Vehicle lateral velocity estimation method and device

Abstract

The application discloses a vehicle lateral velocity estimation method and device. A fusion weight is determined according to a tire side slip angle of a vehicle. The fusion weight comprises a first weight corresponding to a tire model-based dynamics method and a second weight corresponding to a sensor-based kinematics method. The lateral velocity of the vehicle is estimated according to the fusion weight and motion parameters of the vehicle by using the tire model-based dynamics method and / or the sensor-based kinematics method, and a final estimation result of the lateral velocity is obtained. According to the embodiment of the application, the fusion of the tire model-based dynamics method and the sensor-based kinematics method is realized, the problem that the result is inaccurate due to errors of a tire model and intermediate estimation quantity when only the tire model-based dynamics method is used for estimation under a large vehicle load change and a combined working condition is solved, and more accurate lateral velocity estimation is realized.

Description

Technical Field

[0001] This application belongs to the field of automotive electronics technology, and in particular relates to a method and device for estimating vehicle lateral speed. Background Technology

[0002] With the development of the automotive industry, vehicle stability control systems are becoming increasingly widespread. These systems are primarily used to identify the stability state of a vehicle and prevent dangerous conditions such as brake lock-up, drive slippage, and skidding by controlling engine torque and braking pressure at specific wheels. In the process of vehicle stability identification, yaw rate and lateral velocity are mainly used to determine whether the vehicle is skidding. Yaw rate can be directly measured by sensors, while lateral velocity requires the fusion and estimation of signals from numerous vehicle sensors.

[0003] Current vehicle stability control systems primarily employ either tire-model-based dynamics methods or sensor-based kinematic methods to estimate lateral velocity. However, the accuracy of the first method is highly dependent on the tire model; errors in the tire model will lead to inaccurate estimations. The accuracy of the second method, on the other hand, is highly dependent on the accuracy of the sensors; errors in the sensors can also result in inaccurate estimations.

[0004] Therefore, it is evident that the existing methods for estimating vehicle lateral speed mainly suffer from inaccurate estimation results. Summary of the Invention

[0005] This application provides a method, apparatus, device, storage medium, and program for estimating vehicle lateral speed. By fusing a tire-model-based kinematic method and a sensor-based dynamic method, the lateral speed is estimated together. This solves the problem that estimation based solely on the tire-model kinematic method is inaccurate under conditions of large vehicle load variations and combined operating conditions due to errors in the tire model and intermediate estimation quantities, thus achieving more accurate lateral speed estimation.

[0006] In a first aspect, embodiments of this application provide a method for estimating the lateral speed of a vehicle, including:

[0007] Acquire the vehicle's motion parameters, including tire swerve angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal speed.

[0008] Determine the vehicle's tire slip angle.

[0009] The fusion weights are determined based on the tire slip angle. These weights include a first weight corresponding to the first method and a second weight corresponding to the second method. The first method is a dynamic method based on a tire model, and the second method is a sensor-based kinematic method. The first weight is negatively correlated with the tire slip angle, and the second weight is positively correlated with the tire slip angle.

[0010] Using the first method and / or the second method, the lateral velocity of the vehicle is estimated based on the fusion weights and motion parameters to obtain the final estimate of the lateral velocity.

[0011] As one possible implementation, the tire slip angle of the vehicle includes the tire slip angles of multiple wheels in the vehicle, and the fusion weights are determined based on the tire slip angles, including:

[0012] If multiple tire slip angles are all less than the first angle threshold, the first weight is determined to be 1, and the second weight is determined to be 0.

[0013] If the maximum value among multiple tire slip angles is greater than or equal to a first angle threshold, and the second largest value among multiple tire slip angles is greater than a second angle threshold, then the first weight is determined to be 0, and the second weight is determined to be 1.

[0014] If the maximum value among multiple tire slip angles is greater than or equal to the first angle threshold, and the second largest value among multiple tire slip angles is less than or equal to the second angle threshold, the ratio of the second largest value to the second angle threshold is determined as the second weight, and the difference between 1 and the second weight is determined as the first weight.

[0015] As one possible implementation, the lateral velocity of the vehicle is estimated based on the fusion weights and motion parameters using the first and / or second methods to obtain the final estimate of the lateral velocity, including:

[0016] Using a tire-model-based dynamics method, the lateral velocity of the vehicle is estimated based on motion parameters, yielding the first estimate of the lateral velocity.

[0017] Using a sensor-based kinematic method, the lateral velocity of the vehicle is estimated based on motion parameters, resulting in a second estimate of the lateral velocity.

[0018] The first and second estimation results are fused according to the fusion weights to obtain the final estimation result of the lateral velocity.

[0019] As one possible implementation, a tire-model-based dynamics method is used to estimate the vehicle's lateral velocity based on motion parameters, yielding a first lateral velocity estimate, including:

[0020] The observed lateral acceleration, yaw rate, and longitudinal speed from the motion parameters are defined as the first observed parameters.

[0021] The tire swerve angle and longitudinal acceleration in the motion parameters are determined as the first control variables.

[0022] The first observation and the first control quantity are input into a preset dynamics observer to obtain the first observation results corresponding to the motion parameters output by the dynamics observer. The dynamics observer is constructed based on the vehicle dynamics model, the tire model, and the extended Kalman filter. The observation results output by the dynamics observer include longitudinal velocity, lateral velocity, and yaw rate.

[0023] The lateral velocity in the first observation result is determined as the first lateral velocity estimate of the vehicle.

[0024] As one possible implementation, the dynamics observer includes a first state transition equation based on the vehicle dynamics model and a first observation equation based on the vehicle dynamics model and the tire model. The first observation and the first control quantity are input into the preset dynamics observer to obtain the first observation result corresponding to the motion parameters output by the dynamics observer, including:

[0025] The dynamic observer predicts the change in the first parameter based on the first control variable, the first state transition equation, and the observation results of the previous output. The change in the first parameter includes the change in longitudinal velocity, the change in lateral velocity, and the change in yaw rate.

[0026] The dynamics observer predicts the first observation result corresponding to the input motion parameters based on the previous output observation result and the predicted change in the first parameter.

[0027] The dynamic observer corrects the first observation result based on the first observation equation and the first observation, thus obtaining the second observation result.

[0028] The dynamics observer identifies the second observation result as the observation result corresponding to the motion parameters and outputs it.

[0029] As one possible implementation, before estimating the lateral velocity of the vehicle based on motion parameters using a sensor-based kinematic method, the method further includes:

[0030] Determine the target sensor zero-point drift corresponding to the lateral acceleration.

[0031] Using sensor-based kinematic methods, the lateral velocity of the vehicle is estimated based on motion parameters, including:

[0032] Using a sensor-based kinematic method, the lateral velocity of the vehicle is estimated based on motion parameters and the zero-point drift of the target sensor, resulting in a second lateral velocity estimation result for the vehicle.

