Parking control method and device, electronic equipment and medium

Abstract

The invention provides a parking control method and device, electronic equipment and a medium, and relates to the technical field of vehicles, and the method comprises the steps: obtaining a current first motion state of a vehicle and a first expected driving track of the vehicle in a period of time in the future; determining whether the vehicle is in a parking scene or not according to the first motion state and the first expected driving track; when the vehicle is in the parking scene, acquiring a first acceleration for controlling the vehicle to park according to the first motion state and the first expected driving track by using an active-disturbance-rejection control algorithm; and controlling the vehicle to park according to the first acceleration. According to the invention, the parking stability of the vehicle is ensured.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a parking control method, device, electronic device, and medium. Background Art

[0002] During the driving process of a vehicle, there will be situations where it needs to brake and stop in place (or called parking situations), and the vehicle will start again only when the traffic conditions permit. There are many interferences when the vehicle is driving at a low speed, which is not conducive to the stability of vehicle parking. Summary of the Invention

[0003] Embodiments of this application provide a parking control method, device, electronic device, and medium, which are beneficial to ensuring the stability of vehicle parking.

[0004] In a first aspect, embodiments of this application provide a parking control method, including: obtaining a first motion state of the vehicle currently and a first expected driving trajectory of the vehicle within a period of time in the future; determining whether the vehicle is in a parking scenario according to the first motion state and the first expected driving trajectory; in the case that the vehicle is in the parking scenario, using an active disturbance rejection control algorithm, obtaining a first acceleration for controlling vehicle parking according to the first motion state and the first expected driving trajectory; controlling vehicle parking according to the first acceleration.

[0005] Based on the characteristics of the active disturbance rejection control algorithm for dealing with disturbances, etc., embodiments of this application achieve parking control by using the active disturbance rejection control and combining the actual motion state and the expected driving trajectory of the vehicle currently, which helps to weaken and eliminate the influence of the interference during low-speed driving of the vehicle on the parking stability of the vehicle, reduce the error between the expected control effect and the actual control result, and thus is beneficial to ensuring the stability of vehicle parking.

[0006] Optionally, using the active disturbance rejection control algorithm to obtain a first acceleration for controlling vehicle parking according to the first motion state and the first expected driving trajectory includes: using a tracking differentiator to obtain an expected acceleration of the vehicle at the next moment according to the expected speed of the vehicle included in the first expected driving trajectory at the next moment; using an extended state observer to obtain the current acceleration of the vehicle according to the current speed of the vehicle in the first motion state; using an error feedback controller to optimize the expected acceleration according to the error between the expected speed and the current speed of the vehicle and the error between the expected acceleration and the current acceleration of the vehicle, and obtaining the first acceleration.

[0007] By using the active disturbance rejection control algorithm to optimize the parking acceleration according to the error between the expected control effect and the actual control result of the vehicle, it helps to eliminate the influence of the interference during low-speed driving of the vehicle on the parking stability of the vehicle, and makes the actual control of the vehicle meet the expectation.

[0008] Optionally, controlling the vehicle to park according to the first acceleration includes: obtaining a first motor torque for controlling the vehicle to park according to the first acceleration; using the first motor torque to control the vehicle to park.

[0009] By using the motor torque to brake the vehicle, the problems caused by using hydraulic calipers to brake the vehicle can be avoided, which helps to ensure the riding comfort during parking.

[0010] Optionally, obtaining a first motor torque for controlling the vehicle to park according to the first acceleration includes: converting the first acceleration into a second motor torque; using a proportional integral derivative control algorithm to obtain the first motor torque according to the second motor torque.

[0011] By using PID closed-loop control to process the converted motor torque after converting the braking acceleration into the motor torque, it can be ensured that the motor torque accurately tracks the actual deceleration request of the vehicle, and the influence of interference factors such as ground slope and resistance on the braking effect can be offset by the motor torque during braking. At the same time, zero-crossing and limiting processing can be performed on the motor torque, so as to ensure the smoothness and stability of the motor torque execution.

