Track prediction method of three-axis vehicle, vehicle-mounted controller, vehicle and storage medium

Abstract

The invention provides a trajectory prediction method of a three-axis vehicle, a vehicle-mounted controller, a vehicle and a storage medium, and relates to the technical field of automatic driving. In the running process of the vehicle, the vehicle speed of the vehicle at the current moment and the rotating angle of each axle are collected, the slip angle and the cornering stiffness of each axle at the current moment are calculated based on the parameter information of the vehicle and the vehicle speed and the rotating angle of each axle at the current moment, and then the slip angle and the cornering stiffness of each axle are calculated according to the parameter information of the vehicle and the cornering information and the rotating angle of each axle at the current moment. According to the scheme, only the vehicle speed and the rotation angles of all the shafts of the vehicle at the current moment need to be collected, excessive environment information does not need to be collected through a sensor, the anti-interference performance is high, excessive complex calculation processing is not needed, and prediction of the running track of the vehicle can be achieved; meanwhile, the dynamic model adopted in the scheme is constructed based on the characteristics of the three-axle vehicle, so that the accuracy of a calculation result can be ensured.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a trajectory prediction method for a three-axle vehicle, an on-board controller, a vehicle and a storage medium. Background Art

[0002] Trajectory prediction is an important part of autonomous driving technology. Its function is to predict the movement trajectory of the vehicle in the future based on road conditions and maps. The accuracy of the prediction results directly affects the calculation time and accuracy of downstream behavioral decisions and trajectory planning.

[0003] In the related art, for multi-axle vehicles, when performing trajectory prediction, the radius of the vehicle's trajectory circle can be calculated using environmental data information. For example, the surrounding environment information can be collected through a camera or the current vehicle position can be determined through multi-sensor fusion (Global Positioning System, GPS for short) to determine the radius of the vehicle's trajectory circle.

[0004] However, measuring the radius of the vehicle trajectory circle through environmental information is easily affected by the environment, increases the complexity of the system, and has the problem of low reliability. Summary of the invention

[0005] The purpose of the present invention is to provide a trajectory prediction method for a three-axle vehicle, an on-board controller, a vehicle and a storage medium in order to solve the technical problems existing in the prior art in view of the deficiencies in the prior art.

[0006] To achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for predicting a driving trajectory of a three-axle vehicle, the method comprising:

[0008] Acquire parameter information of the vehicle, the current vehicle speed and the turning angle of each axis, wherein the parameter information includes: a first distance between the first axis and the second axis, a second distance between the first axis and the third axis, and a distance from each axis to the center of mass of the vehicle;

[0009] Determine the side slip information of each axis at the current moment according to the parameter information of the vehicle, the vehicle speed and the rotation angle of each axis at the current moment, a pre-built dynamic model and a pre-determined characteristic parameter set of each axis, wherein the characteristic parameter set includes a stiffness factor, a shape factor and a peak factor, and the side slip information includes a side slip angle and a side slip stiffness;

[0010] According to the parameter information of the vehicle, the side deviation information and the turning angle of each axis at the current moment, the trajectory information of the vehicle at the next moment after the current moment is predicted.

[0011] Optionally, determining the side slip information of each axle at the current moment according to the parameter information of the vehicle, the vehicle speed and the rotation angle of each axle at the current moment, a pre-built dynamic model and a pre-determined characteristic parameter set of each axle includes:

[0012] Obtaining the mass of the vehicle;

[0013] Inputting the vehicle speed at the current moment, the rotation angle of each axle, the mass, the first distance, the second distance, and the distance from each axle to the center of mass of the vehicle into the dynamic model;

[0014] Inputting the first distance, the second distance and the rotation angles of each axis at the current moment into a rotation angle relationship model;

[0015] Determining the axial lateral force of each axis at the current moment according to the characteristic parameter set of each axis;

[0016] Inputting the axial lateral force of each axis at the current moment into a pre-constructed lateral force relationship model between the lateral deflection information of each axis and the axial lateral force;

[0017] The dynamic model, the rotation angle relationship model and the cornering force relationship model are solved jointly to obtain the cornering information of each axis at the current moment.