[0033] As one possible implementation, determining the target sensor zero-point drift corresponding to lateral acceleration includes:

[0034] With a first weight of 1, the lateral acceleration estimate is determined using the dynamic observer based on the predicted lateral velocity change and the longitudinal velocity and yaw rate from the previous observation. The difference between the lateral acceleration in the first observation and the estimated lateral acceleration is used as the sensor zero-point offset corresponding to the lateral acceleration. This sensor zero-point offset is defined as the target sensor zero-point drift corresponding to the lateral acceleration.

[0035] If the first weight is not 1, the sensor zero-point drift of the lateral acceleration calculated last time by the dynamic observer with the first weight of 1 is determined as the target sensor zero-point drift of the lateral acceleration.

[0036] As one possible implementation, a sensor-based kinematic method is used to estimate the vehicle's lateral velocity based on motion parameters and the zero-point drift of the target sensor, resulting in a second lateral velocity estimation result for the vehicle, including:

[0037] The longitudinal vehicle speed in the motion parameters is determined as the second observable.

[0038] Lateral acceleration, longitudinal acceleration, and yaw rate from the motion parameters are determined as the second control variables.

[0039] The target sensor's zero-point drift, the second observation, and the second control input are input into a preset kinematics observer to obtain the second observation result corresponding to the motion parameters. The kinematics observer is constructed based on the vehicle kinematics model and an extended Kalman filter. The observation result output by the kinematics observer includes longitudinal velocity and lateral velocity.

[0040] The lateral velocity in the second observation result is determined as the second lateral velocity estimate of the vehicle.

[0041] As one possible implementation, the kinematic observer includes a second state transition equation and a second observation equation established based on the vehicle kinematic model. The target sensor zero-point drift, the second observation quantity, and the second control quantity are input into a pre-set kinematic observer to obtain the second observation result corresponding to the motion parameters output by the kinematic observer, including:

[0042] The kinematic observer corrects the longitudinal acceleration in the second control variable based on the target sensor's zero-point offset.

[0043] The kinematic observer predicts the change in the second parameter based on the corrected second control variable, the second state transition equation, and the observation results of the previous output. The change in the second parameter includes the change in longitudinal velocity and the change in lateral velocity.

[0044] The kinematic observer predicts a third observation corresponding to the input motion parameters based on the previous output observation results and the predicted change in the second parameter.

[0045] The kinematic observer corrects the third observation result based on the second observation equation and the second observation, thus obtaining the fourth observation result.

[0046] The kinematic observer identifies the fourth observation as the observation corresponding to the motion parameters and outputs it.

[0047] Secondly, embodiments of this application also provide a vehicle lateral speed estimation device, comprising:

[0048] The acquisition module is used to acquire the vehicle's motion parameters, including tire steering angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal vehicle speed.

[0049] The slip angle determination module is used to determine the tire slip angle of the vehicle.

[0050] The fusion weight determination module is used to determine the fusion weights based on the tire slip angle. The fusion weights include a first weight corresponding to a first method and a second weight corresponding to a second method. The first method is a dynamic method based on a tire model, and the second method is a kinematic method based on sensors. The first weight is negatively correlated with the tire slip angle, and the second weight is positively correlated with the tire slip angle.

[0051] The fusion module is used to estimate the lateral velocity of the vehicle based on the fusion weights and motion parameters using the first method and / or the second method, so as to obtain the final estimate of the lateral velocity.

[0052] Thirdly, embodiments of this application also provide an electronic device, the device including: a processor and a memory storing computer program instructions.

[0053] When the processor executes computer program instructions, it implements a vehicle lateral speed estimation method as described in the first aspect.

[0054] Fourthly, embodiments of this application also provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the vehicle lateral speed estimation method as described in the first aspect.

[0055] Fifthly, embodiments of this application also provide a computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the vehicle lateral speed estimation method as described in the first aspect.

[0056] The vehicle lateral velocity estimation method, apparatus, device, storage medium, and program of this application embodiment determine fusion weights based on the vehicle's tire slip angle. The fusion weights include a first weight corresponding to a tire model-based dynamics method and a second weight corresponding to a sensor-based kinematics method. Using the tire model-based dynamics method and / or the sensor-based kinematics method, the vehicle's lateral velocity is estimated based on the fusion weights and the vehicle's motion parameters to obtain the final estimated result. According to the embodiments of this application, the fusion of the tire model-based dynamics method and the sensor-based kinematics method is achieved, solving the problem that when only the tire model-based dynamics method is used for estimation, the results are inaccurate due to errors in the tire model and intermediate estimations under conditions of large vehicle load variations and combined operating conditions, thus achieving more accurate lateral velocity estimation. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a schematic flowchart of the vehicle lateral speed estimation method provided in the embodiments of this application.

[0059] Figure 2 This is a schematic diagram of the vehicle lateral speed estimation system provided in the embodiments of this application.

[0060] Figure 3 This is a schematic diagram of the vehicle lateral speed estimation method provided in the embodiments of this application.

[0061] Figure 4 This is a schematic diagram of the vehicle lateral speed estimation device provided in the embodiments of this application.

[0062] Figure 5 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0063] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0064] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0065] Currently, the dynamic method based on tire models is used to estimate the lateral velocity of vehicles. However, the tire model is heavily influenced by the tire model itself. The parameters required by the tire model, such as vertical force, slip ratio, and road adhesion, are all estimated values ​​and are prone to errors. These errors in the tire model will lead to inaccurate estimates of the lateral velocity.

[0066] The sensor-based kinematic method used to estimate the lateral speed of a vehicle is inaccurate because the sensor measurement bias accumulates with integration.

[0067] Therefore, it is evident that both of the above estimation methods suffer from inaccurate estimation results.

[0068] However, the applicant has creatively discovered that the tire model-based dynamics method suffers from inaccuracies in estimation results under conditions of significant load variations and combined operating conditions due to errors in the tire model and intermediate estimates, primarily because of the complex nonlinear characteristics of the tire. However, its estimation results are highly accurate when the vehicle is in the linear region. Conversely, when the vehicle is in the nonlinear region, the sensor-based kinematics method provides even higher accuracy compared to the tire model-based dynamics method. Whether a vehicle is in the linear region can be determined based on its tire slip angle; typically, the tire slip angle is smaller in the linear region and larger in the nonlinear region. Based on this, embodiments of this application provide a vehicle lateral velocity estimation method, apparatus, device, storage medium, and program.

[0069] The method for estimating vehicle lateral speed provided in the embodiments of this application will be described below.

[0070] See Figure 1 This is a flowchart illustrating a method for estimating the lateral speed of a vehicle according to an embodiment of this application. Figure 1 The method may include the following steps S11-S14.

[0071] S11. Obtain the vehicle's motion parameters, including tire swerve angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal speed.

[0072] S11 can be executed if it is determined that the lateral speed of the vehicle needs to be estimated.

