Control method and device of vehicle, electronic equipment and storage medium

Abstract

The application provides a control method of a vehicle, comprising: acquiring a current driving speed, current path information and reference path information of the vehicle; determining path deviation information according to the current path information and the reference path information; determining a high-frequency signal gain corresponding to the current driving speed and the path deviation information, and determining a low-frequency signal gain corresponding to the current driving speed and the path deviation information; determining a control parameter of the vehicle according to the high-frequency signal gain and the low-frequency signal gain, and controlling the vehicle according to the control parameter. The application adjusts the control parameter of the vehicle by determining the control parameter according to the current driving speed of the vehicle, and distinguishing the low-frequency part and the high-frequency part of the path deviation information and the current driving speed of the vehicle, so that the control of the vehicle is more accurate, the driving safety of the vehicle is improved, and the driving experience of the vehicle is improved.

Description

Technical Field

[0001] This application relates to the field of automotive electronics technology, and more specifically, to a vehicle control method, device, electronic device, and storage medium. Background Technology

[0002] Currently, with the development of technology, more and more vehicles are equipped with intelligent driving functions. Lateral control algorithms for intelligent driving include pure tracking algorithms and Stanley's algorithm based on geometric models; model predictive control algorithms and feedback linearization algorithms based on kinematic models; and full-state feedback control and aiming-following control algorithms based on dynamic models. In pure tracking algorithms based on geometric models, there is a problem where the vehicle's control performance deteriorates as vehicle speed changes. How to improve vehicle control performance under constantly changing vehicle speeds has become an urgent problem to be solved. Summary of the Invention

[0003] In view of this, embodiments of this application propose a vehicle control method, apparatus, electronic device, and storage medium to solve the above-mentioned problems.

[0004] According to one aspect of the embodiments of this application, a vehicle control method is provided, the method comprising: acquiring the vehicle's current driving speed, current path information, and reference path information; determining path deviation information based on the current path information and the reference path information; determining a high-frequency signal gain corresponding to the current driving speed and the path deviation information, and determining a low-frequency signal gain corresponding to the current driving speed and the path deviation information; determining control parameters of the vehicle based on the high-frequency signal gain and the low-frequency signal gain, and controlling the vehicle based on the control parameters.

[0005] According to one aspect of the embodiments of this application, a vehicle control device is provided, the device comprising: an acquisition module for acquiring the vehicle's current driving speed, current path information, and reference path information; a path deviation information determination module for determining path deviation information based on the current path information and the reference path information; a signal gain determination module for determining a high-frequency signal gain corresponding to the current driving speed and the path deviation information, and for determining a low-frequency signal gain corresponding to the current driving speed and the path deviation information; and a control module for determining control parameters of the vehicle based on the high-frequency signal gain and the low-frequency signal gain, and controlling the vehicle based on the control parameters.

[0006] According to one aspect of the embodiments of this application, an electronic device is provided, including: a processor; and a memory storing computer-readable instructions, which, when executed by the processor, implement the vehicle control method described above.

[0007] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided that stores computer-readable instructions thereon, which, when executed by a processor, implement the vehicle control method described above.

[0008] In the solution of this application, when the vehicle is in autonomous driving mode, path deviation information is first determined based on the vehicle's current path information and reference path information. Then, based on the vehicle's current speed and path deviation information, the high-frequency signal gain and low-frequency signal gain corresponding to the current speed and path deviation information are determined. Furthermore, the vehicle's control parameters can be determined based on the high-frequency and low-frequency signal gains, allowing for adaptive adjustment of the vehicle's control parameters according to the vehicle's current speed. The solution distinguishes between the low-frequency and high-frequency components of the path deviation information and the vehicle's current speed, determining the respective gains for each component. Adjusting the vehicle's control parameters based on these gains makes vehicle control more accurate, improves driving safety, and enhances the driving experience.

[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0011] Figure 1 This is a flowchart illustrating a method for upgrading a program in a vehicle according to an embodiment of this application.

[0012] Figure 2 This is a flowchart illustrating the specific steps of step 120 according to an embodiment of this application.

