Trajectory prediction method for three-axle vehicles, on-board controller, vehicle and storage medium

Abstract

This application provides a trajectory prediction method for a three-axle vehicle, an onboard controller, a vehicle, and a storage medium, relating to the field of autonomous driving technology. During vehicle operation, the method collects the vehicle's speed and the rotation angles of each axle at the current moment. Based on the vehicle's parameter information and the current speed and rotation angles, the method calculates the sideslip angle and sideslip stiffness of each axle at the current moment. Then, using the vehicle's parameter information, the sideslip information of each axle at the current moment, and the rotation angles, the method predicts the vehicle's trajectory information for the next moment. This solution only requires collecting the vehicle's speed and rotation angles at the current moment, without needing to collect excessive environmental information through sensors. It exhibits strong anti-interference capabilities and requires minimal complex computational processing to predict the vehicle's trajectory. Furthermore, the dynamic model used in this solution is constructed based on the inherent characteristics of a three-axle vehicle, thus ensuring the accuracy of the calculation results.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and more specifically, to a trajectory prediction method for a three-axle vehicle, an onboard controller, a vehicle, and a storage medium. Background Technology

[0002] Trajectory prediction is a crucial component of autonomous driving technology. Its function is to predict the vehicle's trajectory over a future period based on road conditions and maps. The accuracy of the prediction directly impacts the computation time and accuracy of downstream behavioral decisions and trajectory planning.

[0003] In related technologies, when predicting the trajectory of multi-axle vehicles, the radius of the vehicle's trajectory circle can be calculated using environmental data. For example, the radius of the vehicle's trajectory circle can be determined by multi-sensor fusion, such as by collecting surrounding environmental information through cameras or by using GPS (Global Positioning System) to determine the current vehicle position.

[0004] However, measuring the radius of the vehicle trajectory circle using environmental information is easily affected by environmental interference, increases the complexity of the system, and has low reliability. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the prior art by providing 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.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, embodiments of this application provide a method for predicting the trajectory of a three-axle vehicle, the method comprising:

[0008] The vehicle acquires its parameter information, current speed, and rotation angles of each axle. The parameter information includes: a first distance between the first and second axles, a second distance between the first and third axles, and the distance from each axle to the vehicle's center of gravity.

[0009] Based on the vehicle's parameter information, the vehicle speed and the rotation angle of each axle at the current moment, the pre-constructed dynamic model, and the pre-determined characteristic parameter set of each axle, the lateral slip information of each axle at the current moment is determined. The characteristic parameter set includes: stiffness factor, shape factor, and peak factor. The lateral slip information includes: slip angle and lateral slip stiffness.

[0010] Based on the vehicle's parameter information, the lateral deflection information and rotation angle of each axle at the current moment, the trajectory information of the vehicle at the next moment at the current moment is predicted.

[0011] Optionally, determining the sideslip information of each axle at the current moment based on the vehicle's parameter information, the vehicle speed and the steering angle of each axle at the current moment, a pre-constructed dynamic model, and a pre-determined set of characteristic parameters for each axle includes:

[0012] Obtain the mass of the vehicle;

[0013] The current vehicle speed, the rotation angle of each axle, the mass, the first distance, the second distance, and the distance from each axle to the vehicle's center of mass are input into the dynamic model;

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

[0015] Based on the characteristic parameter set of each axis, determine the axial lateral force of each axis at the current moment;

[0016] The lateral force of each axis at the current moment is input into a pre-constructed model of the relationship between the lateral information of each axis and the lateral force of the axis.

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

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

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

[0020] Optionally, predicting the vehicle's trajectory information for the next moment based on the vehicle's parameter information, the lateral slip information and steering angle of each axle at the current moment includes:

[0021] Determine the first difference between the second distance and the first distance;

[0022] The sideslip angle and rotation angle of the second axis at the current moment are summed to obtain a first summation result, and the tangent value of the first summation result is determined;

[0023] The sideslip angle and rotation angle of the third axis at the current moment are summed to obtain a second summation result, and the tangent value of the second summation result is determined.

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

[0025] Determine the ratio of the first difference to the second difference, and use the ratio as the radius of the trajectory circle for the next moment at the current moment.

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

[0027] Obtain the initial feature parameter set for each axle of the vehicle;

[0028] The measured values ​​of the vehicle at the target historical moment are obtained, including the measured vehicle speed, the radius of the measured trajectory circle, and the measured rotation angle of each axle;

[0029] Based on the measured values ​​of the target historical time and the initial feature parameter set, the feature parameter set of each axis is determined.

