Vehicle control method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a vehicle control method and device, electronic equipment and a storage medium. The method comprises the steps that first vehicle body state information of a target vehicle at the current moment is acquired, and road surface information and reference control quantity of the target vehicle at the next moment are determined; inputting the first vehicle body state information, the road surface information and the reference control quantity into a pre-trained prediction model to obtain second vehicle body state information of the target vehicle at the next moment; determining an error value between the second vehicle body state information and third vehicle body state information, wherein the third vehicle body state information is the expected vehicle body state information of the target vehicle at the next moment; under the condition that the error value meets the preset error condition, the reference control quantity serves as the target control quantity of the target vehicle at the next moment, the suspension system of the target vehicle is controlled based on the target control quantity, the control performance of the vehicle suspension system is improved, and therefore the requirement for precise control over the vehicle suspension system at present is met.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of vehicle control, and particularly relate to a vehicle control method and device, an electronic device and a storage medium. BACKGROUND

[0002] With the rapid development of automobile technology, consumers' requirements for vehicle comfort, controllability and safety are increasingly improved. As a core component connecting the vehicle body and the wheels, the performance of the suspension system plays a decisive role in the ride comfort and driving stability of the vehicle. Therefore, precise control of the suspension system is a key link to improve the overall performance of the vehicle.

[0003] In related technologies, the control of the vehicle suspension system is usually a PID control method, that is, the control amount is determined by calculating the current error, error accumulation and error change rate to reduce the deviation between the system output and the expected value. However, PID control relies on an accurate system model, and the vehicle suspension system is a complex nonlinear system, whose parameters change with the vehicle driving state, road conditions and other factors, which makes it difficult for PID control to achieve ideal control effect in actual application, and the response speed often cannot meet the demand of rapidly changing road conditions, and the adaptability is poor. SUMMARY

[0004] The present application provides a vehicle control method, device, electronic device and storage medium to improve the control performance of the vehicle suspension system to meet the demand for precise control of the vehicle suspension system.

[0005] According to an aspect of the present application, a vehicle control method is provided, which comprises:

[0006] obtaining first vehicle body state information of a target vehicle at a current time, determining road surface information and a reference control amount of the target vehicle at a next time;

[0007] inputting the first vehicle body state information, the road surface information and the reference control amount into a pre-trained prediction model to obtain second vehicle body state information of the target vehicle at the next time;

[0008] determining an error value of the second vehicle body state information and third vehicle body state information, wherein the third vehicle body state information is the vehicle body state information expected to be reached by the target vehicle at the next time;

[0009] in the case that the error value meets a preset error condition, taking the reference control amount as a target control amount of the target vehicle at the next time, and controlling the suspension system of the target vehicle based on the target control amount.

[0010] According to another aspect of the present application, a vehicle control device is provided. The device comprises:

[0011] a data acquisition module configured to acquire first body state information of a target vehicle at a current time, determine road surface information and a reference control amount of the target vehicle at a next time;

[0012] a body state prediction module configured to input the first body state information, the road surface information and the reference control amount into a pre-trained prediction model to obtain second body state information of the target vehicle at the next time;

[0013] an error determination module configured to determine an error value of the second body state information and third body state information, wherein the third body state information is body state information expected to be reached by the target vehicle at the next time;

[0014] a vehicle control module configured to, in a case where the error value satisfies a preset error condition, take the reference control amount as a target control amount of the target vehicle at the next time, and control a suspension system of the target vehicle based on the target control amount.

[0015] According to another aspect of the present application, an electronic device is provided. The electronic device comprises:

[0016] one or more processors;

[0017] a storage device configured to store one or more programs,

[0018] when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the vehicle control method according to any one of the embodiments of the present application.

[0019] According to another aspect of the present application, a computer readable storage medium is provided. The computer readable storage medium stores computer instructions for causing a processor to implement the vehicle control method according to any one of the embodiments of the present application when executed.