[0013] Figure 3 This is a schematic diagram illustrating the lateral and longitudinal control of a vehicle according to an embodiment of this application.

[0014] Figure 4 This is a flowchart illustrating the specific steps of step 130 according to an embodiment of this application.

[0015] Figure 5 This is a low-frequency signal gain curve shown by way of an embodiment according to this application.

[0016] Figure 6 This is a flowchart illustrating the specific steps of step 140 according to an embodiment of this application.

[0017] Figure 7 This is a flowchart illustrating a method for upgrading a program in a vehicle according to another embodiment of this application.

[0018] Figure 8 This is an error curve diagram shown according to an embodiment of this application.

[0019] Figure 9 This is an error curve diagram shown according to another embodiment of this application.

[0020] Figure 10 This is an error curve diagram shown according to yet another embodiment of this application.

[0021] Figure 11 This is an error curve diagram shown according to another embodiment of the present application.

[0022] Figure 12 This is a block diagram of a control device in a vehicle according to an embodiment of this application.

[0023] Figure 13 This is a hardware structure diagram of an electronic device according to an embodiment of this application.

[0024] The accompanying drawings have illustrated specific embodiments of the present invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the inventive concept in any way, but rather to illustrate the concept of the invention to those skilled in the art through specific embodiments. Detailed Implementation

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Please see Figure 1 , Figure 1 This application illustrates a vehicle control method according to an embodiment of the present application. In a specific embodiment, the vehicle control method can be applied to, for example... Figure 12 The vehicle control unit 400 and the electronic equipment 500 equipped with the vehicle control unit 400 are shown. Figure 13 The specific process of this embodiment will be described below. Of course, it is understood that this method can be executed by an electronic device with computing capabilities, such as a desktop computer, laptop computer, in-vehicle terminal, or in-vehicle infotainment screen. This method can also be executed interactively by a processing system including a server and a terminal; the method can also be performed by an intelligent driving domain controller or a PID controller. The following will focus on... Figure 1 The process shown will be described in detail. The vehicle control method may specifically include the following steps:

[0028] Step 110: Obtain the vehicle's current speed, current path information, and reference path information.

[0029] In one approach, the vehicle's current speed can be obtained by detecting the speed sensor within the vehicle and acquiring the data from that sensor, or it can be detected by other devices within the vehicle. The vehicle's current driving information can be data detected by the vehicle's Global Navigation Satellite System (GNSS) or Inertial Measurement Unit (IMU).

[0030] In some embodiments, the reference path information may be calculated by the vehicle's intelligent driving system based on the vehicle's current location information and the location information of obstacles in front of the vehicle. Optionally, the reference path information may be real-time reference path information obtained after planning to avoid collisions with obstacles around the vehicle (such as other vehicles, pedestrians, etc.). In other embodiments, the reference path information may also be path information associated with the vehicle's current location information from historical path information stored in the vehicle's local database, or path information associated with the vehicle's current location information from historical path information stored on a cloud server.

[0031] In one approach, when a vehicle is in autonomous driving or assisted driving mode, the intelligent driving system can detect whether there are obstacles around the vehicle (e.g., in front of and behind the vehicle). When an obstacle is present, it can obtain the distance between the obstacle and the vehicle and the location information of the obstacle. Then, based on the distance between the obstacle and the vehicle and the location information of the obstacle, it can plan reference path information and finally control the vehicle to drive according to the reference path information, thereby avoiding collisions between the vehicle and the obstacle.

[0032] As another approach, reference path information can be determined based on the vehicle's current location information, where the distance between the current location information and the historical path information is less than a distance threshold. The distance threshold can be set according to actual needs and is not specifically limited here.

[0033] Step 120: Determine path deviation information based on the current path information and the reference path information.

[0034] In one approach, reference path information can be used as the target path information for the vehicle. If the vehicle needs to move from the position corresponding to the current path information to the position corresponding to the target path information, it is necessary to determine the path deviation between the reference path information and the current path information, and control the vehicle based on this path deviation.

