Vehicle yaw angle prediction method, device and computer-readable storage medium

Abstract

This invention discloses a method, apparatus, and computer-readable storage medium for predicting vehicle yaw angle. The method includes: determining the current yaw angle of the target vehicle based on sensing data of the vehicle's center of gravity speed and yaw angle; determining the turning radius and tire slip angle of the target vehicle's wheels; determining the actual speed of the wheels in the vehicle's direction of travel based on the equivalent speed of the wheels in the wheel's direction of travel, the turning radius, and the tire slip angle; determining the weighted angular velocity of the wheels based on the actual speed of the wheels; and determining the target vehicle yaw angle at a predetermined prediction time based on the current yaw angle and the weighted angular velocity of the wheels. This invention solves the technical problem of inaccurate vehicle yaw angle estimation.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to a method, apparatus, and computer-readable storage medium for predicting vehicle yaw angle. Background Technology

[0002] In related technologies, there is a technical problem of inaccurate estimation of vehicle yaw angle.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and computer-readable storage medium for predicting vehicle yaw angle, in order to at least solve the technical problem of inaccurate vehicle yaw angle estimation.

[0005] According to one aspect of the present invention, a vehicle yaw angle prediction method is provided, comprising: determining the current vehicle yaw angle of the target vehicle based on the vehicle center-of-gravity speed and yaw angle sensing data of the target vehicle; determining the turning radius and tire slip angle of the target vehicle wheels; determining the actual speed of the target vehicle wheels in the vehicle's travel direction based on the equivalent speed of the target vehicle wheels in the wheel travel direction, the turning radius, and the tire slip angle; determining the weighted angular velocity of the wheel yaw angle based on the actual speed of the target vehicle wheels; and determining the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle.

[0006] Optionally, the actual speed of the wheel in the vehicle's direction of travel is determined based on the equivalent speed of the wheel in the target vehicle's direction of travel, the turning radius, and the tire slip angle. This includes: acquiring the first equivalent speed of the wheel using wheel speed sensors in the target vehicle; acquiring the wheel's rotational angular velocity and wheel radius, and calculating the second equivalent speed of the wheel based on the rotational angular velocity and wheel radius; combining the first and second equivalent speeds of the wheel according to a predetermined ratio to obtain the combined equivalent speed of the wheel; and calculating the actual speed of the wheel based on the combined equivalent speed of the wheel, the turning radius, and the tire slip angle.

[0007] Optionally, the vehicle yaw angle weighted angular velocity of the wheels can be determined based on the actual speed of the target vehicle's wheels, including: determining the vehicle yaw angle weighted angular velocity of the wheels based on the actual speed of the wheels, the vehicle's center of gravity speed, and the turning radius.

[0008] Optionally, the target vehicle yaw angle at the predetermined prediction time is determined based on the vehicle yaw angle at the current moment and the weighted angular velocity of the vehicle yaw angle of the wheels. This includes: determining the target vehicle yaw angle at the predetermined prediction time using a target prediction model based on the vehicle yaw angle at the current moment and the weighted angular velocity of the vehicle yaw angle of the wheels. The target prediction model is constructed based on a particle filter algorithm and is trained using multiple sets of sample data.

[0009] Optionally, according to the method of claim 1, determining the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle includes:

[0010]

[0011] Where, ψ K+1 ψ is the yaw angle of the target vehicle at the predetermined prediction time. K The yaw angle of the vehicle at the current moment. The weighted angular velocity of the vehicle's yaw angle at the current moment. The yaw rate is the weighted angular acceleration of the vehicle at the current moment, and T is the time from the current moment to the predetermined prediction moment.

[0012] Optionally, the target vehicle yaw angle at a predetermined prediction time is determined based on the vehicle yaw angle at the current moment and the weighted angular velocity of the vehicle yaw angle of the wheels, including: acquiring the measurement noise of the sensors in the target vehicle; and determining the target vehicle yaw angle at the predetermined prediction time based on the measurement noise, the vehicle yaw angle at the current moment, and the weighted angular velocity of the vehicle yaw angle of the wheels.

[0013] According to another aspect of the present invention, a vehicle yaw angle prediction device is also provided, comprising: a first determining module, configured to determine the current vehicle yaw angle of the target vehicle based on the vehicle center-of-gravity speed and yaw angle sensing data of the target vehicle; a second determining module, configured to determine the turning radius and tire slip angle of the target vehicle's wheels; a third determining module, configured to determine the actual speed of the wheels in the vehicle's travel direction based on the equivalent speed of the target vehicle's wheels in the wheel's travel direction, the turning radius, and the tire slip angle; a fourth determining module, configured to determine the vehicle yaw angle weighted angular velocity of the wheels based on the actual speed of the target vehicle's wheels; and a fifth determining module, configured to determine the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the vehicle yaw angle weighted angular velocity of the wheels.

