GPS augmentation friction estimation

Abstract

A vehicle and a system and method of controlling the vehicle. The system includes a sensor and a processor. The sensor obtains a first estimate of a force on a tire of the vehicle based on dynamics of the vehicle. The processor is configured to obtain a second estimate of the force on the tire using a tire model, determine an estimate of a coefficient of friction between the tire and a road from the first estimate of the force and the second estimate of the force, and use the estimate of the coefficient of friction to control the vehicle.

Description

Technical Field

[0001] This subject matter discloses information relating to the operation of autonomous vehicles, and in particular, to a method for estimating the friction between vehicle tires and the road to improve the operation of an autonomous vehicle along a road based on that friction. Background Technology

[0002] Autonomous vehicles operate to navigate roads autonomously, given knowledge of their environment. They can use the friction between their tires and the road to perform calculations to control the vehicle, prevent slippage, improve braking, and so on. However, road conditions can change rapidly and drastically depending on weather conditions and the location of the road. In some situations, these changes can be rapid and drastic, affecting vehicle control. Therefore, improved knowledge of road friction is desirable for controlling the vehicle when road friction changes. Summary of the Invention

[0003] In one exemplary embodiment, a method for controlling a vehicle is disclosed. A first estimate of the forces on the vehicle's tires is obtained based on the vehicle's dynamics. A second estimate of the forces on the tires is obtained using a tire model. An estimate of the coefficient of friction between the tires and the road is determined from the first and second estimates of the forces. The estimated coefficient of friction is used to control the vehicle.

[0004] In addition to one or more features described herein, the method further includes determining an estimate of the coefficient of friction by reducing the difference between a first estimate and a second estimate of the force. The second estimate of the force is based on a measured tire slip angle. This force is at least one of a front lateral force on the front tire, a rear lateral force on the rear tire, a front longitudinal force on the front tire, and a rear longitudinal force on the rear tire. The method further includes determining low-friction conditions when a metric based on measured yaw parameters and a metric based on model yaw parameters is greater than a threshold. The method further includes outputting an estimate of the coefficient of friction when the estimate is less than 1 for a selected number of sampling times and when the jerk occurring on the vehicle is negative. The tire model is a nonlinear tire model.

[0005] In another exemplary embodiment, a system for controlling a vehicle is disclosed. The system includes sensors and a processor. The sensors obtain a first estimate of the forces on the vehicle's tires based on the vehicle's dynamics. The processor is configured to obtain a second estimate of the forces on the tires using a tire model, determine an estimate of the coefficient of friction between the tires and the road from the first and second estimates of the forces, and use the estimate of the coefficient of friction to control the vehicle.

[0006] In addition to one or more features described herein, the processor is configured to determine an estimate of the coefficient of friction by reducing the difference between a first estimate and a second estimate of the force. The second estimate of the force is based on a measured tire slip angle. This force is at least one of a front lateral force on the front tire, a rear lateral force on the rear tire, a front longitudinal force on the front tire, and a rear longitudinal force on the rear tire. The processor is also configured to determine a low-friction condition when a metric based on measured yaw parameters and a metric based on model yaw parameters is greater than a threshold. The processor is also configured to output an estimate of the coefficient of friction when the estimate is less than one for a selected number of sampling times, and when the jerk occurring at the vehicle is negative. The tire model is a nonlinear tire model.

[0007] In yet another exemplary embodiment, a vehicle is disclosed. The vehicle includes sensors and a processor. The sensors obtain a first estimate of the forces on the vehicle's tires based on the vehicle's dynamics. The processor is configured to obtain a second estimate of the forces on the tires using a tire model, determine an estimate of the coefficient of friction between the tires and the road from the first and second estimates of the forces, and use the estimate of the coefficient of friction to control the vehicle.

