System and method for road friction coefficient estimation

• US 10,442,439 B1
• Filed: 08/18/2017
• Issued: 10/15/2019
• Est. Priority Date: 08/18/2016
• Status: Expired due to Fees

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Seo, YoungWoo, Aikin, Randol, Garg, Kshitiz
• Primary Examiner(s): Shaawat, Mussa A
• Application Number:
  US15/681,134
• Time in Patent Office: 788 Days
• CPC Class Codes: B60T 2210/12 Friction
• CPC Class Codes: B60W 2050/146 Display means
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2552/40 Coefficient of friction
• CPC Class Codes: B60W 2555/00 Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
• CPC Class Codes: B60W 40/00 Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 40/068 Road friction coefficient
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention

Abstract

Aspects of the present disclosure involve systems and methods for obtaining real-time road friction coefficient estimations. In one embodiment, a regression function is learned using a training data set which correlates input data measurements arriving from onboard system sensors and coefficient estimations arriving from an extension system. In another embodiment, the learned regression function can be retrieved to obtain real-time road friction coefficient estimations while the system is in motion.

Claims

1. A method for managing device operation comprising:

receiving lateral force measurements from a first device connected to a first vehicle as the first device is in communication with a road surface;

estimating road friction coefficients of the road surface based at least on the received lateral force measurements;

collecting a first set of road surface characteristics sensor measurements from a first set of sensors of the first vehicle;

combining the estimated road friction coefficients and the collected first set of road surface characteristics sensor measurements to generate a training data set associated with operation of the first vehicle;

deriving a regression function from the training data set, wherein the regression function predicts real-time road friction coefficients of the road surface; and

storing the regression function in a remote server in communication with the first vehicle through a communication network.

2. The method of claim 1 further comprising;

retrieving, at a second vehicle, the regression function for predicting the real-time road friction coefficients from the remote server;

obtaining a second set of road surface characteristics sensor measurements from a second set of sensors;

estimating the real-time road friction coefficients using the regression function and the second set of road surface characteristics sensor measurements obtained from the second set of sensors.

3. The method of claim 1, wherein the first set of sensors include at least one of an object sensor, a textural sensor, a light detection and ranging (LiDAR) sensor, a radio detection and ranging sensor (RADAR), or a camera.

4. The method of claim 2, wherein the second set of sensors include at least one of LiDAR sensors, RADAR sensors, or cameras on the second vehicle.

5. The method of claim 4, wherein a third vehicle transfers a third set of road surface characteristics sensor measurements to the second vehicle, wherein the third set of road surface characteristics sensor measurements is used to estimate the real-time road friction coefficients of the second vehicle through the regression function.

6. The method of claim 1 further comprising transmitting the first set of collected road surface characteristics sensor measurements and the estimated road friction coefficients to the remote server by the first vehicle.

7. The method of claim 1, wherein the first device is an extension trailer connected to the first vehicle and wherein the first device measures the lateral force measurements at a sampling cycle as the trailer is in motion.

8. The method of claim 1, wherein the first device is an onboard mechanical sensor connected to the first vehicle, wherein the onboard mechanical sensor is one of a strain gauge, wheel speed sensor, and a hub force sensor.

9. The method of claim 1, wherein the first vehicle further comprises an inertial measurement unit in conjunction with the first set of road surface characteristics sensor measurements for estimating the real-time road friction coefficients of the first vehicle.

10. The method of claim 1 wherein the first vehicle further comprises a global navigation satellite system unit in conjunction with the first set of road surface characteristics sensor measurements for estimating the real-time road friction coefficients of the first vehicle.

11. A system comprising:

a first device connected to a first vehicle;

a first set of sensors detecting a first set of road surface characteristics sensor measurements;

a processor; and

a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the processor to perform the operations of;

obtaining an estimated road friction coefficients of a road surface from lateral force measurements acquired from the first device as the first device is in communication with the road surface;

receiving the first set of road surface characteristics sensor measurements from the first set of sensors;

generating a training data set associated with the first vehicle using the estimated road friction coefficients and the collected first set of road surface characteristics sensor measurements;

deriving a regression function from the training data set, wherein the regression function predicts real-time road friction coefficients of the road surface; and

storing the regression function in a remote server in communication with the first vehicle through a communication network.

