Friction Coefficient Estimation from Camera and Wheel Speed Data

• US 20150251659A1
• Filed: 12/09/2013
• Published: 09/10/2015
• Est. Priority Date: 12/20/2012
• Status: Active Grant

• Current Assignee: Continental Teves AG & Co. OHG (Continental AG)
• Original Assignee: Continental Teves AG & Co. OHG (Continental AG)
• Inventors: Koebe, Andreas, Fischer, Daniel, Stoelzl, Stefan
• Granted Patent: US 9,643,617 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60T 8/171 Detecting parameters used in the regulation; Measuring values used in the regulation
• CPC Class Codes: B60T 8/172 Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters B60T8/17551 takes precedence
• CPC Class Codes: B60T 8/1725 Using tyre sensors, e.g. Sidewall Torsion sensors [SWT] for tyre pressure and temperature detection B60C23/00
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2520/28 Wheel speed
• CPC Class Codes: B60W 40/068 Road friction coefficient
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road

Abstract

This invention relates to a method and an apparatus, respectively, for estimating a friction coefficient in a moving vehicle. For this purpose, image data from a forward-looking vehicle camera is analyzed in the vehicle so as to allow conclusions with respect to the road surface. The camera image data is used to perform a friction coefficient classification which provides a camera friction coefficient μ_k. A tire slip and a tire vibration are analyzed based on a wheel speed signal and are used to perform a classification of the friction coefficient which provides a wheel friction coefficient μ_w. Proactive estimation of the friction coefficient is performed as a merger of the camera and wheel friction coefficients, wherein the proactive friction coefficient estimation is primarily based on the camera friction coefficient μ_k but the wheel friction coefficient μ_w is continuously taken into account to check the camera friction coefficient μ_k for plausibility.

Claims

1. A method for estimating a friction coefficient in a moving vehicle, wherein image data of a forward-looking camera in the vehicle is analyzed so as to allow conclusions with respect to the road surface and to perform a friction coefficient classification which provides a camera friction coefficient μ

_k, characterized in that

a tire slip and a tire vibration are analyzed based on a wheel speed signal and are used to perform a classification of the friction coefficient which provides a wheel friction coefficient μ

_w, wherein the proactive friction coefficient estimation is a merger of the camera and wheel friction coefficients, wherein the proactive friction coefficient estimation is primarily based on the camera friction coefficient μ

_k, wherein the wheel friction coefficient μ

_w is continuously taken into account to check the camera friction coefficient μ

_k for plausibility.

2. The method according to claim 1, wherein synchronism of the camera friction coefficient μ

_k and the wheel friction coefficient μ

_w is ensured taking into account the travel speed of the vehicle.

3. The method according to claim 1, wherein the camera friction coefficient μ

_k and the wheel friction coefficient μ

_ware each respectively assigned associated reliability information.

4. The method according to claim 3, wherein the camera friction coefficient μ

_k with associated high reliability information is released immediately as a proactive friction coefficient, while if low reliability information is associated with the camera friction coefficient μ

_k, then the system waits for confirmation from the wheel friction coefficient μ

_w, and if the camera friction coefficient μ

_k becomes unavailable, then the system only resorts to the wheel friction coefficient μ

_w.

5. The method according to claim 1, which is adjusted to characteristic field conditions of the vehicle and/or the driver by learning, comprising learning typical interferences in the camera image which can be identified by checking the plausibility of the camera friction coefficient μ

_k using the wheel friction coefficient μ

_w.

6. The method according to claim 1, wherein a mono camera is used as the camera.

7. The method according to claim 1, wherein a stereo camera is used as the camera.

8. The method according to claim 1, wherein a camera of a panoramic view system is used as the camera.

9. The method according to claim 1, wherein the proactive friction coefficient, the camera friction coefficient μ

_k, and/or the wheel friction coefficient μ

_w is transmitted to other driver assistance or driving dynamics control systems.

10. An apparatus for estimating a friction coefficient in a moving vehicle, including a forward-looking camera, at least one wheel speed sensor, and evaluation means, wherein the evaluation means are configured to analyze image data of said camera in the vehicle so as to allow conclusions with respect to the road surface and to perform a friction coefficient classification which provides a camera friction coefficient μ

_k, characterized in that

the wheel speed sensor transmits wheel speed signals to the evaluation means and said evaluation means are further configured to analyze a tire slip and a tire vibration based on a wheel speed signal and to use the same to perform a classification of the friction coefficient which provides a wheel friction coefficient μ

_w, wherein the proactive friction coefficient estimation is a merger of the camera and wheel friction coefficients, wherein the proactive friction coefficient estimation is primarily based on the camera friction coefficient μ

_k, wherein the wheel friction coefficient μ

_w is continuously taken into account to check the camera friction coefficient μ

_k for plausibility.

11. A method of estimating an actual friction coefficient of a road surface on which a vehicle is driving, comprising:

a) obtaining image data of an image of said road surface, from a forward-looking camera of said vehicle;

b) by analyzing said image data, producing therefrom a camera friction coefficient as a first estimate of said actual friction coefficient of said road surface;

c) obtaining a wheel speed signal from a wheel speed sensor of said vehicle;

d) by analyzing said wheel speed signal, determining therefrom a tire slip and a tire oscillation regarding motion of a tire of said vehicle relative to said road surface;

e) based on said tire slip and said tire oscillation, determining a wheel friction coefficient as a second estimate of said actual friction coefficient of said road surface;

f) determining a proactive estimation of said actual friction coefficient of said road surface by merging said camera friction coefficient and said wheel friction coefficient, wherein said proactive estimation of said actual friction coefficient is primarily based on said camera friction coefficient, said wheel friction coefficient is considered secondarily as a plausibility check of said camera friction coefficient; and

g) if said camera friction coefficient passes said plausibility check, then outputting said proactive estimation of said actual friction coefficient corresponding to said camera friction coefficient.