[0035] As another approach, intelligent driving systems typically use a controller to control vehicle movement. This controller can be a proportional-integral-derivative (PID) controller, which consists of a proportional unit (P), an integral unit (I), and a derivative unit (D). Control is achieved through the setting of three parameters: Kp, Ki, and Kd. The PID controller is primarily suitable for systems that are essentially linear and whose dynamic characteristics do not change over time. PID controllers are commonly used for automated vehicle control. The PID controller is a common feedback loop component in automated control applications. It compares collected data with a reference value and uses this difference to calculate a new input value. The purpose of this new input value is to allow the system data to reach or remain at the reference value. In the embodiments of this application, the new parameters required for vehicle control can be calculated using the path deviation between the current path information and the reference path information, and then the vehicle can be controlled based on these new parameters. Optionally, the path deviation can be the distance deviation between the current path information and the reference path information.

[0036] In some embodiments, the current path information includes current location information and current heading angle, and the reference path information includes the target trajectory route, such as... Figure 2 As shown, step 120 includes:

[0037] Step 121: Based on the current location information, determine the target tracking point from the target trajectory route. The target tracking point is the point on the target trajectory route that is closest to the current location information.

[0038] One approach is to define a reference path information set consisting of a series of coordinate points, where different coordinate points can form different trajectory routes. Optionally, the target trajectory route can be determined from the reference path information based on the vehicle's current path information. To accurately determine the path deviation between the vehicle's current path information and the reference path information, it is necessary to identify the target tracking point on the target trajectory route that is closest to the vehicle's current position. Optionally, the target tracking point can be continuously updated based on the vehicle's current position information and the reference path information during the vehicle's movement.

[0039] Step 122: Determine the position deviation based on the current position information and the target tracking point, and determine the heading angle deviation based on the current heading angle and the target heading angle corresponding to the target tracking point.

[0040] As one method, position deviation can be the deviation between the vehicle's x-coordinate in the current position information and the x-coordinate of the target tracking point. When controlling the vehicle, such as... Figure 3As shown, this includes lateral control and longitudinal control. Lateral control refers to path tracking, which uses automatic steering control or the vehicle's steering wheel to ensure the vehicle always travels along the desired path, while maintaining driving safety and passenger comfort. Longitudinal control refers to adjusting vehicle speed to maintain sufficient space between vehicles, using minimal braking to maintain a relatively constant speed, and braking as quickly as possible in emergency situations. Figure 3 As shown, the vehicle can plan and determine reference path information based on the obtained obstacle positions and the vehicle's current position. Based on the coordinate information of the target tracking point in the reference information, the vehicle's steering wheel target angle and vehicle target acceleration are determined. Then, the target angle and target acceleration are sent to the vehicle's lateral actuator (Electric Power Steering, EPS) and longitudinal actuator (Electronic Stability Program, ESP) respectively. The lateral actuator and longitudinal actuator control the vehicle according to the target angle and target acceleration respectively.

[0041] Optionally, the heading angle refers to the angle between the vehicle's current heading and the longitudinal axis of the ground coordinate system, or it can be the angle between the vehicle's center-of-gravity velocity and the lateral axis of the ground coordinate system. The heading angle corresponding to the target tracking point can be the angle between the vehicle at the target tracking point and the longitudinal axis of the ground coordinate system, or it can be the angle between the vehicle's center-of-gravity velocity and the lateral axis of the ground coordinate system at the target tracking point. The heading angle deviation is the difference between the vehicle's current heading angle and the heading angle corresponding to the target tracking point.

[0042] Step 123: Determine the path deviation information based on the position deviation and the heading angle deviation.

[0043] One approach is to determine the position deviation and heading angle deviation as path deviation information. Optionally, a path deviation set can be set up, and the position deviation and heading angle deviation can be stored in the path deviation set. The path deviation information can be determined from the path deviation set.

[0044] Please continue reading. Figure 1 Step 130: Determine the high-frequency signal gain corresponding to the current driving speed and the path deviation information, and determine the low-frequency signal gain corresponding to the current driving speed and the path deviation information.