[0012] Optionally, determining whether the vehicle is in a parking scenario according to the first motion state and the first expected driving trajectory includes: inputting the first motion state and the first expected driving trajectory into the learned neural network to obtain the output result of the learned neural network, and the output result is used to describe whether the vehicle is in a parking scenario; wherein, the learned neural network is obtained by using the neural network for sigmoid regression learning, and the data used for the regression learning includes the motion state and expected driving trajectory of the test vehicle in the parking scenario, as well as the motion state and expected driving trajectory of the test vehicle in the non-parking scenario.

[0013] By performing neural network regression learning according to the test data in the parking scenario and non-parking scenario, and inputting the currently obtained actual running data and expected running data of the vehicle into the learned neural network, an accurate judgment of whether the vehicle is currently in a parking scenario can be achieved.

[0014] Optionally, the parking control method further includes: when the vehicle is not in a parking scenario, obtaining vehicle control information according to the first motion state and the first expected driving trajectory, wherein the error between the predicted driving trajectory obtained according to the vehicle control information and the first expected driving trajectory meets the error requirement; controlling the vehicle to drive according to the vehicle control information.

[0015] By determining in real time whether the vehicle is in a parking scenario during vehicle operation, using active disturbance rejection control to control the vehicle to park stably in the parking scenario, and controlling the vehicle to continue driving along the desired driving trajectory in a non-parking scenario, it is possible to achieve the effect of controlling the vehicle to park stably in a timely manner as needed during the vehicle's travel along the planned path.

[0016] In a second aspect, an embodiment of the present application provides a parking control device, including: a first acquisition module, configured to acquire the current first motion state of the vehicle and the first desired driving trajectory of the vehicle within a future period of time; a determination module, configured to determine whether the vehicle is in a parking scenario according to the first motion state and the first desired driving trajectory; a second acquisition module, configured to, when the vehicle is in a parking scenario, use an active disturbance rejection control algorithm to acquire an acceleration for controlling the vehicle to park according to the first motion state and the first desired driving trajectory; and a control module, configured to control the vehicle to park according to the acceleration.

[0017] In a third aspect, an embodiment of the present application provides an electronic chip, including: a processor, configured to execute computer program instructions stored in a memory, wherein when the computer program instructions are executed by the processor, the electronic chip is triggered to execute the method according to any one of the first aspect.

[0018] In a fourth aspect, an embodiment of the present application provides an electronic device. The electronic device includes at least one processor. The processor is coupled to a memory. The memory is used to store computer program instructions. The processor is used to execute the computer program instructions. When the computer program instructions are executed by the processor, the electronic device is triggered to execute the method according to any one of the first aspect.

[0019] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer is caused to execute the method according to any one of the first aspect.

[0020] In a sixth aspect, an embodiment of the present application provides a computer program product. The computer program product includes a computer program. When the computer program runs on a computer, the computer is caused to execute the method according to any one of the first aspect.

[0021] The technical effects of the foregoing aspects can be referred to each other and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments.

[0023] Figure 1 It is a schematic flowchart of a parking control method provided by an embodiment of the present application;

[0024] Figure 2 This is a block schematic diagram of a parking control device provided by an embodiment of the present application;

[0025] Figure 3 This is a structural schematic diagram of a computer device provided by an embodiment of the present application. Specific implementation manners

[0026] To better understand the technical solution of the present application, the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0027] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts fall within the scope of protection of the present application.

[0028] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0029] It should be understood that the term "at least one" used herein means one or more, and "a plurality" means two or more. The term "and / or" used herein is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. Herein, A and B may be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c may be single or multiple.

[0030] It should be understood that although the terms first, second, etc. may be used in the embodiments of the present application to describe set thresholds, these set thresholds should not be limited to these terms. These terms are only used to distinguish the set thresholds from each other. For example, without departing from the scope of the embodiments of the present application, the first set threshold may also be referred to as the second set threshold, and similarly, the second set threshold may also be referred to as the first set threshold.

[0031] Such as Figure 1As shown in the figure, the embodiment of the present application provides a parking control method, which may include steps 101 to 104. In one embodiment, the execution subject of the parking control method may be a vehicle with an autonomous driving function, and the vehicle can implement parking control based on its autonomous driving system. In one embodiment, the vehicle may be a four-wheel car.

[0032] In one embodiment, during the running of the vehicle, the parking control method can be periodically executed to enable timely and stable parking control of the vehicle when a parking situation is encountered.

