Adhesion coefficient determination method and apparatus, and vehicle, medium and program product

Abstract

Disclosed herein are an adhesion coefficient determination method and apparatus, and a vehicle, a medium and a program product. The method comprises: acquiring sensor data collected by at least one sensor of a vehicle; on the basis of the sensor data, determining travelling information of the vehicle, wherein the travelling information comprises at least one of the following: a slip rate, longitudinal stiffness, a tire longitudinal force, a tire lateral force, a tire vertical load, a front-axle lateral force and a rack force; on the basis of the travelling information, or the sensor data and the travelling information, determining at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient, wherein the at least one reference adhesion coefficient comprises at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient and a lateral adhesion coefficient; and on the basis of the at least one reference adhesion coefficient and the confidence level of each reference adhesion coefficient, determining a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient. The method in the embodiments of the present application improves the accuracy of adhesion coefficient determination.

Description

Adhesion coefficient determination method, device, vehicle, medium and program product

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on March 15, 2024, with application number 202410301415.6 and application name “Method, device, vehicle, medium and program product for determining adhesion coefficient”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The embodiments of the present application relate to, but are not limited to, the field of vehicle technology, and in particular to a method, device, vehicle, medium, and program product for determining an adhesion coefficient. Background Art

[0003] The vehicle's adhesion coefficient plays a key role in the vehicle's longitudinal drive anti-skid control. Therefore, accurately determining the vehicle's adhesion coefficient is crucial for the vehicle's anti-skid control. Summary of the Invention

[0004] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0005] The present invention provides a method, apparatus, vehicle, medium, and program product for determining an adhesion coefficient. The method of the present invention can accurately determine a target adhesion coefficient and a confidence level for the target adhesion coefficient, thereby improving the matching of the adhesion coefficient with the actual vehicle conditions and thereby enhancing the vehicle's anti-skid control capability.

[0006] In a first aspect, an embodiment of the present application provides a method for determining an adhesion coefficient, comprising:

[0007] acquiring sensor data collected by at least one sensor of the vehicle;

[0008] Determining driving information of the vehicle based on the sensor data, the driving information including at least one of the following: slip ratio, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, front axle lateral force, and rack force;

[0009] Determining at least one reference adhesion coefficient and a confidence level for each reference adhesion coefficient based on driving information, or sensor data and driving information; the at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient;

[0010] A target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient are determined based on at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient.

[0011] In one implementation, the driving information includes a slip ratio of each wheel, a longitudinal stiffness of each wheel, and a tire longitudinal force of each wheel; the reference adhesion coefficient includes a longitudinal adhesion coefficient;

[0012] Determining at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on driving information, or sensor data and driving information, includes:

[0013] determining a longitudinal adhesion coefficient of each wheel based on the slip rate of each wheel, the longitudinal stiffness of each wheel, the tire longitudinal force of each wheel, and the obtained adhesion coefficients of multiple scenarios;

[0014] determining, based on the longitudinal adhesion coefficients of the plurality of wheels, a first longitudinal adhesion coefficient and a first longitudinal confidence factor of the vehicle in a low adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence factor of the vehicle in a high adhesion state;

[0015] A longitudinal adhesion coefficient of the vehicle and a confidence level of the longitudinal adhesion coefficient are determined based on the first longitudinal adhesion coefficient, the first longitudinal confidence level, the second longitudinal adhesion coefficient, and the second longitudinal confidence level.

[0016] In one implementation, the sensor data includes vehicle speed, the driving information includes multiple front axle lateral forces and multiple rack forces of the vehicle at multiple moments, the reference adhesion coefficient includes a lateral adhesion coefficient;

[0017] Determining at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on driving information, or sensor data and driving information, includes:

[0018] determining first statistical information based on the plurality of front axle lateral forces;

[0019] determining second statistical information based on the plurality of front axle lateral forces and the plurality of rack forces;

[0020] Fitting the first statistical information and the second statistical information by a least square method to obtain a lateral adhesion coefficient;

[0021] determining at least one influencing factor according to the vehicle speed, the lateral adhesion coefficient and / or the front axle lateral force at a current moment, wherein the multiple moments include the current moment;

[0022] A confidence level of the lateral adhesion coefficient is determined based on at least one influencing factor.

[0023] In one implementation, the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes a steady-state adhesion coefficient;

[0024] Determining at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on driving information, or sensor data and driving information, includes:

[0025] Determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel;

[0026] Get the activation intervention time of the vehicle's stability control system;

[0027] The steady-state adhesion coefficient and the confidence level of the steady-state adhesion coefficient of the vehicle at a current moment are determined based on the instantaneous adhesion coefficient and the activation intervention duration of the vehicle at multiple moments; the multiple moments include multiple historical moments and the current moment.

[0028] In one implementation, the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes a dynamic adhesion coefficient;

[0029] Determining at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on driving information, or sensor data and driving information, includes:

[0030] Determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel;

[0031] Determine longitudinal stability factor and lateral stability factor;

[0032] determining, based on the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor, a first dynamic adhesion coefficient and a first dynamic confidence factor of the vehicle in a high adhesion state, and a second dynamic adhesion coefficient and a second dynamic confidence factor of the vehicle in a low adhesion state;

[0033] The dynamic adhesion coefficient of the vehicle and the confidence level of the dynamic adhesion coefficient are determined based on the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level.

[0034] In one implementation, the sensor data includes tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel steering angle, slope, longitudinal acceleration, and lateral acceleration; the driving information includes tire longitudinal force, tire lateral force, tire vertical load, and longitudinal stiffness;

[0035] Determine the vehicle's driving information based on sensor data, including:

[0036] Based on the three-degree-of-freedom model, the tire longitudinal force, tire lateral force and tire vertical load of each wheel are determined according to the tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel rotation angle, slope, longitudinal acceleration and lateral acceleration;

[0037] The longitudinal stiffness is determined by using the least square method based on multiple tire longitudinal forces and multiple slip ratios of each wheel at multiple times.

[0038] In a second aspect, an embodiment of the present application provides a device for determining an adhesion coefficient, comprising:

[0039] an acquisition module, configured to acquire sensor data collected by at least one sensor of the vehicle;

[0040] a processing module, configured to determine driving information of the vehicle based on the sensor data, the driving information including at least one of the following: slip ratio, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, front axle lateral force, and rack force;

[0041] The processing module is further configured to determine at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on driving information, or sensor data and driving information; the at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient;

[0042] The fusion module is configured to determine a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient based on at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient.

[0043] In one implementation, the driving information includes a slip ratio of each wheel, a longitudinal stiffness of each wheel, and a tire longitudinal force of each wheel; the reference adhesion coefficient includes a longitudinal adhesion coefficient;

[0044] Processing module, specifically used for:

[0045] determining a longitudinal adhesion coefficient of each wheel based on the slip rate of each wheel, the longitudinal stiffness of each wheel, the tire longitudinal force of each wheel, and the obtained adhesion coefficients of multiple scenarios;

[0046] determining, based on the longitudinal adhesion coefficients of the plurality of wheels, a first longitudinal adhesion coefficient and a first longitudinal confidence factor of the vehicle in a low adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence factor of the vehicle in a high adhesion state;

[0047] A longitudinal adhesion coefficient of the vehicle and a confidence level of the longitudinal adhesion coefficient are determined based on the first longitudinal adhesion coefficient, the first longitudinal confidence level, the second longitudinal adhesion coefficient, and the second longitudinal confidence level.

[0048] In one implementation, the sensor data includes vehicle speed, the driving information includes multiple front axle lateral forces and multiple rack forces of the vehicle at multiple moments, the reference adhesion coefficient includes a lateral adhesion coefficient;

[0049] Processing module, specifically used for:

[0050] determining first statistical information based on the plurality of front axle lateral forces;

[0051] determining second statistical information based on the plurality of front axle lateral forces and the plurality of rack forces;

[0052] Fitting the first statistical information and the second statistical information by a least square method to obtain a lateral adhesion coefficient;

[0053] determining at least one influencing factor according to the vehicle speed, the lateral adhesion coefficient and / or the front axle lateral force at a current moment, wherein the multiple moments include the current moment;

[0054] A confidence level of the lateral adhesion coefficient is determined based on at least one influencing factor.

[0055] In one implementation, the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes a steady-state adhesion coefficient;

[0056] Processing module, specifically used for:

[0057] Determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel;

[0058] Get the activation intervention time of the vehicle's stability control system;

[0059] The steady-state adhesion coefficient and the confidence level of the steady-state adhesion coefficient of the vehicle at a current moment are determined based on the instantaneous adhesion coefficient and the activation intervention duration of the vehicle at multiple moments; the multiple moments include multiple historical moments and the current moment.

[0060] In one implementation, the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes a dynamic adhesion coefficient;

[0061] Processing module, specifically used for:

[0062] Determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel;

[0063] Determine longitudinal stability factor and lateral stability factor;

[0064] determining, based on the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor, a first dynamic adhesion coefficient and a first dynamic confidence factor of the vehicle in a high adhesion state, and a second dynamic adhesion coefficient and a second dynamic confidence factor of the vehicle in a low adhesion state;

[0065] The dynamic adhesion coefficient of the vehicle and the confidence level of the dynamic adhesion coefficient are determined based on the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level.

[0066] In one implementation, the sensor data includes tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel steering angle, slope, longitudinal acceleration, and lateral acceleration; the driving information includes tire longitudinal force, tire lateral force, tire vertical load, and longitudinal stiffness;

[0067] Processing module, specifically used for:

[0068] Based on the three-degree-of-freedom model, the tire longitudinal force, tire lateral force and tire vertical load of each wheel are determined according to the tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel rotation angle, slope, longitudinal acceleration and lateral acceleration;

[0069] The longitudinal stiffness is determined by using the least square method based on multiple tire longitudinal forces and multiple slip ratios of each wheel at multiple times.

[0070] In a third aspect, an embodiment of the present application provides a vehicle, comprising:

[0071] a processor, and a memory communicatively coupled to the processor;

[0072] a memory configured to store computer-executable instructions;

[0073] The processor is configured to execute computer-executable instructions stored in the memory to implement the method for determining the adhesion coefficient of the first aspect.

[0074] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are configured to implement the method for determining the adhesion coefficient of the first aspect.

[0075] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the method for determining the adhesion coefficient as in the first aspect.