[0030] Optionally, determining the feature parameter set for each axis based on the measured values ​​at the plurality of historical moments and the initial feature parameter set includes:

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

[0032] B. Determine the radius of the current trajectory circle based on the current feature parameter set and the measured value;

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

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

[0035] Secondly, embodiments of this application also provide an in-vehicle controller, including: 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 via the bus, and the processor executes the machine-readable instructions to perform the steps of the method provided in the first aspect.

[0036] Thirdly, embodiments of this application also provide a vehicle, including the vehicle controller and angle acquisition unit described in the second aspect; the vehicle controller and the angle acquisition unit are communicatively connected.

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

[0038] The vehicle controller is used to perform the method as provided in the first aspect.

[0039] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs any of the methods described in the first aspect.

[0040] The beneficial effects of this application are:

[0041] This application provides a trajectory prediction method, on-board controller, vehicle, and storage medium for a three-axle vehicle. During vehicle operation, the method collects the vehicle's speed and the rotation angles of each axle at the current moment. Based on the vehicle's parameter information and the current speed and rotation angles, the method calculates the sideslip angle and sideslip stiffness of each axle at the current moment. Then, using the vehicle's parameter information, the sideslip information of each axle at the current moment, and the rotation angles, the method predicts the vehicle's trajectory information for the next moment. This solution only requires collecting the vehicle's speed and rotation angles at the current moment, without needing to collect excessive environmental information through sensors, thus exhibiting strong anti-interference capabilities. In other words, by combining multiple known parameter information, the current speed and rotation angles of each axle, a pre-constructed dynamic model, and a pre-determined set of characteristic parameters for each axle, the method can predict the vehicle's trajectory information for the next moment without requiring excessive complex computational processing. The dynamic model is constructed based on the inherent characteristics of a three-axle vehicle, ensuring the accuracy of the calculation results. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A schematic flowchart illustrating a trajectory prediction method for a three-axle vehicle provided in an embodiment of this application;

[0044] Figure 2 This application provides a schematic diagram of the angles of each axle of a three-axle vehicle during driving, as shown in the embodiments of the present application.

[0045] Figure 3 A flowchart illustrating another trajectory prediction method for a three-axle vehicle provided in this application embodiment;

[0046] Figure 4 A flowchart illustrating another trajectory prediction method for a three-axle vehicle provided in this application embodiment;

[0047] Figure 5A flowchart illustrating another trajectory prediction method for a three-axle vehicle provided in this application embodiment;

[0048] Figure 6 A flowchart illustrating another trajectory prediction method for a three-axle vehicle provided in this application embodiment;

[0049] Figure 7 This is a schematic diagram of the structure of an on-board controller provided in an embodiment of this application;

[0050] Figure 8 This is a structural schematic diagram of a vehicle provided in an embodiment of this application.

[0051] Icons: 100 - Vehicle; 101 - Vehicle controller; 102 - Angle acquisition unit. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0053] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0054] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence 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 described in detail through the following embodiments.

[0056] In related technologies, most multi-axle vehicles calculate the radius of their trajectory circle using two methods: one is to use environmental data, such as collecting surrounding environmental information through cameras or determining the current vehicle position through GPS, and the other is to directly calculate the trajectory radius based on the geometric relationship of the vehicle's steering.

[0057] However, measuring the radius of the vehicle trajectory circle through environmental information is easily affected by environmental interference, increases the complexity of the system, and has low reliability. In addition, the method of calculating the trajectory radius through the vehicle's steering geometry is mostly suitable for two-axle vehicles. Due to their own characteristics, three-axle or multi-axle vehicles are statically indeterminate systems between the wheels when the vehicle is turning. When the steering angle of each axle is not properly configured or the steering angle capability is insufficient, sideslip angle will be generated on each axle. If the wheel steering angle is directly used as the calculation input, the accuracy of the calculation result will be low.