[0008] In addition to one or more features described herein, the processor is configured to determine an estimate of the coefficient of friction by reducing the difference between a first estimate of the force and a second estimate of the force. The second estimate of the force is based on a measured tire slip angle. This force is at least one of a front lateral force on the front tire, a rear lateral force on the rear tire, a front longitudinal force on the front tire, and a rear longitudinal force on the rear tire. The processor is also configured to determine low-friction conditions when a metric based on measured yaw parameters and a metric based on model yaw parameters is greater than a threshold. The processor is also configured to output an estimate of the coefficient of friction when the estimate is less than 1 for a selected number of sampling times, and when the jerk occurring at the vehicle is negative.

[0009] The above-described features and advantages, as well as other features and advantages of this disclosure, will become apparent when the following detailed description is taken in conjunction with the accompanying drawings. Attached Figure Description

[0010] Other features, advantages, and details appear only as examples in the following detailed description, which is described in detail with reference to the accompanying drawings, in which:

[0011] Figure 1 An autonomous vehicle is shown in an exemplary embodiment;

[0012] Figure 2 A top view of the chassis of the autonomous vehicle is shown;

[0013] Figure 3A flowchart is shown for controlling an autonomous vehicle using the methods disclosed herein;

[0014] Figure 4 A flowchart is shown for a method used to estimate the coefficient of friction between a vehicle and a road;

[0015] Figure 5 A flowchart is shown for a method used to determine low-friction conditions;

[0016] Figure 6 A phase diagram of the yaw parameters for low-friction roads (e.g., icy roads) is shown.

[0017] Figure 7 The phase diagrams of yaw rate and differential yaw rate of a high-friction road are shown;

[0018] Figure 8 A graph of the model-based lateral force on the tire is shown;

[0019] Figure 9 A graph illustrating the operation of the optimization method for lateral forces is shown; and

[0020] Figure 10 A flowchart is shown for a method to prevent excessive flipping between low and high friction coefficient estimates. Detailed Implementation

[0021] The following description is exemplary in nature only and is not intended to limit this disclosure, its application, or use. It should be understood that in all the drawings, corresponding reference numerals denote the same or corresponding parts and features. As used herein, the term module refers to processing circuitry, which may include application-specific integrated circuits (ASICs), electronic circuitry, processors (shared, dedicated, or grouped), and memory executing one or more software or firmware programs, combinational logic circuitry, and / or other suitable components that provide the described functionality.

[0022] According to an exemplary embodiment, Figure 1 An autonomous vehicle 10 is illustrated. In an exemplary embodiment, the autonomous vehicle 10 is a so-called Level 4 or Level 5 automation system. A Level 4 system signifies "high automation," referring to the driving mode-specific performance of the automated driving system in all aspects of a dynamic driving task, even if the driver does not appropriately respond to intervention requests. A Level 5 system signifies "full automation," referring to the full-time performance of the automated driving system in all road and environmental conditions that are manageable by the driver for all aspects of a dynamic driving task. It should be understood that the systems and methods disclosed herein can also be used with any autonomous vehicle operating at Levels 1 to 5.

[0023] Autonomous vehicle 10 typically includes at least a navigation system 20, a propulsion system 22, a transmission system 24, a steering system 26, a braking system 28, a sensing system 30, an actuator system 32, and a controller 34. Navigation system 20 determines a road-level route plan for autonomous driving of autonomous vehicle 10. Propulsion system 22 provides power to generate power for autonomous vehicle 10 and, in various embodiments, may include an internal combustion engine, an electric motor, such as a traction electric motor, and / or a fuel cell propulsion system. Transmission system 24 is configured to transmit power from propulsion system 22 to two or more wheels 16 of autonomous vehicle 10 according to a selectable speed ratio. Steering system 26 affects the position of two or more wheels 16. Although described for illustrative purposes as including a steering wheel 27, in some embodiments contemplated within the scope of this disclosure, steering system 26 may not include a steering wheel 27. Braking system 28 is configured to provide braking torque to two or more wheels 16.

[0024] The sensing system 30 senses objects in the external environment of the autonomous vehicle 10 and determines various parameters of these objects to determine the position and relative speed of various remote vehicles within the autonomous vehicle environment. The sensing system 30 may include sensors such as digital cameras, radar, and lidar. The object parameters are provided to the controller 34 for vehicle navigation.