[0076] Embodiments of the present application provide a method, apparatus, vehicle, medium, and program product for determining an adhesion coefficient. In this method, a vehicle can obtain sensor data collected by at least one sensor of the vehicle and determine the vehicle's driving information based on the sensor data. The vehicle can determine at least one reference adhesion coefficient and a confidence level for each reference adhesion coefficient based on the driving information, or both the sensor data and the driving information. The at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient. The vehicle can determine a target adhesion coefficient and a confidence level for the target adhesion coefficient based on the at least one reference adhesion coefficient and the confidence level for each reference adhesion coefficient. Through the above-described method, the target adhesion coefficient and the confidence level for the target adhesion coefficient can be accurately determined, thereby improving the matching of the adhesion coefficient with the actual vehicle conditions and, in turn, enhancing the vehicle's anti-skid control capability.

[0077] Still other aspects will become apparent upon reading and understanding the accompanying drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] The drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0079] FIG1 is a schematic diagram of an application scenario applicable to an embodiment of the present application;

[0080] FIG2 a is a flow chart of a first embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application;

[0081] FIG2 b is a schematic diagram of a process for determining an adhesion coefficient according to an embodiment of the present application;

[0082] FIG2c is a schematic diagram of determining driving information according to an embodiment of the present application;

[0083] FIG3 is a flow chart of a second embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application;

[0084] FIG4 is a flow chart of a third embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application;

[0085] FIG5 is a flow chart of a fourth embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application;

[0086] FIG6 is a flow chart of a fifth embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application;

[0087] FIG7 is a schematic structural diagram of an apparatus for determining an adhesion coefficient according to an embodiment of the present application;

[0088] FIG8 is a structural diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0089] The embodiments described are part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments made by ordinary technicians in this field under the inspiration of these embodiments are within the scope of protection of this application.

[0090] The terms "first," "second," "third," "fourth," etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential sequence. It should be understood that the numbers used in this way are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations, are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or apparatus.

[0091] A vehicle's adhesion coefficient plays a key role in implementing longitudinal anti-skid control. Related technologies typically estimate the adhesion coefficient based directly on the ratio of longitudinal driving force to vertical load, and then implement anti-skid control based on this coefficient. However, this method of estimating the adhesion coefficient directly based on the ratio of longitudinal driving force to vertical load can mismatch the actual vehicle conditions, leading to inaccurate anti-skid control and compromising passenger safety.

[0092] An embodiment of the present application provides a method for determining an adhesion coefficient. A vehicle can obtain sensor data collected by at least one sensor of the vehicle and determine the vehicle's driving information based on the sensor data. The vehicle can determine at least one reference adhesion coefficient and a confidence level for each reference adhesion coefficient based on the driving information, or both the sensor data and the driving information. The at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient. The vehicle can determine a target adhesion coefficient and a confidence level for the target adhesion coefficient based on the at least one reference adhesion coefficient and the confidence level for each reference adhesion coefficient. This method accurately determines the target adhesion coefficient and the confidence level for the target adhesion coefficient, thereby improving the matching of the adhesion coefficient with the vehicle's actual conditions and, in turn, enhancing the vehicle's anti-skid control capability.

[0093] The principles and features of the embodiments of the present application are described below in conjunction with the accompanying drawings. The examples given are only used to explain the embodiments of the present application and are not used to limit the scope of the embodiments of the present application.

[0094] FIG1 is a schematic diagram of an application scenario applicable to an embodiment of the present application. The application scenario includes a vehicle 10, and the vehicle 10 includes a plurality of wheels. For example, FIG1 shows four wheels, namely a left front wheel 101, a right front wheel 102, a left rear wheel 103, and a right rear wheel 104. In addition, a plurality of sensors (not shown in FIG1 ) may be installed on the vehicle 10. For example, a wheel speed sensor, an inertial measurement unit (IMU) sensor (motion sensor), and a steering wheel angle sensor may be installed on the vehicle 10. In addition, other sensors may also be installed on the vehicle 10, which is not limited in the embodiment of the present application and may be determined according to the sensor data and driving information used to calculate the reference adhesion coefficient and its confidence in actual conditions.

[0095] In this application scenario, vehicle 10 can obtain sensor data collected by at least one sensor of vehicle 10 and determine driving information of vehicle 10 based on the sensor data. Vehicle 10 can determine at least one reference adhesion coefficient and a confidence level for each reference adhesion coefficient based on the driving information, or both the sensor data and the driving information. The at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient. Vehicle 10 can determine a target adhesion coefficient for vehicle 10 and a confidence level for the target adhesion coefficient based on the at least one reference adhesion coefficient and the confidence level for each reference adhesion coefficient.

[0096] The embodiment of the present application does not limit the actual form of the vehicle included in Figure 1. In the application of the solution, it can be set according to actual needs.

[0097] The technical solution of the present application is described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0098] FIG2a is a flow chart of a first embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application. Referring to FIG2a , the method specifically includes the following steps:

[0099] S201: Acquire sensor data collected by at least one sensor of a vehicle.

[0100] In this embodiment, the vehicle may obtain sensor data collected by at least one sensor of the vehicle.

[0101] For example, sensor data may include one or more of the following:

[0102] Vehicle speed, tire driving force, vehicle mass, wheel speed, wheel rotation angle (wheel steering angle), slope, acceleration (wheel acceleration, longitudinal acceleration and lateral acceleration).

[0103] In addition, sensor data may also include the number of teeth of the wheel rotation, rolling direction, angular velocity (including roll angular velocity, yaw angular velocity, etc.), steering wheel angle, load, tire torque, Euler angle, etc.

[0104] In one implementation, after acquiring sensor data, the vehicle may pre-process the sensor data. For example, the vehicle may perform low-pass filtering, differential filtering, and compensation on the sensor data. For another example, the vehicle may calculate the rate of change of the sensor data based on the sensor data. For example, the vehicle may calculate the rate of change of the longitudinal acceleration based on the longitudinal acceleration.

[0105] For example, after obtaining the acceleration (or angular velocity) collected by the inertial measurement unit sensor in static and dynamic conditions, the vehicle can calculate the variance value of the acceleration (or angular velocity) and use the variance value as the zero drift to compensate for the data collected by the inertial measurement unit sensor.

[0106] For another example, after obtaining the braking torque of each wheel, the vehicle can calculate a correction value of the braking torque based on the braking torques of multiple wheels based on the least mean square algorithm, and compensate the braking torque of each wheel based on the correction value of the braking torque.

[0107] S202: Determine the vehicle's driving information based on the sensor data.

[0108] In this embodiment, the vehicle can determine the vehicle's driving information based on sensor data.

[0109] The driving information includes at least one of the following: slip ratio, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, front axle lateral force, and rack force. In one implementation, the driving information may also include the vehicle's driving direction.

[0110] Specifically, the vehicle can determine the tire longitudinal force, tire lateral force, and tire vertical load of each wheel based on the three-degree-of-freedom model according to the vehicle mass, tire driving force (tire driving force of each wheel), wheel speed (wheel speed of each wheel), wheel acceleration (wheel acceleration of each wheel), wheel turning angle (wheel turning angle of each wheel), slope, longitudinal acceleration, and lateral acceleration corresponding to the vehicle at the current moment.

[0111] The vehicle can determine the longitudinal stiffness of each wheel based on multiple tire longitudinal forces and multiple slip rates of each wheel at multiple moments using the least squares method. In addition, the vehicle can also determine the longitudinal slip stiffness of each wheel.

[0112] In addition, if the vehicle's speed is not obtained through sensors, the vehicle can also calculate the slope and vehicle speed (the vehicle's longitudinal speed) based on the vertical acceleration and longitudinal acceleration detected by the IMU sensor and the wheel speed detected by the wheel speed sensor using the extended Kalman filter method. In addition, the vehicle can also calculate the longitudinal speed quality factor based on the longitudinal speed.

[0113] The vehicle can also calculate the wheel's rolling radius based on vehicle speed (longitudinal speed and speed detected by the Global Positioning System (GPS)) using the least squares method. The vehicle can determine that the wheel's tire mode is the spare tire mode when it detects that the wheel's rolling radius is less than a preset rolling radius. The vehicle can determine that the wheel's tire mode is not the spare tire mode when it detects that the wheel's rolling radius is greater than or equal to the preset rolling radius.

[0114] The vehicle can also determine the vehicle mass based on the least squares method according to the tire driving force and vehicle acceleration (such as lateral acceleration, longitudinal acceleration, etc.).

[0115] The vehicle can determine the direction of travel (forward, backward or stationary) based on the number of teeth rotated and the rolling direction of the wheels.

[0116] The vehicle can also calculate acceleration and angular velocity based on the vehicle speed and longitudinal force using the tire brush model without obtaining acceleration and angular velocity from sensors. After calculating the acceleration, the vehicle can also calculate the acceleration fluctuation value based on the acceleration.

[0117] In addition, it should be noted that the vehicle can also calculate the slip ratio deviation of each wheel after obtaining multiple slip ratios of each wheel. The vehicle can also calculate the yaw acceleration based on the yaw angular velocity.

[0118] Figure 2c is a schematic diagram of determining driving information provided by an embodiment of the present application. For example, Figure 2c illustrates determining driving direction, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, acceleration, angular velocity, tire mode, vehicle mass, driving direction, slope, vehicle speed, and longitudinal slip stiffness.

[0119] S203: Determine at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on the driving information, or the sensor data and the driving information.

[0120] In this embodiment, the vehicle may determine at least one reference adhesion coefficient and the confidence level of each reference adhesion coefficient based on driving information, or sensor data and driving information.

[0121] The at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient.

[0122] Accordingly, the confidence level of the at least one reference adhesion coefficient includes at least one of the following: the confidence level of the steady-state adhesion coefficient, the confidence level of the dynamic adhesion coefficient, the confidence level of the longitudinal adhesion coefficient, and the confidence level of the lateral adhesion coefficient.

[0123] S204: Determine a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient according to at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient.

[0124] In this embodiment, the vehicle may determine a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient based on at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient.

[0125] In one implementation, a vehicle may obtain the sensor status of at least one sensor and the tire mode of at least one wheel. If the vehicle determines that the sensor status of at least one sensor is in a failed state and / or the tire mode of at least one wheel is in spare tire mode, it may determine a target adhesion coefficient as a first preset value and a confidence level of the target adhesion coefficient as a second preset value. For example, the first preset value may be 1, and the second preset value may be 0. Furthermore, the vehicle may determine the confidence level as Level 0.

[0126] In one implementation, when the reference adhesion coefficient includes the dynamic adhesion coefficient, the lateral adhesion coefficient, and the longitudinal adhesion coefficient, the vehicle may determine the target adhesion coefficient to be a third preset value and the confidence level of the target adhesion coefficient to be a fourth preset value when the confidence levels of the dynamic adhesion coefficient, the longitudinal adhesion coefficient, and the lateral adhesion coefficient are all less than or equal to 0. For example, the third preset value may be 1, and the fourth preset value may be 1. Furthermore, when the confidence levels of the dynamic adhesion coefficient, the longitudinal adhesion coefficient, and the lateral adhesion coefficient are all less than or equal to 0, the vehicle may determine the confidence level to be Level 1.

