle data determining a SAT mode to be linear based on the SAT data, and the lateral acceleration data; and determining the steering mode to be fast steering based on the steering rate data; and
wherein the selecting the feature set comprises selecting the third feature set based on the slip angle being greater than the threshold, the SAT mode being linear, and the steering mode being fast steering.

10. The method of claim 1 , further comprising:
determining a SAT mode to be nonlinear, and
wherein the selecting the feature set comprises selecting the fourth feature set based on the SAT mode being nonlinear.

11. The method of claim 1 , further comprising determining a surface value based on the surface type.

12. The method of claim 1 , further comprising confirming the surface type based on an evaluation of lateral acceleration.

13. The method of claim 1 , further comprising determining a final surface type based on a plurality of determined surface types within a temporal moving window.

14. A system for determining road surface information in a vehicle, comprising:
a condition assessment module that processes slip angle data, self-aligning torque data, lateral acceleration data, steering angle data, and steering rate data;
a feature set determination module that selects a feature set from a plurality of feature sets based on an evaluation of the slip angle data, the self-aligning torque data, the lateral acceleration data, the steering angle data, and the steering rate data; wherein a first feature set of the plurality of feature sets includes self-aligning torque (SAT) and slip angle; wherein a second feature set of the plurality of feature sets includes SAT, SAT variance and slip angle; wherein a third feature set of the plurality of feature sets includes lateral acceleration, steering rate and slip angle; and wherein a fourth feature set of the plurality of feature sets includes lateral acceleration; and
a surface classification module that processes the steering data obtained during a steering maneuver and associated with the selected feature set using a pattern classification technique to determine a surface type,
wherein the pattern classification technique compares real-time data and pre-stored data associated with the selected feature set using at least one of a linear discriminant analysis and a support vector machine analysis.

15. The system of claim 14 further comprising a decision making module that determines a final surface type based on a plurality of determined surface types within a temporal moving window.

Assignees

• Gm Global Tech Operations Llc

Inventors

• Ghoneim Youssef A
• Zhao Qingrong
• Zhang Qi
• Litkouhi Bakhtiar B
• Si Yingmei
• Sidlosky David M
• Boatman Valarie V
• Ahmed Sami

Classifications

• B60W2520/20

  Sideslip angle · CPC title

• B60W2520/14

  Yaw · CPC title

• B60W40/068 Primary

  Road friction coefficient · CPC title

• B60W2540/18

  Steering angle · CPC title

• B60W2520/125

  Lateral acceleration · CPC title