[0073] See Figure 2 This is a schematic diagram of a vehicle lateral speed estimation system provided in an embodiment of this application, as shown below. Figure 2 As shown, it may include a conversion module 210, an IMU (Inertial Measurement Unit) module 220, a longitudinal vehicle speed estimation module 230, and a lateral vehicle speed estimation module 240. The vehicle lateral speed estimation method provided in this application embodiment can be applied to the lateral vehicle speed estimation module 240.

[0074] When the lateral vehicle speed estimation module 240 estimates the lateral vehicle speed based on the vehicle lateral speed estimation method provided in this application embodiment, the conversion module 210 can receive the steering wheel angle, and then convert it to obtain the tire angle according to the preset correspondence between the steering wheel angle and the tire angle, and output the tire angle to the lateral vehicle speed estimation module 240. The steering wheel angle can be measured by the steering wheel angle sensor in the steering controller, and then sent to the conversion module 210 by the steering controller through CAN communication. The tire angle output by the conversion module 210 to the lateral vehicle speed estimation module 240 can include the rotation angle of all wheels in the vehicle. The IMU module 220 can provide the lateral vehicle speed estimation module 240 with the lateral acceleration, longitudinal acceleration and yaw rate of the vehicle. The longitudinal vehicle speed estimation module 230 can provide the longitudinal vehicle speed of the vehicle to the lateral vehicle speed estimation module 240. In this way, the motion parameters of the vehicle can be obtained.

[0075] S12. Determine the vehicle's tire slip angle.

[0076] The tire slip angle of a vehicle indicates whether it is in a linear region. Generally, the tire slip angle is smaller when the vehicle is in a linear region, and larger when the vehicle is in a non-linear region. Therefore, by determining the tire slip angle, it is possible to determine whether the vehicle is in a linear or non-linear region.

[0077] As an example, the tire slip angle can be determined by the tire steering angle, lateral acceleration and yaw rate obtained from the motion parameters in S11. There are already mature technologies for the specific determination method, so it will not be described in detail here.

[0078] Besides using the methods described above to determine the tire slip angle, other methods can also be used to determine the tire slip angle of a vehicle, and there are no specific limitations on these methods.

[0079] S13. Determine the fusion weights based on the tire slip angle. The fusion weights include the first weight corresponding to the first method and the second weight corresponding to the second method. The first method is a dynamic method based on the tire model, and the second method is a kinematic method based on the sensor. The first weight is negatively correlated with the tire slip angle, and the second weight is positively correlated with the tire slip angle.

[0080] As discussed above, when the tire slip angle is small, the lateral velocity estimation result obtained by the tire model-based dynamics method is more accurate, while when the tire slip angle is large, the lateral velocity estimation result obtained by the sensor-based kinematics method is more accurate. Therefore, determining the fusion weights based on the principle that the first weight is negatively correlated with the tire slip angle and the second weight is positively correlated with the tire slip angle ensures that when the tire slip angle is small, the first weight corresponding to the tire model-based dynamics method is larger, and when the tire slip angle is large, the second weight corresponding to the sensor-based kinematics method is larger.

[0081] S14. Using the first method and / or the second method, estimate the lateral velocity of the vehicle based on the fusion weights and motion parameters to obtain the final estimate of the lateral velocity.

[0082] By estimating lateral velocity using fusion weights, the final estimation result of lateral velocity can rely more on the dynamic method based on the tire model when the tire slip angle is small, and more on the kinematic method based on the sensor when the tire slip angle is large, thus making the final estimation result more accurate.

[0083] This application provides a method for estimating vehicle lateral velocity. A fusion weight is determined based on the vehicle's tire slip angle. The fusion weight includes a first weight corresponding to a tire model-based dynamics method and a second weight corresponding to a sensor-based kinematics method. Using the tire model-based dynamics method and / or the sensor-based kinematics method, the vehicle's lateral velocity is estimated based on the fusion weight and the vehicle's motion parameters to obtain the final estimated result. According to this application, the fusion of the tire model-based dynamics method and the sensor-based kinematics method is achieved, combining the advantages of both methods. This solves the problem that when only the tire model-based dynamics method is used for estimation, the results are inaccurate due to errors in the tire model and intermediate estimations under conditions of large vehicle load variations and combined operating conditions, resulting in more accurate lateral velocity estimation.

[0084] In some embodiments, a vehicle typically includes multiple wheels, and the tire slip angles of these multiple wheels can more accurately determine whether the vehicle is in a linear zone. Therefore, in S12, the tire slip angles of multiple wheels of the vehicle can be determined, for example, the tire slip angles of all wheels can be determined, or the tire slip angles of a representative subset of wheels can be obtained, for example, the tire slip angles of one front wheel and one rear wheel can be obtained.

[0085] Thus, in S13 above, the fusion weight can be determined based on the tire slip angles of multiple wheels in the vehicle.

[0086] In some embodiments, determining the fusion weight based on the tire slip angles of multiple wheels may include:

[0087] When multiple tire slip angles are all less than the first angle threshold, the first weight is determined to be 1 and the second weight is determined to be 0. Here, multiple tire slip angles refer to the tire slip angles of multiple wheels in the vehicle.

[0088] The first angle threshold can be determined based on the road surface adhesion coefficient and tire characteristics (such as tire type, tire material, etc.).

[0089] For example, the maximum tire slip angle of the vehicle within the linear zone can be determined based on the road surface adhesion coefficient and tire characteristics, and this maximum tire slip angle can be used as a first angle threshold. For instance, on high-adhesion roads, the linear zone of the tire slip angle is generally within approximately 6°, so 6° can be used as the first angle threshold.

[0090] Typically, during the initial stage of vehicle steering, the tire slip angles of each wheel are small, and the vehicle is still in the linear region. At this time, using a tire model-based dynamics method to estimate lateral velocity can achieve high accuracy. Therefore, when the tire slip angles of multiple wheels are all less than a first angle threshold, it can be assumed that the tire slip angles of all wheels are small, and the vehicle is still in the linear region. In this case, the first weight corresponding to the tire model-based dynamics method in the fusion weight is set to 1, and the weight corresponding to the sensor-based kinematics method is set to 0. Thus, the final lateral velocity estimation result will rely entirely on the more accurate tire model-based dynamics method, resulting in a higher precision estimate.

[0091] In some embodiments, determining the fusion weight based on the tire slip angles of multiple wheels may further include:

[0092] If the maximum value among multiple tire slip angles is greater than or equal to the first angle threshold, and the second largest value among multiple tire slip angles is greater than the second angle threshold, then the first weight is determined to be 0, and the second weight is determined to be 1.

[0093] The second angle threshold can be a value smaller than the first angle threshold, set according to actual needs. For example, if the first angle threshold is 6°, the second angle threshold can be set to 3°.

[0094] When the maximum tire slip angle is greater than the first angle threshold, it can be considered that the vehicle may have entered the nonlinear region, but it cannot be accurately determined whether the vehicle has actually entered the nonlinear region. In order to accurately determine whether the vehicle has actually entered the nonlinear region, it is further determined whether the second maximum tire slip angle is greater than the second angle threshold. If the second maximum tire slip angle is greater than the second angle threshold, it is determined that the vehicle has actually entered the nonlinear region. If the second maximum tire slip angle is not greater than the second angle threshold, it can be determined that the vehicle is in the transition zone between the linear region and the nonlinear region.