[0020] The technical scheme of the embodiment of the present application fully collects the key information of the current state of the vehicle and the road conditions that the vehicle may face in the future by acquiring the first vehicle body state information of the target vehicle at the current time, determining the road surface information and the reference control amount of the target vehicle at the next time. Compared with the traditional PID control which only depends on the current or past part of the information, the technical scheme can more comprehensively determine the vehicle state and the change of the external environment. The first vehicle body state information, the road surface information and the reference control amount are input into the prediction model which is trained in advance to obtain the second vehicle body state information of the target vehicle at the next time. The technical scheme can estimate the state of the vehicle at the next time in advance by means of the prediction model, so that the control is no longer limited to the current, which helps the suspension system to make adjustment in advance and better adapt to the upcoming road conditions, thereby improving the driving stability and comfort of the vehicle. Then, the error value of the second vehicle body state information and the third vehicle body state information (the vehicle body state information that the target vehicle expects to reach at the next time) is determined to determine the gap between the current estimated state and the expected state by calculating the error value. In the case that the error value meets the preset error condition, the reference control amount is taken as the target control amount of the target vehicle at the next time, and the suspension system of the target vehicle is controlled based on the target control amount. The control mode of error judgment can flexibly adjust the control amount according to the actual situation to ensure that the suspension system always runs towards the expected state. The technical scheme of the embodiment of the present application solves the problems of slow response speed and poor adaptability of the existing PID control in the control of the vehicle suspension system, and achieves the beneficial effects of improving the driving stability, ride comfort and reducing the control energy consumption of the vehicle.

[0021] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating any labor.

[0023] Figure 1 A flowchart of a vehicle control method provided by the embodiment of the present application is shown in the figure.

[0024] Figure 2 A structural schematic diagram of a vehicle suspension system suitable for the vehicle control method provided by the embodiment of the present application is shown in the figure.

[0025] Figure 3 A flowchart of a vehicle control method provided by an embodiment of the present application is shown in FIG. 1.

[0026] Figure 4 A flowchart of an optional embodiment of a vehicle control method provided by an embodiment of the present application is shown in FIG. 2.

[0027] Figure 5 A structural diagram of a vehicle control device provided by an embodiment of the present application is shown in FIG. 3.

[0028] Figure 6 A structural diagram of an electronic device provided by an embodiment of the present application is shown in FIG. 4. DETAILED DESCRIPTION

[0029] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely below in combination with the drawings in the embodiment of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the personnel of ordinary skill in the art without creative labor should belong to the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0031] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the type, use range, use scenario and the like of the personal information involved in the present disclosure should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0032] For example, in response to receiving the active request of the user, prompt information is sent to the user to explicitly prompt the user that the operation requested to be performed will require obtaining and using the personal information of the user. Thus, the user can voluntarily choose whether to provide personal information to the electronic device, application program, server or storage medium, etc. software or hardware performing the operation of the technical solution of the present disclosure according to the prompt information.

[0033] As an optional but non-limiting implementation manner, in response to receiving the active request of the user, the manner of sending the prompt information to the user may, for example, be a pop-up window manner, and the prompt information may be presented in the pop-up window in the form of text. In addition, the pop-up window may also carry a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0034] It can be understood that the above notification and user authorization obtaining process is only illustrative and does not limit the implementation manners of the present disclosure, and other manners meeting relevant laws and regulations can also be applied to the implementation manners of the present disclosure.

[0035] It can be understood that the data involved in the technical solution (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of relevant laws and regulations and relevant provisions.

[0036] Figure 1 A flowchart of a vehicle control method provided by the embodiment of the present application is shown. The embodiment can be applied to the case of controlling a vehicle. The method can be executed by a vehicle control device, which can be realized in the form of hardware and / or software, and can be configured in an electronic device such as a computer or a server. As shown in the figure, the method of the embodiment includes: Figure 1

[0037] S110, acquiring first vehicle body state information of a target vehicle at a current time, determining road surface information of the target vehicle at a next time and a reference control amount.