[0025] The controller 34 includes a processor 36 and a computer-readable storage device or computer-readable storage medium 38. The storage medium includes a program or instructions 39 that, when executed by the processor 36, operate the autonomous vehicle 10 based on sensor system outputs. The controller 34 establishes a trajectory for the autonomous vehicle 10 based on the outputs of the sensing system 30. The controller 34 can provide the trajectory to the actuator system 32 to control the propulsion system 22, transmission system 24, steering system 26, and / or braking system 28 to navigate the autonomous vehicle 10 relative to an object 50. The computer-readable storage medium 38 may also include a program or instructions 39 that, when executed by the processor 36, determines friction conditions or the coefficient of friction between one or more wheels 16 of the autonomous vehicle 10 and the road, and uses the coefficient of friction to control the operation of the autonomous vehicle.

[0026] The communication system 60 is capable of communicating with remote devices such as traffic servers, infrastructure equipment, and Global Positioning Satellite (GPS) systems, and providing data from these remote devices to the controller 34. In various embodiments, for example, the controller 34 uses GPS data to determine the vehicle speed and angle or orientation of the autonomous vehicle 10. This information can be used to determine forces on the autonomous vehicle 10, thereby determining the forces on the tires of the autonomous vehicle 10.

[0027] Figure 2A top view of the chassis 200 of the autonomous vehicle 10 is shown. The chassis 200 includes a front axle 202, a rear axle 204, and a drive axle 206 connecting the front axle 202 to the rear axle 204. The front axle 202 includes a left front wheel 208 and a right front wheel 210. The rear axle 204 includes a left rear wheel 212 and a right rear wheel 214. The center of gravity 216 can be found along the drive axle 206 or the chassis 200. The front axle length “a” is the distance along the drive axle 206 from the center of gravity 216 to the front axle 202. The rear axle length “b” is the distance along the drive axle 206 from the center of gravity 216 to the rear axle 204.

[0028] The chassis 200 is shown in a body-centered coordinate system 225. This body-centered coordinate system 225 includes a longitudinal axis (x), a transverse axis (y), and a yaw axis (z) pointing outwards from the page. Rotation about the longitudinal axis is determined by the roll angle. Rotation about the horizontal axis is represented by the pitch angle θ. Rotation about the yaw axis is represented by the yaw angle ψ.

[0029] Force arrows are shown to indicate the forces on the tires. Right front wheel 210 shows the longitudinal front wheel force (F). xf ) and lateral front wheel force (F yf The right rear wheel 214 shows the longitudinal rear wheel force (F). xr ) and lateral rear wheel force (F yr The steering angle δ is shown at position 208 on the left front wheel. The steering angle δ is the angle between the longitudinal axis and the direction the tire is pointing. The slip angle is the angle between the direction the tire is pointing and the actual direction the tire is traveling.

[0030] Figure 3A flowchart 300 for controlling an autonomous vehicle 10 using the methods disclosed herein is shown. The autonomous vehicle 10 includes various modules running on a processor 36 of the autonomous vehicle 10, including a vehicle operation module 302 for changing the state of the autonomous vehicle 10 (i.e., via acceleration, deceleration, braking, steering, etc.), an integrated control system 304 providing instructions or signals to the vehicle operation module 302, and a driver input 306 providing driver instructions to the vehicle operation module 302. In other embodiments, the modules disclosed herein may operate on separate processors or circuits. The vehicle operation module 302 changes the state of the autonomous vehicle 10 based on data from the integrated control system 304 and from the driver input 306. The autonomous vehicle 10 also includes a vehicle state estimation module 308 operating at the processor 36, which evaluates the changes in the state of the autonomous vehicle 10 and generates parameters that can be used at the integrated control system for subsequent instructions to the autonomous vehicle 10. In various embodiments, the vehicle state estimation module 308 determines the coefficient of friction between the tires and the road of the autonomous vehicle 10 based on the current state or current dynamics of the vehicle. The integrated control system 304 uses the coefficient of friction to determine the control signal sent to the vehicle operation module 302.