[0018] Optionally, determining the axial lateral force of each axis at the current moment according to the characteristic parameter set of each axis includes:

[0019] The characteristic parameter set of each axle is input into the pre-constructed tire magic formula to solve and obtain the axial lateral force of each axle at the current moment.

[0020] Optionally, predicting the trajectory information of the vehicle at the next moment after the current moment based on the parameter information of the vehicle, the side deviation information and the turning angle of each axle at the current moment includes:

[0021] determining a first difference between the second distance and the first distance;

[0022] Summing the sideslip angle and the rotation angle of the second axis at the current moment to obtain a first summation result, and determining a tangent value of the first summation result;

[0023] Summing the sideslip angle and the rotation angle of the third axis at the current moment to obtain a second summation result, and determining a tangent value of the second summation result;

[0024] Determine a second difference between the tangent value of the first summation result and the tangent value of the second summation result;

[0025] A ratio of the first difference to the second difference is determined, and the ratio is used as the trajectory circle radius at a moment next to the current moment.

[0026] Optionally, the process of determining the characteristic parameter set of each axis includes:

[0027] Obtaining an initial characteristic parameter set of each axle on the vehicle;

[0028] Acquire the measured values ​​of the vehicle at the target historical moment, the measured values ​​including: measured vehicle speed, measured trajectory circle radius and measured turning angles of each axis;

[0029] The characteristic parameter set of each axis is determined according to the measured value of the target historical moment and the initial characteristic parameter set.

[0030] Optionally, determining the characteristic parameter set of each axis according to the measured values ​​at the multiple historical moments and the initial characteristic parameter set includes:

[0031] A. using the initial feature parameter set as an initial current feature parameter set;

[0032] B. determining the radius of the current trajectory circle according to the current feature parameter set and the measured value;

[0033] C. updating the current feature parameter set according to the difference between the current trajectory circle radius and the measured trajectory circle radius to obtain a new current feature parameter set;

[0034] D. Execute steps BC repeatedly until the difference between the current trajectory circle radius and the measured trajectory circle radius meets a preset condition.

[0035] In the second aspect, an embodiment of the present application also provides a vehicle-mounted controller, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the method provided in the first aspect.

[0036] In a third aspect, an embodiment of the present application further provides a vehicle, comprising the vehicle-mounted controller and the angle acquisition unit provided in the second aspect; the vehicle-mounted controller is in communication connection with the angle acquisition unit;

[0037] The angle acquisition unit is used to acquire the rotation angle of each axis of the vehicle at the current moment, and send the rotation angle of each axis at the current moment to the vehicle controller;

[0038] The vehicle-mounted controller is used to execute the method provided in the first aspect.

[0039] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, any of the methods provided in the first aspect is executed.

[0040] The beneficial effects of this application are:

[0041] The embodiment of the present application provides a trajectory prediction method for a three-axle vehicle, a vehicle controller, a vehicle and a storage medium. During the driving process of the vehicle, the vehicle speed and the turning angle of each axle at the current moment are collected, and based on the parameter information of the vehicle and the speed and the turning angle of each axle at the current moment, the slip angle and the side stiffness of each axle at the current moment are calculated, and then the trajectory information of the vehicle at the next moment at the current moment is predicted by the parameter information of the vehicle, the side slip information and the turning angle of each axle at the current moment, that is, this scheme only needs to collect the vehicle speed and the turning angle of each axle at the current moment, and does not need to collect too much environmental information through sensors, and has strong anti-interference ability; that is, in this scheme, by combining multiple known parameter information, the speed and the turning angle of each axle at the current moment, the pre-constructed dynamic model and the pre-determined characteristic parameter set of each axle, the trajectory information of the vehicle at the next moment at the current moment can be predicted, and the prediction of the vehicle driving trajectory can be realized without too much complex calculation processing. Among them, the dynamic model is constructed based on the characteristics of the three-axle vehicle itself, so that the accuracy of the calculation result can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A schematic diagram of a flow chart of a trajectory prediction method for a three-axle vehicle provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of the angles of each axle of a three-axle vehicle when traveling provided by an embodiment of the present application;

[0045] Figure 3 A schematic flow chart of another trajectory prediction method for a three-axle vehicle provided in an embodiment of the present application;

[0046] Figure 4 A schematic flow chart of another trajectory prediction method for a three-axle vehicle provided in an embodiment of the present application;

[0047] Figure 5A schematic flow chart of another trajectory prediction method for a three-axle vehicle provided in an embodiment of the present application;

[0048] Figure 6 A schematic flow chart of another trajectory prediction method for a three-axle vehicle provided in an embodiment of the present application;

[0049] Figure 7 A schematic diagram of the structure of a vehicle-mounted controller provided in an embodiment of the present application;

[0050] Figure 8 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application.