[0127] In one implementation, when the reference adhesion coefficient includes a dynamic adhesion coefficient, a lateral adhesion coefficient, and a longitudinal adhesion coefficient, the vehicle may determine a target number of reference adhesion coefficients having a reference adhesion coefficient greater than a first preset threshold and a corresponding confidence level greater than a second preset threshold.

[0128] When the target number is greater than 1, the vehicle can determine the target adhesion coefficient based on the following formula:

[0129] Among them, FusionMue(t) is the target adhesion coefficient, LatRfeMue(t) is the lateral adhesion coefficient, LongRfeMue(t) is the longitudinal adhesion coefficient, MueFromVehDyn(t) is the dynamic adhesion coefficient, and MaxConfdIdx_B(i) is the target number.

[0130] In addition, the vehicle may determine that the confidence level of the target adhesion coefficient is a fifth preset value. For example, the fifth preset value may be 0.01.

[0131] When the target number is 1, the vehicle can determine the target adhesion coefficient based on the following formula:

[0132] Among them, FusionMue(t) is the target adhesion coefficient, LatRfeMue(t) is the lateral adhesion coefficient, LatConfd(t) is the confidence of the lateral adhesion coefficient, LongRfeMue(t) is the longitudinal adhesion coefficient, LongConfd(t) is the confidence of the longitudinal adhesion coefficient, MueFromVehDyn(t) is the dynamic adhesion coefficient, VehDynConfd(t) is the confidence of the dynamic adhesion coefficient, and MaxConfdIdx_B(i) is the target number.

[0133] The vehicle may determine a mean square value of the confidence level of the dynamic adhesion coefficient, the confidence level of the longitudinal adhesion coefficient, and the confidence level of the lateral adhesion coefficient as the confidence level of the target adhesion coefficient.

[0134] In addition, the vehicle may also determine a confidence level based on each reference adhesion coefficient having a confidence level greater than a second preset threshold. For example, the vehicle may determine the confidence level to be Level 2 if the confidence level of the lateral adhesion coefficient is greater than the second preset threshold. The vehicle may determine the confidence level to be Level 3 if the confidence level of the longitudinal adhesion coefficient is greater than the second preset threshold. The vehicle may determine the confidence level to be Level 4 if the confidence level of the dynamic adhesion coefficient is greater than the second preset threshold. The vehicle may determine the confidence level to be Level 5 if the confidence level of two reference adhesion coefficients is greater than the second preset threshold. The vehicle may determine the confidence level to be Level 6 if the confidence level of all three reference adhesion coefficients is greater than the second preset threshold.

[0135] In one implementation, when the reference adhesion coefficient includes a steady-state adhesion coefficient, the vehicle may obtain a historical target adhesion coefficient of the vehicle at a previous moment. The vehicle may determine the target adhesion coefficient based on the following formula:

[0136] Among them, FusionMue(t) is the target adhesion coefficient, MueFromStabCtrl(t) is the steady-state adhesion coefficient, and FusionMue(t-1) is the historical target adhesion coefficient. is the adjustment coefficient, TS is the value corresponding to the activation duration, and TC is the time constant.

[0137] In addition, the vehicle may also determine the confidence level of the target adhesion coefficient as a sixth preset value. For example, the sixth preset value may be 7.

[0138] Figure 2b is a schematic diagram of a process for determining an adhesion coefficient according to an embodiment of the present application. As shown in Figure 2b, the vehicle can determine at least one reference adhesion coefficient (Figure 2b shows a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient), as well as a confidence level for each reference adhesion coefficient. The vehicle can determine a target adhesion coefficient and a confidence level for the target adhesion coefficient based on the at least one reference adhesion coefficient and the confidence level for each reference adhesion coefficient. It should be noted that when determining the steady-state adhesion coefficient and the dynamic adhesion coefficient, the vehicle must first determine the instantaneous adhesion coefficient, and then determine the steady-state adhesion coefficient and the dynamic adhesion coefficient based on the instantaneous adhesion coefficient.

[0139] The beneficial effects of this embodiment include: a vehicle can obtain sensor data collected by at least one sensor of the vehicle and determine driving information based on the sensor data. The vehicle can determine at least one reference adhesion coefficient and a confidence level for each reference adhesion coefficient based on the driving information, or both the sensor data and the driving information. The at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient. The vehicle can determine a target adhesion coefficient and a confidence level for the target adhesion coefficient based on the at least one reference adhesion coefficient and the confidence level for each reference adhesion coefficient. This approach allows for accurate determination of the target adhesion coefficient and the confidence level for the target adhesion coefficient, thereby improving the match between the adhesion coefficient and the actual vehicle conditions and, in turn, enhancing the vehicle's anti-skid control capabilities.

[0140] The following describes the process of determining the longitudinal adhesion coefficient and confidence level of a vehicle through a second method embodiment.

[0141] FIG3 is a flow chart of a second embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application. Referring to FIG3 , the method specifically includes the following steps:

[0142] S301: Determine the longitudinal adhesion coefficient of each wheel according to the slip rate of each wheel, the longitudinal stiffness of each wheel, the tire longitudinal force of each wheel, and the acquired adhesion coefficients of multiple scenarios.

[0143] In this embodiment, the vehicle can determine the longitudinal adhesion coefficient of each wheel based on the slip rate of each wheel, the longitudinal stiffness of each wheel, the tire longitudinal force of each wheel, and the obtained adhesion coefficients of multiple scenarios.

[0144] Specifically, for any wheel, the vehicle can use the following formula to calculate the longitudinal adhesion coefficient of the wheel: MueInst(k)=E(X)=P(1)*0.15+P(2)*0.3+P(3)*0.45+P(4)*0.6+P(5)*0.75+P(6)*0.9

[0145] It should be noted that P(1) is the probability corresponding to the adhesion coefficient of the first scene, and 0.15 is the adhesion coefficient of the first scene; P(2) is the probability corresponding to the adhesion coefficient of the second scene, and 0.35 is the adhesion coefficient of the second scene; P(3) is the probability corresponding to the adhesion coefficient of the third scene, and 0.45 is the adhesion coefficient of the third scene; P(4) is the probability corresponding to the adhesion coefficient of the fourth scene, and 0.6 is the adhesion coefficient of the fourth scene; P(5) is the probability corresponding to the adhesion coefficient of the fifth scene, and 0.75 is the adhesion coefficient of the second scene; P(6) is the probability corresponding to the adhesion coefficient of the sixth scene, and 0.9 is the adhesion coefficient of the sixth scene.

[0146] It should also be noted that MueInst(k) is the longitudinal adhesion coefficient of the wheel, FxNorm(k) is the tire longitudinal force (it should be noted that the tire longitudinal force here is the normalized longitudinal force), LongStfns(k) is the longitudinal stiffness, Slip(k) is the slip rate, and k takes values ​​of 1, 2, 3, and 4, representing the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively. It should also be noted that when calculating the probability corresponding to the adhesion coefficient of each scenario, max(P) can use the initial value 1 / 6, An initial value of 1 can be used.

[0147] S302: Determine, based on the longitudinal adhesion coefficients of the plurality of wheels, a first longitudinal adhesion coefficient and a first longitudinal confidence factor of the vehicle in a low adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence factor of the vehicle in a high adhesion state.

[0148] In this embodiment, the vehicle can determine a first longitudinal adhesion coefficient and a first longitudinal confidence of the vehicle in a low adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence of the vehicle in a high adhesion state based on the longitudinal adhesion coefficients of multiple wheels.

[0149] In the process of determining the first dynamic adhesion coefficient and the first longitudinal confidence factor:

[0150] The vehicle can obtain the activation duration of the stability control system and the rate of change of longitudinal acceleration. The vehicle can also obtain the slip ratio deviation of each wheel. It should be noted that the vehicle can determine that the vehicle activates the stability control system when it recognizes that the brake flag of a wheel (tire) is in position and the braking torque of the wheel (tire) is greater than zero. The vehicle can also determine that the vehicle activates the stability control system when it recognizes that the control flag of the anti-lock braking system of a wheel (tire) is in position and the braking torque of the wheel (tire) is greater than zero. The vehicle can also determine that the vehicle activates the stability control system when it recognizes that there is a wheel (tire) that corresponds to the wheel (tire) identifier in the torque lift request and the slip ratio of the wheel (tire) is greater than zero.

[0151] For any wheel, if the vehicle determines that the wheel meets multiple low-adhesion estimation conditions, the vehicle analyzes the wheel based on the wheel's slip ratio deviation and the wheel's tire longitudinal force (normalized longitudinal force) to determine whether the wheel meets the first condition or the second condition. The multiple low-adhesion estimation conditions include: the wheel's longitudinal adhesion coefficient is greater than a first threshold, the wheel's estimation flag is valid, the activation duration is greater than a second threshold, and the longitudinal acceleration rate of change is less than a third threshold. It should be noted that the vehicle determines the wheel's estimation flag as valid if the wheel's corresponding adhesion coefficient variance is less than a sixth threshold; and determines the wheel's estimation flag as invalid if the wheel's corresponding adhesion coefficient variance is greater than or equal to a fifth threshold. It should also be noted that the first threshold can be any value between 0 and 0.4, the third threshold can be any value between 200 milliseconds and 500 milliseconds, and the fourth threshold can be any value between 3000 and 5000.

[0152] After analyzing all wheels, the vehicle can determine the number of wheels that meet the first condition and the number of wheels that meet the second condition.

[0153] The vehicle may use the following formula to determine the first longitudinal adhesion coefficient under the first condition and the first longitudinal confidence factor under the first condition: mueFastTrackConfd=cnfdLvlMidWheels

[0154] Among them, nofMidWheels is the number of wheels that meet the first condition, mueFastTrack is the first longitudinal adhesion coefficient under the first condition, mueFastTrackConfd is the first confidence level under the first condition, and cnfdLvlMidWheels is the first calibration parameter.

[0155] The vehicle can use the following formula to determine the first longitudinal adhesion coefficient under the second condition and the first longitudinal confidence under the second condition: mueFastTrackConfd=cnfdLvlLargeWheels

[0156] Among them, nofLargeWheels is the number of wheels that meet the second condition, mueFastTrack is the first longitudinal adhesion coefficient under the first condition, mueFastTrackConfd is the first longitudinal confidence under the second condition, and cnfdLvlLargeWheels is the second calibration parameter.