[0014] Optionally, the third determining module includes: an acquisition unit, used to acquire the first equivalent speed of the wheel using wheel speed sensors in the target vehicle; a first calculation unit, used to acquire the rotational angular velocity and wheel radius of the wheel, and calculate the second equivalent speed of the wheel based on the rotational angular velocity and wheel radius; a synthesis unit, used to synthesize the first equivalent speed and the second equivalent speed of the wheel according to a predetermined ratio to obtain the comprehensive equivalent speed of the wheel; and a second calculation unit, used to calculate the actual speed of the wheel based on the comprehensive equivalent speed of the wheel, the turning radius, and the tire slip angle.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the vehicle yaw angle prediction method of any of the above.

[0016] According to another aspect of the present invention, a computer device is also provided, comprising: a memory and a processor, the memory storing a computer program; and a processor for executing the computer program stored in the memory, wherein the computer program, when running, causes the processor to execute the vehicle yaw angle prediction method described above.

[0017] In this embodiment of the invention, a particle filtering method is used to obtain driving sensor data of the target vehicle from different sensors. This sensor data is then fused to determine the current yaw angle of the target vehicle. Based on the turning radius, tire slip angle, equivalent speed of the wheel in the wheel's direction of travel, and actual speed of the wheel in the vehicle's direction of travel, the weighted angular velocity of the wheel's yaw angle is iteratively determined. Then, based on the current yaw angle of the target vehicle and the weighted angular velocity of the wheel's yaw angle, the target vehicle yaw angle at the predetermined prediction time is determined. This achieves the goal of determining the target vehicle yaw angle based on multiple vehicle sensor data, thereby improving the technical effect of vehicle yaw angle prediction accuracy and solving the technical problem of inaccurate vehicle yaw angle estimation. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of a vehicle yaw angle prediction method according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of the radius of curvature provided according to an optional embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the side slip angle calculation provided by an optional embodiment of the present invention;

[0022] Figure 4 This is a structural block diagram of a vehicle yaw angle prediction device provided according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Terminology Explanation

[0026] Particle filtering is a process that approximates the probability density function by finding a set of random samples propagating in the state space, replacing the integral operation with the sample mean, and thus obtaining the minimum variance estimate of the system state. These samples are figuratively called "particles", hence the name particle filtering.

[0027] The turning radius refers to the distance from the center of the turn to the point where the outer front steering wheel contacts the ground during vehicle operation.

[0028] According to an embodiment of the present invention, a method for predicting vehicle yaw angle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1This is a flowchart of a vehicle yaw angle prediction method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Based on the vehicle center of gravity speed and yaw angle sensing data of the target vehicle, determine the current vehicle yaw angle of the target vehicle.

[0031] Step S104: Determine the turning radius and tire slip angle of the target vehicle's wheels;

[0032] Step S106: Based on the equivalent speed of the target vehicle's wheels in the direction of wheel travel, the turning radius, and the tire slip angle, determine the actual speed of the wheels in the direction of vehicle travel.

[0033] Step S108: Determine the vehicle yaw angle weighted angular velocity of the wheels based on the actual speed of the target vehicle's wheels;

[0034] Step S110: Based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle, determine the target vehicle yaw angle at the predetermined prediction time.

[0035] Through the above steps, using particle filtering, driving sensor data of the target vehicle is acquired from different sensors. This data is then fused to determine the current yaw angle of the target vehicle. Based on the turning radius, tire slip angle, equivalent wheel speed in the wheel's direction of travel, and actual wheel speed in the vehicle's direction of travel, the weighted angular velocity of the wheel's yaw angle is iteratively determined. Finally, based on the current yaw angle of the target vehicle and the weighted angular velocity of the wheel's yaw angle, the target vehicle yaw angle at the predetermined prediction time is determined. This achieves the goal of determining the target vehicle yaw angle based on multiple vehicle sensor data, thereby improving the accuracy of vehicle yaw angle prediction and solving the technical problem of inaccurate vehicle yaw angle estimation.