[0058] To address the aforementioned issues, this application proposes a method for predicting the trajectory of a three-axle vehicle. During vehicle operation, the method collects the vehicle's speed and the rotation angles of each axle at the current moment. Based on the vehicle's parameter information and the current speed and rotation angles, the method calculates the sideslip angle and sideslip stiffness of each axle at the current moment. Then, using the vehicle's parameter information, the sideslip information of each axle at the current moment, and the rotation angles, the method predicts the vehicle's trajectory for the next moment. This solution only requires collecting the vehicle's speed and rotation angles at the current moment, eliminating the need for extensive environmental data collection via sensors, thus exhibiting strong anti-interference capabilities. By combining multiple known parameter information, the current speed and rotation angles of each axle, a pre-constructed dynamic model, and a pre-determined set of characteristic parameters for each axle, the method can predict the vehicle's trajectory for the next moment. Therefore, the prediction method provided in this application can predict the vehicle's trajectory without requiring excessive complex computational processing. Furthermore, the dynamic model is constructed based on the inherent characteristics of a three-axle vehicle, ensuring the accuracy of the calculation results.

[0059] The following will explain the implementation principle and corresponding beneficial effects of the method for predicting the driving trajectory of a three-axle vehicle provided in this application through several specific embodiments.

[0060] refer to Figure 1 The diagram shown is a flowchart illustrating a method for predicting the trajectory of a three-axle vehicle according to an embodiment of this application. Optionally, the execution entity of this method is the on-board controller or other processing unit on the vehicle. It should be understood that in other embodiments, the order of some steps in the method for predicting the 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. Obtain vehicle parameter information, current vehicle speed, and rotation angle of each axle.

[0062] Among them, reference Figure 2 As shown, O is the origin of the vehicle coordinate system. The vehicle's parameter information includes: the first distance between the first and second axes is L1, the second distance between the first and third axes is L2, and the distance from each axis to the vehicle's center of mass, i.e., the distance from the first axis to the vehicle's center of mass is l. a The distance from the second axle to the vehicle's center of gravity is l b The distance from the third axle to the vehicle's center of gravity is l c It is understandable that the vehicle's parameter information consists of fixed parameters on the vehicle.

[0063] Wherein, the vehicle speed v at the current moment x Let be the vehicle's speed along the x-axis in the vehicle coordinate system; let δ be the rotation angle of the first axis. 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 vehicle speed and the angle of rotation of each axle may change over time; that is, vehicle speed and the angle of rotation of each axle are not fixed parameters. For example, vehicle speed and the angle of rotation of each axle can be collected in real time by sensors installed on the vehicle to predict vehicle trajectory information.

[0064] It should be noted that for vehicles that only steer on the first and third axles, the steering angle of the second axle is δ. m =0.

[0065] S102. Based on the vehicle's parameter information, the current vehicle speed and the rotation angle of each axle, the pre-constructed dynamic model, and the pre-determined characteristic parameter set of each axle, determine the lateral slip information of each axle at the current moment.

[0066] The dynamic model is constructed based on the inherent characteristics of a three-axle vehicle, thus ensuring the accuracy of subsequent predictions. Similarly, if it is necessary to predict the trajectory of a four-axle vehicle, a dynamic model that better reflects the characteristics of a four-axle vehicle can be constructed.

[0067] The characteristic parameter set (B, C, D) includes: stiffness factor B, shape factor C, and peak factor D. These factors characterize the axle lateral stiffness of the vehicle. The characteristic parameter set for each axle is pre-optimized based on measured values ​​from historical moments of the three-axle vehicle; that is, the characteristic parameter set for each axle is the optimal parameter set that best matches the actual response of the vehicle.

[0068] Lateral slip information includes: slip angle and slip stiffness. (Continue to refer to...) Figure 2As shown, the sideslip angle and sideslip stiffness of the first axis are α and α, respectively. f k f The sideslip angle and sideslip stiffness of the second axis are α and α, respectively. m k m The sideslip angle and sideslip stiffness of the third axis are α and α, respectively. r k r .

[0069] S103. Based on the vehicle's parameter information, the lateral slip information and turning angle of each axle at the current moment, predict the trajectory information of the vehicle at the next moment.

[0070] Optionally, the vehicle's trajectory information for the next moment can be predicted based on the vehicle's parameter information, the lateral slip information of each axle at the current moment, and the steering angle. This solution only needs to collect the vehicle's speed and steering angle of each axle at the current moment, eliminating the need for extensive environmental data collection via sensors. It exhibits strong anti-interference capabilities and requires minimal complex computational processing to predict the vehicle's trajectory. Furthermore, the dynamic model is constructed based on the inherent characteristics of a three-axle vehicle, ensuring the accuracy of the calculation results.