[0157] The vehicle can analyze the number of wheels that meet the first condition and the number of wheels that meet the second condition:

[0158] The vehicle may determine the first longitudinal adhesion coefficient under the second condition as the first longitudinal adhesion coefficient and the first longitudinal confidence level under the second condition as the first longitudinal confidence level when it is determined that the number of wheels satisfying the second condition is greater than 0 and the number of wheels satisfying the first condition is 0.

[0159] The vehicle may determine the first longitudinal adhesion coefficient under the first condition as the first longitudinal adhesion coefficient and the first longitudinal confidence level under the first condition as the first longitudinal confidence level when it is determined that the number of wheels satisfying the first condition is greater than 0 and the number of wheels satisfying the second condition is 0.

[0160] If the vehicle determines that the number of wheels meeting the first condition is greater than 0 and the number of wheels meeting the second condition is greater than 0, the vehicle may determine the maximum value of the first longitudinal adhesion coefficient under the first condition and the first longitudinal adhesion coefficient under the second condition as the first longitudinal adhesion coefficient, and determine the confidence level corresponding to the maximum value as the first longitudinal confidence level. In one implementation, after determining the confidence level corresponding to the maximum value as the first longitudinal confidence level, the vehicle may further adjust the first longitudinal confidence level. For example, the vehicle may add the first longitudinal confidence level to a third calibration parameter to adjust the first longitudinal confidence level.

[0161] When the number of wheels meeting the first condition is determined to be 0 and the number of wheels meeting the second condition is determined to be 0, the vehicle may determine the preset longitudinal adhesion coefficient as the first longitudinal adhesion coefficient and the preset confidence level as the first longitudinal confidence level. For example, the preset longitudinal adhesion coefficient may be 1 and the preset confidence level may be 0.

[0162] In determining the second dynamic adhesion coefficient and the second longitudinal confidence factor:

[0163] For any wheel, if the vehicle determines that the wheel meets multiple high-adhesion estimation conditions, it then determines whether the wheel meets the third condition or the fourth condition based on the tire longitudinal force (normalized longitudinal force) of the wheel. The multiple high-adhesion estimation conditions include: the wheel's longitudinal adhesion coefficient being greater than a fourth threshold, the wheel's estimation flag being in the valid flag, and the activation duration being greater than a fifth threshold. It should be noted that the fourth threshold can be any value between 0.4 and 1, and the fifth threshold can be any value between 300 milliseconds and 500 milliseconds.

[0164] After analyzing all wheels, the vehicle can determine the number of wheels that meet the third condition and the number of wheels that meet the fourth condition.

[0165] The vehicle can use the following formula to determine the second longitudinal adhesion coefficient under the third condition and the second longitudinal confidence under the third condition: mueFastTrackConfd=cnfdLvlMidWheels

[0166] Among them, nofMidWheels is the number of wheels that meet the third condition, mueFastTrack is the second longitudinal adhesion coefficient under the third condition, mueFastTrackConfd is the second longitudinal confidence under the third condition, and cnfdLvlMidWheels is the fourth calibration parameter.

[0167] The vehicle can use the following formula to determine the second longitudinal adhesion coefficient and the second longitudinal confidence factor under the fourth condition: mueFastTrackConfd=cnfdLvlLargeWheels

[0168] Among them, nofLargeWheels is the number of wheels that meet the fourth condition, mueFastTrack is the second longitudinal adhesion coefficient under the fourth condition, mueFastTrackConfd is the second longitudinal confidence under the fourth condition, and cnfdLvlLargeWheels is the fifth calibration parameter.

[0169] The vehicle can analyze the number of wheels that meet the third condition and the number of wheels that meet the fourth condition:

[0170] When it is determined that the number of wheels satisfying the fourth condition is greater than 0 and the number of wheels satisfying the third condition is 0, the vehicle may determine the second longitudinal adhesion coefficient under the fourth condition as the second longitudinal adhesion coefficient and the second longitudinal confidence level under the fourth condition as the second longitudinal confidence level.

[0171] When it is determined that the number of wheels satisfying the third condition is greater than 0 and the number of wheels satisfying the fourth condition is 0, the vehicle may determine the second longitudinal adhesion coefficient under the third condition as the second longitudinal adhesion coefficient and the second longitudinal confidence level under the third condition as the second longitudinal confidence level.

[0172] If the vehicle determines that the number of wheels meeting the third condition is greater than 0 and the number of wheels meeting the fourth condition is greater than 0, it may determine the maximum of the second longitudinal adhesion coefficient under the third condition and the second longitudinal adhesion coefficient under the fourth condition as the second longitudinal adhesion coefficient, and determine the confidence level corresponding to the maximum value as the second longitudinal confidence level. In one implementation, after determining the confidence level corresponding to the maximum value as the second longitudinal confidence level, the vehicle may further adjust the second longitudinal confidence level. For example, the vehicle may add the second longitudinal confidence level to the sixth calibration parameter to adjust the second longitudinal confidence level.

[0173] When the number of wheels meeting the third condition is determined to be 0 and the number of wheels meeting the fourth condition is determined to be 0, the vehicle may determine the preset longitudinal adhesion coefficient as the second longitudinal adhesion coefficient and the preset confidence level as the second longitudinal confidence level. For example, the preset longitudinal adhesion coefficient may be 1 and the preset confidence level may be 0.

[0174] S303: Determine the longitudinal adhesion coefficient of the vehicle and the confidence level of the longitudinal adhesion coefficient according to the first longitudinal adhesion coefficient, the first longitudinal confidence level, the second longitudinal adhesion coefficient, and the second longitudinal confidence level.

[0175] Specifically, the vehicle can determine the adhesion coefficient variance corresponding to each wheel based on the longitudinal adhesion coefficient of the wheel, the probability corresponding to each scene adhesion coefficient, and multiple scene adhesion coefficients.

[0176] For example, the vehicle can use the following formula to calculate the variance of the adhesion coefficient corresponding to each wheel: MueVarInst(k)=D(x)=p1[x1-E(X)] 2 +p2[x2-E(X)] 2 +…+p n [x n -E(X)]2

[0177] Among them, MueVarInst(k) is the adhesion coefficient variance corresponding to the wheel; n is the number of scene adhesion coefficients; E(X) is MueInst(k), which is the adhesion coefficient of the wheel (expected value); x is the scene adhesion coefficient; P is the probability corresponding to each scene adhesion coefficient.

[0178] The following formula can be used to determine the scene adhesion coefficient variance of the vehicle: MueVarCmn = offset1 + offset2 + offset3 + offset4 + offset5 MueVarInst(k) = MueVarInst(k) * Factor1 * Factor2 * Factor3 * MueVarCmn

[0179] Among them, MueVarInst(k) is the adhesion coefficient variance corresponding to the wheel; k takes values ​​of 1, 2, 3, and 4, representing the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively; InstLongRfeMueVar is the scene adhesion coefficient variance of the vehicle; Factor1 is the correction coefficient determined by the vehicle based on the slip rate lookup table; Factor2 is the correction coefficient determined by the vehicle based on the load lookup table; Factor3 is the correction coefficient determined by the vehicle based on the tire longitudinal force (normalized longitudinal force) lookup table; offset1 is the correction coefficient determined by the vehicle based on the tire torque lookup table; offset2 is the correction coefficient determined by the vehicle based on the acceleration fluctuation value lookup table; offset3 is the correction coefficient determined by the vehicle based on the longitudinal speed mass factor lookup table; offset4 is the correction coefficient determined by the vehicle based on the Euler angle lookup table; offset5 is the correction coefficient determined by the vehicle based on the longitudinal speed.

[0180] The vehicle compares the first longitudinal confidence level and the second longitudinal confidence level when determining that the scene adhesion coefficient variance of the vehicle is less than a variance threshold.

[0181] If the first longitudinal confidence level is greater than or equal to the second longitudinal confidence level, the vehicle may determine the first longitudinal adhesion coefficient as the longitudinal adhesion coefficient. In addition, the vehicle may also determine the first longitudinal confidence level as the confidence level of the longitudinal adhesion coefficient.

[0182] If the first longitudinal confidence level is less than the second longitudinal confidence level, the vehicle may determine the second longitudinal adhesion coefficient as the longitudinal adhesion coefficient. In addition, the vehicle may further determine the second longitudinal confidence level as the confidence level of the longitudinal adhesion coefficient.

[0183] The vehicle may determine the preset longitudinal adhesion coefficient as the longitudinal adhesion coefficient when the variance of the vehicle's scenario adhesion coefficient is greater than or equal to a variance threshold. Additionally, the vehicle may determine a preset confidence level as the confidence level of the longitudinal adhesion coefficient. For example, the preset longitudinal adhesion coefficient may be 1, and the preset confidence level may be 0.

[0184] The beneficial effects of this embodiment are as follows: The vehicle can determine a first longitudinal adhesion coefficient and a first longitudinal confidence level in a low-adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence level in a high-adhesion state based on the longitudinal adhesion coefficients of multiple wheels. The vehicle can also determine the longitudinal adhesion coefficient of the vehicle based on the first longitudinal adhesion coefficient, the first longitudinal confidence level, the second longitudinal adhesion coefficient, and the second longitudinal confidence level. This approach accurately determines the longitudinal adhesion coefficient, thereby improving the accuracy of determining the vehicle's adhesion coefficient, thereby enhancing the vehicle's controllability under driving conditions and improving the safety of passengers.

[0185] The following describes the process of determining the lateral adhesion coefficient and confidence level of a vehicle through a third method embodiment.

[0186] FIG4 is a flow chart of a third embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application. Referring to FIG4 , the method specifically includes the following steps:

[0187] S401: Determine first statistical information based on multiple front axle lateral forces.

[0188] In this embodiment, the vehicle can obtain multiple rack forces and multiple front axle lateral forces of the vehicle at multiple historical moments.

[0189] It should be noted that the vehicle meets at least one of the following conditions at the historical moment:

[0190] The vehicle speed (longitudinal speed) is within a preset range; the vehicle's vertical acceleration is less than or equal to a vertical acceleration threshold; the vehicle's roll angular velocity is less than or equal to a roll angular velocity threshold; and the sensor signal's signal state is normal.

[0191] Next, the process of obtaining rack force in the vehicle is described.

[0192] In this embodiment, for any historical moment, the vehicle can obtain the steering resistance torque, the self-aligning torque, and the effective lever arm of the vehicle at that historical moment. It should be noted that the steering resistance torque, the self-aligning torque, and the effective lever arm can be sensor data.

[0193] The vehicle can determine the rack force based on the following formula:

[0194] Among them, Ffrack is the rack force, M r is the steering resistance torque, M g is the aligning torque, and l is the effective lever arm.

[0195] Next, the process of obtaining the lateral force on the front axle of the vehicle is described.