[0033] Step 101, obtain the current first motion state of the vehicle and the first expected driving trajectory of the vehicle within a period of time in the future.

[0034] In one embodiment, the motion state of the vehicle can be collected in real time through the vehicle's sensors (such as speed sensors, positioning sensors, etc.).

[0035] In one embodiment, the current motion state of the vehicle may include some or all of the actual motion information of the vehicle such as vehicle speed, vehicle acceleration, vehicle position (such as the vehicle's horizontal and vertical coordinate values), and vehicle heading angle.

[0036] In a feasible implementation manner, the motion state collected in real time by the vehicle sensors can be subjected to fusion filtering processing, and the processed running state can be used as the obtained current motion state of the vehicle. Through the fusion filtering processing, it helps to ensure the accurate acquisition of the current motion state of the vehicle.

[0037] In one embodiment, the vehicle may include a path planning module. The path planning module can combine the current state of the vehicle (such as vehicle position, vehicle speed, vehicle heading angle, etc.) and the current environment of the vehicle (such as whether there are obstacles in front of the vehicle, etc.) to real-time plan the expected driving trajectory of the vehicle within a period of time in the future (such as within the next 5 seconds). For example, if there is an obstacle close to the vehicle in front of the vehicle, the expected driving trajectory may be the vehicle braking trajectory or the vehicle obstacle avoidance driving trajectory.

[0038] In a feasible embodiment, the expected driving trajectory may include some or all of the expected motion information of the vehicle such as the expected position of the vehicle, the expected heading angle of the vehicle, the expected speed of the vehicle, and the expected acceleration of the vehicle at a series of future time points. In another feasible embodiment, the expected driving trajectory may be the expected driving trajectory line within a period of time in the future.

[0039] By obtaining the current motion state and the expected driving trajectory of the vehicle, it can be determined whether the vehicle is in a parking scenario, so as to timely control the vehicle to park when it is determined that the vehicle is in a parking scenario.

[0040] Step 102: Determine whether the vehicle is in a parking scenario based on the first motion state and the first expected driving trajectory.

[0041] In a feasible implementation, the trajectory change trend reflected by the expected driving trajectory, the current vehicle speed, acceleration magnitude and other motion states of the vehicle can be combined, and the pre-set parking scenario determination rules can be referred to determine whether the vehicle is in a parking scenario.

[0042] In another feasible implementation, Step 102 includes: inputting the first motion state and the first expected driving trajectory into the learned neural network to obtain the output result of the learned neural network, and the output result is used to describe whether the vehicle is in a parking scenario. Among them, the learned neural network is obtained by using the neural network for softmax regression learning, and the data used for regression learning includes the motion states and expected driving trajectories of the test vehicle in the parking scenario, and the motion states and expected driving trajectories of the test vehicle in the non-parking scenario.

[0043] In one embodiment, all previous working condition test data can be fed back. The expected motion information such as the future planned speed and acceleration information in the test data, as well as the current actual motion state information of the vehicle are used as inputs, and softmax regression learning is performed using a neural network. The learned neural network is used to judge whether the vehicle enters the parking working condition. After completing the regression learning of the neural network, the current motion state and expected driving trajectory of the vehicle can be used as input data, input into the learned neural network, and based on the output result of the learned neural network, it is judged whether the vehicle is in a parking scenario.

[0044] In one embodiment, the output result of the learned neural network can be information indicating whether the vehicle is in a parking scenario. In another embodiment, the output result can also be information indicating the scenario where the vehicle is located.

[0045] By performing neural network regression learning based on the test data in the parking scenario and non-parking scenario, and inputting the currently obtained actual vehicle operation data and vehicle expected operation data into the learned neural network, an accurate judgment of whether the vehicle is currently in a parking scenario can be realized.

[0046] Step 103: In the case where the vehicle is in a parking scenario, use the active disturbance rejection control algorithm to obtain the first acceleration for controlling the vehicle to park according to the first motion state and the first expected driving trajectory.

[0047] There are more interferences when the vehicle is driving at a low speed, such as the road gradient, the road friction, and the influence of the vehicle's environment. The existence of these interferences makes it easy to have a corresponding degree of error between the expected vehicle control effect and the actual vehicle control result. Especially when driving at a low speed in a complex terrain environment, the existence of interferences will be unfavorable to the stability and safety of vehicle parking.