[0095] When the second largest value is greater than the second angle threshold, it is determined that the vehicle has truly entered the nonlinear region. Based on the finding that the estimation results of the sensor-based kinematic method are more accurate when the vehicle is in the nonlinear region, the first weight is set to 0 and the second weight is set to 1. In this way, the final estimation result of the lateral velocity will rely entirely on the sensor-based kinematic method with higher estimation accuracy, so that the final estimation result has higher accuracy.

[0096] In some embodiments, determining the fusion weight based on the tire slip angles of multiple wheels may further include:

[0097] If the maximum value among multiple tire slip angles is greater than or equal to a first angle threshold, and the second largest value among multiple tire slip angles is less than or equal to a second angle threshold, the first weight and the second weight are determined based on the second largest value and the second angle threshold.

[0098] As described above, when the largest tire slip angle is greater than or equal to the first angle threshold, and the second largest tire slip angle is not greater than the second angle threshold, the vehicle can be determined to be in the transition zone between the linear and nonlinear regions. In this transition zone, as the second largest tire slip angle increases, the vehicle gradually transitions from the linear to the nonlinear region. The estimation accuracy of the tire model-based dynamics method gradually decreases, while the estimation accuracy of the sensor-based kinematics method gradually surpasses that of the tire model-based dynamics method. Therefore, when determining the first and second weights based on the second largest value and the second angle threshold, the first weight can be gradually decreased and the second weight gradually increased as the second largest value increases, thereby improving the accuracy of the final estimation result.

[0099] In some embodiments, determining the first weight and the second weight based on the second maximum value and the second angle threshold may include:

[0100] The ratio of the second largest value to the second angle threshold is determined as the second weight, and the difference between 1 and the second weight is determined as the first weight.

[0101] For example, the first weight can be calculated using the following formula:

[0102]

[0103] The second weight is calculated using the following formula:

[0104]

[0105] In the formula, Indicates the first weight. Indicates the second weight. This represents the second angle threshold. This represents the second largest value among the tire slip angles of multiple wheels.

[0106] By determining the first and second weights in the above manner, the first weight can be gradually reduced and the second weight can be gradually increased as the second largest value increases, thereby making the final estimation result more accurate.

[0107] In some embodiments, when estimating the lateral velocity of the vehicle based on fusion weights and motion parameters using the first method and / or the second method in S14 to obtain the final estimated result of the lateral velocity, the following may be included:

[0108] Using a tire-model-based dynamics method, the lateral velocity of the vehicle is estimated based on motion parameters, yielding the first estimate of the lateral velocity.

[0109] Using a sensor-based kinematic method, the lateral velocity of the vehicle is estimated based on motion parameters, resulting in a second estimate of the lateral velocity.

[0110] The first and second estimation results are fused according to the fusion weights to obtain the final estimation result of the lateral velocity.

[0111] Using the above method, when estimating the lateral velocity of a vehicle, the lateral velocity is estimated using both a tire-model-based dynamic method and a sensor-based kinematic method, resulting in a first and a second estimation result. Then, the first and second estimation results are fused using a fusion weight to obtain the final estimation result of the lateral velocity. By using the fusion method, the two calculation methods based on kinematics and dynamics jointly provide the lateral velocity estimation result, which solves the problem of inaccurate results caused by tire model and intermediate estimation errors in the dynamic method under conditions of large vehicle load variations and combined operating conditions, thus achieving a more accurate lateral velocity estimation.

[0112] The process of fusing the first and second estimation results using fusion weights may include:

[0113] The first estimation result is multiplied by the first weight to obtain the first product, the second estimation result is multiplied by the second weight to obtain the second product, the first product and the second product are added together, and the sum is taken as the final estimation result of the lateral velocity.

[0114] For example, the first estimation result and the second estimation result can be merged according to the following formula:

[0115]

[0116] In the formula, This represents the final estimated result of the lateral velocity. This represents the first estimate of the lateral velocity. The second estimate represents the lateral velocity.

[0117] In some embodiments, a tire-model-based dynamics method is used to estimate the lateral velocity of the vehicle based on motion parameters to obtain a first estimate of the lateral velocity, which may include:

[0118] The observed lateral acceleration, yaw rate, and longitudinal speed from the motion parameters are defined as the first observed parameters.

[0119] The tire swerve angle and longitudinal acceleration in the motion parameters are determined as the first control variables.

[0120] The first and second observations are input into a pre-defined dynamics observer to obtain the first observation result corresponding to the motion parameters output by the dynamics observer. The dynamics observer is constructed based on a vehicle dynamics model, a tire model, and an extended Kalman filter. The observation result output by the dynamics observer includes longitudinal velocity, lateral velocity, and yaw rate. The lateral velocity in the first observation result is determined as the estimated first lateral velocity of the vehicle. The dynamics observer can be pre-built based on a vehicle dynamics model and an extended Kalman filter (EKF), and is not limited to a specific form, that is, it is not limited to the specific dynamics model used. For example, a dynamics observer can be built based on a linear tire model and a bicycle model with front wheel steering. The state-space parameters observed by the dynamics observer, that is, the parameters included in the first observation result output, include the longitudinal velocity of the vehicle motion. u Lateral velocity v and yaw rate .

[0121] The working principle of the dynamics observer is mainly to obtain the predicted motion state of the vehicle through model recursion, and then determine the vehicle's driving state based on the predicted motion state and the motion parameters measured by sensors, thereby obtaining the estimated lateral velocity. The dynamics observer includes a first state transition equation based on the vehicle dynamics model and a first observation equation based on the vehicle dynamics model and the tire model. Based on this, the first observation and the first control quantity are input into the preset dynamics observer to obtain the first observation result corresponding to the motion parameters output by the dynamics observer, which may include:

[0122] The dynamic observer predicts the change in the first parameter based on the first control variable, the first state transition equation, and the observation results of the previous output. The change in the first parameter includes the change in longitudinal velocity, the change in lateral velocity, and the change in yaw rate.

[0123] The dynamics observer predicts the first observation result corresponding to the input motion parameters based on the previous output observation result and the predicted change in the first parameter.

[0124] The dynamic observer corrects the first observation result based on the first observation equation and the first observation, thus obtaining the second observation result.

[0125] The dynamics observer identifies the second observation result as the observation result corresponding to the motion parameters and outputs it.

[0126] The first observation result is the predicted motion state of the vehicle obtained through model recursion, and the second observation result is the driving state of the vehicle determined by the predicted motion state and the motion parameters measured by the sensors.