[0071] In summary, this application provides a method for predicting the trajectory of a three-axle vehicle. During vehicle operation, the method collects the vehicle's speed and the rotation angles of each axle at the current moment. Based on the vehicle's parameter information and the current speed and rotation angles, the method calculates the sideslip angle and sideslip stiffness of each axle at the current moment. Then, using the vehicle's parameter information, the sideslip information of each axle at the current moment, and the rotation angles, the method predicts the vehicle's trajectory information for the next moment. This solution only requires collecting the vehicle's speed and rotation angles at the current moment, without needing to collect excessive environmental information through sensors, thus exhibiting strong anti-interference capabilities. In other words, by combining multiple known parameter information, the current speed and rotation angles of each axle, a pre-constructed dynamic model, and a pre-determined set of characteristic parameters for each axle, the method can predict the vehicle's trajectory information for the next moment without requiring excessive complex computational processing. Furthermore, the dynamic model is constructed based on the inherent characteristics of a three-axle vehicle, ensuring the accuracy of the calculation results.

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

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

[0074] S301. Obtain the vehicle's mass, and input the current vehicle speed, rotation angle of each axle, mass, first distance, second distance, and distance from each axle to the vehicle's center of mass into the dynamic model.

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

[0076]

[0077] The steady-state motion of the three-axle vehicle satisfies the above dynamic model, which means that the vehicle's mass m and its current speed v can be expressed as follows: x The angle of rotation with respect to each axis (i.e., δ) f δ m δ r The distance between the first and second axles is L1, the distance between the first and third axles is L2, and the distance from the first axle to the vehicle's center of gravity is l. a The distance from the second axle to the vehicle's center of gravity is l b The distance from the third axle to the vehicle's center of gravity is l c Input it into the dynamic model shown in the above formula (1).

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

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

[0080]

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

[0082] S303. Determine the axial lateral force of each axis at the current moment based on the characteristic parameter set of each axis.

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

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

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

[0086]

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

[0088] S305. Solve the dynamic model, rotation angle relationship model and lateral force relationship model simultaneously to obtain the lateral deflection information of each axis at the current moment.

[0089] Optionally, by simultaneously solving equations (1) to (3) above, the sideslip angle and sideslip stiffness of each axis at the current moment can be obtained. That is, the sideslip angle and sideslip stiffness of the first axis are obtained as α. f k f The sideslip angle and sideslip stiffness of the second axis are α and α, respectively. m k m The rotation angle of the third axis is α. r k r .

[0090] Optionally, step S304 above includes:

[0091] The characteristic parameter set of each axis is input into the pre-constructed axial lateral stiffness model, and the axial lateral force of each axis at the current moment is obtained by solving.

[0092] Optionally, the axial side stiffness model includes: the tire magic formula.

[0093] In this practical example, considering that tire stiffness is the main factor affecting the axle side stiffness of a vehicle, a simplified version of the tire magic formula shown in formula (4) is proposed to describe the axle side stiffness 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 formula (4) above 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 axial force on the second axis is F.m The axial force on the third axis is F. r .

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

[0097] Therefore, this solution is mainly based on the dynamic model of a three-axle vehicle and the axial side stiffness model of each axle. It directly predicts the radius of the trajectory circle of the vehicle at the next moment by simply collecting the vehicle speed and the rotation angle of each axle at the current moment, based on the vehicle's own physical characteristics. No additional environmental signals are required. The system has a simple structure, low cost, strong anti-interference ability, and high accuracy.

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

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

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

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

[0102] S403. Sum the sideslip angle and rotation angle of the third axis at the current moment to obtain the second summation result, and determine the tangent value of the second summation result.

[0103] S404. Determine the second difference between the tangent of the first summation result and the tangent 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 from the current moment.

[0105] In this embodiment, the radius of the vehicle trajectory circle can be calculated as follows: First, determine the first difference between the second distance L2 and the first distance L1, i.e., Δ = L2 - L1; then, sum the sideslip angle and the turning angle of the second axis at the current moment to obtain the first summation result, i.e., δ. m +α m And determine the tangent of the first summation result, i.e., tan(δ). m +α m Simultaneously, it is also necessary to determine the sideslip angle and rotation angle of the third axis at the current moment and sum them to obtain the second summation result, i.e., δ. r +αr And determine the tangent of the second summation result, i.e., tan(δ). r +α r Then, calculate the tangent value tan(δ) of the first summation result. m +α m The second difference between tan(δ) and the tangent of the second summation result. 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 radius R of the trajectory circle at the next moment, as shown in the following formula (5):

[0106]

[0107] Therefore, in the solution provided in this application, it is only necessary to obtain the vehicle speed and the steering angle of each axle at different times to predict the radius of the vehicle trajectory circle at the next time. Furthermore, this application employs steering on the first and third axles, but in practical applications, it can also be any combination of steering on other axles besides the first axle.