12. The system of claim 11 further comprising:

a second vehicle comprising a second set of sensors collecting a second set of road surface characteristics sensor measurements, wherein the second vehicle retrieves the regression function for predicting the real-time road friction coefficients from the remote server and estimates the real-time road friction coefficients using the regression function and the second set of road surface characteristics sensor measurements obtained from the second set of sensors.

13. The system of claim 11, wherein the first set of sensors include at least one of an object sensor, a textural sensor, light detection and ranging (LiDAR) sensor, a radio detection and ranging (RADAR), or a camera.

14. The system of claim 12, wherein the second set of sensors include LiDAR sensors, RADAR sensors, or cameras on the second vehicle.

15. The system of claim 14, wherein a third vehicle transfers a third set of road surface characteristics sensor measurements to the second vehicle, wherein the third set of road surface characteristics sensor measurements is used to estimate the real-time road friction coefficients of the second vehicle through the regression function.

16. The system of claim 12, wherein the processor further transmits the first set of collected road surface characteristics sensor measurements and the estimated road friction coefficients to the remote server.

17. The system of claim 12, wherein the first device is an extension trailer is connected to the first vehicle, and wherein the first device measures the lateral force measurements at a sampling cycle as the extension trailer is in motion.

18. The system of claim 12, wherein the first device is an onboard mechanical sensor connected to the first vehicle and wherein the onboard mechanical sensor is one of a strain gauge, wheel speed sensor, and a hub force sensor.

19. The system of claim 12, wherein the first vehicle further comprises an inertial measurement unit and a global navigation satellite system unit in conjunction with the first set of road surface characteristics sensor measurements for estimating the real-time road friction coefficients of the first vehicle.

20. A non-transitory computer-readable data storage medium comprising instructions that, when executed by at least one processor of a first vehicle, cause the first vehicle to perform operations comprising:

receiving lateral force measurements from a first device connected to the first vehicle as the first device is in communication with a road surface;

estimating road friction coefficients of the road surface based at least on the received lateral force measurements;

collecting a first set of road surface characteristics sensor measurements from a first set of sensors of the first vehicle;

combining the estimated road friction coefficients and the collected first set of road surface characteristics sensor measurements to generate a training data set associated with operation of the first vehicle;

deriving a regression function from the training data set, wherein the regression function predicts real-time road friction coefficients of the road surface; and

storing the regression function in a remote server in communication with the first vehicle through a communication network.

21. The non-transitory computer-readable data storage medium of claim 20, further comprising;

retrieving, at a second vehicle, the regression function for predicting the real-time road friction coefficients from the remote server;

obtaining a second set of road surface characteristics sensor measurements from a second set of sensors;

estimating the real-time road friction coefficients using the regression function and the second set of road surface characteristics sensor measurements obtained from the second set of sensors.

22. The non-transitory computer-readable data storage medium of claim 21, wherein the second set of sensors include at least one of LiDAR sensors, RADAR sensors, or cameras on the second vehicle.

23. The method of claim 21, wherein a third vehicle transfers a third set of road surface characteristics sensor measurements to the second vehicle, wherein the third set of road surface characteristics sensor measurements is used to estimate the real-time road friction coefficients of the second vehicle through the regression function.

24. The non-transitory computer-readable data storage medium of claim 20, further comprising transmitting the first set of collected road surface characteristics sensor measurements and the estimated road friction coefficients to the remote server by the first vehicle.

25. The non-transitory computer-readable data storage medium of claim 20, wherein the first device is an extension trailer connected to the first vehicle and wherein the first device measures the lateral force measurements at a sampling cycle as the trailer is in motion.

26. The non-transitory computer-readable data storage medium of claim 20, wherein the first device is an onboard mechanical sensor connected to the first vehicle, wherein the onboard mechanical sensor is one of a strain gauge, wheel speed sensor, and a hub force sensor.

27. The non-transitory computer-readable data storage medium of claim 20, wherein the first vehicle further comprises an inertial measurement unit in conjunction with the first set of road surface characteristics sensor measurements for estimating the real-time road friction coefficients.

28. The non-transitory computer-readable data storage medium of claim 20, wherein the first vehicle further comprises a global navigation satellite system unit in conjunction with the first set of road surface characteristics sensor measurements for estimating the real-time road friction coefficients.

29. The non-transitory computer-readable data storage medium of claim 20, wherein the first set of sensors include at least one of an object sensor, a textural sensor, a light detection and ranging (LiDAR) sensor, a radio detection and ranging sensor (RADAR), or a camera.