[0048] The Active Disturbance Rejection Control (ADRC) algorithm evolved from the PID (Proportional Integral Derivative) control algorithm and is a control algorithm that can handle nonlinearity, uncertainty, and disturbances. The ADRC inherits the advantages of PID control, such as error feedback control, and improves the disadvantages in PID control, such as the error extraction method and weighted error. The unique feature of the ADRC algorithm is that it classifies all uncertain factors acting on the controlled object as "unknown disturbances", and estimates and compensates for the unknown disturbances using the input and output data of the object.

[0049] Based on the characteristics of the ADRC algorithm in dealing with disturbances, etc., the embodiments of this application achieve parking control by using the ADRC and combining the current actual motion state and the expected driving trajectory of the vehicle, which helps to weaken and eliminate the influence of the interferences during low-speed driving of the vehicle on the parking stability of the vehicle, reduce the error between the expected control effect and the actual control result, and thus is beneficial to ensuring the stability of vehicle parking.

[0050] When the embodiments of this application apply the ADRC algorithm to the application scenario of vehicle parking control, the controlled object is the vehicle, and the unknown disturbances estimated and compensated by the ADRC algorithm can be the combination of all interferences that will cause an error between the expected control effect and the actual control result of the vehicle during vehicle parking. In this way, obtaining the parking acceleration through the ADRC algorithm helps to make the vehicle parking meet the expectations and improve the stability and safety of vehicle parking.

[0051] In an embodiment of this application, the step of using the ADRC algorithm to obtain the first acceleration for controlling vehicle parking according to the first motion state and the first expected driving trajectory may include: using a tracking differentiator to obtain the expected acceleration of the vehicle at the next moment according to the expected speed of the vehicle included in the first expected driving trajectory; using an extended state observer to obtain the current acceleration of the vehicle according to the current speed of the vehicle in the first motion state; using an error feedback controller to optimize the expected acceleration according to the error between the expected speed and the current speed of the vehicle, and the error between the expected acceleration and the current acceleration of the vehicle, to obtain the first acceleration.

[0052] In one embodiment, the desired driving trajectory may include the desired vehicle positions, desired vehicle heading angles, and desired vehicle speeds at a series of future time points.

[0053] In one embodiment, the desired speed of the vehicle at the next moment in the desired driving trajectory may be obtained, and the desired acceleration of the vehicle at the next moment may be obtained by differentiating the desired speed.

[0054] In one embodiment, the current actual speed of the vehicle may be observed through a speed sensor of the vehicle, and the current actual acceleration of the vehicle may be obtained by differentiating the actual speed, which is used as the observed vehicle acceleration.

[0055] In this way, the current speed error may be obtained based on the acquired desired speed and actual speed, and the current acceleration error may be obtained based on the acquired desired acceleration and actual acceleration.

[0056] Since the interference during low-speed driving of the vehicle may cause the existence of speed error and acceleration error, which is not conducive to the stable parking of the vehicle, in order to reduce the influence of interference on the parking stability of the vehicle, the acquired desired acceleration may be optimized according to the current speed error and acceleration error to compensate for the interference. By performing parking control according to the optimized desired acceleration, the influence of interference on the parking stability of the vehicle may be weakened.

[0057] In one embodiment, an active disturbance rejection controller may be used to obtain a first acceleration. The active disturbance rejection controller may include a tracking differentiator, an extended state observer, and an error feedback controller.

[0058] The tracking differentiator may pre-arrange a transition process for the input signal, extract the input signal containing random noise and its differential signal, and solve the contradiction between PID overshoot and rapidity. In such a feasible implementation, the tracking differentiator may be used to differentiate the desired speed to obtain the desired acceleration and perform a transition process.

[0059] The extended state observer may estimate the real-time action value of the internal and external disturbances of the system and give compensation in the feedback, and eliminate the influence of the disturbances by the compensation method, so as to have the function of anti-interference. In such a feasible implementation, the extended state observer may be used to observe the output state (i.e., the actual operating state of the vehicle) to observe the actual speed of the vehicle, and then observe the actual acceleration of the vehicle and the interference according to the observed actual speed of the vehicle.