[0045] In some embodiments, due to the characteristics of PID controllers, if the frequency of the input signal is too high when determining the control parameters of the PID, the accuracy of the determined control parameters will be low, which will cause the vehicle to be unable to travel accurately along the expected route. Therefore, the input signal of the PID controller is usually first input into a low-pass filter to filter out the influence of high-frequency signals on the determination of control parameters. However, the accuracy of determining the control parameters of the PID controller based on the output signal after the low-pass filter is not high enough. Therefore, in the embodiments of this application, in addition to determining the low-frequency signal gain based on the current driving speed and path deviation information, the high-frequency signal gain can also be determined based on the current driving speed and path deviation information to improve the accuracy of the control parameter determination.

[0046] In some embodiments, such as Figure 4 As shown, step 130 includes:

[0047] Step 131: Input the current driving speed and the path deviation information into the filter to perform noise reduction processing on the current driving speed and the path deviation information to obtain target information, wherein the target information is the current driving speed and the path deviation information after noise reduction processing.

[0048] In one approach, the current velocity and path deviation information is used as input signals and fed into a filter to filter out the influence of other signals (such as interference noise signals) or signals of other frequencies on the subsequent determination of control parameters.

[0049] The target information refers to the current driving speed and path deviation information output by the filter, that is, the current driving speed and path deviation information after filtering out the fixed frequency. Optionally, this filter can be a low-pass filter.

[0050] In some embodiments, step 131 includes: inputting the current driving speed and the path deviation information into the notch filter to perform fixed-frequency denoising processing on the current driving speed and the path deviation information to obtain reference information; inputting the reference information into the low-pass filter to perform high-frequency signal denoising processing on the reference information to obtain the target information.

[0051] As one approach, filters include notch filters and low-pass filters. Typically, the location information of the target tracking point and the corresponding heading angle determined by a vehicle's positioning system (such as GPS) are subject to fixed-frequency interference due to the inherent characteristics of the positioning system, leading to inaccurate path deviation information. In the embodiments of this application, to avoid the influence of fixed frequencies on the path deviation information, the current driving speed and path deviation information are first input into a notch filter for appropriate filtering processing.

[0052] Reference information refers to the current driving speed and path deviation information output by the notch filter after eliminating fixed-frequency interference.

[0053] As one approach, reference information is input into a low-pass filter to eliminate the influence of high-frequency signals in the reference information on the determination of subsequent control parameters. Optionally, a mapping relationship between different vehicle speeds and the cutoff frequency of the low-pass filter can be preset, where the cutoff frequency refers to the maximum frequency of signals allowed to pass through the low-pass filter; signals higher than this cutoff frequency cannot pass through. The cutoff frequency corresponding to the current driving speed can be determined based on the mapping relationship between different vehicle speeds and the cutoff frequency of the low-pass filter, and then the low-pass filter determines the target information corresponding to the current driving speed and path deviation information based on this cutoff frequency. Optionally, this mapping relationship can be different driving speeds corresponding to different cutoff frequencies, or different driving speed ranges can be set to correspond to different cutoff frequencies. This mapping relationship can be set according to actual needs and is not specifically limited here.

[0054] Step 132: Determine the high-frequency error value of the target information based on the target information and the path deviation information.

[0055] As one approach, since the frequency of the target information after passing through the low-pass filter is lower than or equal to the cutoff frequency of the low-pass filter, the target information can be subtracted from the path deviation information to obtain information higher than the cutoff frequency. This information is the high-frequency error value of the target information.

[0056] Step 133: Based on the first control parameter table, determine the first parameter corresponding to the first target information and the current driving speed, wherein the first control parameter table includes the target information, the current driving speed and the first parameter.

[0057] One approach is to pre-set a first control parameter table, which is a two-dimensional table consisting of target information, current driving speed, and a first parameter. As shown in Table 1, different current driving speeds and different target information correspond to different first parameters.

[0058] Table 1 First Control Parameters Table

[0059]

[0060] like Figure 1As shown, if the current driving speed is 2 km / h and the target information is 6 cm, then the first parameter used is 3.1. Table 1 above is only an example. The first control parameter table can be set according to actual needs, and is not limited here. It is easy to understand that step 133 can also be executed before step 132. This application does not limit the execution order of steps 133 and 132.