[0051] Icon: 100- vehicle; 101- vehicle controller; 102- angle acquisition unit. DETAILED DESCRIPTION

[0052] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0053] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0054] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0055] First, the prior art involved in this application will be introduced in detail through the following examples.

[0056] In the related technology, when calculating the radius of the vehicle trajectory circle of most multi-axle vehicles, one method is to use environmental data information for calculation, such as collecting surrounding environment information through a camera or determining the current vehicle position through GPS and other means to perform multi-sensor fusion to determine the vehicle's trajectory circle radius; the other method is to directly calculate the trajectory radius through the geometric relationship of the vehicle's turning.

[0057] However, measuring the radius of the vehicle trajectory circle through environmental information is easily affected by the environment, increases the complexity of the system, and has low reliability; and the method of calculating the trajectory radius through the vehicle steering geometry is mostly suitable for two-axle vehicles. Due to their own characteristics, three-axle or multi-axle vehicles are hyperstatic systems between the wheels when the vehicle is turning. When the steering angles of each axle are improperly configured or the turning angle capacity is insufficient, a side slip angle will be generated on each axle. At this time, if the wheel turning angle is directly used as the calculation input, the accuracy of the calculation result will be low.

[0058] In view of the above problems, the present application proposes a prediction method for the driving trajectory of a three-axle vehicle. During the driving process of the vehicle, the vehicle speed and the turning angle of each axle at the current moment are collected, and based on the parameter information of the vehicle, the speed of the vehicle at the current moment and the turning angle of each axle, the slip angle and the sideways stiffness of each axle at the current moment are calculated. Then, the vehicle's parameter information, the slip information and the turning angle of each axle at the current moment are used to predict the trajectory information of the vehicle at the next moment of the current moment. That is, this scheme only needs to collect the vehicle speed and the turning angle of each axle at the current moment, that is, it is not necessary to collect too much environmental information through sensors, and it has strong anti-interference ability; combined with multiple known parameter information, the speed of the vehicle at the current moment and the turning angle of each axle, the pre-constructed dynamic model and the pre-determined characteristic parameter set of each axle, the trajectory information of the vehicle at the next moment of the current moment can be predicted, that is, the prediction method provided by the present application can be used without too much complex calculation processing to realize the prediction of the vehicle's driving trajectory. Among them, the dynamic model is constructed based on the characteristics of the three-axle vehicle itself, so that the accuracy of the calculation results can be ensured.

[0059] The following will illustrate the implementation principles and corresponding beneficial effects of the three-axle vehicle driving trajectory prediction method steps provided in this application through multiple specific embodiments.

[0060] refer to Figure 1 As shown, it is a flow chart of a method for predicting the driving trajectory of a three-axle vehicle provided in an embodiment of the present application; optionally, the execution subject of the method is a vehicle controller or other processing unit on the above vehicle. It should be understood that in other embodiments, the order of some steps in the method for predicting the driving trajectory of a three-axle vehicle can be interchanged according to actual needs, or some steps can be omitted or deleted. Figure 1 As shown, the method includes:

[0061] S101, obtaining vehicle parameter information, current vehicle speed and the rotation angle of each axle.

[0062] Among them, reference Figure 2 As shown, O is the origin of the vehicle coordinate system, and the parameter information of the vehicle includes: the first distance between the first axis and the second axis is L1, the second distance between the first axis and the third axis is L2, and the distance from each axis to the center of mass of the vehicle, that is, the distance from the first axis to the center of mass of the vehicle is l a , the distance from the second axis to the center of mass of the vehicle is l b , the distance from the third axis to the center of mass of the vehicle is l c It can be understood that the parameter information of the vehicle is a fixed parameter on the vehicle.