[0060] The error feedback controller can suppress disturbances. It can calculate the error based on the signal given by the tracking differentiator and its derivative, and the system state and its derivative obtained by the extended state observer, and calculate the control quantity using the method of nonlinear combination to compensate for the disturbances. In such a feasible implementation, an error feedback controller can be used to perform feedback control on the error between the expected values (expected speed and expected acceleration) and the observed values (observed actual speed and actual acceleration) through a feedback control rate, and compensate for the observed disturbances to obtain the final acceleration request (i.e., obtain the above-mentioned first acceleration).

[0061] By using the active disturbance rejection control algorithm to optimize the parking acceleration according to the error between the expected control effect and the actual control result of the vehicle, it helps to eliminate the influence of disturbances during low-speed driving of the vehicle on the parking stability of the vehicle, making the actual control of the vehicle meet the expectations.

[0062] Step 104: Control the vehicle to park according to the first acceleration.

[0063] The sign of the first acceleration can be a negative sign, that is, the first acceleration is the deceleration used to achieve vehicle deceleration and stop. By controlling the vehicle according to the first acceleration, the corresponding vehicle stopping effect can be achieved to achieve the purpose of vehicle parking.

[0064] At the next moment when controlling the vehicle to park according to the first acceleration, the vehicle speed can be zero speed, that is, the parking has been completed, or it can be non-zero speed, that is, the parking has not been completed. In the case where the parking has not been completed, the parking acceleration can be obtained multiple times in a loop until the vehicle completes parking.

[0065] After step 104, the steps of obtaining the current operating state and the expected driving trajectory of the vehicle can be executed again, and the obtained data can be used for the execution of the next parking control process.

[0066] In an embodiment of the present application, step 104 includes: obtaining the first motor torque for controlling the vehicle to park according to the first acceleration; using the first motor torque to control the vehicle to park.

[0067] After obtaining the acceleration for realizing vehicle parking, the motor torque for realizing vehicle parking can be obtained according to the conversion relationship between the acceleration and the motor torque, and the motor torque is used to stop the vehicle. Feasibly, the motor torque for realizing the purpose of vehicle stopping may be called motor negative torque.

[0068] Using a hydraulic caliper to stop the vehicle is another feasible implementation for controlling the vehicle to park, but the hydraulic caliper has poor linearity, which easily leads to problems such as uncomfortable braking and slow starting. By using the motor torque to stop the vehicle in the embodiment of the present application, the problems caused by using the hydraulic caliper to stop the vehicle can be avoided, which helps to ensure the riding comfort during parking.

[0069] In one embodiment of the present application, obtaining a first motor torque for controlling vehicle parking according to a first acceleration includes: converting the first acceleration into a second motor torque; using a proportional-integral-derivative control algorithm to obtain the first motor torque according to the second motor torque. That is, the second motor torque serves as the input of the PID control algorithm, and the output of the PID control algorithm is the first motor torque.

[0070] By converting the braking acceleration into a motor torque and then using PID closed-loop control to process the converted motor torque, it can be ensured that the motor torque accurately tracks the actual deceleration request of the vehicle, and it can be ensured that the motor torque during braking can offset the influence of interference factors such as ground slope and resistance on the braking effect. At the same time, zero-crossing and limiting processing can also be performed on the motor torque, so as to ensure the smoothness and stability of the motor torque execution.

[0071] Feasibly, the first motor torque can be sent to the vehicle chassis so that the vehicle chassis can perform vehicle braking processing accordingly.

[0072] Different from converting the braking acceleration into a motor torque after PID processing, in the embodiment of the present application, by performing PID processing after converting the braking acceleration into a motor torque, the PID processing can act on the smooth and stable control of the motor torque execution, so as to ensure the stability of the vehicle chassis to achieve braking control.

[0073] When the vehicle encounters a parking situation, in step 102, it can usually be determined that the vehicle is in a parking scenario, and in this case, braking processing can be performed to make a short stop. When the vehicle does not encounter a parking situation, in step 102, it can usually be determined that the vehicle is not in a parking scenario, and in this case, vehicle driving control can be performed according to the conventional closed-loop control strategy to continue driving.