[0061] Step 134: Determine the high-frequency signal gain based on the high-frequency error value and the first parameter.

[0062] One approach is to multiply the high-frequency error value by the first parameter to obtain the high-frequency signal gain. It's understood that the first parameter is a scaling factor; multiplying the scaling factor by the high-frequency error value yields the high-frequency signal gain. For example, if the high-frequency signal gain is G1, then G1 = X * Q, where X is the high-frequency signal error and Q is the first parameter.

[0063] In some embodiments, after step 131, the method further includes: determining a second parameter and a third parameter corresponding to the current driving speed based on a second control parameter table, wherein the second control parameter table includes the current driving speed, the target information, the second parameter, and the third parameter; if the absolute value of the target information is greater than the second parameter, then determining the low-frequency signal gain based on the target information, the second parameter, and the third parameter.

[0064] One approach is to pre-set a second control parameter table, which consists of the current driving speed, target information, a second parameter, and a third parameter. As shown in Table 2, different current driving speeds and target information correspond to different second and third parameters.

[0065] Table 2 Second Control Parameters Table

[0066]

[0067] Where P1 is the second parameter and P2 is the third parameter. The target information mentioned above can be the low-pass error value of the path deviation information after passing through a low-pass filter. Table 2 above is only an example. The second control parameter table can be set according to actual needs, and is not limited here.

[0068] As one approach, path deviation information is the position deviation determined by the vehicle's current position information and the target tracking point's position information in the current path information, as well as the heading angle deviation between the vehicle's current heading angle and the heading angle corresponding to the target tracking point. The position deviation and heading angle deviation may be negative. When the position deviation or heading angle deviation is negative, the low-frequency signal gain is determined based on the absolute value of the position deviation or heading angle deviation, that is, the low-frequency signal gain is determined based on the absolute value of the target information.

[0069] Optionally, after determining the second and third parameters according to the second control parameter table, the existence of low-frequency signal gain is determined based on the relationship between the absolute value of the target information and the second parameter. Optionally, if the absolute value of the target information is not greater than the second parameter, it can be determined that the target information has no low-frequency signal gain; if the absolute value of the target information is greater than the second parameter, the low-frequency signal gain is determined based on the target information, the second parameter, and the third parameter. Specifically, the target information can be subtracted from the second parameter first, and then multiplied by the third parameter to obtain the low-frequency signal gain. For example, if the low-frequency signal gain is G2, then G2 = (ERR - P1) * P2, where ERR is the target information; P1 is the second parameter; and P2 is the third parameter. The figure shows a low-frequency signal gain curve. Figure 5 As shown, when the absolute value of the target information is not greater than the second parameter, there is no low-frequency signal gain; when the absolute value of the target information is greater than the second parameter, if the target information is greater than zero, the low-frequency signal gain increases with the increase of the target information; if the target information is less than zero, the low-frequency signal gain decreases with the increase of the absolute value of the target information. This can be understood as follows: if the target information is greater than zero, the corresponding low-frequency signal gain is positively compensated; if the target information is less than zero, the corresponding low-frequency signal gain is negatively compensated.

[0070] Please continue reading. Figure 1 Step 140: Determine the control parameters of the vehicle based on the high-frequency signal gain and the low-frequency signal gain, and control the vehicle based on the control parameters.

[0071] In one approach, the high-frequency signal gain and low-frequency signal gain are determined based on the path deviation information. Therefore, the control parameters can be adjusted based on the high-frequency signal gain and low-frequency signal gain. In other words, the path deviation information is used as the input signal for the feedback control of the controller to adjust the control parameters.

[0072] Optionally, the control parameters can be determined based on the position information of the target tracking point and the heading angle corresponding to the target tracking point. Since the path deviation information determines the high-frequency signal gain and low-frequency signal gain, the control parameters are adjusted based on the high-frequency signal gain and low-frequency signal gain to obtain the target control parameters. In turn, the vehicle can be controlled based on the target control parameters, thereby improving the accuracy of the control parameters and ensuring the safety of vehicle driving and the accuracy of intelligent driving.