[0036] As an optional embodiment, determining the actual speed of the wheel in the vehicle's travel direction based on the equivalent speed of the wheel in the wheel's travel direction, the turning radius, and the tire slip angle includes: acquiring the first equivalent speed of the wheel using wheel speed sensors in the target vehicle; acquiring the wheel's rotational angular velocity and wheel radius, and calculating the second equivalent speed of the wheel based on the rotational angular velocity and wheel radius; combining the first and second equivalent speeds of the wheel according to a predetermined ratio to obtain the combined equivalent speed of the wheel; and calculating the actual speed of the wheel based on the combined equivalent speed of the wheel, the turning radius, and the tire slip angle.

[0037] In this embodiment of the invention, the equivalent speed of the wheel obtained by the wheel speed sensor can be directly used to calculate the actual vehicle speed. Alternatively, the equivalent speed of the wheel can be calculated using the wheel's rotational angular velocity and wheel radius. The first and second equivalent speeds of the wheel can be determined using either of these methods. These first and second equivalent speeds are then combined according to a predetermined ratio (e.g., 0.4:0.6) to obtain a more accurate combined equivalent speed, thereby improving the accuracy of the actual speed calculated based on the equivalent speed. The predetermined ratio can be set according to actual conditions, taking into account factors such as the sensitivity of the wheel speed sensor, the measurement error of the rotational angular velocity and wheel radius, etc.

[0038] As an optional embodiment, the vehicle yaw angle weighted angular velocity of the wheel is determined based on the actual speed of the target vehicle's wheel, including: determining the vehicle yaw angle weighted angular velocity of the wheel based on the actual speed of the wheel, the vehicle's center of gravity speed, and the turning radius.

[0039] After determining the actual speed of the wheels, the vehicle's center of gravity speed, and the turning radius, the weighted angular velocity of the wheels can be calculated using the following formula:

[0040]

[0041] Of these, one of the two formulas can be used to calculate the vehicle's yaw-weighted angular velocity. Where v WFL,x and v WFL,y These are the velocity components of the wheel's actual speed in the preset x and y directions, v G,x and v G,y These are the velocity components of the vehicle's center of gravity in the preset x and y directions, respectively. r is the angular velocity weighted by the vehicle's yaw angle. FL γ is the distance from the wheel's center of mass to the vehicle's center of mass. FL It is the angle between the direction of wheel travel and the direction of vehicle travel.

[0042] As an optional embodiment, determining the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle includes: determining the target vehicle yaw angle at the predetermined prediction time using a target prediction model based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle. The target prediction model is constructed based on a particle filter algorithm and trained using multiple sets of sample data. In this embodiment of the invention, a prediction model for vehicle yaw angle can be constructed based on a particle filter algorithm, and the prediction model can be continuously adjusted and optimized by training it using multiple sets of sample data to obtain the target prediction model. Alternatively, the prediction accuracy and / or precision of the prediction model can be evaluated, and if the prediction model achieves the required prediction accuracy and / or precision for vehicle yaw angle, it is determined as the target prediction model.

[0043] As an optional embodiment, the target vehicle yaw angle at a predetermined prediction time is determined based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle, including:

[0044]

[0045] Where, ψ K+1 ψ is the yaw angle of the target vehicle at the predetermined prediction time. K The yaw angle of the vehicle at the current moment. The weighted angular velocity of the vehicle's yaw angle at the current moment. The yaw rate is the weighted angular acceleration of the vehicle at the current moment, and T is the time from the current moment to the predetermined prediction moment.

[0046] As an optional embodiment, determining the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle includes: acquiring the measurement noise of the sensors in the target vehicle; and determining the target vehicle yaw angle at the predetermined prediction time based on the measurement noise, the current vehicle yaw angle, and the weighted angular velocity of the wheel yaw angle. Considering that when using the target vehicle's sensors to acquire and transmit data, different levels of measurement noise may exist in the measurement data depending on the actual performance of the sensors, a unified sensor measurement noise parameter applicable to all sensors can be preset to address this noise. This parameter can then be used to process all data obtained by the sensors. Alternatively, different measurement noise parameters can be determined for different sensors, and the corresponding sensor data can be processed using these parameters to minimize the vehicle yaw angle prediction error caused by sensor measurement noise.

[0047] It should be noted that, in this embodiment of the invention, model process noise parameters can also be set for the target prediction model to improve the accuracy and precision of model calculation.

[0048] It should be noted that, in the embodiments of the present invention, the target vehicle may be equipped with different sensors, wherein the sensors may include at least two of the following sensors: wheel speed sensor, combined inertial navigation sensor, and braking system inertial sensor.