[0031] Figure 4 A flowchart 400 is shown, illustrating a method for estimating the coefficient of friction between a vehicle and a road, as in... Figure 3 This is performed as in vehicle state estimation module 308. The method begins at block 402, where various dynamic parameters of the vehicle are acquired using data. In one embodiment, the data is acquired using sensors such as an inertial measurement unit (IMU) and may include parameters such as force, acceleration, angular rate, orientation, and velocity on the vehicle using sensors such as accelerometers, gyroscopes, etc. In various embodiments, these measurements may be performed along three or six degrees of freedom. In block 402, the coefficient of friction is initially set to μ = 1.

[0032] In box 404, dynamic parameters are used to determine whether the vehicle is experiencing a low coefficient of friction. A flag is set when the vehicle experiences such a low coefficient of friction. This flag can be set when the difference between the measured parameters and the model-based parameter estimates exceeds a selected threshold. In one embodiment, the parameters include the vehicle's yaw rate and the time derivative of the yaw rate. In another embodiment, the parameters include the wheel's rotational speed and the time derivative of the rotational speed.

[0033] When the difference between the measured parameter and the model-based parameter estimate is less than a threshold (i.e., when a high friction state is detected), the flag is set to 'flag=0 (Flage=0)'. If a high friction state is detected, the method loops back to box 402 to obtain further measurements. When the difference between the measured parameter and the model-based estimate is greater than or equal to the threshold (i.e., when a low friction state is detected), the flag is set to 'flag=1'. If a low friction state is detected, the method continues to box 406.

[0034] In box 406, dynamic parameter measurements are used to dynamically estimate the forces on the tire. Additionally, dynamic parameter measurements are used to estimate the tire's slip angle.

[0035] In block 408, an estimated value for the friction coefficient is determined. This estimate is determined based on a dynamic force estimate and a model-based force estimate obtained in block 406. The model-based force estimate is partly based on the slip angle obtained in block 406. In various embodiments, the value determined for the friction coefficient is to reduce, minimize, or substantially minimize the difference between the dynamic force estimate and the model-based force estimate. In one embodiment, this value can be determined based on the difference between the dynamic force estimate and the model-based force estimate, using any suitable optimization method applied to the cost function.

[0036] In box 410, a low-friction estimate is obtained and provided to the selection criteria in box 412. In box 412, an algorithm is used to determine the actual coefficient of friction at the tire, which can be determined by a high coefficient of friction (e.g., μ). n =1, where n is the iteration exponent) or represented by the low friction coefficient estimated in box 410. The algorithm applies standards to avoid excessive jumps between high friction coefficients and effective low coefficients, as explained in this paper. When using a high friction coefficient, the method returns box 402 (where μ = 1, where n is the iteration exponent) or is represented by the low friction coefficient estimated in box 410. n+1 =1). When using a low coefficient of friction, the method returns to box 410 (where μ = 1). n+1 =μ n ).

[0037] Figure 5 A flowchart 500 illustrates a method for determining low-friction conditions, as performed in block 404. Low-friction conditions are determined by comparing measured yaw parameters with predicted or model-based yaw parameters. In block 502, the lateral slip of the front wheel is determined. If the lateral slip is less than or equal to a slip threshold, the method loops back to block 502. If the lateral slip is greater than the slip threshold, the method proceeds to block 504. In block 504, the deviation between the measured yaw parameters and the model-based yaw parameters is determined. In one embodiment, a nonlinear bicycle model is used to calculate the model-based yaw parameters, as shown in equations (1) and (2):

[0038]

[0039]

[0040] Where M is the mass of the vehicle, and V x It is the longitudinal speed of the vehicle. It is the time derivative of the vehicle's lateral velocity. It is the yaw rate. αf is the time derivative of the yaw rate or yaw acceleration, αr is the slip angle of the front tire, αf is the slip angle of the rear tire, and g is the gravitational constant. It should be understood that other models may be used to determine the yaw rate parameter in various embodiments.