[0074] In one embodiment of the present application, the parking control method further includes: when the vehicle is not in a parking scenario, obtaining vehicle control information according to a first motion state and a first desired driving trajectory, wherein the error between the predicted driving trajectory obtained according to the vehicle control information and the first desired driving trajectory meets the error requirement; controlling the vehicle to drive according to the vehicle control information.

[0075] In a feasible implementation manner, vehicle control information can be obtained according to the current operating state of the vehicle (such as position, heading angle, speed, etc.) and the desired driving trajectory, so that when the vehicle drives with the vehicle control information, the actual driving trajectory of the vehicle can be closer to the desired driving trajectory, so as to achieve the effect of controlling the vehicle to drive along the desired driving trajectory.

[0076] In one embodiment, the vehicle control information may include wheel angle and acceleration to support lateral and longitudinal control of vehicle driving.

[0077] By determining in real time whether the vehicle is in a parking scenario during vehicle operation, using active disturbance rejection control to control the vehicle to park stably in the parking scenario, and controlling the vehicle to continue driving along the desired driving trajectory in the non-parking scenario, it is possible to achieve the effect of controlling the vehicle to park stably in a timely manner as needed during the vehicle's regular path driving.

[0078] After controlling the vehicle to drive according to the vehicle control information, the steps of obtaining the current operating state and the desired driving trajectory of the vehicle can be executed again, and the obtained data can be used for the execution of the next parking control process.

[0079] As Figure 2 shown, an embodiment of the present application provides a parking control device 200, including: a first acquisition module 201, configured to acquire the current first motion state of the vehicle and the first desired driving trajectory of the vehicle within a period of time in the future; a determination module 202, configured to determine whether the vehicle is in a parking scenario according to the first motion state and the first desired driving trajectory; a second acquisition module 203, configured to, when the vehicle is in a parking scenario, use an active disturbance rejection control algorithm to acquire the acceleration for controlling the vehicle to park according to the first motion state and the first desired driving trajectory; and a control module 204, configured to control the vehicle to park according to the acceleration.

[0080] An embodiment of the present application provides an electronic chip, including: a processor, which is configured to execute computer program instructions stored in a memory, wherein when the computer program instructions are executed by the processor, the electronic chip is triggered to execute the method described in any embodiment of the present application.

[0081] An embodiment of the present application provides an electronic device. The electronic device includes at least one processor, the processor is coupled to a memory, the memory is used to store computer program instructions, and the processor is used to execute the computer program instructions. When the computer program instructions are executed by the processor, the electronic device is triggered to execute the method described in any embodiment of the present application.

[0082] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When it runs on a computer, the computer is enabled to execute the method described in any embodiment of the present application.

[0083] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program runs on a computer, the computer is enabled to execute the method described in any embodiment of the present application.

[0084] Figure 3 is a schematic diagram of a computer device provided by an embodiment of the present application. As Figure 3As shown, the computer device 20 of this embodiment includes: a processor 21 and a memory 22. The memory 22 is used to store a computer program 23 that can run on the processor 21. When the computer program 23 is executed by the processor 21, it implements the steps in the method embodiment of this application. To avoid repetition, they are not elaborated here one by one. Alternatively, when the computer program 23 is executed by the processor 21, it implements the functions of each model / unit in the device embodiment of this application. To avoid repetition, they are not elaborated here one by one.

[0085] The computer device 20 includes but is not limited to the processor 21 and the memory 22. Those skilled in the art can understand that Figure 3 merely examples of the computer device 20, which do not constitute a limitation on the computer device 20, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0086] The processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0087] The memory 22 may be an internal storage unit of the computer device 20, such as the hard disk or memory of the computer device 20. The memory 22 may also be an external storage device of the computer device 20, such as a plug-in hard disk equipped on the computer device 20, a smart media (SM) card, a secure digital (SD) card, a flash card, etc. Further, the memory 22 may also include both the internal storage unit and the external storage device of the computer device 20. The memory 22 is used to store the computer program 23 and other programs and data required by the computer device. The memory 22 may also be used to temporarily store data that has been output or will be output.

[0088] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be an indirect coupling or communication connection through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms.