[0127] For example, taking a bicycle as the vehicle dynamics model, the first state transition equation set in the dynamics observer is as follows:

[0128]

[0129]

[0130]

[0131] In the formula, This represents the change in longitudinal velocity. This represents the change in yaw rate. This represents the change in lateral velocity. This represents the longitudinal velocity in the previously output observation results. This represents the yaw rate from the previously output observation. This represents the lateral velocity from the previously output observations. and Indicates the lateral stiffness of the front and rear axles of the vehicle. This represents the distance from the center of the front and rear axles to the vehicle's center of gravity, and I represents the vehicle's yaw moment of inertia. Indicates vehicle mass. Indicates the front axle steering angle of the vehicle. Represents the longitudinal acceleration in the first control variable, where, , , , I and It can be preset according to the vehicle model. Based on the tire angle in the first control quantity.

[0132] Based on the first state transition equation described above, we can determine the vehicle's previous state... Predict the change in state This allows for the prediction of the current vehicle's motion state. In other words, it allows for the prediction of the first observation result corresponding to the input motion parameters based on the previous output observation results and the predicted change in the first parameter.

[0133] Similarly, based on the bicycle model and the linear tire model, we can obtain the observations obtained from the sensor measurements and the longitudinal velocity and vehicle state. The first observation equation for the relationship is given by the following equation:

[0134]

[0135]

[0136]

[0137] In the formula, This indicates lateral acceleration.

[0138] The first observation equation described above provides the relationship between the onboard sensors and the vehicle's motion state, which is used to correct the vehicle state predicted by the model based on the first observation, i.e., the first observation result. After prediction and correction, a first estimate of the lateral velocity based on the extended Kalman method and vehicle dynamics can be obtained. Typically, when the vehicle's tire slip angle is small, the dynamics-based estimation method has high accuracy and is almost unaffected by sensor offset.

[0139] In some embodiments, the estimation accuracy of sensor-based kinematics methods is highly dependent on the accuracy of sensor measurements. If there is a deviation in the sensor measurement, the deviation will accumulate with integration when the sensor-based kinematics method estimates the lateral velocity, leading to inaccurate estimation results. Conversely, as long as the sensor offset is corrected, the sensor-based kinematics method can obtain accurate estimation results. Therefore, to improve the estimation accuracy of sensor-based kinematics methods, before estimating the lateral velocity of the vehicle based on motion parameters using sensor-based kinematics methods, the target sensor zero-point drift corresponding to the lateral acceleration can be determined first. Then, the sensor-based kinematics method can be used to estimate the lateral velocity of the vehicle based on the motion parameters and the target sensor zero-point drift, resulting in a second lateral velocity estimation result for the vehicle.

[0140] In this way, the offset of the sensor can be corrected based on the zero-point drift of the target sensor, thereby solving the problem of cumulative error in kinematic methods and making the estimation results of sensor-based kinematic methods more accurate.

[0141] In some embodiments, a dynamic observer can be used to determine the target sensor zero-point drift corresponding to lateral acceleration. Specific determination methods may include:

[0142] With a first weight of 1, the lateral acceleration estimate is determined using the dynamic observer based on the predicted lateral velocity change and the longitudinal velocity and yaw rate from the previous observation. The difference between the lateral acceleration in the first observation and the estimated lateral acceleration is used as the sensor zero-point offset corresponding to the lateral acceleration. This sensor zero-point offset is defined as the target sensor zero-point drift corresponding to the lateral acceleration.

[0143] If the first weight is not 1, the sensor zero-point drift of the lateral acceleration calculated last time by the dynamic observer with the first weight of 1 is determined as the target sensor zero-point drift of the lateral acceleration.

[0144] For example, the lateral acceleration of a vehicle can be calculated using the following formula:

[0145]

[0146] Calculate the sensor zero-point offset corresponding to the lateral acceleration using the following formula:

[0147]

[0148] In the formula, This indicates the sensor zero-point offset corresponding to lateral acceleration. This represents the value of lateral acceleration in the first observation.

[0149] When the tire slip angle of the vehicle is small, the estimation accuracy of the tire model-based dynamics method is very high and almost unaffected by sensor offset. Therefore, when the first weight is 1, the accurate lateral velocity can be obtained by the tire model-based dynamics method. Based on this, when the first weight is 1, the actual vehicle lateral acceleration can be derived in real time from the lateral velocity output by the dynamics observer. The difference between the actual vehicle lateral acceleration and the lateral acceleration measured by the sensor is then used to update the sensor zero-point offset corresponding to the lateral acceleration. The updated sensor zero-point offset is then used as the target sensor zero-point offset.

[0150] By using the above method, when determining the zero-point offset of the target sensor, a lateral acceleration reference value is given by a dynamic method based on a tire model, which can correct the sensor zero-point offset in real time and ensure that an accurate target sensor zero-point offset can be obtained.

[0151] Furthermore, after subtracting the actual vehicle lateral acceleration from the lateral acceleration measured by the sensor, a window averaging can be performed to achieve smooth filtering. Then, the value obtained after window averaging is used to update the sensor zero-point offset corresponding to the lateral acceleration.

[0152] When the tire slip angle is large, that is, when the first weight is not 1, the dynamic method based on the tire model can no longer obtain the accurate lateral velocity, and thus cannot accurately predict the sensor zero drift offset. However, the sensor offset has a much longer time dimension than the vehicle steering. Therefore, in this case, the sensor zero offset corresponding to the lateral acceleration obtained in the last update when the first weight is 1 can be used as the target sensor zero offset.

[0153] After determining the target sensor zero-point drift corresponding to the lateral acceleration, the vehicle's lateral velocity can be estimated using sensor-based kinematic methods based on motion parameters and the target sensor zero-point drift, thus obtaining the vehicle's second lateral velocity estimation result.

[0154] In some embodiments, when using a sensor-based kinematic method to estimate the lateral velocity of a vehicle based on motion parameters and the zero-point drift of a target sensor to obtain a second lateral velocity estimation result for the vehicle, the method may include:

[0155] The longitudinal vehicle speed in the motion parameters is determined as the second observable.

[0156] Lateral acceleration, longitudinal acceleration, and yaw rate from the motion parameters are determined as the second control variables.

[0157] The target sensor zero-point drift, the second observation, and the second control quantity are input into a preset kinematic observer to obtain the second observation result corresponding to the motion parameters output by the kinematic observer. The kinematic observer is constructed based on the vehicle kinematic model and the extended Kalman filter. The observation result output by the kinematic observer includes longitudinal velocity and lateral velocity. The lateral velocity in the second observation result is determined as the second lateral velocity estimation result of the vehicle.

[0158] Similar to dynamics observers, kinematics observers can be built using extended Kalman filters and vehicle kinematics models. The specific kinematic model used is not limited; for example, it can be based on a bicycle kinematics model. The state-space parameters observed by the kinematics observer, i.e., the parameters included in the second observation result, include the longitudinal velocity of the vehicle. u and lateral velocity v .