[0041] The nonlinear bicycle model in equations (1) and (2) receives input in the form of a steering angle δ and assumes a friction coefficient μ = 1. The output of the nonlinear bicycle model is the lateral velocity V. y Model-based yaw rate and model-based yaw The time derivative of (or model-based yaw acceleration). The yaw rate is measured at the vehicle using, for example, an inertial measurement unit (IMU). (or the measured yaw rate) and the measured yaw rate The time derivative of (or the measured yaw acceleration). The metric m between these values ​​is calculated as shown in equation (3):

[0042]

[0043] In box 506, the metric is compared to a yaw rate threshold. When the metric is greater than the yaw rate threshold and the absolute value of the slip angle is greater than 1, the method proceeds to box 508, where a flag is set to 1 ("FLAG=1 (flag=1)") to indicate a low-friction condition. Returning to box 506, when the metric is less than or equal to the yaw rate threshold, the method proceeds to box 510, where a flag is set to 0 ("FLAG=0 (flag=0)") to indicate a high-friction condition.

[0044] Figure 6 A phase diagram 600 for yaw parameters used on low-friction roads (e.g., icy roads) is shown. The yaw rate is shown along the x-axis, and the differential yaw rate along the y-axis. The phase diagram includes a first curve 602, which represents the measured yaw rate and the measured differential yaw rate (the time derivative of the yaw rate). A second curve 604 represents the yaw rate and differential yaw rate determined from a model (e.g., a nonlinear bicycle model). Figure 6 It is evident that, due to icy road conditions, the first curve 602 and the second curve 604 deviate considerably from each other in many cases.

[0045] Figure 7 A phase diagram 700 showing the yaw rate and differential yaw rate of a high-friction road is presented. The yaw rate is shown along the x-axis, and the differential yaw rate along the y-axis. A first curve 702 represents the measured yaw rate and the measured differential yaw rate. A second curve 704 represents the yaw rate and the differential yaw rate determined from the model. The first curve 702 and the second curve 704 remain very close to each other and rarely deviate from each other by more than a selected yaw deviation threshold.

[0046] for Figure 6 and 7 The phase diagram, the measure of equation (3) can be used to determine the deviation between the first curve (602, 702) and the second curve (604, 704) and compare this deviation with the yaw deviation threshold. When the deviation is greater than the yaw deviation threshold, a flag (i.e., "FLAG=1") is set to indicate that low friction conditions are being experienced. When the deviation is less than the yaw deviation threshold, the flag (i.e., "FLAG=0") is removed to indicate that high friction conditions are being experienced.

[0047] The process of estimating the forces on the wheels performed in box 406 is discussed with respect to equations (4)-(5). Lateral forces on the front wheels (e.g., left front wheel 208 or right front wheel 210) are also discussed. It can be determined by lateral acceleration, yaw rate and steering angle, as shown in equation (4):

[0048]

[0049] Where M is the mass of the vehicle, A y It measures the lateral acceleration of the vehicle, I. z It is the moment of inertia of the vehicle about the z-axis. This is the measured yaw rate, and δ is the steering angle. Lateral acceleration and steering angle can be determined from vehicle sensors. Similarly, the lateral force on the rear wheels... Use equation (5) to determine:

[0050]

[0051] The slip angle is the difference between the direction of wheel movement and the direction the wheel is pointing. The slip angle of the front tire... The estimated value can be obtained using equation (6):

[0052]

[0053] Where δf is the steering angle of the front tires. It is an estimate of the vehicle's lateral velocity, V. x It measures the vehicle's forward speed, and It's the yaw rate. Similarly, the rear tire slip angle. The estimated value can be obtained using equation (7):

[0054]

[0055] Where δr is the steering angle of the rear tire. Using the tire force model, the lateral force on the front tire is given by equation (8):

[0056]

[0057] Where c f and d f These are the model coefficients. Similarly, the lateral force on the rear tire is given by equation (9):

[0058]

[0059] Where c r and d r These are model coefficients.