[0089] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in various embodiments of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0091] The integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit stored in a storage medium includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0092] In the embodiments of this application, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, commodity, or device. Without further limitations, the element defined by the statement "including one..." does not exclude the existence of other identical elements in the process, method, commodity, or device including the said element.

[0093] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present application may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0094] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments of the present application can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled professionals may use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0095] Those skilled in the art can clearly understand that for the convenience and brevity of description, the same or similar parts among the various embodiments in the present application can be referred to each other. For example, for the specific working processes of the systems, devices, and units described in the embodiments of the present application, reference may be made to the corresponding processes in the method embodiments of the present application, which will not be elaborated herein.

[0096] The above are only specific embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of protection of the present application.

Claims

1. A parking control method, characterized in that: include: Acquire a current first motion state of a vehicle and a first expected driving trajectory of the vehicle within a period of time in the future; determining whether the vehicle is in a parking scenario according to the first motion state and the first expected driving trajectory; When the vehicle is in a parking scenario, using an active disturbance rejection control algorithm, according to the first motion state and the first expected driving trajectory, obtain a first acceleration for controlling the parking of the vehicle; The vehicle is controlled to park according to the first acceleration.

2. The method according to claim 1, characterized in that The using an active disturbance rejection control algorithm to obtain a first acceleration for controlling the parking of the vehicle according to the first motion state and the first expected driving trajectory includes: Using a tracking differentiator, obtaining an expected acceleration of the vehicle at a next moment according to an expected speed of the vehicle at a next moment included in the first expected driving trajectory; Using an extended state observer, obtaining a current acceleration of the vehicle according to a current speed of the vehicle in the first motion state; An error feedback controller is used to optimize the expected acceleration according to an error between the expected speed and the current speed of the vehicle and an error between the expected acceleration and the current acceleration of the vehicle to obtain the first acceleration.

3. The method according to claim 1 or 2, characterized in that: The controlling the vehicle to park according to the first acceleration includes: acquiring, according to the first acceleration, a first motor torque for controlling parking of the vehicle; The vehicle is parked using the first electric machine torque.

4. The method according to claim 3, characterized in that The step of acquiring a first motor torque for controlling parking of the vehicle according to the first acceleration includes: converting the first acceleration into a second motor torque; The first motor torque is obtained according to the second motor torque by using a proportional-integral-derivative control algorithm.

5. The method according to claim 1 or 2, characterized in that: The determining, according to the first motion state and the first expected driving trajectory, whether the vehicle is in a parking scenario includes: Inputting the first motion state and the first expected driving trajectory into a learned neural network to obtain an output result of the learned neural network, wherein the output result is used to describe whether the vehicle is in a parking scene; Among them, the learned neural network is obtained by using a neural network to perform normalized exponential function regression learning, and the data used for regression learning includes the motion state and expected driving trajectory of the test vehicle in a parking scenario, as well as the motion state and expected driving trajectory of the test vehicle in a non-parking scenario.

6. The method according to claim 1 or 2, characterized in that: The method further comprises: When the vehicle is not in a parking scenario, acquiring vehicle control information according to the first motion state and the first expected driving trajectory, wherein an error between a predicted driving trajectory obtained according to the vehicle control information and the first expected driving trajectory meets an error requirement; The vehicle is controlled to travel according to the vehicle control information.

7. A parking control device, characterized in that: include: A first acquisition module is used to acquire a current first motion state of the vehicle and a first expected driving trajectory of the vehicle within a period of time in the future; a determination module, configured to determine whether the vehicle is in a parking scenario according to the first motion state and the first expected driving trajectory; A second acquisition module is used to acquire an acceleration for controlling parking of the vehicle according to the first motion state and the first expected driving trajectory by using an active disturbance rejection control algorithm when the vehicle is in a parking scene; A control module is used to control the parking of the vehicle according to the acceleration.

8. An electronic chip, characterized in that: include: A processor, configured to execute computer program instructions stored in a memory, wherein when the computer program instructions are executed by the processor, the electronic chip is triggered to execute the method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: The electronic device comprises at least one processor, the processor is coupled to a memory, the memory is used to store computer program instructions, and the processor is used to execute computer program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to execute the method as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 6.

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