[0038] The target vehicle can be understood as a vehicle that needs to adjust the vehicle body state. In the embodiment of the present disclosure, the target vehicle has an active suspension system. The active suspension structure of the active suspension system is shown in the figure. In the embodiment of the present application, the active suspension system of the target vehicle can include a vehicle body posture sensor, a road surface preview sensor, an electro-hydraulic or electromagnetic actuator, a control unit (ECU) and an MPC algorithm module. The vehicle body posture sensor can include a height sensor and an accelerometer. The height sensor can be used to acquire the Z-axis height of the vehicle body of the target vehicle. The accelerometer can be used to acquire the acceleration of the target vehicle at the current time. The road surface preview sensor can include a laser radar or a camera. Figure 2 The first vehicle body state information can be understood as the vehicle body state information of the target vehicle at the current time. The vehicle body state information can include the Z-axis height of the vehicle body and the acceleration. The road surface information can be understood as the road characteristic parameter of the region to be traveled by the target vehicle. Optionally, the road surface information can include one of the following: road curvature radius, road slope and road adhesion coefficient. The reference control amount can be understood as the control amount of the target vehicle at the next time. The control amount can include the vehicle body stiffness and the damping.

[0039] The first vehicle body state information can be understood as the vehicle body state information of the target vehicle at the current time. The vehicle body state information can include the Z-axis height of the vehicle body and the acceleration. The road surface information can be understood as the road characteristic parameter of the region to be traveled by the target vehicle. Optionally, the road surface information can include one of the following: road curvature radius, road slope and road adhesion coefficient. The reference control amount can be understood as the control amount of the target vehicle at the next time. The control amount can include the vehicle body stiffness and the damping.​

[0040] In the embodiment of the present application, the first vehicle body state information of the target vehicle at the current time can be obtained by a vehicle body posture sensor installed on the target vehicle. In the embodiment of the present application, the road surface information of the target vehicle at the next time can be determined by a road surface preview sensor pre-installed on the target vehicle. In the embodiment of the present application, the reference control amount of the target vehicle at the next time can be determined by pre-setting a control amount, and the pre-set control amount is determined as the reference control amount of the target vehicle at the next time.

[0041] S120, input the first vehicle body state information, the road surface information and the reference control amount into the pre-trained prediction model to obtain the second vehicle body state information of the target vehicle at the next time.

[0042] The pre-trained prediction model can be used to obtain the second vehicle body state information of the target vehicle at the next time based on the first vehicle body state information, the road surface information and the reference control amount. In the embodiment of the present application, the prediction model can be obtained by building a system simulation model corresponding to the target vehicle through Modelica modeling. Then, the training samples of the training model can be collected through the system simulation model. Then, the initial network model can be trained based on the sample data in the training sample and the expected data corresponding to the sample data to obtain the prediction model. In the embodiment of the present application, the prediction model obtained by building a system simulation model corresponding to the target vehicle through Modelica modeling can reduce the complexity of the prediction model, and improve the calculation speed of the prediction model on the basis of ensuring the prediction accuracy of the model. The second vehicle body state information can be understood as the vehicle body state information of the target vehicle at the next time.

[0043] Specifically, the prediction model is pre-trained to obtain the pre-trained prediction model. Then, the first vehicle body state information, the road surface information and the reference control amount can be input into the pre-trained prediction model to obtain the model output result, that is, the second vehicle body state information of the target vehicle at the next time.

[0044] S130, determine the error value of the second vehicle body state information and the third vehicle body state information, wherein the third vehicle body state information is the vehicle body state information expected to be reached by the target vehicle at the next time.

[0045] The third body state information can be understood as the body state information that the target vehicle expects to reach at the next moment. In the embodiment of the present application, the third body state information can be the target body state information pre-set for each vehicle. For example, the third body state information can be the optimal body state information of the target vehicle. In the embodiment of the present application, the third body state information can be the default body state information or the pre-configured body state information. In the embodiment of the present application, the error value of the second body state information and the third body state information can directly reflect the deviation degree of the current body state from the target body state, so as to guide the adjustment of the suspension system parameters.

[0046] In the embodiment of the present application, there are various ways to determine the error value of the second body state information and the third body state information.

[0047] As an optional implementation in the embodiment of the present application, the determination of the error value of the second body state information and the third body state information can include: calculating the difference value of the second body state information and the third body state information, and taking the difference value as the error value of the second body state information and the third body state information.

[0048] As another optional implementation in the embodiment of the present application, the determination of the error value of the second body state information and the third body state information can include: constructing a cost function; wherein the cost function takes the body vertical acceleration, the suspension dynamic travel, the tire ground force and the actuator energy consumption as the target; and determining the error value of the second body state information and the third body state information based on the cost function.