[0159] The working principle of the kinematic observer is mainly to obtain the predicted motion state of the vehicle through model recursion, and then determine the vehicle's driving state based on the predicted motion state and the motion parameters measured by the sensors, thereby obtaining the estimated lateral velocity. The kinematic observer includes a second state transition equation based on the vehicle kinematic model and a second observation equation based on the vehicle kinematic model. Based on this, the target sensor zero-point drift, the second observation quantity, and the second control quantity are input into the preset kinematic observer to obtain the second observation result corresponding to the motion parameters output by the kinematic observer, which may include:

[0160] The kinematic observer corrects the longitudinal acceleration in the second control variable based on the target sensor's zero-point offset.

[0161] The kinematic observer predicts the change in the second parameter based on the corrected second control variable, the second state transition equation, and the observation results of the previous output. The change in the second parameter includes the change in longitudinal velocity and the change in lateral velocity.

[0162] The kinematic observer predicts a third observation corresponding to the input motion parameters based on the previous output observation results and the predicted change in the second parameter.

[0163] The kinematic observer corrects the third observation result based on the second observation equation and the second observation, thus obtaining the fourth observation result.

[0164] The kinematic observer identifies the fourth observation as the observation corresponding to the motion parameters and outputs it.

[0165] For example, taking the kinematic model used by the kinematic observer as a bicycle motion model, the second state transition equation set in the kinematic observer can be as follows:

[0166]

[0167]

[0168] In the formula, This represents the change in lateral velocity. This represents the change in longitudinal velocity. This represents the longitudinal velocity in the previously output observation results. This represents the lateral velocity from the previously output observations. This represents the yaw rate in the second control variable. This represents the corrected lateral acceleration value.

[0169] The corresponding correction formula is shown below:

[0170]

[0171] In the formula, This represents the longitudinal acceleration in the second control variable. This indicates the zero-point offset of the target sensor.

[0172] The second observation equation set in the kinematic observer can be as follows:

[0173]

[0174] In sensor-based kinematics methods, the sensor offset is corrected based on the zero-degree offset of the target sensor according to the lateral acceleration. This solves the problem of accumulated error in sensor-based kinematics methods, enabling accurate lateral velocity to be obtained even when the tire slip angle is large, thereby improving the reliability range of lateral velocity estimation.

[0175] See Figure 3 This is a schematic diagram illustrating a vehicle lateral speed estimation method provided in an embodiment of this application. Figure 3 As shown, estimating lateral velocity may include:

[0176] Acquire the vehicle's motion parameters, including tire swerve angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal vehicle speed.

[0177] The vehicle's driving state is determined based on tire steering angle, lateral acceleration, and yaw rate, i.e., the tire slip angle is determined, and then the fusion weight is determined based on the tire slip angle.

[0178] The tire steering angle, lateral acceleration, longitudinal acceleration, yaw rate, and longitudinal vehicle speed are input into the dynamics observer to obtain the lateral vehicle speed output by the dynamics observer, which is the first estimate of the lateral vehicle speed.

[0179] Based on the accurate lateral vehicle speed and lateral acceleration output by the dynamics observer, the zero-degree drift of the target sensor for lateral acceleration is determined.

[0180] The lateral acceleration, longitudinal acceleration, yaw rate, longitudinal vehicle speed, and target sensor zero-degree drift are input into the kinematics observer to obtain the lateral vehicle speed output by the kinematics observer, which is also the second estimate of the lateral vehicle speed.

[0181] The first and second estimation results are weighted and fused according to the fusion weights to obtain the final estimation result of the lateral vehicle speed.

[0182] The vehicle lateral velocity estimation method provided in this application, compared with traditional solutions, uses a tire model-based dynamics method to predict lateral acceleration, thereby determining the corresponding sensor zero-degree drift. Furthermore, it corrects the sensor in real time based on the zero-degree drift, eliminating sensor error and solving the problem of accumulated error in sensor-based kinematics methods. Moreover, by fusing tire model-based kinematics and sensor-based dynamics methods, it jointly provides the lateral velocity estimation result, solving the problem of inaccurate estimation results caused by tire model and intermediate estimation errors under conditions of large vehicle load variations and combined operating conditions, achieving more accurate lateral velocity estimation.

[0183] Based on the vehicle lateral speed estimation method provided in the above embodiments, this application also provides specific implementation methods of the vehicle lateral speed estimation device. Please refer to the following embodiments.

[0184] See Figure 4 The vehicle lateral speed estimation device provided in this application embodiment includes the following modules:

[0185] The acquisition module 401 is used to acquire the vehicle's motion parameters, including tire steering angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal vehicle speed.

[0186] Side slip angle determination module 402 is used to determine the tire side slip angle of the vehicle.

[0187] The fusion weight determination module 403 is used to determine the fusion weights based on the tire slip angle. The fusion weights include a first weight corresponding to a first method and a second weight corresponding to a second method. The first method is a dynamic method based on a tire model, and the second method is a kinematic method based on sensors. The first weight is negatively correlated with the tire slip angle, and the second weight is positively correlated with the tire slip angle.

[0188] The fusion module 404 is used to estimate the lateral velocity of the vehicle based on the fusion weights and motion parameters using the first method and / or the second method, so as to obtain the final estimate of the lateral velocity.

[0189] The vehicle lateral velocity estimation device of this application determines fusion weights based on the vehicle's tire slip angle. The fusion weights include a first weight corresponding to a tire model-based dynamics method and a second weight corresponding to a sensor-based kinematics method. Using the tire model-based dynamics method and / or the sensor-based kinematics method, the vehicle's lateral velocity is estimated based on the fusion weights and the vehicle's motion parameters to obtain the final estimated result. According to this application embodiment, the fusion of the tire model-based dynamics method and the sensor-based kinematics method is achieved, solving the problem that when only the tire model-based dynamics method is used for estimation, the results are inaccurate due to errors in the tire model and intermediate estimations under conditions of large vehicle load variations and combined operating conditions, thus achieving more accurate lateral velocity estimation.

[0190] In some embodiments, the tire slip angle of a vehicle includes the tire slip angles of multiple wheels in the vehicle. The fusion weight determination module 403 can be used to:

[0191] If multiple tire slip angles are all less than the first angle threshold, the first weight is determined to be 1, and the second weight is determined to be 0.

[0192] If the maximum value among multiple tire slip angles is greater than or equal to a first angle threshold, and the second largest value among multiple tire slip angles is greater than a second angle threshold, then the first weight is determined to be 0, and the second weight is determined to be 1.

[0193] If the maximum value among multiple tire slip angles is greater than or equal to the first angle threshold, and the second largest value among multiple tire slip angles is less than or equal to the second angle threshold, the ratio of the second largest value to the second angle threshold is determined as the second weight, and the difference between 1 and the second weight is determined as the first weight.

[0194] In some embodiments, the fusion module 404 includes:

[0195] The first estimation submodule is used to estimate the lateral velocity of the vehicle based on the motion parameters using a tire model-based dynamics method, and obtain the first estimation result of the lateral velocity.

[0196] The second estimation submodule is used to estimate the lateral velocity of the vehicle based on motion parameters using a sensor-based kinematic method, thus obtaining a second estimation result of the lateral velocity.