[0073] In some embodiments, such as Figure 6 As shown, step 140 includes:

[0074] Step 141: Determine the curvature corresponding to the reference path information, and determine the first reference angle of the vehicle's steering wheel based on the curvature.

[0075] In one approach, the reference path information can be historical path information stored in the vehicle's local database or cloud database, and its corresponding curvature is also stored in the vehicle's local database or cloud database, which can be directly retrieved from the local database or cloud database.

[0076] As another approach, the reference path information includes the target driving trajectory, and the curvature corresponding to the reference path information can be determined based on the target driving trajectory.

[0077] Optionally, a first reference angle of the steering wheel required for the vehicle to travel along the target trajectory can be determined based on the curvature of the target trajectory. This first reference angle of the steering wheel, determined based on the curvature, can be determined by the vehicle's controller, corresponding to feedforward control of the vehicle's controller.

[0078] As another method, the torque required for the vehicle to travel along the reference path information can be determined based on the curvature.

[0079] Step 142: Determine the second reference angle of the vehicle's steering wheel based on the control parameters.

[0080] In one approach, the control parameters can be those of a PID controller. The control parameters of a PID controller include proportional control parameters (P control parameters), integral control parameters (I control parameters), and derivative control parameters (D control parameters).

[0081] In one approach, a mapping relationship between control parameters and a second reference angle of the steering wheel can be preset, and the second reference angle of the steering wheel corresponding to the control parameters of the vehicle can be determined based on this mapping relationship.

[0082] Alternatively, the second reference angle of the vehicle's steering wheel can be calculated based on the control parameters and the functional relationship between the input and output of the PID controller. Optionally, the functional relationship between the input and output of the PID controller is: Where, k p T is the proportional control parameter. I For integral control parameters; T D Here, u(t) represents the differential control parameter; e(t) represents the input information, i.e., the target information; the upper and lower limits of integration for the integral function are 0 and t, respectively, where t is determined according to actual needs and is not limited here. Optionally, u(t) can be used as the second reference angle of the steering wheel.

[0083] Step 143: Determine the target angle of the vehicle's steering wheel based on the first reference angle and the second reference angle.

[0084] One approach is to superimpose the first reference angle and the second reference angle, using the superimposed angle value as the target angle. Since the first reference angle is determined based on reference path information, and the second reference angle is determined by control parameters adjusted based on the high-frequency signal gain and low-frequency signal gain determined by the target information, the target angle is obtained by combining feedforward control and feedback control, thus improving the accuracy of the obtained target angle.

[0085] Step 144: Control the vehicle according to the target angle.

[0086] In one approach, the vehicle can control the steering wheel to turn at the target angle, thereby controlling the vehicle to move according to the target angle. In this way, the vehicle can travel along the target trajectory in the reference path information, making the intelligent driving of the vehicle more accurate.

[0087] In the embodiments of this application, when the vehicle is in autonomous driving mode, path deviation information is first determined based on the vehicle's current path information and reference path information. Then, based on the vehicle's current speed and path deviation information, the high-frequency signal gain and low-frequency signal gain corresponding to the current speed and path deviation information are determined. Furthermore, the vehicle's control parameters can be determined based on the high-frequency and low-frequency signal gains, allowing for adaptive adjustment of the vehicle's control parameters according to the vehicle's current speed. The low-frequency and high-frequency components of the path deviation information and the vehicle's current speed are distinguished, and their respective gains are determined. Adjusting the vehicle's control parameters based on these gains makes vehicle control more accurate, improves driving safety, and enhances the driving experience.