[0049] The body vertical acceleration can reflect the ride comfort of the vehicle, which needs to be minimized. The suspension dynamic travel can avoid the suspension from hitting the limit, which needs to be limited within a reasonable range to reflect the safety of the vehicle ride. The tire ground force can reflect the handling, which can ensure the tire to be in contact with the ground and prevent the suspension from being excessively compressed to cause instability. The actuator energy consumption can reduce the energy consumption of the control system (such as the hydraulic / electromagnetic actuator).

[0050] Specifically, the cost function taking the body vertical acceleration, the suspension dynamic travel, the tire ground force and the actuator energy consumption as the target is pre-established. Thus, the second body state information and the third body state information can be brought into the cost function to obtain the calculation result of the cost function. Thus, the calculation result of the cost function can be determined as the error value of the second body state information and the third body state information.

[0051] It should be noted that, in the embodiments of the present application, the cost function can be constructed in the following manner: first, the cost function is obtained by calculating two sets of result errors, one set of reference values, i.e., the body state that the vehicle is expected to reach at the next moment, and the other set of input different control amounts and the current initial body state and the road surface information at the next moment to the prediction model to predict the body state of the vehicle at the next moment under the current control amount, and the cost function is constructed by using the above two sets of values.

[0052] To prevent system overshoot or hardware damage, suspension displacement, actuator output force and motor current can also be introduced as hard constraints to control the control amount of the target vehicle.

[0053] The suspension displacement constraint can be used to limit the relative displacement between the vehicle body and the wheels to be less than the gap between the physical limit blocks. The suspension displacement constraint can prevent the suspension from colliding with the limit blocks, resulting in impact noise, structural damage or reduced ride comfort. The actuator output force constraint can be used to limit the force generated by the actuator to be less than its maximum output capacity. The actuator output force constraint can avoid overloading of the actuator (such as excessive pressure of a hydraulic actuator or burning of a coil of an electromagnetic actuator). The motor current constraint can be used to limit the motor current in an electromagnetic suspension to be less than the rated value. The motor current constraint can prevent the motor from overheating, demagnetization or damage to the drive circuit.

[0054] S140, in the case where the error value meets a preset error condition, taking the reference control amount as a target control amount of the target vehicle at the next moment, and controlling the suspension system of the target vehicle based on the target control amount.

[0055] The preset error condition can be set according to actual needs, which is not limited here. The target control amount can be understood as the control amount needed to control the target vehicle at the next moment.

[0056] Specifically, the error condition is set in advance, i.e., the preset error condition. In the case where the error value meets the preset error condition, the reference control amount can be taken as the target control amount of the target vehicle at the next moment. Then the suspension system of the target vehicle can be controlled based on the target control amount. The control amount of the target vehicle is set as the target control amount based on the target control amount to control the target vehicle to travel on the road surface at the next moment with the target control amount.

[0057] On the basis of the above-mentioned embodiments, the method can further comprise: in the case that the error value does not satisfy the preset error condition, the reference control quantity of the target vehicle at the next time can be updated, and the step of inputting the first vehicle body state information, the road surface information and the reference control quantity into the prediction model trained in advance to obtain the second vehicle body state information of the target vehicle at the next time is executed again, so as to obtain the target control quantity of the target vehicle at the next time.

[0058] It should be noted that, compared with the traditional PID control, the technical scheme of the embodiment of the present application is no longer limited by the precise system model, and can better adapt to the changes of the vehicle suspension system parameters, greatly improving the response speed and adaptability. Whether in rapid lane changing, emergency braking or driving on rough roads, the suspension system can be timely and accurately adjusted, effectively reducing the bumping and shaking of the vehicle during driving, and significantly improving the ride comfort. In addition, due to more accurate and efficient control, the energy consumption of the vehicle suspension system control can also be reduced, achieving the goal of energy saving and emission reduction.