[0197] The result fusion submodule is used to fuse the first estimation result and the second estimation result according to the fusion weight to obtain the final estimation result of the lateral velocity.

[0198] In some embodiments, the first estimation submodule may include:

[0199] The first determining unit is used to determine the observed lateral acceleration, yaw rate, and longitudinal vehicle speed from the motion parameters as the first observed parameters.

[0200] The second determining unit is used to determine the tire swerve angle and longitudinal acceleration from the motion parameters as the first control quantity. The dynamic observation unit is used to input the first and second observations into a preset dynamic observer to obtain the first observation result corresponding to the motion parameters output by the dynamic observer. The dynamic observer is constructed based on the vehicle dynamics model, the tire model, and the extended Kalman filter. The observation results output by the dynamic observer include longitudinal velocity, lateral velocity, and yaw rate.

[0201] The first estimation result determination unit is used to determine the lateral velocity in the first observation result as the first lateral velocity estimation result of the vehicle.

[0202] In some embodiments, the dynamics observer includes a first state transition equation based on a vehicle dynamics model and a first observation equation based on the vehicle dynamics model and a tire model. Based on this, the dynamics observation unit can be used for:

[0203] The dynamic observer predicts the change in the first parameter based on the first control variable, the first state transition equation, and the observation results of the previous output. The change in the first parameter includes the change in longitudinal velocity, the change in lateral velocity, and the change in yaw rate.

[0204] The dynamics observer predicts the first observation result corresponding to the input motion parameters based on the previous output observation result and the predicted change in the first parameter.

[0205] The dynamic observer corrects the first observation result based on the first observation equation and the first observation, thus obtaining the second observation result.

[0206] The dynamics observer identifies the second observation result as the observation result corresponding to the motion parameters and outputs it.

[0207] In some embodiments, the apparatus may further include: a sensor drift determination module (not shown), for:

[0208] Before estimating the lateral velocity of the vehicle based on motion parameters using sensor-based kinematic methods, the target sensor zero-point drift corresponding to the lateral acceleration is determined.

[0209] Correspondingly, the second estimation submodule can be used for:

[0210] Using a sensor-based kinematic method, the lateral velocity of the vehicle is estimated based on motion parameters and the zero-point drift of the target sensor, resulting in a second lateral velocity estimation result for the vehicle.

[0211] In some embodiments, the sensor drift determination module may be used for:

[0212] With a first weight of 1, the lateral acceleration estimate is determined using the dynamic observer based on the predicted lateral velocity change and the longitudinal velocity and yaw rate from the previous observation. The difference between the lateral acceleration in the first observation and the estimated lateral acceleration is used as the sensor zero-point offset corresponding to the lateral acceleration. This sensor zero-point offset is defined as the target sensor zero-point drift corresponding to the lateral acceleration.

[0213] If the first weight is not 1, the sensor zero-point drift of the lateral acceleration calculated last time by the dynamic observer with the first weight of 1 is determined as the target sensor zero-point drift of the lateral acceleration.

[0214] In some embodiments, the second estimation submodule may include:

[0215] The first determining unit is used to determine the longitudinal vehicle speed from the motion parameters as the second observation.

[0216] The second determining unit is used to determine the lateral acceleration, longitudinal acceleration, and yaw rate from the motion parameters as the second control variables.

[0217] The kinematic observation unit is used to input the target sensor zero-point drift, the second observation, and the second control quantity into a preset kinematic observer to obtain the second observation result corresponding to the motion parameters output by the kinematic observer. The kinematic observer is constructed based on the vehicle kinematic model and an extended Kalman filter. The observation results output by the kinematic observer include longitudinal velocity and lateral velocity.

[0218] The second estimation result determination unit is used to determine the lateral velocity in the second observation result as the second lateral velocity estimation result of the vehicle.

[0219] In some embodiments, the kinematic observer includes a second state transition equation and a second observation equation established based on a vehicle kinematic model. Based on this, the kinematic observation unit can be used for:

[0220] The kinematic observer corrects the longitudinal acceleration in the second control variable based on the target sensor's zero-point offset.

[0221] The kinematic observer predicts the change in the second parameter based on the corrected second control variable, the second state transition equation, and the observation results of the previous output. The change in the second parameter includes the change in longitudinal velocity and the change in lateral velocity.

[0222] The kinematic observer predicts a third observation corresponding to the input motion parameters based on the previous output observation results and the predicted change in the second parameter.

[0223] The kinematic observer, based on the second observation equation, uses motion parameters to correct the third observation result, thus obtaining the fourth observation result.

[0224] The kinematic observer identifies the fourth observation as the observation corresponding to the motion parameters and outputs it.

[0225] The vehicle lateral speed estimation device provided in this application embodiment can realize all the processes implemented in the above-described vehicle lateral speed estimation method embodiment. To avoid repetition, it will not be described again here.

[0226] Figure 5 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0227] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.

[0228] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0229] Memory 502 may include a large-capacity memory for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 502 is non-volatile solid-state memory. Memory 502 may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, typically, memory 502 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it performs the operations described in any of the vehicle lateral speed estimation methods in the above embodiments.

[0230] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the vehicle lateral speed estimation methods in the above embodiments.

[0231] In one example, the electronic device may also include a communication interface 503 and a bus 510. Wherein, as... Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0232] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0233] Bus 510 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0234] Furthermore, in conjunction with the vehicle lateral speed estimation methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions, which, when executed by a processor, implement any of the vehicle lateral speed estimation methods in the above embodiments.

[0235] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0236] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0237] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0238] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0239] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method of estimating a lateral velocity of a vehicle, characterized by, include: Acquire the vehicle's motion parameters, including tire swerve angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal vehicle speed. Determine the tire slip angle of the vehicle. The fusion weights are determined based on the tire slip angle. These fusion weights include a first weight corresponding to a first method and a second weight corresponding to a second method. The first method is a dynamic method based on a tire model, and the second method is a sensor-based kinematic method. The first weight is negatively correlated with the tire slip angle, and the second weight is positively correlated with the tire slip angle. Using the first method and / or the second method, the lateral velocity of the vehicle is estimated based on the fusion weights and the motion parameters to obtain the final estimated result of the lateral velocity; The tire slip angle of the vehicle includes the tire slip angles of multiple wheels in the vehicle, and the step of determining the fusion weight based on the tire slip angle includes: If multiple tire slip angles are less than a first angle threshold, the first weight is determined to be 1, and the second weight is determined to be 0. If the maximum value among the plurality of tire slip angles is greater than or equal to the first angle threshold, and the second largest value among the plurality of tire slip angles is greater than the second angle threshold, then the first weight is determined to be 0, and the second weight is determined to be 1. If the maximum value among the plurality of tire slip angles is greater than or equal to the first angle threshold, and the second largest value among the plurality of tire slip angles is less than or equal to the second angle threshold, the ratio of the second largest value to the second angle threshold is determined as the second weight, and the difference between 1 and the second weight is determined as the first weight.