[0063] Among them, the current speed v x is the speed of the vehicle coordinate system along the x-axis; the rotation angle of the first axis is δ f , the rotation angle of the second axis is δ m , the rotation angle of the third axis is δ r It is understandable that the vehicle speed and the rotation angle of each axle at different times may change over time, that is, the vehicle speed and the rotation angle of each axle are not fixed parameters. For example, the vehicle speed and the rotation angle of each axle can be collected in real time by sensors installed on the vehicle to predict the vehicle trajectory information.

[0064] It should be noted that for a vehicle with only the first and third axes turning, the turning angle of the second axis is δ m =0.

[0065] S102, determining the side slip information of each axis at the current moment according to the parameter information of the vehicle, the vehicle speed and the rotation angle of each axis at the current moment, the pre-built dynamic model and the pre-determined characteristic parameter set of each axis.

[0066] The dynamic model is constructed based on the characteristics of the three-axle vehicle, so that the accuracy of the subsequent prediction results can be ensured. Similarly, if the driving trajectory of a four-axle vehicle needs to be predicted, a dynamic model that is more in line with the four-axle vehicle can be constructed based on the characteristics of the four-axle vehicle.

[0067] Among them, the characteristic parameter set (B, C, D) includes: stiffness factor B, shape factor C and peak factor D. Stiffness factor B, shape factor C and peak factor D can be used to characterize the axle lateral stiffness of the vehicle. The characteristic parameter set of each axis is optimized based on the measured values ​​of the historical moments of the three-axle vehicle in advance, that is, the characteristic parameter set of each axis is the optimal parameter set that matches the actual response of the vehicle.

[0068] The side slip information includes: side slip angle and side slip stiffness. Figure 2As shown, the slip angle and cornering stiffness of the first axis are α f , k f , the slip angle and cornering stiffness of the second axis are α m , k m , the slip angle and cornering stiffness of the third axis are α r , k r .

[0069] S103, predicting the trajectory information of the vehicle at the next moment after the current moment based on the parameter information of the vehicle, the side deviation information and the turning angle of each axis at the current moment.

[0070] Optionally, based on the vehicle's parameter information, the side deviation information and the turning angle of each axle at the current moment, the vehicle's trajectory information at the next moment can be predicted. That is, this solution only needs to collect the vehicle's speed and the turning angle of each axle at the current moment, that is, it does not need to collect too much environmental information through sensors, has strong anti-interference ability, and does not require too much complex calculation processing, so as to achieve the prediction of the vehicle's driving trajectory. Among them, the dynamic model is constructed based on the characteristics of the three-axle vehicle itself, so that the accuracy of the calculation results can be ensured.

[0071] In summary, the embodiment of the present application provides a method for predicting the driving trajectory of a three-axle vehicle. During the driving process of the vehicle, the vehicle speed and the turning angle of each axle at the current moment are collected, and based on the parameter information of the vehicle, the speed of the vehicle at the current moment and the turning angle of each axle, the slip angle and the lateral stiffness of each axle at the current moment are calculated, and then the trajectory information of the vehicle at the next moment of the current moment is predicted by the parameter information of the vehicle, the slip information and the turning angle of each axle at the current moment, that is, this scheme only needs to collect the speed of the vehicle at the current moment and the turning angle of each axle, and does not need to collect too much environmental information through sensors, and has strong anti-interference ability; that is, in this scheme, by combining multiple known parameter information, the speed of the vehicle at the current moment and the turning angle of each axle, the pre-constructed dynamic model and the pre-determined characteristic parameter set of each axle, the trajectory information of the vehicle at the next moment of the current moment can be predicted, and the prediction of the driving trajectory of the vehicle can be realized without too much complex calculation processing. Among them, the dynamic model is constructed based on the characteristics of the three-axle vehicle itself, so that the accuracy of the calculation results can be ensured.

[0072] The following example will explain in detail how to obtain the side deviation information of each axis at the current moment.

[0073] Optionally, refer to Figure 3 As shown, the above step S102 includes:

[0074] S301, obtaining the mass of the vehicle, and inputting the current vehicle speed, the turning angle of each axle, the mass, the first distance, the second distance, and the distance from each axle to the center of mass of the vehicle into a dynamic model.