[0088] Please see Figure 7 , Figure 7 This application illustrates a vehicle control method according to an embodiment of the present application. In a specific embodiment, the vehicle control method can be applied to, for example... Figure 12 The vehicle control unit 400 and the electronic equipment 500 equipped with the vehicle control unit 400 are shown. Figure 13 The specific process of this embodiment will be described below. Of course, it is understood that this method can be executed by an electronic device with computing capabilities, such as a desktop computer, laptop computer, in-vehicle terminal, or in-vehicle infotainment screen. This method can also be executed interactively by a processing system including a server and a terminal; this method can also be performed by an intelligent driving domain controller or a PID controller. The following will focus on... Figure 7 The process shown will be described in detail. The vehicle control method may specifically include the following steps:

[0089] Step 210: When the vehicle is in autonomous driving mode, obtain the vehicle's current driving speed, current path information, and reference path information;

[0090] Step 220: Determine path deviation information based on the current path information and the reference path information;

[0091] Step 230: Determine the high-frequency signal gain based on the current driving speed and the path deviation information, and determine the low-frequency signal gain based on the current driving speed and the path deviation information.

[0092] For a detailed description of steps 210-230, please refer to steps 110-130, which will not be repeated here.

[0093] Step 240: Based on the mapping relationship between the current driving speed and the control parameters, determine the proportional control parameter, integral control parameter, and derivative control parameter corresponding to the current driving speed, and use the proportional control parameter, integral control parameter, and derivative control parameter as the reference control parameters of the vehicle.

[0094] One approach is to pre-set the mapping relationship between different driving speeds and control parameters, including proportional control parameters, integral control parameters, and derivative control parameters, thereby enabling the control parameters to adaptively adjust according to the vehicle's driving speed. Optionally, different driving speed ranges can be set, with each speed range corresponding to a unique control parameter.

[0095] Step 250: Determine the adjustment value of the proportional control parameter based on the high-frequency signal gain and the low-frequency signal gain, and adjust the proportional control parameter based on the adjustment value.

[0096] One approach is to use the high-frequency signal gain and low-frequency signal gain as adjustment values ​​for the proportional control parameter, add these adjustment values ​​to the proportional control parameter in the reference control parameters, and use the sum as the target proportional control parameter. Alternatively, as... Figure 6 As shown, the low-frequency signal gain can be positive, negative, or even zero, depending on the target information and the second parameter.

[0097] In a PID controller, the proportional control parameter is related to the vehicle's movement. Increasing the proportional coefficient can make the vehicle more responsive, accelerate the adjustment speed, and reduce steady-state error. However, an excessively large proportional coefficient can increase overshoot, increase the number of oscillations, lengthen the settling time, and degrade dynamic performance. Therefore, it is necessary to adjust the proportional control parameter based on the high-frequency signal gain and low-frequency signal gain corresponding to the path deviation information. This will make the vehicle's movement more stable, reduce the error between the vehicle's trajectory and the target trajectory, and make the vehicle's movement more accurate. This effectively improves control performance and provides users with a better performance experience.

[0098] Step 260: Adjust the reference control parameters according to the adjusted proportional control parameters to obtain the control parameters of the vehicle, and control the vehicle according to the control parameters.

[0099] One approach is to determine the second reference angle of the vehicle's steering wheel based on the adjusted control parameters, and then superimpose it with the first reference angle of the vehicle's steering wheel determined based on the curvature corresponding to the reference path information to obtain the target angle of the vehicle's steering wheel. Finally, the vehicle is controlled based on the target angle.

[0100] As another approach, the third reference angle of the vehicle's steering wheel can be determined based on the high-frequency signal gain and the low-frequency signal gain. Then, the third reference angle, the first reference angle, and the second reference angle are superimposed to obtain the target reference angle. Finally, the vehicle is controlled based on the target reference angle.

[0101] Figure 8 and Figure 9 This refers to the error information curves obtained by controlling the vehicle using fixed control parameters in existing technologies. Specifically, it shows the values ​​of position error and heading angle error at different target tracking points. Smaller values ​​for position error and heading angle error indicate better vehicle control performance. Figure 7 In the case of 40 target tracking points, the vehicle's maximum heading angle error is approximately 0.038 rad, and the vehicle's maximum position error (lateral distance error) is approximately 0.027 m. Figure 8 Is Figure 7Based on the previous version, the length of the target tracking points was increased by 40. The maximum heading angle error of the vehicle was about 0.05 rad, and the maximum position error of the vehicle was 0.1 m. Figure 10 and Figure 11 The path deviation information curve is obtained by controlling the vehicle according to the vehicle control method of this application. Figure 10 correspond Figure 8 With 40 target tracking points, the vehicle's maximum heading angle error is approximately 0.01 rad, and the maximum position error is -0.0025 m. Figure 11 correspond Figure 9 With an additional 40 target tracking points added to the original 40, the maximum heading angle error of the vehicle is approximately 0.022 rad, and the maximum position error is approximately -0.01 m. Therefore, it can be concluded that the method of this application can improve the accuracy of vehicle control and ensure driving safety.