[0196] The vehicle can determine the front axle lateral force on the left front wheel and the front axle lateral force on the right front wheel based on the following formula:

[0197] Where m is the vehicle mass, a y is the lateral acceleration, is the yaw angular acceleration, I z is the moment of inertia (the moment of inertia of the vehicle around the z axis), L f is the first distance from the center of mass to the front axle, L r is the second distance from the center of mass to the rear axle, δ is the front wheel turning angle, T yaw is the yaw moment (the yaw moment generated by differential braking / traction), F xfr is the longitudinal force of the right front wheel, F xfl It should be noted that the above parameters can be sensor data obtained by the vehicle.

[0198] After determining the front axle lateral force of the left front wheel and the front axle lateral force of the right front wheel, the vehicle can determine the sum of the front axle lateral force of the left front wheel and the front axle lateral force of the right front wheel as the front axle lateral force of the vehicle at the historical moment.

[0199] After obtaining multiple front axle lateral forces, the vehicle can determine first initial statistical information based on the multiple front axle lateral forces.

[0200] The following describes a process in which the vehicle determines the first initial statistical information.

[0201] The vehicle may determine a number n of multiple front axle lateral forces, where n is an integer greater than 1. The vehicle may determine a first accumulated value of the multiple front axle lateral forces and a second accumulated value of the squares of the multiple front axle lateral forces. The vehicle may determine first initial statistical information based on the number n, the first accumulated value, and the second accumulated value.

[0202] In one implementation, the first initial statistical information may be expressed in the form of a matrix:

[0203] Among them, H T H is the first initial matrix, F yf is the front axle lateral force, n is the number of front axle lateral forces, is the first accumulated value of multiple front axle lateral forces, It is a second accumulated value of the squares of multiple front axle lateral forces.

[0204] Next, a process of determining the difference coefficients of multiple front axle lateral forces and the standard deviations of multiple front axle lateral forces for a vehicle will be described.

[0205] Specifically, the vehicle can use the following formula to determine the difference coefficient of the longitudinal forces of multiple tires:

[0206] Among them, F yf is the front axle lateral force, rb is the difference coefficient, and n is the number of multiple tire longitudinal forces.

[0207] The vehicle can use the following formula to determine the standard deviation of multiple front axle lateral forces:

[0208] in, is the standard deviation, E(F yf 2 ) is the expected value of the square of the front axle lateral force, E(F yf ) 2 is the square of the expected value of the lateral force on the front axle.

[0209] After determining the first initial statistical information, the vehicle can compare the difference coefficients of multiple front axle lateral forces with a preset difference threshold, compare the standard deviations of multiple front axle lateral forces with a preset standard deviation threshold, and determine whether the steering angle of the vehicle is 0.

[0210] If the difference coefficient of multiple front axle lateral forces is less than a preset difference threshold, the standard deviation of multiple front axle lateral forces is less than the standard deviation threshold, and / or the steering angle of the vehicle is 0, the vehicle determines the adjustment coefficient and determines the product of the first initial statistical information and the adjustment coefficient as the first statistical information.

[0211] If the difference coefficient of multiple front axle lateral forces is greater than or equal to a preset difference threshold, the standard deviation of multiple front axle lateral forces is greater than or equal to the standard deviation threshold, and the steering angle of the vehicle is not 0, the vehicle can determine the first initial statistical information as the first statistical information.

[0212] S402: Determine second statistical information based on multiple front axle lateral forces and multiple rack forces.

[0213] In this embodiment, the vehicle may determine the second statistical information based on a plurality of front axle lateral forces and a plurality of rack forces.

[0214] Specifically, the vehicle may determine the second initial statistical information based on a plurality of front axle lateral forces and a plurality of rack forces.

[0215] The following describes a process in which the vehicle determines the second initial statistical information.

[0216] The vehicle may determine the product of the front axle lateral force and the rack force corresponding to each historical moment, obtain multiple products, and obtain a third accumulated value of the multiple products. The vehicle may also determine a fourth accumulated value of the multiple rack forces. The vehicle may determine second initial statistical information based on the third accumulated value and the fourth accumulated value.

[0217] In one implementation, the second initial statistical information may be expressed in the form of a matrix:

[0218] Among them, H T Y is the second initial matrix, F yf is the lateral force on the front axle, F frack is the rack force, n is the number of front axle lateral forces, is the third accumulated value, is the fourth accumulated value.

[0219] After determining the second initial statistical information, the vehicle can compare the difference coefficients of multiple front axle lateral forces with a preset difference threshold, compare the standard deviations of multiple front axle lateral forces with a preset standard deviation threshold, and determine whether the steering angle of the vehicle is 0.

[0220] If the difference coefficient of multiple front axle lateral forces is less than a preset difference threshold, the standard deviation of multiple front axle lateral forces is less than the standard deviation threshold, and / or the steering angle of the vehicle is 0, the vehicle determines the adjustment coefficient and determines the product of the second initial statistical information and the adjustment coefficient as the second statistical information.

[0221] If the difference coefficient of multiple front axle lateral forces is greater than or equal to a preset difference threshold, the standard deviation of multiple front axle lateral forces is greater than or equal to the standard deviation threshold, and the steering angle of the vehicle is not 0, the vehicle can determine the second initial statistical information as the second statistical information.

[0222] S403: Perform fitting processing on the first statistical information and the second statistical information by using the least square method to obtain a lateral adhesion coefficient.

[0223] In this embodiment, the vehicle may perform fitting processing on the first statistical information and the second statistical information by using a least square method to obtain a lateral adhesion coefficient.

[0224] For example, when the first statistical information is the first initial statistical information and the second statistical information is the second initial statistical information, the vehicle may calculate the selected lateral adhesion coefficient based on the following formula: β = (H T H) -1 H T Y

[0225] Determine the first matrix (H TH) and the second matrix (H T Y), the vehicle can obtain the inverse matrix of the first matrix ((H T H) -1 The vehicle can take the inverse matrix of the first matrix ((H T H) -1 ) and the second matrix (H T The vehicle can determine the first bit in the target matrix (β) as the selected lateral adhesion coefficient (Mue y ).

[0226] Next, determine the inverse matrix of the first matrix for the vehicle ((H T H) -1 ) process is described.

[0227] The vehicle can calculate the inverse matrix of the first matrix (H T H) -1 : det(H T H)=(F yf,1 -F yf,2 ) 2 +…(F yf,1 -F yf,n ) 2 +(F yf,2 -F yf,3 ) 2 +…(F yf,2 -F yf,n ) 2 …+(F yf,n-1 -F yf,n ) 2

[0228] Among them, H T H is the first matrix, (H T H) -1 is the inverse matrix of the first matrix, (H T H) * is the adjoint matrix of the first matrix, det(H T H) is the determinant of the first matrix.

[0229] The vehicle may determine an average residual based on a selected lateral adhesion coefficient, a plurality of front axle lateral forces, and a plurality of rack forces.

[0230] Specifically, the vehicle can calculate the residual based on the following formula: ‖Hβ-Y‖ 2 =Y T Y-(Y T H)β

[0231] Among them, ‖Hβ-Y‖2 is the residual, F frack is the rack force, F fy is the lateral force on the front axle, Mue y is the candidate lateral adhesion coefficient.

[0232] The vehicle may determine the ratio of the residual to the amount of lateral force on the front axle as the average residual.

[0233] After calculating the average residual, the vehicle can compare the average residual with a preset residual threshold.

[0234] If the average residual is greater than or equal to a preset residual threshold, the vehicle may determine that the lateral adhesion coefficient is 1 and determine that the confidence level of the lateral adhesion coefficient is 0.

[0235] If the average residual is less than the preset residual threshold, the vehicle determines the candidate lateral adhesion coefficient as the lateral adhesion coefficient.

[0236] S404: Determine at least one influencing factor according to the vehicle speed, the lateral adhesion coefficient and / or the front axle lateral force at the current moment.

[0237] In this embodiment, if the average residual is less than the preset residual threshold, the vehicle can determine the rate of change of the lateral adhesion coefficient based on the lateral adhesion coefficient; determine the ratio of the front axle lateral force to the load based on the front axle lateral force at the current moment; and determine the average vehicle speed and the vehicle speed standard deviation based on the vehicle speed.

[0238] The vehicle can query a table stored in the vehicle to determine the first influencing factor based on the rate of change of the lateral adhesion coefficient. It should be noted that the rate of change of the lateral adhesion coefficient is determined by the vehicle based on the current lateral adhesion coefficient, the previous lateral adhesion coefficient, and a time interval.

[0239] The vehicle can determine the second influencing factor by querying a stored table based on the ratio of the front axle lateral force to the load. It should be noted that the front axle lateral force refers to the front axle lateral force at one of the multiple moments. For example, it can be the front axle lateral force at the current moment (the last moment).

[0240] The vehicle can query a table stored in the vehicle according to the average vehicle speed to determine the third influencing factor.

[0241] The vehicle can query a table stored in the vehicle to determine the fourth influencing factor based on the standard deviation of the vehicle speed.

[0242] S405: Determine the confidence level of the lateral adhesion coefficient according to at least one influencing factor.

[0243] In this embodiment, the vehicle may determine the confidence level of the lateral adhesion coefficient based on at least one influencing factor.

[0244] For example, the vehicle may determine the confidence level of the lateral adhesion coefficient based on the following formula according to the first influencing factor, the second influencing factor, the third influencing factor, and the fourth influencing factor. y =Factor1*Factor2*Factor3*Factor4

[0245] Among them, Cond y Indicates the confidence level of the lateral adhesion coefficient. Factor 1 is the first influencing factor. Factor 2 is the second influencing factor. Factor 3 is the third influencing factor. Factor 4 is the fourth influencing factor.

[0246] In this embodiment of the present application, a vehicle can determine first statistical information based on multiple front axle lateral forces, and second statistical information based on multiple rack forces and multiple front axle lateral forces. The vehicle can then perform a least squares fit on the first and second statistical information to obtain a lateral adhesion coefficient. This method improves the accuracy of determining the lateral adhesion coefficient, thereby improving the accuracy of determining the vehicle's adhesion coefficient, thereby enhancing vehicle stability control and improving passenger safety.

[0247] The following describes the process of determining the steady-state adhesion coefficient and confidence level of a vehicle through a fourth method embodiment.

[0248] FIG5 is a flow chart of a fourth embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application. Referring to FIG5 , the method specifically includes the following steps:

[0249] S501: Determine an instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel.

[0250] In this embodiment, the vehicle information may include acceleration and tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel.

[0251] The vehicle can determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel.

[0252] Next, the process of determining the instantaneous adhesion coefficient of the vehicle is described.