[0059] The technical scheme of the embodiment of the present application comprehensively collects the key information such as the current state of the vehicle and the road conditions that the vehicle may face in the future by acquiring the first vehicle body state information of the target vehicle at the current time, determining the road surface information and the reference control quantity of the target vehicle at the next time. Compared with the traditional PID control which only relies on the current or past part of the information, the present technical scheme can more comprehensively determine the vehicle state and the changes in the external environment. The first vehicle body state information, the road surface information and the reference control quantity are input into the prediction model trained in advance to obtain the second vehicle body state information of the target vehicle at the next time. By means of the prediction model, the present technical scheme can estimate the state of the vehicle at the next time in advance, so that the control is no longer limited to the current, which helps the suspension system to make adjustments in advance and better adapt to the upcoming road conditions, thereby improving the driving stability and comfort of the vehicle. Then, the error value of the second vehicle body state information and the third vehicle body state information (the vehicle body state information that the target vehicle is expected to reach at the next time) is determined, so as to determine the gap between the current estimated state and the expected state by calculating the error value. In the case that the error value satisfies the preset error condition, the reference control quantity is taken as the target control quantity of the target vehicle at the next time, and the suspension system of the target vehicle is controlled based on the target control quantity. The error judgment control mode can flexibly adjust the control quantity according to the actual situation, so as to ensure that the suspension system always runs towards the expected state. The technical scheme of the embodiment of the present application solves the problems of slow response speed and poor adaptability of the existing PID control in the control of the vehicle suspension system, and achieves the beneficial effects of improving the driving stability, ride comfort and reducing the control energy consumption of the vehicle.

[0060] Figure 3 A flowchart of a vehicle control method provided by an embodiment of the present application is shown in the figure. On the basis of the foregoing embodiment, the method can optionally comprise: determining a reference control amount of the target vehicle at a next time point, comprising: obtaining a plurality of initial control amounts, and selecting one set of control amounts from the plurality of initial control amounts as the reference control amount of the target vehicle at the next time point. Wherein, the same or similar technical features as the foregoing embodiment are not described again here.

[0061] As shown in the figure, the method of the embodiment specifically comprises: Figure 3

[0062] S210, obtaining first vehicle body state information of the target vehicle at a current time point, and determining road surface information of the target vehicle at a next time point.

[0063] S220, obtaining a plurality of initial control amounts, and selecting one set of control amounts from the plurality of initial control amounts as the reference control amount of the target vehicle at the next time point.

[0064] Wherein, the initial control amount can be understood as a reference control amount of the target vehicle at the next time point which is set in advance.

[0065] Specifically, a plurality of initial control amounts are obtained. One set of control amounts can be randomly selected from the plurality of initial control amounts. Thus, the selected control amounts can be used as the reference control amount of the target vehicle at the next time point. Alternatively, according to the order of the plurality of initial control amounts, one set of initial control amounts in a preset order (first, third or last) can be used as the reference control amount of the target vehicle at the next time point.

[0066] In the embodiment of the present application, there are various ways to obtain a plurality of initial control amounts, which can be set according to actual needs, and are not specifically limited here.

[0067] As an optional implementation in the embodiment of the present application, the obtaining of the plurality of initial control amounts comprises: obtaining a plurality of initial control amounts based on a control amount parameter range. Wherein, the control amount parameter range can be understood as the parameter range of the control amount.

[0068] Specifically, for each control variable, the control amount parameter range of the control variable is determined. All sampling points of the control variable within the control variable parameter range are obtained by uniform adoption within the control amount parameter range. Then, the sampling points of different control variables can be combined to obtain a plurality of initial control variables.

[0069] For example, the control amount can include suspension stiffness and damping coefficient. The parameter range of the suspension stiffness can be A min -A max . The parameter range of the damping coefficient can be B min ​-B max The plurality of initial control variables can include three groups of initial control variables, a first group of initial control variables including A1 and B1, a second group of initial control variables including A2 and B2, and a third group of initial control variables including A3 and B3. A1, A2, and A3 belong to A min -A max The parameter range. B1, B2, and B3 belong to B min -B max The parameter range.

[0070] As another optional implementation in the embodiment of the present application, the plurality of initial control variables can be obtained by using a preset heuristic algorithm. Optionally, the preset heuristic algorithm can be a particle swarm algorithm. Specifically, a preset number of groups of initial control variables can be generated based on the particle swarm algorithm and the control variable parameter range of each control variable. It should be noted that the number of groups of initial control variables can be set according to actual needs, which is not limited herein. For example, 100, 200, or 500, etc.