[0075] The dynamic model constructed based on the characteristics of the three-axle vehicle is shown in the following formula (1):

[0076]

[0077] The motion of the three-axle vehicle in steady state satisfies the above dynamic model, that is, the mass of the vehicle is m, the current speed v is x The rotation angles with each axis (i.e., δ f , δ m , δ r ), the distance between the first axis and the second axis is L1, the distance between the first axis and the third axis is L2, and the distance from the first axis to the center of mass of the vehicle is l a , the distance from the second axis to the center of mass of the vehicle is l b , the distance from the third axis to the center of mass of the vehicle is l c , which is input into the kinetic model shown in the above formula (1).

[0078] S302: Input the first distance, the second distance, and the rotation angles of each axis at the current moment into a rotation angle relationship model.

[0079] The rotation angle relationship model is shown in the following formula (2):

[0080]

[0081] Among them, the rotation angles of each axis of the three-axle vehicle at different times all satisfy the rotation angle relationship model shown in the above formula (2). Therefore, the distance between the first axis and the second axis L1, the distance between the first axis and the third axis L2 and the rotation angle δ of the first axis can be f , the rotation angle of the second axis δ m and the rotation angle δ of the third axis r All are input into the rotation angle relationship model shown in the above formula (2).

[0082] S303: Determine the axial lateral force of each axis at the current moment according to the characteristic parameter set of each axis.

[0083] Optionally, the axial lateral force F of each axle at the current moment can be obtained based on the axle stiffness model (such as the tire magic formula or the axle stiffness model) pre-built for the three-axle vehicle and the characteristic parameter set of each axle. Among them, the axial lateral force of the first axle is F f , the lateral force of the second axis is F m The lateral force of the third axis is F r .

[0084] S304: Input the axial lateral force of each axis at the current moment into a pre-constructed axial lateral force relationship model between the axial lateral information and the axial lateral force of each axis.

[0085] The side slip force relationship model between the side slip information of each axis and the side slip force of the axis is shown in the following formula (3):

[0086]

[0087] Therefore, the axial lateral force of the first axis can be expressed as F f , the lateral force of the second axis is F m The lateral force of the third axis is F r Input the cornering force relationship model shown in the above formula (3).

[0088] S305 , jointly solving the dynamics model, the rotation angle relationship model and the cornering force relationship model to obtain the cornering information of each axis at the current moment.

[0089] Optionally, the above formula (1)-formula (3) are solved simultaneously to obtain the slip angle and the cornering stiffness of each axis at the current moment. That is, the slip angle and the cornering stiffness of the first axis are solved to be α and α respectively. f , k f , the slip angle and cornering stiffness of the second axis are α m , k m , the rotation angle of the third axis is α r , k r .

[0090] Optionally, the above step S304 includes:

[0091] The characteristic parameter set of each axis is input into the pre-built axle lateral stiffness model to solve the axle lateral force of each axis at the current moment.

[0092] Optionally, the axle cornering stiffness model includes: tire magic formula.

[0093] In this example, considering that the tire stiffness is the main factor affecting the axle cornering stiffness of the vehicle, it is proposed that the simplified tire magic formula shown in the following formula (4) can be used to describe the axle cornering characteristics of the vehicle, as follows:

[0094] F=Dsin(Carctan(Bα)) (4)

[0095] Therefore, the characteristic parameter set of each axis can be input into the tire magic formula shown in the above formula (4) to calculate the axial lateral force F of each axis at the current moment. Among them, the axial lateral force of the first axis is F f , the lateral force of the second axis is Fm The lateral force of the third axis is F r .

[0096] Optionally, in addition to using the tire magic formula shown in formula (4) above, other forms of axle stiffness models, such as PAC-2002, etc., can also be used to calculate the axial lateral force of each axle at the current moment.

[0097] Therefore, this scheme is mainly based on the dynamic model of the three-axle vehicle and the lateral stiffness model of each axle. According to the physical characteristics of the vehicle itself, it only needs to collect the vehicle speed and the turning angle of each axle at the current moment to predict the trajectory circle radius of the vehicle at the next moment at the current moment. No additional environmental signals are required. The system has a simple structure, low cost, strong anti-interference ability and high accuracy.