[0253] Specifically, at any moment, the vehicle can determine the first adhesion coefficient of the vehicle based on the tire longitudinal force, tire lateral force, and tire vertical load of each wheel.

[0254] First, the vehicle can use the following formula to determine the adhesion coefficient of each wheel:

[0255] Where MueUtilWhl(k) represents the wheel adhesion coefficient. Fx_N(k) represents the tire longitudinal force, Fy_N(k) represents the tire lateral force, and Fz_N(k) represents the tire vertical load. k = 1 represents the left front wheel, k = 2 represents the right front wheel, k = 3 represents the left rear wheel, and k = 4 represents the right rear wheel.

[0256] The vehicle can determine whether to activate the stability control system.

[0257] If activated, the vehicle can calculate the first adhesion coefficient based on the following formula according to the adhesion coefficient of each wheel, the tire longitudinal force and the tire lateral force corresponding to each wheel.

[0258] Among them, MueUtilFromTqMdl represents the first adhesion coefficient.

[0259] If not activated, the vehicle determines the vehicle's turning state and determines a first adhesion coefficient based on the turning state and the adhesion coefficient of each tire. Specifically, if the turning state is a left turn, the maximum adhesion coefficient between the right front tire and the right rear tire is determined as the first adhesion coefficient. If the turning state is a right turn or a straight-ahead state, the maximum adhesion coefficient between the left front tire and the left rear tire is determined as the first adhesion coefficient.

[0260] After determining the first adhesion coefficient, the vehicle may determine a gain coefficient according to the first adhesion coefficient, the tire longitudinal force, the tire lateral force, and the tire vertical load of each wheel.

[0261] Specifically, the vehicle can determine the gain coefficient based on the following formula.

[0262] Wherein, Gain represents the gain coefficient, and MueUtilFromTqMdl represents the first adhesion coefficient.

[0263] The vehicle may determine the second adhesion coefficient according to the gain coefficient and the acceleration of the vehicle (longitudinal acceleration, lateral acceleration, and acceleration due to gravity).

[0264] Specifically, the vehicle may determine the second adhesion coefficient based on the following formula.

[0265] Among them, MueUtilWhlIMU represents the second adhesion coefficient, Gain represents the gain coefficient, g represents the acceleration of gravity, a x represents the longitudinal acceleration, a y It should be noted that a x and ay Detected by the inertial motion unit (IMU) sensor.

[0266] After determining the first adhesion coefficient and the second adhesion coefficient, the vehicle fuses the first adhesion coefficient and the second adhesion coefficient to determine the instantaneous adhesion coefficient of the vehicle at that moment.

[0267] Specifically, the vehicle may determine a first weight value of the first adhesion coefficient and a second weight value of the second adhesion coefficient according to an actual yaw angle, a tire slip rate, a longitudinal vehicle speed, and / or a second adhesion coefficient.

[0268] For example, a vehicle determines a first weighted value of the first adhesion coefficient and a second weighted value of the second adhesion coefficient according to an actual yaw angle, a tire slip rate, a longitudinal vehicle speed, and a second adhesion coefficient.

[0269] The vehicle may determine a first correction weight coefficient, a second correction weight coefficient, a third correction weight coefficient, and a fourth correction weight coefficient according to an actual yaw angle, a tire slip rate, a longitudinal vehicle speed, and a second adhesion coefficient, respectively.

[0270] The vehicle may determine the first weight value based on the following formula according to the first modified weight coefficient, the second modified weight coefficient, the third modified weight coefficient, and the fourth modified weight coefficient: λ1 = Factor1 * Factor2 * Factor3 * Factor4

[0271] Wherein, λ1 represents the first weight value, Factor1 represents the first modified weight coefficient, Factor2 represents the second modified weight coefficient, Factor3 represents the third modified weight coefficient, and Factor4 represents the fourth modified weight coefficient.

[0272] After determining the first weight value, the vehicle can determine the second weight value based on the following formula: λ1=1-λ2

[0273] Wherein, λ1 represents the first weight value. λ2 represents the second weight value.

[0274] The vehicle can determine the target adhesion coefficient based on the following formula: MueUtilizedInst = λ2*MueUtilWhlIMU+λ1*MueUtilFromTqMdl

[0275] Among them, λ1 represents the first weight value, λ2 represents the second weight value, MueUtilFromTqMdl represents the first adhesion coefficient, and MueUtilWhlIMU represents the second adhesion coefficient.

[0276] S502: Obtain activation intervention duration of the vehicle's stability control system.

[0277] In this embodiment, the vehicle may obtain the activation intervention duration of the vehicle's stability control system.

[0278] Specifically, the vehicle can determine the instantaneous adhesion coefficient, vertical acceleration change rate, and road bump coefficient of the vehicle at each moment.

[0279] The vehicle may determine a fifth correction weight coefficient, a sixth correction weight coefficient, and a seventh correction weight coefficient based on the instantaneous adhesion coefficient, the vertical acceleration change rate, and the road bump coefficient, respectively.

[0280] The vehicle can determine the weight coefficient of the instantaneous adhesion coefficient of the vehicle at time s based on the fifth correction weight coefficient, the sixth correction weight coefficient, and the seventh correction weight coefficient based on the following formula: sWeight = Factor5*Factor6*Factor7

[0281] Wherein, sWeight represents the weight coefficient of the instantaneous adhesion coefficient, Factor5 represents the fifth correction weight coefficient, Factor6 represents the sixth correction weight coefficient, and Factor7 represents the seventh correction weight coefficient.

[0282] After calculating the weight coefficients of the vehicle's instantaneous adhesion coefficients at multiple moments, the vehicle can determine the activation intervention duration of the vehicle's stability control system based on the following formula, the weight coefficients of the vehicle's instantaneous adhesion coefficients at multiple moments, and the length of the time period.

[0283] Where batchTimefirst represents the activation intervention duration of the vehicle's stability control system. TS represents the duration of the time period. sWeight represents the weight coefficient of the vehicle's instantaneous adhesion coefficient at a given moment.

[0284] S503: Determine the steady-state adhesion coefficient of the vehicle at the current moment and the confidence level of the steady-state adhesion coefficient based on the instantaneous adhesion coefficients and activation intervention durations of the vehicle at multiple moments.

[0285] In this embodiment, the vehicle can determine the steady-state adhesion coefficient and the confidence level of the steady-state adhesion coefficient at the current moment based on the instantaneous adhesion coefficient and activation intervention duration of the vehicle at multiple moments, where the multiple moments include multiple historical moments and the current moment.

[0286] First, the vehicle may determine an average instantaneous adhesion coefficient according to a plurality of instantaneous adhesion coefficients and a weight coefficient of each instantaneous adhesion coefficient.

[0287] For example, the vehicle may determine the average instantaneous adhesion coefficient based on the following formula according to multiple instantaneous adhesion coefficients and a weight coefficient of each instantaneous adhesion coefficient:

[0288] Where, MueMean represents the average adhesion coefficient, MueUtilizedInst represents the instantaneous adhesion coefficient, and sWeight represents the weight coefficient of the vehicle's instantaneous adhesion coefficient at a moment.

[0289] Then, the vehicle may determine a candidate steady-state adhesion coefficient and a confidence level of the candidate steady-state adhesion coefficient at the current moment according to the multiple instantaneous adhesion coefficients and the activation intervention duration.

[0290] Specifically, the vehicle may compare the activation intervention duration with a first duration and a second duration, wherein the second duration is greater than the first duration.

[0291] If the activation intervention duration is greater than or equal to the first duration and less than the second duration, the vehicle may determine a standard deviation of the instantaneous adhesion coefficient based on the multiple instantaneous adhesion coefficients and the average instantaneous adhesion coefficient.

[0292] For example, the vehicle may determine the instantaneous adhesion coefficient standard deviation based on the following formula according to the plurality of instantaneous adhesion coefficients and the average instantaneous adhesion coefficient:

[0293] Where, MueStd represents the standard deviation of the instantaneous adhesion coefficient, MueUtilizedInst represents the instantaneous adhesion coefficient, and sWeight represents the weight coefficient of the vehicle's instantaneous adhesion coefficient at a certain moment.

[0294] After determining the instantaneous adhesion coefficient standard deviation, the vehicle may determine whether the average instantaneous adhesion coefficient is greater than or equal to a preset average instantaneous adhesion coefficient, or whether the instantaneous adhesion coefficient standard deviation is less than or equal to a preset adhesion coefficient standard deviation. If so, the vehicle determines the maximum of the average instantaneous adhesion coefficient and the instantaneous adhesion coefficient at the current moment as the candidate steady-state adhesion coefficient and sets the confidence level to a first steady-state confidence level. If not, the vehicle may determine the candidate steady-state adhesion coefficient to be zero and set the confidence level to zero.

[0295] If the activation intervention duration is greater than or equal to the second duration, the vehicle may determine a candidate steady-state adhesion coefficient and a confidence level based on the instantaneous adhesion coefficient at the current moment, the average instantaneous adhesion coefficient, and the preset adhesion coefficient.

[0296] For example, the vehicle may use the following formula to determine the selected steady-state adhesion coefficient: MueFromStabCtrl = MueMean + max(min((MueUtilizedInst-MueMean), Ratelimit), -Ratelimit)

[0297] Among them, MueFromStabCtrl represents the steady-state adhesion coefficient to be selected. MueUtilizedInst represents the instantaneous adhesion coefficient at the current moment. MueMean represents the average instantaneous adhesion coefficient. Ratelimit represents the preset adhesion coefficient.

[0298] In addition, the vehicle may determine that the confidence level is a second steady-state confidence level, wherein the second steady-state confidence level is greater than zero.

[0299] Finally, the vehicle can determine the steady-state adhesion coefficient based on the average instantaneous adhesion coefficient, the candidate steady-state adhesion coefficient and the confidence level.

[0300] Specifically, the vehicle may determine whether the selected steady-state adhesion coefficient is within a first value range, the confidence level is within a second value range, and the average instantaneous adhesion coefficient is within a third value range. For example, the first value range may be 0.7 to 1, the second value range may be 0 to 1, and the third value range may be 0.2 to 1.

[0301] If yes, the vehicle determines the adjustment step length. After determining the adjustment step length, the vehicle can determine the sum of the candidate steady-state adhesion coefficient and the adjustment step length as the steady-state adhesion coefficient. If no, the vehicle directly determines the candidate steady-state adhesion coefficient as the steady-state adhesion coefficient.

[0302] The beneficial effects of this embodiment include obtaining instantaneous adhesion coefficients of the vehicle at multiple moments, including multiple historical moments and the current moment; obtaining the activation intervention duration of the vehicle's stability control system; and determining the vehicle's steady-state adhesion coefficient at the current moment based on the multiple instantaneous adhesion coefficients and the activation intervention duration. This method ensures that the determined steady-state adhesion coefficient of the vehicle at the current moment matches the actual vehicle condition at the current moment, thereby improving the accuracy of the determined adhesion coefficient.