[0108] The following examples will illustrate how to optimize and obtain the optimal set of feature parameters for each axle 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 the initial feature parameter set for each axle of the vehicle.

[0111] For example, the initial set of characteristic parameters for each axis can be set to actual empirical values.

[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 as (B... f C f D f The second and third axes have the same structural stiffness, meaning they can use the same set of characteristic parameters, i.e., (B). r C r D r ).

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

[0114] Alternatively, the optimization objective can be set as a set of characteristic parameters for each axis, i.e., i = 1, 2, 3...n, where the value of n is determined by the number of tire specifications, suspension hard point parameters, suspension bushing and elastic component parameter sets for each axis. Axes with the same factors can share a set of parameters.

[0115] S502, Obtain the measured values ​​of the vehicle at the target historical moment.

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

[0117] In one feasible approach, for example, during the data acquisition process, the steering function of all wheels except the front wheels is disabled, and a sampling period of 10ms is used to collect the vehicle's measured speed, the radius of the measured trajectory circle, and the measured rotation angle of the first axle at the target historical moment. The optimization condition is: vehicle speed v x The rotation angle δ of the first axis f Measured trajectory circle radius R real .

[0118] S503. Determine the characteristic parameter set for each axis based on the measured values ​​of the target's historical time and the initial characteristic parameter set.

[0119] In this embodiment, for example, the vehicle's parameter information, the initial feature parameter set of each axle, and the collected vehicle speed v are used. x Angle δ with respect to the first axis f By inputting the formulas (1) to (5) above, the radius R of the current trajectory circle can be calculated. sim And based on the current trajectory circle radius R sim The radius R of the measured trajectory circle real This allows us to obtain the optimal set of characteristic parameters for each axle that best matches the actual response of the vehicle.

[0120] Optionally, refer to Figure 6 As shown, step S503 above 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 based on the current feature parameter set and the measured values.

[0123] S603. Based on the difference between the radius of the current trajectory circle and the radius of the measured trajectory circle, update the current feature parameter set to obtain new current feature parameters.

[0124] In this embodiment, the current feature parameter set, measured values, and basic vehicle parameters can be input into the dynamic model to calculate the parameter set (B) for the current iteration. f C f D f ) and (B r C r D r The radius R of the corresponding trajectory circle sim,i And calculate the radius R of the current trajectory circle. sim,i The radius R of the measured trajectory circle real The current difference, i.e., obj i =|R sim,i -R real Then, based on the difference between the optimization algorithm and the current situation, 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 the new current feature parameters; and repeat steps S602-S603 until the difference between the current trajectory circle radius and the measured trajectory circle radius obj=|R sim -R real |Once the minimum value is obtained, optimization terminates, yielding the feature parameter set (B) of the first axis that best matches the actual vehicle response. f C f D f ) and the feature parameter set of the third axis (B r C r D r ).

[0125] In summary, the trajectory circle radius prediction for a three-axle vehicle proposed in this application only requires measuring the vehicle speed and the rotation angle of each axle, and based on an optimization algorithm, obtaining the feature parameter set (B) of the first axle that best matches the actual vehicle response. f C f D f ) and the feature parameter set of the third axis (B r C r D r It has the advantages of being simple to operate and highly feasible.

[0126] Optionally, the solution provided in this application can also be applied to multi-axle vehicles (greater or greater than 3 axles) using a similar processing method to predict the trajectory circle radius of the multi-axle vehicles (greater or greater than 3 axles).

[0127] Optionally, refer to Figure 7As shown, this application also provides an in-vehicle controller 101, which can be a processing chip integrated into a vehicle and has data processing functions. The in-vehicle 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 embodiments. The specific implementation and technical effects are similar, and will not be described in detail here.

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

[0130] Angle acquisition unit 102 is used to acquire the rotation angle of each axle of vehicle 100 at the current moment and send the rotation angle of each axle at the current moment to vehicle controller 101; wherein, angle acquisition unit 102 can be set on the tire of each axle, that is, the rotation angle of each axle at the current moment during the vehicle's driving process can be acquired through angle acquisition unit 102.