[0098] The following example will explain in detail how to determine the trajectory information of the vehicle at the next moment after the current moment.

[0099] Optionally, refer to Figure 4 As shown, the above step S103 includes:

[0100] S401: Determine a first difference between a second distance and a first distance.

[0101] S402: summing the sideslip angle and the rotation angle of the second axis at the current moment to obtain a first summation result, and determining a tangent value of the first summation result.

[0102] S403, summing the sideslip angle and the turning angle of the third axis at the current moment to obtain a second summation result, and determining a tangent value of the second summation result.

[0103] S404: Determine a second difference between the tangent value of the first summation result and the tangent value of the second summation result.

[0104] S405: Determine the ratio of the first difference to the second difference, and use the ratio as the radius of the trajectory circle at the next moment after the current moment.

[0105] In this embodiment, the following calculation method can be used to calculate the radius of the vehicle trajectory circle. Specifically, first determine the first difference between the second distance L2 and the first distance L1, that is, Δ=L2-L1; then, sum the sideslip angle and the turning angle of the second axis at the current moment to obtain a first summation result, that is, δ m +α m , and determine the tangent value of the first summation result, that is, tan(δ m +α m ); At the same time, it is also necessary to determine the sideslip angle of the third axis at the current moment and sum it with the rotation angle to obtain the second summation result, that is, δ r +αr , and determine the tangent value of the second summation result, that is, tan(δ r +α r ); Then, calculate the tangent value tan(δ m +α m ) and the second difference tan(δ r +α r ), that is, tan(δ m +α m )-tan(δ m +α m ); Finally, the ratio of the first difference to the second difference is calculated to obtain the trajectory circle radius R of the next moment of the current moment, which is shown in the following formula (5):

[0106]

[0107] Therefore, in the solution provided by the present application, it is only necessary to obtain the vehicle speed and the turning angle of each axle at different times to predict the radius of the vehicle trajectory circle at the next moment of the current moment. In addition, the present application adopts the steering of the first axis and the third axis, and in actual application, it can also be any combination of the steering of other axes except the first axis.

[0108] The following embodiments will explain in detail how to optimize and obtain the optimal characteristic parameter set of each axis that best matches the actual response of the vehicle.

[0109] Optionally, refer to Figure 5 As shown, the process of determining the characteristic parameter set of each axis includes:

[0110] S501. Obtain an initial characteristic parameter set of each axis on the vehicle.

[0111] Exemplarily, for example, the initial characteristic parameter set of each axis may be set to an actual empirical value.

[0112] Optionally, since the tire stiffness of the front and rear axles of a vehicle is generally different, the characteristic parameter set of each axle can be determined according to the actual situation. For example, the characteristic parameter set of the first axle can be set to (B f ,C f ,D f ); The structural stiffness of the second axis and the third axis is the same, that is, the second axis and the third axis can use the same characteristic parameter set, that is, (B r ,C r ,D r ).

[0113] Therefore, in this embodiment, the optimized variables are: the characteristic parameter set of the first axis (B f ,C f ,Df ) and the characteristic parameter set of the third axis (B r ,C r ,D r ), a total of six parameters.

[0114] Optionally, the optimization target can also be set as the characteristic parameter set of each axis, that is, i=1,2,3…n, where the value of n is determined by the number of tire specifications, suspension hard point parameters, suspension bushings and elastic part parameter sets of each axle. Axes with the same above factors can share a set of parameter sets.

[0115] S502: Obtain the actual measured value of the vehicle at the target historical moment.

[0116] The measured values ​​include: the measured vehicle speed, the measured trajectory circle radius and the measured turning angles of each axis.

[0117] In one feasible method, for example, during the acquisition process, the steering functions of all wheels except the front wheels are turned off, and the actual vehicle speed, actual trajectory radius, and actual turning angle of the first axis of the vehicle at the target historical moment are collected with a sampling period of 10 ms. That is, the optimization condition is: vehicle speed v x , the rotation angle of the first axis δ f , measured trajectory circle radius R real .

[0118] S503: Determine a characteristic parameter set of each axis according to the measured value at the target historical moment and the initial characteristic parameter set.