[0049] It should be noted that, in the embodiments of the present invention, the turning radius, tire slip angle, equivalent speed in the wheel's direction of travel, actual speed in the vehicle's direction of travel, and weighted angular velocity of the vehicle's yaw angle can be calculated for each wheel of the target vehicle. Alternatively, only the above data for one or a portion of the wheels can be calculated, and then the yaw angle of the target vehicle at the predetermined prediction time can be predicted based on the calculation results and the current yaw angle of the target vehicle.

[0050] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method, which will be described below.

[0051] The vehicle yaw rate signal is an important signal used in the software of the autonomous driving system. This signal represents the vehicle's driving posture in the preset XY plane. The autonomous vehicle uses this signal when driving in the center of a curve or changing lanes.

[0052] However, in related technologies, relying solely on sensors to estimate vehicle yaw angles often results in problems such as zero drift (i.e., the inertial navigation signal has a corresponding deviation when the vehicle is stationary) and temperature drift (i.e., it is sensitive to temperature, and the signal error will increase at high temperatures).

[0053] To address the aforementioned technical problems, an optional embodiment of the present invention fuses GPS information, inertial navigation information, and vehicle wheel speed information, and uses particle filtering to solve the problems of zero drift and temperature drift in the yaw rate signal, thereby improving the accuracy of the yaw rate signal.

[0054] The optional embodiments of the present invention can be applied under the following conditions:

[0055] 1. The vehicle is equipped with wheel speed sensors, a combined inertial navigation sensor, and a braking system inertial sensor (compared to the combined inertial navigation sensor, this sensor can only provide acceleration and yaw rate signals in the X and Y axes of the centroid coordinate system).

[0056] 2. Vehicles traveling on level roads

[0057] 3. The vehicle's center of gravity is in a fixed position, which is determined by the vehicle test.

[0058] Figure 2This is a schematic diagram of the radius of curvature provided according to an optional embodiment of the present invention, such as... Figure 2 As shown, the optional embodiments of the present invention involve the following calculations:

[0059] 1. Radius at the vehicle's center of gravity

[0060]

[0061] Where, ω G This is the yaw rate signal of the vehicle's center of gravity, which can be directly measured using a combination of inertial navigation or braking system inertial sensors. G The vehicle's center-of-gravity velocity can be obtained by converting the velocity and attitude information from the combined inertial navigation system (INS). The combined INS outputs a velocity signal in the North-East-Ground coordinate system. Combined with the attitude information from the INS, the velocity signals in the XYZ axes of the vehicle coordinate system can be obtained through a rotation matrix. This signal can also be obtained by integrating the inertial sensors of the braking system. By discretizing and integrating the acceleration signals of the X and Y axes, the velocity signals of the corresponding axes can be obtained. Finally, the resultant velocity signal and the center-of-gravity sideslip angle signal are obtained according to the vector addition formula.

[0062] 2. Vehicle center of gravity sideslip angle

[0063]

[0064] Among them, v G,x and v G,y These are the velocity components of the vehicle's center of gravity in the preset x and y directions.

[0065] 3. Wheel turning radius (taking the left wheel of the front axle of the vehicle (subscript FL) as an example)

[0066]

[0067] Where, r FL It is the distance from the center of mass to the center of rotation of the left wheel on the front axle, and it is a fixed value, θ. FL It is related to the sideslip angle of the center of mass, and is equal to the sum of the fixed angle value and the sideslip angle of the center of mass.

[0068] Figure 3 This is a schematic diagram of slip angle calculation provided by an optional embodiment of the present invention, wherein α is the tire slip angle, v R v is the equivalent speed of the wheel. W δ represents the actual speed of the wheel. W For the turning angle of the wheel, such as Figure 3 As shown, optional embodiments of the present invention also involve the following calculations:

[0069] 4. Vehicle center of gravity speed

[0070]

[0071] Among them, v WFL,x and v WFL,y These are the velocity components of the wheel's actual speed in the preset x and y directions, v G,x and v G,y These are the velocity components of the vehicle's center of gravity in the preset x and y directions, respectively. r is the angular velocity weighted by the vehicle's yaw angle. FL γ is the distance from the wheel's center of mass to the vehicle's center of mass. FL It is the angle between the direction of wheel travel and the direction of vehicle travel.

[0072] 5. Tire slip angle

[0073]

[0074] Where, δ W The turning angle of the wheel.