[0303] The following describes the process of determining the dynamic adhesion coefficient and confidence level of a vehicle through a fifth method embodiment.

[0304] FIG6 is a flow chart of a fifth embodiment of a method for determining an adhesion coefficient provided in an embodiment of the present application. Referring to FIG6 , the method specifically includes the following steps:

[0305] S601: Determine an instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel.

[0306] In this embodiment, the instantaneous adhesion coefficient is determined based on the acceleration and the tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel.

[0307] S602: Determine the longitudinal stability factor and the lateral stability factor.

[0308] In this embodiment, the vehicle may determine a longitudinal stability factor and a lateral stability factor.

[0309] In the process of determining the longitudinal stability factor and lateral stability factor of a vehicle:

[0310] The vehicle can use the following formula to determine the slip error of each wheel:

[0311] Among them, SlipError(k) is the slip rate error, Slip(k) is the slip rate calculated based on the wheel rolling speed and linear velocity; LongStfns(k) is the wheel longitudinal stiffness; Fxnorm(k) is the normalized wheel longitudinal force; k takes the value of 1, 2, 3, 4, representing the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively.

[0312] The vehicle can use the following formula to determine the left slip rate variance and the left slip rate variation variance of the left wheel of the vehicle, and the right slip rate variance and the right slip rate variation variance of the right wheel of the vehicle: SlipVarianceLeft=SlipError(1) 2 +SlipError(3) 2 SlipVarianceRight=SlipError(2) 2 +SlipError(4) 2

[0313] Among them, SlipError(1) is the slip error of the left front wheel, SlipError(2) is the slip error of the right front wheel, SlipError(3) is the slip error of the left rear wheel, SlipError(4) is the slip error of the right rear wheel, SlipVarianceLeft is the left slip variance of the left wheel, SlipDotVarianceLeft is the left slip variance of the left wheel, SlipVarianceRight is the right slip variance of the right wheel, and SlipDotVarianceRight is the right slip variance of the right wheel.

[0314] The vehicle can determine the longitudinal correction factor and the lateral correction factor of the vehicle.

[0315] Specifically, the vehicle can determine a correction coefficient based on the left slip rate variance, the right slip rate variance, the wheel steering angle, the longitudinal force (tire longitudinal force), the wheel acceleration, the lateral acceleration, the yaw rate, the longitudinal vehicle speed, and the acceleration detected by the inertial measurement unit sensor. The vehicle can also determine a longitudinal correction factor based on the correction coefficient, the yaw rate, the longitudinal vehicle speed, and the acceleration detected by the inertial measurement unit sensor. The vehicle can also determine a lateral correction factor based on the correction coefficient, the lateral acceleration, and the longitudinal force.

[0316] The vehicle can use the following formula to determine the longitudinal stability factor: SlipVariance = max(SlipVarianceLeft, SlipVarianceRight) SlipDotVariance = max(SlipDotVarianceLeft, SlipDotVarianceRight)

[0317] Among them, indInst(1) is the longitudinal stability factor, SlipVarianceLeft is the left slip rate variance of the left wheel, SlipDotVarianceLeft is the left slip rate variation variance of the left wheel, SlipVarianceRight is the right slip rate variance of the right wheel, SlipDotVarianceRight is the right slip rate variation variance of the right wheel, threshold1 is the seventh calibration parameter, threshold2 is the eighth calibration parameter, and ScaleLongitudinal is the longitudinal correction factor.

[0318] The vehicle's lateral stability factor can be determined using the following formula:

[0319] Where indInst(2) is the lateral stability factor, ScaleLateral is the lateral correction factor, d1 is the lateral force offset, d2 is the yaw rate offset, and d3 is the roll rate offset.

[0320] S603: Determine a first dynamic adhesion coefficient and a first dynamic confidence level of the vehicle in a high adhesion state, and a second dynamic adhesion coefficient and a second dynamic confidence level of the vehicle in a low adhesion state based on the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor.

[0321] In this embodiment, the vehicle may determine a first dynamic adhesion coefficient and a first dynamic confidence factor of the vehicle in a high adhesion state according to the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor.

[0322] In an implementation of determining the first dynamic adhesion coefficient and the first dynamic confidence factor:

[0323] The vehicle can obtain the vehicle's sensor status and speed (longitudinal), and the sensor status is valid or invalid.

[0324] The vehicle may determine that the high adhesion estimation condition is not met if the sensor status is determined to be a failure state and / or the vehicle speed is less than a preset threshold. The vehicle may directly determine the first dynamic adhesion coefficient to be the dynamic adhesion coefficient of the vehicle at the previous moment and determine the first dynamic confidence level to be 0.

[0325] The vehicle determines that the high adhesion estimation condition is met when it is determined that the sensor state is valid and the vehicle speed is greater than or equal to a preset threshold.

[0326] The vehicle may determine the instantaneous adhesion coefficient as the first dynamic adhesion coefficient and determine the first dynamic confidence level to be 1 if it is determined that the longitudinal stability factor and / or the lateral stability factor is greater than the first factor and the instantaneous adhesion coefficient is greater than the first coefficient. Furthermore, the vehicle may determine the first dynamic adhesion coefficient as the preset dynamic adhesion coefficient and determine the first dynamic confidence level to be 1 if it is determined that both the longitudinal stability factor and the lateral stability factor are less than the second factor, the instantaneous adhesion coefficient is greater than the second coefficient, and the duration for which the instantaneous adhesion coefficient is greater than the second coefficient is greater than or equal to a preset duration.

[0327] It should be noted that the first factor is greater than the second factor, and the first coefficient is greater than the second coefficient. For example, the first factor can be 1, and the first coefficient can be any value between 0.65 and 0.9. The second factor can be 0.6, and the second coefficient can be any value between 0.5 and 0.65. The preset duration can be any value between 100 ms and 300 ms, the preset dynamic adhesion coefficient can be any value between 0.75 and 1, and the preset confidence can be any value between 0.5 and 0.8.

[0328] In this embodiment, the vehicle may determine the second dynamic adhesion coefficient and the second confidence level of the vehicle in a low adhesion state according to the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor.

[0329] In the implementation process of determining the second dynamic adhesion coefficient and the second dynamic confidence factor:

[0330] In one implementation, the vehicle may determine whether a stability control system is activated. If the stability control system is activated, the vehicle may determine the vehicle's dynamic adhesion coefficient at a previous moment as a second dynamic adhesion coefficient and the vehicle's confidence level at the previous moment as a second dynamic confidence level. If the stability control system is not activated, the vehicle may determine the vehicle's dynamic adhesion coefficient at the previous moment as the second dynamic adhesion coefficient and the second dynamic confidence level as zero.

[0331] In one implementation, the vehicle may determine whether the vehicle satisfies multiple conditions. If the vehicle satisfies the multiple conditions, the vehicle's dynamic adhesion coefficient at the previous moment is determined as the second dynamic adhesion coefficient, and the vehicle's confidence level at the previous moment is determined as the second dynamic confidence level. If the vehicle does not satisfy at least one of the multiple conditions, the vehicle's dynamic adhesion coefficient at the previous moment is determined as the second dynamic adhesion coefficient, and the second dynamic confidence level is determined to be 0.

[0332] Among them, multiple conditions include:

[0333] The instantaneous adhesion coefficient is greater than the third coefficient; illustratively, the third coefficient may be any value between 0 and 0.35.

[0334] The longitudinal stability factor and the lateral stability factor are both greater than 1;

[0335] The vehicle speed (longitudinal speed) is greater than a preset speed; for example, the preset speed may be any value between 5 kph and 10 kph, where kph refers to kilometers per hour;

[0336] The driving direction is forward (the gear is forward).

[0337] S604: Determine the dynamic adhesion coefficient of the vehicle and the confidence level of the dynamic adhesion coefficient according to the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level.

[0338] In this embodiment, the vehicle may determine the dynamic adhesion coefficient of the vehicle according to the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level.

[0339] Specifically, the vehicle may compare the first dynamic confidence level and the second dynamic confidence level.

[0340] If the first dynamic confidence level is greater than or equal to the second dynamic confidence level, the vehicle determines the dynamic adhesion coefficient to be the first dynamic adhesion coefficient. Alternatively, the vehicle may also determine the confidence level of the dynamic adhesion coefficient to be the first dynamic confidence level.

[0341] If the first dynamic confidence level is less than the second dynamic confidence level, the vehicle determines the dynamic adhesion coefficient to be the second dynamic adhesion coefficient. Alternatively, the vehicle may also determine the confidence level of the dynamic adhesion coefficient to be the second dynamic confidence level.

[0342] The beneficial effects of this embodiment are as follows: The vehicle can determine a first dynamic adhesion coefficient and a first dynamic confidence level for the vehicle in a high-adhesion state, and a second dynamic adhesion coefficient and a second dynamic confidence level for the vehicle in a low-adhesion state, based on the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor. The vehicle can then perform adhesion coefficient fusion processing based on the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level to obtain the vehicle's dynamic adhesion coefficient. This approach accurately determines the dynamic adhesion coefficient, improving the accuracy of determining the vehicle's dynamic adhesion coefficient, thereby enhancing the vehicle's controllability under driving conditions and improving the safety of passengers.

[0343] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0344] FIG7 is a schematic diagram of the structure of the device for determining the adhesion coefficient provided in an embodiment of the present application. As shown in FIG7 , the device for determining the adhesion coefficient 70 includes an acquisition module 71, a processing module 72, and a fusion module 73.

[0345] an acquisition module 71 for acquiring sensor data collected by at least one sensor of the vehicle;

[0346] a processing module 72 for determining driving information of the vehicle based on the sensor data, the driving information including at least one of the following: slip ratio, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, front axle lateral force, and rack force;

[0347] The processing module 72 is further configured to determine at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on the driving information, or the sensor data and the driving information; the at least one reference adhesion coefficient includes at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient;

[0348] The fusion module 73 is configured to determine a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient based on at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient.

[0349] The device for determining the adhesion coefficient provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0350] In one implementation, the driving information includes a slip ratio of each wheel, a longitudinal stiffness of each wheel, and a tire longitudinal force of each wheel; the reference adhesion coefficient includes a longitudinal adhesion coefficient;

[0351] The processing module 72 is specifically configured to:

[0352] determining a longitudinal adhesion coefficient of each wheel based on the slip rate of each wheel, the longitudinal stiffness of each wheel, the tire longitudinal force of each wheel, and the obtained adhesion coefficients of multiple scenarios;

[0353] determining, based on the longitudinal adhesion coefficients of the plurality of wheels, a first longitudinal adhesion coefficient and a first longitudinal confidence factor of the vehicle in a low adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence factor of the vehicle in a high adhesion state;

[0354] A longitudinal adhesion coefficient of the vehicle and a confidence level of the longitudinal adhesion coefficient are determined based on the first longitudinal adhesion coefficient, the first longitudinal confidence level, the second longitudinal adhesion coefficient, and the second longitudinal confidence level.