[0119] In this embodiment, for example, the above-mentioned vehicle parameter information, the initial characteristic parameter set of each axis, the collected vehicle speed v x Angle δ with the first axis f Input into the above formula (1)-formula (5), the radius R of the current trajectory circle can be calculated sim , and based on the current trajectory circle radius R sim The measured trajectory circle radius R real , the optimal characteristic parameter set of each axis that best matches the actual response of the vehicle can be obtained.

[0120] Optionally, refer to Figure 6 As shown, the above step S503 includes:

[0121] S601: Use the initial feature parameter set as the initial current feature parameter set.

[0122] S602: Determine the radius of the current trajectory circle according to the current feature parameter set and the measured value.

[0123] S603: Update the current feature parameter set according to the difference between the current trajectory circle radius and the measured trajectory circle radius to obtain new current feature parameters.

[0124] In this embodiment, the above-mentioned current characteristic parameter set, measured values, and basic parameters of the vehicle can be input into the dynamic model to calculate the parameter set (B f ,C f ,D f ) and (B r ,C r ,D r ) corresponds to the radius of the trajectory circle R sim,i ; and calculate the radius R of the current trajectory circle sim,i The measured trajectory circle radius R real The current difference of obj i =|R sim,i -R real |; Then, based on the optimization algorithm and the current difference, such as genetic algorithm, simulated annealing algorithm, etc., determine the iterative update direction of the current feature parameter set, update the current feature parameter set, and obtain new current feature parameters; and execute steps S602-S603 repeatedly until the difference between the current trajectory circle radius and the measured trajectory circle radius obj=|R sim -R real | Get the minimum value, terminate the optimization, and obtain the characteristic parameter set of the first axis that best matches the actual response of the vehicle (B f ,C f ,D f ) and the characteristic parameter set of the third axis (B r ,C r ,D r ).

[0125] In summary, the trajectory circle radius prediction of the three-axle vehicle proposed in this application only needs to measure the vehicle speed and the turning angle of each axle, and obtain the characteristic parameter set of the first axis (B ) that best matches the actual response of the vehicle based on the optimization algorithm. f ,C f ,D f ) and the characteristic parameter set of the third axis (B r ,C r ,D r ), which has the advantages of simple operation and high feasibility.

[0126] Optionally, the solution provided in the present application can also adopt a processing method similar to that of the present application for multi-axle vehicles (greater than or equal to 3 axes) to predict the trajectory circle radius of the multi-axle (greater than or equal to 3 axes).

[0127] Optionally, refer to Figure 7As shown, the present application also provides a vehicle-mounted controller 101 , which may be a processing chip integrated in a vehicle and has a data processing function. The vehicle-mounted controller 101 includes: a processor 701 and a memory 702 .

[0128] The memory 702 is used to store programs, and the processor 701 calls the programs stored in the memory 702 to execute the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0129] Optionally, refer to Figure 8 As shown, the present application also provides a vehicle 100, including: an onboard controller 101 and an angle acquisition unit 102; the onboard controller and the angle acquisition unit are communicatively connected. Exemplarily, the onboard controller 101 and the angle acquisition unit 102 can be communicatively connected via a wired (or wireless) direction.

[0130] The angle acquisition unit 102 is used to collect the rotation angle of each axle of the vehicle 100 at the current moment, and send the rotation angle of each axle at the current moment to the vehicle controller 101; wherein, the angle acquisition unit 102 can be set on the tire where each axle is located, that is, the rotation angle of each axle at the current moment during the driving process of the vehicle can be collected through the angle acquisition unit 102.

[0131] The vehicle-mounted controller 101 is used to execute the trajectory prediction method of the three-axle vehicle provided in the above embodiment to realize the prediction of the radius of the vehicle trajectory circle at the next moment from the current moment.

[0132] Optionally, the present invention also provides a program product, such as a computer-readable storage medium, comprising a program, which is used to execute the above method embodiment when executed by a processor.

[0133] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0136] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to perform some steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), disk or optical disk and other media that can store program codes.