[0071] As another optional implementation in the embodiment of the present application, the plurality of initial control variables can be obtained by using a preset heuristic algorithm. Optionally, the preset heuristic algorithm can be a particle swarm algorithm. Specifically, a preset number of groups of initial control variables can be generated based on the particle swarm algorithm and the control variable parameter range of each control variable. It should be noted that the number of groups of initial control variables can be set according to actual needs, which is not limited herein. For example, 100, 200, or 500, etc.

[0072] In the embodiment of the present application, the selected one group of control variables from the plurality of initial control variables as the reference control variable of the target vehicle at the next time can include: for each group of initial control variables, determining a local control variable corresponding to the initial control variable based on the cost function and an algorithm for solving a nonlinear optimization problem; and selecting one group of global control variables from the local control variables based on the cost function as the reference control variable of the target vehicle at the next time.

[0073] The local control variable can be understood as the control variable obtained by optimizing the determined initial control variable based on the cost function and the algorithm for solving the nonlinear optimization problem (Sequential Quadratic Programming, SQP). In the embodiment of the present application, the initial control variable is optimized by the cost function combined with the algorithm for solving the nonlinear optimization problem, which can ensure that the local control variable is in a reasonable interval, thereby improving the comprehensive performance and adaptability of the vehicle controller. It should be noted that in the embodiment of the present application, the result of the prediction model can be used to construct the cost function of SQP.

[0074] In the embodiments of the present disclosure, based on the cost function, a set of global control variables is selected from each set of local control variables as the reference control amount of the target vehicle at the next moment, which can include: bringing each set of local control variables into the cost function to obtain the cost value of each set of local control variables. Thus, the local control variable with the minimum cost value can be determined. Further, the local control variable with the minimum cost value can be taken as the reference control amount of the target vehicle at the next moment.

[0075] S230, inputting the first vehicle body state information, the road surface information and the reference control amount into the prediction model trained in advance to obtain second vehicle body state information of the target vehicle at the next moment.

[0076] S240, determining an error value of the second vehicle body state information and third vehicle body state information, wherein the third vehicle body state information is the vehicle body state information expected to be reached by the target vehicle at the next moment.

[0077] S250, in the case that the error value meets a preset error condition, taking the reference control amount as a target control amount of the target vehicle at the next moment, and controlling the suspension system of the target vehicle based on the target control amount.

[0078] Referring to Figure 4 , a reference path (a trajectory or road surface information expected to be tracked by the vehicle at the next moment) of the vehicle and an initial state (first vehicle body state information measured or estimated by a sensor, such as position, speed, attitude, etc.) and a control amount initial value (an output of the last control period or a preset reference control amount) of the vehicle are obtained. An optimization target is constructed, based on the reference path (road surface information of the vehicle at the next moment), the MPC converts the control problem in the future period into a mathematical optimization problem to calculate a series of expected control amounts in the future prediction period. And the initial state, the control amount initial value and the expected control amount are input into the prediction model. Thus, whether the control amount initial value can be used as the control amount of the vehicle at the next moment can be determined by the prediction model. If yes, the control amount initial value can be determined as the MPC output control amount.

[0079] It should be noted that the predicted path needs to be based on the current vehicle state, and is not calculated in advance. The predicted path only outputs a future part of the value (a prediction time domain), and the starting point of each prediction is extremely dependent on the system state at the current time. For example, in the case of vehicle path following, after the controller outputs the control quantity, the actual position of the vehicle will deviate from the prediction due to various disturbances. Therefore, in the next control period, the current actual position of the vehicle must be input as a new "initial state" into the prediction model to re-plan the reference path for the next time. In this way, the control decision is always based on the latest real situation, forming an effective feedback loop.

[0080] The technical scheme of the embodiment of the application realizes the function of determining the reference control quantity of the target vehicle at the next time by obtaining a plurality of initial control quantities and selecting one of the plurality of initial control quantities as the reference control quantity of the target vehicle at the next time.

[0081] Figure 5 A structural schematic diagram of a vehicle control device provided by the embodiment of the application is shown in FIG. 1. Figure 5 As shown in the figure, the device comprises a data acquisition module 310, a vehicle body state prediction module 320, an error determination module 330 and a vehicle control module 340.