[0060] Figure 8 A model-based graph 800 is shown for the lateral forces on a tire. The slip angle is shown in radians (rad) along the x-axis, and the force is shown in Newtons (N) along the y-axis. Curve 802 represents the lateral forces on a tire with a high coefficient of friction (e.g., μ = 1), and curve 804 represents the lateral forces on a tire with a low coefficient of friction (e.g., μ = 0.2).

[0061] Equation (10) shows an optimization method for estimating the coefficient of friction of the front wheel.

[0062]

[0063] The optimization method of Equation (10) determines a value for the coefficient of friction that reduces or minimizes the difference between the lateral tire force measured in Equation (4) and the lateral tire force modeled in Equation (8). Similarly, Equation (11) shows an optimization method for estimating the coefficient of friction for positioning the rear wheels.

[0064] The optimization method of Equation (11) determines the value of the friction coefficient, which reduces or minimizes the difference between the lateral tire force measured by Equation (5) and the lateral tire force modeled by Equation (9). (Friction coefficient) The overall estimate can be the minimum value of the coefficients determined in equations (10) and (11), as shown in equation (12):

[0065]

[0066] A similar estimate of the friction coefficient can be determined using the longitudinal force. The longitudinal slip ratio can be determined using equation (13):

[0067] σ=(Rω-V x ) / max(Rω,V x (13)

[0068] Where R is the radius of the wheel, ω is the rotational speed or velocity of the wheel, and V x This refers to the vehicle's longitudinal velocity. A nonlinear longitudinal model can be used to determine the wheel's rotational speed ω. The nonlinear longitudinal model takes the torque T on the vehicle as input and assumes a friction coefficient μ = 1. The model outputs the model's rotational speed ω. b and rotational speed The time derivative. These parameters can be correlated with the corresponding measured values ​​of the rotational speed ω and the rotational speed. The time derivatives are compared. A metric can be used to determine the deviation between the modeling parameters and the measurement parameters, as shown in equation (14):

[0069]

[0070] The metric *m* can be compared to a rotational speed deviation threshold to determine when the wheel is in a low-friction state, using the same method disclosed herein for yaw parameters. Once it is determined that the metric is greater than the rotational speed deviation threshold, the longitudinal force on the tire can be used to determine the coefficient of friction.

[0071] The dynamic longitudinal force on the wheel is given in equation (15):

[0072]

[0073] Where T is the torque on the wheel, and I w R is the moment of inertia of the wheel, and R is the radius of the wheel. The model of the longitudinal force on the front tire is given by equation (16):

[0074]

[0075] The model of the longitudinal force on the rear tire is given by equation (17):

[0076]

[0077] The coefficient of friction can be determined by minimizing the difference between the model longitudinal force and the measured longitudinal force, as shown in equation (18):

[0078]

[0079] Equation (18) can be applied to the front and rear tires respectively.

[0080] Figure 9Figure 900 illustrates the operation of the optimization method for lateral forces. Force is shown in Newtons (N) along the y-axis and time in seconds (sec) along the x-axis. Curve 902 shows the predicted force with a friction coefficient μ = 1 between the tire and the road. Curve 904 shows the predicted force with a friction coefficient μ = 0.8. Curve 906 shows the predicted force with a friction coefficient μ = 0.6. Curve 908 shows the predicted force with a friction coefficient μ = 0.4. Curve 910 shows the predicted force with a friction coefficient μ = 0.2. Curve 912 shows the force measured on the tire. The predicted force of μ = 0.2 (curve 910) best matches the measured force (curve 912).

[0081] Figure 10 A flowchart 1000 illustrates a method for preventing excessive switching between estimates of low and high friction coefficients. Figure 4 In each iteration of flowchart 400, a criterion is checked to see if the output friction coefficient should be changed (i.e., in box 412). This criterion is used to move between outputting a high friction coefficient 1002 and a low friction coefficient 1004. When the method currently outputs a high friction coefficient, and the estimate generated using the steps disclosed herein (e.g., via any one of equations (10), (11), (12), and (18)) satisfies a first set of conditions, the method then switches to outputting an estimate of a low friction coefficient. The first set of conditions includes an estimate less than 1 for a selected number of sampling times, or an estimate less than 1 when the vehicle's lateral jerk is negative. The selected number of sampling times can be an adjustable parameter. The sampling time is the time interval at which measurements can be performed to calculate friction. For example, several measurements and calculations can be performed per second.