Claims

1. A trajectory prediction method for a three-axle vehicle, characterized in that: The method comprises: Acquire parameter information of the vehicle, the current vehicle speed and the turning angle of each axis, wherein the parameter information includes: a first distance between the first axis and the second axis, a second distance between the first axis and the third axis, and a distance from each axis to the center of mass of the vehicle; Determine the side slip information of each axis at the current moment according to the parameter information of the vehicle, the vehicle speed and the rotation angle of each axis at the current moment, a pre-built dynamic model and a pre-determined characteristic parameter set of each axis, wherein the characteristic parameter set includes a stiffness factor, a shape factor and a peak factor, and the side slip information includes a side slip angle and a side slip stiffness; According to the parameter information of the vehicle, the side deviation information and the turning angle of each axis at the current moment, the trajectory information of the vehicle at the next moment after the current moment is predicted.

2. The method according to claim 1, characterized in that Determining the side slip information of each axle at the current moment according to the parameter information of the vehicle, the vehicle speed and the rotation angle of each axle at the current moment, a pre-built dynamic model and a pre-determined characteristic parameter set of each axle includes: obtaining the mass of the vehicle; Inputting the vehicle speed at the current moment, the rotation angle of each axle, the mass, the first distance, the second distance, and the distance from each axle to the center of mass of the vehicle into the dynamic model; Inputting the first distance, the second distance and the rotation angle of each axis at the current moment into a rotation angle relationship model; Determining the axial lateral force of each axis at the current moment according to the characteristic parameter set of each axis; Inputting the axial lateral force of each axis at the current moment into a pre-constructed lateral force relationship model between the lateral deflection information of each axis and the axial lateral force; The dynamic model, the rotation angle relationship model and the cornering force relationship model are solved jointly to obtain the cornering information of each axis at the current moment.

3. The method according to claim 2, characterized in that Determining the axial lateral force of each axis at the current moment according to the characteristic parameter set of each axis includes: The characteristic parameter set of each axis is input into the pre-constructed axis lateral stiffness model to solve and obtain the axis lateral force of each axis at the current moment.

4. The method according to claim 3, characterized in that The axle cornering stiffness model includes: a tire magic formula.

5. The method according to claim 1, characterized in that The predicting of the trajectory information of the vehicle at the next moment after the current moment based on the parameter information of the vehicle, the side deviation information and the turning angle of each axis at the current moment includes: determining a first difference between the second distance and the first distance; Summing the sideslip angle and the rotation angle of the second axis at the current moment to obtain a first summation result, and determining a tangent value of the first summation result; Summing the sideslip angle and the rotation angle of the third axis at the current moment to obtain a second summation result, and determining a tangent value of the second summation result; Determine a second difference between the tangent value of the first summation result and the tangent value of the second summation result; A ratio of the first difference to the second difference is determined, and the ratio is used as the trajectory circle radius at a moment next to the current moment.

6. The method according to any one of claims 1 to 5, characterized in that: The process of determining the characteristic parameter set of each axis includes: Obtaining an initial characteristic parameter set of each axle on the vehicle; Acquire the measured values ​​of the vehicle at the target historical moment, the measured values ​​including: measured vehicle speed, measured trajectory circle radius and measured turning angles of each axis; The characteristic parameter set of each axis is determined according to the measured value of the target historical moment and the initial characteristic parameter set.

7. The method according to claim 6, characterized in that The step of determining the characteristic parameter set of each axis according to the measured value at the target historical moment and the initial characteristic parameter set includes: A. using the initial feature parameter set as an initial current feature parameter set; B. determining the radius of the current trajectory circle according to the current feature parameter set and the measured value; C. updating the current feature parameter set according to the difference between the current trajectory circle radius and the measured trajectory circle radius to obtain a new current feature parameter set; D. Execute steps BC repeatedly until the difference between the current trajectory circle radius and the measured trajectory circle radius meets a preset condition.

8. A vehicle-mounted controller, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the on-board controller is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of any method as described in claims 1-7.

9. A vehicle, characterized in that: The vehicle-mounted controller and the angle acquisition unit of claim 8 are included; the vehicle-mounted controller is in communication connection with the angle acquisition unit; The angle acquisition unit is used to acquire the rotation angle of each axis of the vehicle at the current moment, and send the rotation angle of each axis at the current moment to the vehicle controller; The vehicle-mounted controller is used to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is executed.

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