[0082] The data acquisition module 310 is configured to acquire first vehicle body state information of a target vehicle at a current time, determine road surface information and a reference control quantity of the target vehicle at a next time.

[0083] The vehicle body state prediction module 320 is configured to input the first vehicle body state information, the road surface information and the reference control quantity into a prediction model that has been pre-trained to obtain second vehicle body state information of the target vehicle at the next time.

[0084] The error determination module 330 is configured to determine an error value of the second vehicle body state information and third vehicle body state information, wherein the third vehicle body state information is vehicle body state information that the target vehicle is expected to reach at the next time.

[0085] The vehicle control module 340 is configured to, in a case where the error value satisfies a preset error condition, take the reference control quantity as a target control quantity of the target vehicle at the next time, and control a suspension system of the target vehicle based on the target control quantity.

[0086] The technical scheme of the embodiment of the present application comprehensively collects the key information of the current state of the vehicle and the road conditions that the vehicle may face in the future by acquiring the first vehicle body state information of the target vehicle at the current time, determining the road surface information and the reference control amount of the target vehicle at the next time. Compared with the traditional PID control which only relies on the current or past part of the information, the technical scheme can more comprehensively determine the vehicle state and the change of the external environment. The first vehicle body state information, the road surface information and the reference control amount are input into the prediction model which is trained in advance to obtain the second vehicle body state information of the target vehicle at the next time. The technical scheme can estimate the state of the vehicle at the next time in advance by means of the prediction model, so that the control is no longer limited to the current, which helps the suspension system to make adjustments in advance and better adapt to the upcoming road conditions, thereby improving the driving stability and comfort of the vehicle. Then, the error value of the second vehicle body state information and the third vehicle body state information (the vehicle body state information that the target vehicle expects to reach at the next time) is determined to determine the gap between the current estimated state and the expected state by calculating the error value. In the case that the error value meets the preset error condition, the reference control amount is taken as the target control amount of the target vehicle at the next time, and the suspension system of the target vehicle is controlled based on the target control amount. The control mode of error judgment can flexibly adjust the control amount according to the actual situation to ensure that the suspension system always operates towards the expected state. The technical scheme of the embodiment of the present application solves the problems of slow response speed and poor adaptability of the existing PID control in the control of the vehicle suspension system, and achieves the beneficial effects of improving the driving stability, ride comfort and reducing the control energy consumption of the vehicle.

[0087] Optionally, the error determination module 330 is configured to construct a cost function, wherein the cost function takes the vehicle body vertical acceleration, the suspension dynamic travel, the tire ground force and the actuator energy consumption as targets; and determine the error value of the second vehicle body state information and the third vehicle body state information based on the cost function.

[0088] Optionally, the data acquisition module 310 is configured to acquire a plurality of initial control amounts, and select one control amount from the plurality of initial control amounts as the reference control amount of the target vehicle at the next time.

[0089] Optionally, the data acquisition module 310 is configured to obtain a plurality of initial control amounts based on a control amount parameter range; and / or obtain a plurality of initial control amounts by using a preset heuristic algorithm.

[0090] Optionally, the data acquisition module 310 is configured to determine, for each group of the initial control variables, a local control variable corresponding to the initial control variable based on the cost function and an algorithm for solving a nonlinear optimization problem; and select a global control variable from the local control variables based on the cost function, as the reference control amount of the target vehicle at the next time.

[0091] Optionally, the apparatus further comprises a reference control amount updating module. The reference control amount updating module is configured to, in a case where the error value does not satisfy the preset error condition, update the reference control amount of the target vehicle at the next time, and return to execute the step of inputting the first vehicle body state information, the road surface information, and the reference control amount into the pre-trained prediction model to obtain the second vehicle body state information of the target vehicle at the next time.

[0092] Optionally, the target vehicle has an active suspension system.

[0093] The vehicle control apparatus provided by the embodiments of the present application can execute the vehicle control method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0094] It should be noted that the units and modules included in the vehicle control apparatus are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for the convenience of mutual differentiation, and are not used to limit the protection scope of the embodiments of the present application.

[0095] Figure 6 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0096] As Figure 6As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. Various programs and data required for the operation of the electronic device 10 can also be stored in the random access memory (RAM) 13. The processor 11, the read-only memory (ROM) 12, and the random access memory (RAM) 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0097] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0098] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the vehicle control method.