[0355] The device for determining the adhesion coefficient provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0356] In one implementation, the sensor data includes vehicle speed, the driving information includes multiple front axle lateral forces and multiple rack forces of the vehicle at multiple moments, the reference adhesion coefficient includes a lateral adhesion coefficient;

[0357] The processing module 72 is specifically configured to:

[0358] determining first statistical information based on the plurality of front axle lateral forces;

[0359] determining second statistical information based on the plurality of front axle lateral forces and the plurality of rack forces;

[0360] Fitting the first statistical information and the second statistical information by a least square method to obtain a lateral adhesion coefficient;

[0361] determining at least one influencing factor according to the vehicle speed, the lateral adhesion coefficient and / or the front axle lateral force at a current moment, wherein the multiple moments include the current moment;

[0362] A confidence level of the lateral adhesion coefficient is determined based on at least one influencing factor.

[0363] The device for determining the adhesion coefficient provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0364] In one implementation, the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes a steady-state adhesion coefficient;

[0365] The processing module 72 is specifically configured to:

[0366] Determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel;

[0367] Get the activation intervention time of the vehicle's stability control system;

[0368] The steady-state adhesion coefficient and the confidence level of the steady-state adhesion coefficient of the vehicle at a current moment are determined based on the instantaneous adhesion coefficient and the activation intervention duration of the vehicle at multiple moments; the multiple moments include multiple historical moments and the current moment.

[0369] The device for determining the adhesion coefficient provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0370] In one implementation, the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes a dynamic adhesion coefficient;

[0371] The processing module 72 is specifically configured to:

[0372] Determine the instantaneous adhesion coefficient based on the acceleration and the tire longitudinal force, tire lateral force and tire vertical load corresponding to each wheel;

[0373] Determine longitudinal stability factor and lateral stability factor;

[0374] determining, based on the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor, a first dynamic adhesion coefficient and a first dynamic confidence factor of the vehicle in a high adhesion state, and a second dynamic adhesion coefficient and a second dynamic confidence factor of the vehicle in a low adhesion state;

[0375] The dynamic adhesion coefficient of the vehicle and the confidence level of the dynamic adhesion coefficient are determined based on the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level.

[0376] The device for determining the adhesion coefficient provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0377] In one implementation, the sensor data includes tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel steering angle, slope, longitudinal acceleration, and lateral acceleration; the driving information includes tire longitudinal force, tire lateral force, tire vertical load, and longitudinal stiffness;

[0378] The processing module 72 is specifically configured to:

[0379] Based on the three-degree-of-freedom model, the tire longitudinal force, tire lateral force and tire vertical load of each wheel are determined according to the tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel rotation angle, slope, longitudinal acceleration and lateral acceleration;

[0380] The longitudinal stiffness is determined by using the least square method based on multiple tire longitudinal forces and multiple slip ratios of each wheel at multiple times.

[0381] The device for determining the adhesion coefficient provided in the embodiment of the present application can implement the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar and will not be repeated here.

[0382] Figure 8 is a structural diagram of a vehicle provided in an embodiment of the present application. As shown in Figure 8 , vehicle 80 includes a processor 81 and a memory 82. Processor 81 is communicatively coupled to memory 82, which is configured to store computer-executable instructions. Processor 81 is configured to execute the computer-executable instructions stored in memory 82 to implement the technical solutions of any of the aforementioned method embodiments.

[0383] Optionally, the memory 82 may be independent or integrated with the processor 81. Optionally, when the memory 82 is a device independent of the processor 81, the vehicle 80 may further include a bus configured to connect the above devices.

[0384] The vehicle is configured to execute the technical solution in any of the aforementioned method embodiments, and its implementation principles and technical effects are similar and will not be described in detail here.

[0385] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are configured to implement the technical solution provided by any of the aforementioned method embodiments.

[0386] An embodiment of the present application also provides a computer program product, including a computer program, which is configured to implement the technical solution provided by the aforementioned method embodiment when executed by a processor.

[0387] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing relevant hardware (such as a processor) through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module / unit in the above embodiment can be implemented in the form of hardware, for example, by implementing its corresponding function through an integrated circuit, or in the form of a software functional module, for example, by executing a program / instruction stored in a memory by a processor to implement its corresponding function. This application is not limited to any particular form of combination of hardware and software.

[0388] Finally, it should be noted that each of the above embodiments is only used to illustrate the technical solution of the present application, rather than to limit it. Although the present application has been described in detail with reference to each of the above embodiments, a person skilled in the art should understand that the technical solution described in each of the above embodiments can still be modified, or some or all of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solution of each embodiment of the present application.

Claims

1. A method for determining an adhesion coefficient, comprising: acquiring sensor data collected by at least one sensor of the vehicle; determining driving information of the vehicle based on the sensor data, the driving information comprising at least one of the following: slip ratio, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, front axle lateral force, and rack force; determining, based on the driving information, or the sensor data and the driving information, at least one reference adhesion coefficient and a confidence level for each reference adhesion coefficient; the at least one reference adhesion coefficient comprising at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient; A target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient are determined based on the at least one reference adhesion coefficient and the confidence level of each reference adhesion coefficient.

2. The method according to claim 1, wherein the driving information includes a slip ratio of each wheel, a longitudinal stiffness of each wheel, and a tire longitudinal force of each wheel; and the reference adhesion coefficient includes the longitudinal adhesion coefficient; in: determining a longitudinal adhesion coefficient of each wheel according to the slip rate of each wheel, the longitudinal stiffness of each wheel, the tire longitudinal force of each wheel, and the obtained multiple scene adhesion coefficients; determining, based on the longitudinal adhesion coefficients of the plurality of wheels, a first longitudinal adhesion coefficient and a first longitudinal confidence factor of the vehicle in a low adhesion state, and a second longitudinal adhesion coefficient and a second longitudinal confidence factor of the vehicle in a high adhesion state; A longitudinal adhesion coefficient of the vehicle and a confidence level of the longitudinal adhesion coefficient are determined based on the first longitudinal adhesion coefficient, the first longitudinal confidence level, the second longitudinal adhesion coefficient, and the second longitudinal confidence level.

3. The method of claim 1 , wherein the sensor data comprises vehicle speed, and the driving information comprises a plurality of front axle lateral forces and a plurality of rack forces of the vehicle at a plurality of moments; The reference adhesion coefficient includes the lateral adhesion coefficient; in: determining first statistical information based on the plurality of front axle lateral forces; determining second statistical information based on the plurality of front axle lateral forces and the plurality of rack forces; performing fitting processing on the first statistical information and the second statistical information by a least squares method to obtain the lateral adhesion coefficient; determining at least one influencing factor according to the vehicle speed, the lateral adhesion coefficient and / or the front axle lateral force at a current moment, wherein the multiple moments include the current moment; A confidence level of the lateral adhesion coefficient is determined based on at least one influencing factor.

4. The method according to claim 1, wherein the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes the steady-state adhesion coefficient; in: determining an instantaneous adhesion coefficient according to the acceleration and the tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel; Obtaining an activation intervention duration of a stability control system of the vehicle; The steady-state adhesion coefficient of the vehicle at a current moment and a confidence level of the steady-state adhesion coefficient are determined based on the instantaneous adhesion coefficients of the vehicle at multiple moments and the activation intervention duration; the multiple moments include multiple historical moments and the current moment.

5. The method according to claim 1, wherein the driving information includes tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel, the sensor data includes acceleration, and the reference adhesion coefficient includes the dynamic adhesion coefficient; in: determining an instantaneous adhesion coefficient according to the acceleration and the tire longitudinal force, tire lateral force, and tire vertical load corresponding to each wheel; Determine longitudinal stability factor and lateral stability factor; determining, based on the instantaneous adhesion coefficient, the longitudinal stability factor, and the lateral stability factor, a first dynamic adhesion coefficient and a first dynamic confidence factor of the vehicle in a high adhesion state, and a second dynamic adhesion coefficient and a second dynamic confidence factor of the vehicle in a low adhesion state; A dynamic adhesion coefficient of the vehicle and a confidence level of the dynamic adhesion coefficient are determined based on the first dynamic adhesion coefficient, the first dynamic confidence level, the second dynamic adhesion coefficient, and the second dynamic confidence level.

6. The method according to any one of claims 1 to 5, wherein the sensor data includes tire driving force, vehicle mass, wheel speed, wheel acceleration, wheel steering angle, slope, longitudinal acceleration, and lateral acceleration; and the driving information includes the tire longitudinal force, the tire lateral force, the tire vertical load, and the longitudinal stiffness; in: Determining, based on a three-degree-of-freedom model, a tire longitudinal force, a tire lateral force, and a tire vertical load of each wheel according to the tire driving force, the vehicle mass, the wheel speed, the wheel acceleration, the wheel turning angle, the slope, the longitudinal acceleration, and the lateral acceleration; The longitudinal stiffness is determined by using a least square method according to a plurality of tire longitudinal forces and a plurality of slip ratios of each wheel at a plurality of moments.

7. A device for determining an adhesion coefficient, comprising: an acquisition module, configured to acquire sensor data collected by at least one sensor of the vehicle; a processing module, configured to determine driving information of the vehicle based on the sensor data, the driving information comprising at least one of the following: slip ratio, longitudinal stiffness, tire longitudinal force, tire lateral force, tire vertical load, front axle lateral force, and rack force; The processing module is further configured to determine at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient based on the driving information, or the sensor data and the driving information; the at least one reference adhesion coefficient comprising at least one of the following: a steady-state adhesion coefficient, a dynamic adhesion coefficient, a longitudinal adhesion coefficient, and a lateral adhesion coefficient; A fusion module is configured to determine a target adhesion coefficient of the vehicle and a confidence level of the target adhesion coefficient based on the at least one reference adhesion coefficient and a confidence level of each reference adhesion coefficient.

8. A vehicle comprising: a processor, and a memory communicatively connected to the processor; The memory is configured to store computer-executable instructions; The processor is configured to execute the computer-executable instructions stored in the memory to implement the method for determining the adhesion coefficient according to any one of claims 1 to 6.

9. A computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the method for determining the adhesion coefficient according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method for determining the adhesion coefficient according to any one of claims 1 to 6 is implemented.

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