[0075] 6. Equivalent speed of wheels

[0076] v RFL =ω RFL *r stac

[0077] Where, ω RFL The angular velocity of the wheel is measured by a wheel speed sensor, r. stac The static radius of the wheel is obtained through static measurement.

[0078] 7. The relationship between the equivalent speed and the actual speed of a wheel

[0079] v WFL =v RFL *cos(α FL )

[0080] After calculating the vehicle yaw angle weighted angular velocity using the above formulas, the particle filter algorithm can be used to predict the vehicle yaw angle at the next moment based on the current vehicle yaw angle.

[0081] The weighting method for the vehicle yaw angle weighted angular velocity is as follows:

[0082] 1. The equivalent speed of the wheel is calculated by the inertial sensor and then compared with the equivalent vehicle speed obtained by the wheel speed sensor by a predetermined ratio (e.g., 0.4:0.6) to obtain the comprehensive equivalent speed;

[0083] 2. The actual vehicle speed is calculated using the relationship between the equivalent speed and the actual speed of the wheel.

[0084] 3. Using the actual wheel speed, the weighted angular velocity of the vehicle's yaw angle is calculated based on the vehicle's center of gravity velocity calculation formula.

[0085] In an optional embodiment of the present invention, the particle filter algorithm for predicting vehicle yaw angle includes several steps: particle initialization, particle search, particle correction, and particle resampling. The state equation of this particle filter algorithm can be expressed as:

[0086]

[0087] Where, ψ K+1 ψ is the yaw angle of the target vehicle at the predetermined prediction time. K The yaw angle of the vehicle at the current moment. The weighted angular velocity of the vehicle's yaw angle at the current moment. The yaw rate is the weighted angular acceleration of the vehicle at the current moment, and T is the time from the current moment to the predetermined prediction moment.

[0088] Furthermore, the parameters in the particle filter algorithm can be adjusted. For example, based on the actual performance of the sensor, the measurement noise variance R' = 0.16 can be obtained, the model process noise Q = 0.15 can be assumed, the particle filter weights can be assumed to follow a Gaussian distribution, or other weight calculation methods can be set. If the Gaussian distribution method is used, the weight calculation formula is as follows:

[0089]

[0090] Among them, particle resampling can be done through random sampling or other sampling methods depending on the actual application requirements.

[0091] The yaw rate signal calculation method provided by the optional embodiment of the present invention solves the problem of zero yaw rate drift when the vehicle is stationary and traveling straight. When turning, the signal accuracy is improved by 5% compared with the original ordinary signal filtering method.

[0092] According to an embodiment of the present invention, a vehicle yaw angle prediction device is also provided. Figure 4 This is a structural block diagram of a vehicle yaw angle prediction device provided according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes: a first determining module 41, a second determining module 42, a third determining module 43, a fourth determining module 44, and a fifth determining module 45. The device will be described below.

[0093] The first determining module 41 is used to determine the current yaw angle of the target vehicle based on the vehicle center of gravity speed and yaw angle sensing data of the target vehicle; the second determining module 42 is connected to the first determining module 41 and is used to determine the turning radius and tire slip angle of the target vehicle's wheels; the third determining module 43 is connected to the second determining module 42 and is used to determine the actual speed of the wheels in the vehicle's direction of travel based on the equivalent speed of the target vehicle's wheels in the wheel's direction of travel, the turning radius, and the tire slip angle; the fourth determining module 44 is connected to the third determining module 43 and is used to determine the weighted angular velocity of the wheel's yaw angle based on the actual speed of the target vehicle's wheels; the fifth determining module 45 is connected to the fourth determining module 44 and is used to determine the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel's yaw angle.

[0094] As an optional embodiment, the third determining module 43 includes: an acquisition unit, used to acquire the first equivalent speed of the wheel using wheel speed sensors in the target vehicle; a first calculation unit, used to acquire the rotational angular velocity and wheel radius of the wheel, and calculate the second equivalent speed of the wheel based on the rotational angular velocity and wheel radius; a synthesis unit, used to synthesize the first equivalent speed and the second equivalent speed of the wheel according to a predetermined ratio to obtain the comprehensive equivalent speed of the wheel; and a second calculation unit, used to calculate the actual speed of the wheel based on the comprehensive equivalent speed of the wheel, the turning radius, and the tire slip angle.

[0095] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the vehicle yaw angle prediction method described above.

[0096] According to an embodiment of the present invention, a computer device is also provided, comprising: a memory and a processor, wherein the memory stores a computer program; and the processor is configured to execute the computer program stored in the memory, wherein the computer program, when running, causes the processor to execute any of the above-described vehicle yaw angle prediction methods.