[0099] In some embodiments, the vehicle control method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the read-only memory (ROM) 12 and / or the communication unit 19. When the computer program is loaded into the random access memory (RAM) 13 and executed by the processor 11, one or more steps of the vehicle control method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the vehicle control method by any other appropriate means, such as by means of firmware.

[0100] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a load programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0101] Computer programs used to implement the processes of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer program, when executed, can cause instructions defined in the flow charts and / or block diagrams to be implemented on the computer or other programmable apparatus. The computer programs can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0102] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store computer programs for use by or in connection with an instruction execution system, apparatus, or device. Computer-readable storage media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0103] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0104] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0105] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0106] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in a different order, as long as the desired results of the present disclosure are achieved, and the present disclosure is not limited herein.

[0107] The specific embodiments described above are not intended to be limiting, and persons skilled in the art will appreciate that various modifications, combinations, sub-combinations and alternatives can be made to the specific embodiments without departing from the spirit and principles of the disclosure. Accordingly, the disclosure is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A vehicle control method, characterized in that: include: Acquire first vehicle body state information of a target vehicle at a current moment, and determine road surface information and a reference control amount of the target vehicle at a next moment; Inputting the first vehicle body state information, the road surface information, and the reference control amount into a pre-trained prediction model to obtain second vehicle body state information of the target vehicle at a next moment; determining an error value between the second vehicle state information and third vehicle state information, wherein the third vehicle state information is vehicle state information that the target vehicle is expected to reach at a next moment; When the error value satisfies a preset error condition, the reference control amount is used as a target control amount of the target vehicle at a next moment, and the suspension system of the target vehicle is controlled based on the target control amount.

2. The method according to claim 1, wherein determining the error value between the second vehicle state information and the third vehicle state information comprises: Constructing a cost function; wherein the cost function targets vehicle body vertical acceleration, suspension travel, tire ground contact force, and actuator energy consumption; Based on the cost function, an error value between the second vehicle body state information and the third vehicle body state information is determined.

3. The method according to claim 1, characterized in that Determining a reference control amount of the target vehicle at a next moment includes: A plurality of groups of initial control variables are obtained, and a group of control variables is selected from the plurality of groups of initial control variables as a reference control variable for the target vehicle at a next moment.

4. The method according to claim 3, characterized in that The obtaining of multiple groups of initial control variables includes: A plurality of groups of initial control variables are obtained based on a range of control variable parameters; and / or a plurality of groups of initial control variables are obtained using a preset heuristic algorithm.

5. The method according to claim 3, characterized in that The selecting a set of control variables from the multiple sets of initial control variables as a reference control variable for the target vehicle at the next moment includes: For each group of the initial control variables, determining local control variables corresponding to the initial control variables based on the cost function and an algorithm for solving the nonlinear optimization problem; Based on the cost function, a set of global control variables is selected from each set of local control variables as a reference control variable for the target vehicle at the next moment.

6. The method according to claim 1, characterized in that The method further comprises: When the error value does not satisfy the preset error condition, the reference control amount of the target vehicle at the next moment is updated, and the step of inputting the first vehicle body state information, the road surface information and the reference control amount into the pre-trained prediction model is returned to obtain the second vehicle body state information of the target vehicle at the next moment.

7. The method according to claim 1, characterized in that The target vehicle has an active suspension system.

8. A vehicle control device, characterized in that: include: A data acquisition module is used to obtain first vehicle body state information of the target vehicle at a current moment, and determine road surface information and a reference control amount of the target vehicle at a next moment; a vehicle body state prediction module, configured to input the first vehicle body state information, the road surface information, and the reference control variable into a pre-trained prediction model to obtain second vehicle body state information of the target vehicle at a next moment; an error determination module, configured to determine an error value between the second vehicle state information and third vehicle state information, wherein the third vehicle state information is vehicle state information that the target vehicle is expected to achieve at a next moment; A vehicle control module is configured to use the reference control amount as the target control amount of the target vehicle at the next moment when the error value satisfies a preset error condition, and control the suspension system of the target vehicle based on the target control amount.

9. An electronic device, characterized in that: It is characterized by: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle control method according to any one of claims 1 to 7 when executed.

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