[0131] The vehicle controller 101 is used to execute the trajectory prediction method for a three-axle vehicle provided in the above embodiment, so as to predict 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, including a program that, when executed by a processor, is used to perform the above-described method embodiments.

[0133] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

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

[0135] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0136] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A trajectory prediction method of a tri-axle vehicle, characterized by, The method comprises: acquiring parameter information of the vehicle, a current speed and a rotation angle of each axle, wherein the parameter information comprises a first distance between a first axle and a second axle, a second distance between the first axle and a third axle, and a distance from each axle to a center of mass of the vehicle; determining side slip information of each axle at the current time according to the parameter information of the vehicle, the current speed and the rotation angle of each axle, a pre-constructed dynamic model and a pre-determined characteristic parameter set of each axle, wherein the characteristic parameter set comprises a stiffness factor, a shape factor and a peak factor, and the side slip information comprises a side slip angle and a side slip stiffness; predicting trajectory information of the vehicle at a next time from the current time according to the parameter information of the vehicle, the side slip information and the rotation angle of each axle at the current time; wherein the determination of the side slip information of each axle at the current time according to the parameter information of the vehicle, the current speed and the rotation angle of each axle, the pre-constructed dynamic model and the pre-determined characteristic parameter set of each axle comprises: acquiring a mass of the vehicle; inputting the current speed, 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 axle at the current time into a rotation angle relationship model; determining axle side forces of each axle at the current time according to the characteristic parameter set of each axle; inputting the axle side forces of each axle at the current time into a side slip force relationship model between the side slip information and the axle side forces of each axle pre-constructed; solving the dynamic model, the rotation angle relationship model and the side slip force relationship model to obtain the side slip information of each axle at the current time.

2. The method of claim 1, wherein, the determination of the axle side forces of each axle at the current time according to the characteristic parameter set of each axle comprises: inputting the characteristic parameter set of each axle into the pre-constructed axle side slip stiffness model to obtain the axle side forces of each axle at the current time.

3. The method of claim 2, wherein, the axle side slip stiffness model comprises a tire magic formula.

4. The method of claim 1, wherein, the prediction of the trajectory information of the vehicle at the next time from the current time according to the parameter information of the vehicle, the side slip information and the rotation angle of each axle at the current time comprises: determining a first difference between the second distance and the first distance; summing the side slip angle and the rotation angle of the second axle at the current time to obtain a first summation result, and determining a tangent value of the first summation result; summing the side slip angle and the rotation angle of the third axle at the current time to obtain a second summation result, and determining a tangent value of the second summation result; determining a second difference between the tangent value of the first summation result and the tangent value of the second summation result; determining a ratio of the first difference to the second difference, and taking the ratio as a trajectory circle radius at the next time from the current time.

5. The method according to any one of claims 1 to 4, characterized in that, the determination process of the characteristic parameter set of each axle comprises: acquiring an initial characteristic parameter set of each axle on the vehicle; acquiring a measured value of the vehicle at a target historical time, the measured value including: a measured vehicle speed, a measured track circle radius, and a measured rotation angle of each axle; determining a feature parameter set of each axle according to the measured value at the target historical time and the initial feature parameter set.

6. The method of claim 5, wherein, The determining a feature parameter set of each axle according to the measured value at the target historical time and the initial feature parameter set includes: A. taking the initial feature parameter set as an initial current feature parameter set; B. determining a current track circle radius according to the current feature parameter set and the measured value; C. updating the current feature parameter set according to a difference between the current track circle radius and the measured track circle radius to obtain a new current feature parameter set; D. cyclically executing steps B-C until the difference between the current track circle radius and the measured track circle radius meets a preset condition.

7. An in-vehicle controller characterized by comprising: including: a processor, a storage medium, and a bus, the storage medium storing machine readable instructions executable by the processor, when a vehicle-mounted controller is running, the processor and the storage medium communicate through the bus, the processor executes the machine readable instructions to perform the steps of the method of any one of claims 1-6.

8. A vehicle characterized by comprising: including the vehicle-mounted controller and the angle acquisition unit of claim 7; the vehicle-mounted controller and the angle acquisition unit are in communication connection; the angle acquisition unit is configured to acquire a rotation angle of each axle of the vehicle at a current time, and send the rotation angle of each axle at the current time to the vehicle-mounted controller; the vehicle-mounted controller is configured to perform the method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, the storage medium stores a computer program, and the computer program is executed by the processor to perform the method of any one of claims 1-6.

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