[0082] Similarly, when the method is currently outputting a low coefficient of friction and the estimate generated using the steps disclosed herein satisfies the second set of conditions, the method switches to outputting a high coefficient of friction. The second set of conditions includes the estimate being equal to 1 for a selected number of sampling times, or the estimate being equal to 1 when the lateral jerk on the vehicle is positive.

[0083] Although the foregoing disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and equivalents can be substituted for elements thereof without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from its essential scope. Therefore, this disclosure is intended to be limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A method for controlling a vehicle, comprising: The first estimate of the forces on the tires of the vehicle is obtained based on the vehicle's dynamics, the measured yaw rate, the time derivative of the measured yaw rate, and the vehicle's slip angle. A second estimate of the force on the tire is obtained using a tire model, along with the model-based yaw rate and the time derivative of the model-based yaw rate of the vehicle. The metric is determined based on the difference between the measured yaw rate and the model-based yaw rate, as well as the difference between the time derivative of the measured yaw rate and the time derivative of the model-based yaw rate. Low friction conditions are determined when the metric is greater than the yaw rate threshold and the absolute value of the slip angle is greater than 1. The estimated value of the coefficient of friction between the tire and the road is determined from the first estimate of the force and the second estimate of the force; and Based on low friction conditions, the estimated value of the friction coefficient is used to control the vehicle.

2. The method of claim 1 further comprises determining the estimated value of the friction coefficient by reducing the difference between the first estimated value of the force and the second estimated value of the force.

3. The method according to claim 1, wherein, The force is at least one of the following: (i) a frontal lateral force on the front tire; (ii) a rearal lateral force on the rear tire; (iii) a frontal longitudinal force on the front tire; (iv) a rearal longitudinal force on the rear tire.

4. The method of claim 1, further comprising outputting an estimate of the friction coefficient when: (i) the estimate of the friction coefficient is less than 1 for a selected number of sampling times; and (ii) the estimate of the friction coefficient is less than 1 when the acceleration of the vehicle is negative.

5. A system for controlling a vehicle, comprising: Sensors are used to obtain a first estimate of the forces on the tires of a vehicle based on vehicle dynamics, measuring the yaw rate and the time derivative of the measured yaw rate; The processor is configured as follows: Determine the vehicle's slip angle; A second estimate of the force on the tire is obtained using a tire model, along with the model-based yaw rate and the time derivative of the model-based yaw rate of the vehicle. The metric is determined based on the difference between the measured yaw rate and the model-based yaw rate, as well as the difference between the time derivative of the measured yaw rate and the time derivative of the model-based yaw rate. Low friction conditions are determined when the metric is greater than the yaw rate threshold and the absolute value of the slip angle is greater than 1. The estimated value of the coefficient of friction between the tire and the road is determined from the first estimate of the force and the second estimate of the force; and Based on low friction conditions, the estimated value of the friction coefficient is used to control the vehicle.

6. The system according to claim 5, wherein, The processor is also configured to determine an estimate of the coefficient of friction by reducing the difference between a first estimate of the force and a second estimate of the force.

7. The system according to claim 5, wherein, The force is at least one of the following: (i) a frontal lateral force on the front tire; (ii) a rearal lateral force on the rear tire; (iii) a frontal longitudinal force on the front tire; and (iv) a rearal longitudinal force on the rear tire.

8. The system according to claim 5, wherein, The processor is also configured to output an estimate of the coefficient of friction when: (i) the estimated coefficient of friction is less than 1 for a selected number of sampling times; and (ii) the estimated coefficient of friction is less than 1 when the vehicle experiences negative acceleration.

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