[0097] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0098] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0103] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting vehicle yaw angle, characterized in that, include: Based on the vehicle center of gravity speed and yaw angle sensing data of the target vehicle, the current vehicle yaw angle of the target vehicle is determined. Determine the turning radius and tire slip angle of the target vehicle's wheels; Based on the equivalent speed of the target vehicle's wheels in the direction of wheel travel, the turning radius, and the tire slip angle, the actual speed of the wheels in the direction of vehicle travel is determined. Based on the actual speed of the wheels of the target vehicle, determine the vehicle yaw angle weighted angular velocity of the wheels; Based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle, the target vehicle yaw angle at the predetermined prediction time is determined; The step of determining the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle includes: Where, ψ K+1 ψ is the yaw angle of the target vehicle at the predetermined prediction time. K The yaw angle of the vehicle at the current moment. The weighted angular velocity of the vehicle's yaw angle at the current moment. The yaw rate is the weighted angular acceleration of the vehicle at the current moment, and T is the time from the current moment to the predetermined prediction moment.

2. The method according to claim 1, characterized in that, Determining the actual speed of the wheels in the vehicle's direction of travel based on the equivalent speed of the target vehicle's wheels in the wheel's travel direction, the turning radius, and the tire slip angle includes: The first equivalent speed of each wheel is obtained using the wheel speed sensors in the target vehicle. The rotational angular velocity and wheel radius of the wheel are obtained respectively, and the second equivalent speed of the wheel is calculated based on the rotational angular velocity and the wheel radius; According to a predetermined ratio, the first and second equivalent speeds of the wheel are combined to obtain the combined equivalent speed of the wheel. The actual speed of the wheel is calculated based on the combined equivalent speed of the wheel, the turning radius, and the tire slip angle.

3. The method according to claim 1, characterized in that, Determining the vehicle yaw angle-weighted angular velocity of the wheels based on the actual speed of the target vehicle's wheels includes: Based on the actual speed of the wheels, the vehicle's center of gravity speed, and the turning radius, the vehicle yaw angle weighted angular velocity of the wheels is determined.

4. The method according to any one of claims 1 to 3, characterized in that, Determining the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle includes: Acquire the measurement noise of the sensors in the target vehicle; Based on the measured noise, the vehicle yaw angle at the current moment and the weighted angular velocity of the vehicle yaw angle of the wheels are used to determine the target vehicle yaw angle at the predetermined prediction time.

5. A vehicle yaw angle prediction device, characterized in that, include: The first determining module is used to determine the current yaw angle of the target vehicle based on the vehicle center of gravity speed and yaw angle sensing data of the target vehicle. The second determining module is used to determine the turning radius and tire slip angle of the target vehicle's wheels; The third determining module is used to determine the actual speed of the wheel in the vehicle's direction of travel based on the equivalent speed of the target vehicle's wheel in the wheel's travel direction, the turning radius, and the tire slip angle. The fourth determining module is used to determine the vehicle yaw angle weighted angular velocity of the wheels based on the actual speed of the wheels of the target vehicle. The fifth determining module is used to determine the target vehicle yaw angle at a predetermined prediction time based on the current vehicle yaw angle and the weighted angular velocity of the wheel yaw angle. The fifth determining module is further configured to determine the yaw angle of the target vehicle in the following manner: Where, ψ K+1 ψ is the yaw angle of the target vehicle at the predetermined prediction time. K The yaw angle of the vehicle at the current moment. The weighted angular velocity of the vehicle's yaw angle at the current moment. The yaw rate is the weighted angular acceleration of the vehicle at the current moment, and T is the time from the current moment to the predetermined prediction moment.

6. The apparatus according to claim 5, characterized in that, The third determining module includes: The acquisition unit is used to acquire the first equivalent speed of each wheel using the wheel speed sensor in the target vehicle. The first calculation unit is used to obtain the rotational angular velocity and the wheel radius of the wheel respectively, and calculate the second equivalent speed of the wheel based on the rotational angular velocity and the wheel radius; The integration unit is used to integrate the first and second equivalent speeds of the wheel according to a predetermined ratio to obtain the integrated equivalent speed of the wheel. The second calculation unit is used to calculate the actual speed of the wheel based on the wheel's comprehensive equivalent speed, the turning radius, and the tire slip angle.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the vehicle yaw angle prediction method according to any one of claims 1 to 4.

8. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the vehicle yaw angle prediction method according to any one of claims 1 to 4.

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