2. The method of claim 1, wherein, The step of estimating the lateral velocity of the vehicle based on the fusion weights and the motion parameters using the first method and / or the second method to obtain the final estimation result of the lateral velocity includes: Using a tire-model-based dynamics method, the lateral velocity of the vehicle is estimated based on the motion parameters, resulting in a first estimate of the lateral velocity. Using a sensor-based kinematic method, the lateral velocity of the vehicle is estimated based on the motion parameters, resulting in a second estimate of the lateral velocity. The first estimation result and the second estimation result are fused according to the fusion weight to obtain the final estimation result of the lateral velocity.

3. The method of claim 2, wherein, The method of estimating the lateral velocity of the vehicle using a tire-model-based dynamics approach, based on the motion parameters, to obtain a first lateral velocity estimation result for the vehicle, includes: The lateral acceleration, yaw rate, and longitudinal speed among the motion parameters are defined as the first observations. The tire swerve angle and longitudinal acceleration from the aforementioned motion parameters are determined as the first control variables. The first observation and the first control quantity are input into a preset dynamics observer to obtain the first observation result corresponding to the motion parameters output by the dynamics observer. The dynamics observer is constructed based on a vehicle dynamics model, a tire model, and an extended Kalman filter. The observation result output by the dynamics observer includes longitudinal velocity, lateral velocity, and yaw rate. The lateral velocity in the first observation result is determined as the first lateral velocity estimation result of the vehicle.

4. The method of claim 3, wherein, The dynamics observer includes a first state transition equation based on the vehicle dynamics model and a first observation equation based on the vehicle dynamics model and the tire model. The first observation and the first control quantity are input into the preset dynamics observer to obtain a first observation result output by the dynamics observer corresponding to the motion parameters, including: The dynamics observer predicts the change in the first parameter based on the first control variable, the first state transition equation, and the previous output observation results. The change in the first parameter includes the change in longitudinal velocity, the change in lateral velocity, and the change in yaw rate. The dynamics observer predicts the first observation result corresponding to the input motion parameters based on the previous output observation result and the predicted change in the first parameter. The dynamics observer corrects the first observation result based on the first observation equation and the first observation, thus obtaining the second observation result. The dynamics observer determines the second observation result as the observation result corresponding to the motion parameters and outputs it.

5. The method of claim 4, wherein, Before estimating the lateral velocity of the vehicle based on the motion parameters using a sensor-based kinematic method, the method further includes: Determine the target sensor zero-point drift corresponding to the lateral acceleration. The method of estimating the lateral velocity of the vehicle using a sensor-based kinematics approach based on the motion parameters includes: Using the sensor-based kinematic method, the lateral velocity of the vehicle is estimated based on the motion parameters and the zero-point drift of the target sensor, resulting in a second lateral velocity estimation result for the vehicle.

6. The method of claim 5, wherein, The determination of the target sensor zero-point drift corresponding to the lateral acceleration includes: With the first weight set to 1, the dynamic observer determines an estimated lateral acceleration value based on the predicted lateral velocity change and the longitudinal velocity and yaw rate from the previous observation. The difference between the lateral acceleration in the first observation and the estimated lateral acceleration value is used as the sensor zero-point offset corresponding to the lateral acceleration. This sensor zero-point offset is defined as the target sensor zero-point drift corresponding to the lateral acceleration. If the first weight is not 1, the sensor zero-point drift of the lateral acceleration calculated last time by the dynamic observer when the first weight is 1 is determined as the target sensor zero-point drift of the lateral acceleration.

7. The method according to claim 5 or 6, characterized in that, The method of using the sensor-based kinematics to estimate the lateral velocity of the vehicle based on the motion parameters and the zero-point drift of the target sensor, to obtain a second lateral velocity estimation result for the vehicle, includes: The longitudinal vehicle speed in the aforementioned motion parameters is determined as the second observation. The lateral acceleration, longitudinal acceleration, and yaw rate among the motion parameters are determined as the second control variables. The target sensor zero-point drift, the second observation, and the second control quantity are input into a preset kinematic observer to obtain a second observation result corresponding to the motion parameters, output by the kinematic observer. The kinematic observer is constructed based on a vehicle kinematic model and an extended Kalman filter. The observation result output by the kinematic observer includes longitudinal velocity and lateral velocity. The lateral velocity in the second observation result is determined as the second lateral velocity estimate of the vehicle.

8. The method of claim 7, wherein, The kinematic observer includes a second state transition equation and a second observation equation established based on the vehicle kinematic model. The step of inputting the target sensor zero-point drift, the second observation quantity, and the second control quantity into a preset kinematic observer to obtain a second observation result output by the kinematic observer corresponding to the motion parameters includes: The kinematic observer corrects the longitudinal acceleration in the second control quantity based on the zero-point offset of the target sensor. The kinematic observer predicts the change in the second parameter based on the corrected second control variable, the second state transition equation, and the previous output observation results. The change in the second parameter includes the change in longitudinal velocity and the change in lateral velocity. The kinematic observer predicts a third observation corresponding to the input kinematic parameters based on the previous output observation results and the predicted change in the second parameter. The kinematic observer corrects the third observation result based on the second observation equation and the second observation, thus obtaining a fourth observation result. The kinematic observer determines the fourth observation result as the observation result corresponding to the motion parameters and outputs it.

9. A vehicle lateral velocity estimation device characterized by comprising: include: The acquisition module is used to acquire the vehicle's motion parameters, including tire steering angle, lateral acceleration, longitudinal acceleration, yaw rate, and / or longitudinal vehicle speed. The slip angle determination module is used to determine the tire slip angle of the vehicle. The fusion weight determination module is used to determine fusion weights based on the tire slip angle. The fusion weights include a first weight corresponding to a first method and a second weight corresponding to a second method. The first method is a dynamic method based on a tire model, and the second method is a kinematic method based on sensors. The first weight is negatively correlated with the tire slip angle, and the second weight is positively correlated with the tire slip angle. The fusion module is used to estimate the lateral velocity of the vehicle based on the fusion weights and the motion parameters using the first method and / or the second method, to obtain the final estimation result of the lateral velocity; The tire slip angle of the vehicle includes the tire slip angles of multiple wheels in the vehicle. The fusion weight determination module is specifically used for: If multiple tire slip angles are less than a first angle threshold, the first weight is determined to be 1, and the second weight is determined to be 0. If the maximum value among the plurality of tire slip angles is greater than or equal to the first angle threshold, and the second largest value among the plurality of tire slip angles is greater than the second angle threshold, then the first weight is determined to be 0, and the second weight is determined to be 1. If the maximum value among the plurality of tire slip angles is greater than or equal to the first angle threshold, and the second largest value among the plurality of tire slip angles is less than or equal to the second angle threshold, the ratio of the second largest value to the second angle threshold is determined as the second weight, and the difference between 1 and the second weight is determined as the first weight.

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