[0102] In the embodiments of this application, a reference control parameter corresponding to the current driving speed is determined based on the mapping relationship between control parameters and the current driving speed. Then, the proportional control parameter in the reference control parameter is adjusted according to the high-frequency signal gain and the low-frequency signal gain to obtain the adjusted proportional control parameter. Next, the target control parameter is determined based on the adjusted proportional control parameter and the reference control parameter. Finally, the vehicle is controlled according to the target control parameter. This embodiment can adaptively adjust the control parameters according to the vehicle's current driving speed; that is, it can adaptively adjust the proportional control parameter, integral control parameter, and derivative control parameter according to the vehicle's current driving speed, making the vehicle control more accurate.

[0103] Figure 12 This is a block diagram of a vehicle control device according to an embodiment of this application, such as... Figure 12 As shown, the vehicle control device 400 includes: an acquisition module 410, a path deviation information determination module 420, a signal gain determination module 430, and a control module 440.

[0104] The acquisition module 410 is used to acquire the vehicle's current driving speed, current path information, and reference path information; the path deviation information determination module 420 is used to determine path deviation information based on the current path information and the reference path information; the signal gain determination module 430 is used to determine the high-frequency signal gain corresponding to the current driving speed and the path deviation information, and to determine the low-frequency signal gain corresponding to the current driving speed and the path deviation information; the control module 440 is used to determine the vehicle's control parameters based on the high-frequency signal gain and the low-frequency signal gain, and to control the vehicle based on the control parameters.

[0105] In some embodiments, the signal gain determination module 430 includes: a target information determination submodule, configured to input the current driving speed and the path deviation information into a filter, perform denoising processing on the current driving speed and the path deviation information to obtain target information, wherein the target information is the denoised current driving speed and the path deviation information; a high-frequency error value determination submodule, configured to determine the high-frequency error value of the target information based on the target information and the path deviation information; a first determination submodule, configured to determine a first parameter corresponding to the target information and the current driving speed based on a first control parameter table, wherein the first control parameter table includes the target information, the current driving speed, and the first parameter; and a high-frequency signal gain determination submodule, configured to determine the high-frequency signal gain based on the high-frequency error value and the first parameter.

[0106] In some embodiments, the signal gain determination module 430 further includes: a second determination submodule, configured to determine a second parameter and a third parameter corresponding to the current driving speed based on a second control parameter table, wherein the second control parameter table includes the current driving speed, the target information, the second parameter and the third parameter; and a low-frequency signal gain determination submodule, configured to determine the low-frequency signal gain based on the target information, the second parameter and the third parameter if the absolute value of the target information is greater than the second parameter.

[0107] In some embodiments, the target information determination submodule includes: a reference information determination unit, configured to input the current driving speed and the path deviation information into the notch filter, and perform fixed-frequency denoising processing on the current driving speed and the path deviation information to obtain reference information; and a target information determination unit, configured to input the reference information into the low-pass filter, and perform high-frequency signal denoising processing on the reference information to obtain the target information.

[0108] In some embodiments, the path deviation information determination module 420 includes: a target tracking point determination submodule, configured to determine a target tracking point from the target trajectory route based on the current position information, wherein the target tracking point is the point on the target trajectory route that is closest to the current position information; a deviation determination submodule, configured to determine a position deviation based on the current position information and the target tracking point, and to determine a heading angle deviation based on the current heading angle and the target heading angle corresponding to the target tracking point; and a path deviation information determination submodule, configured to determine the path deviation information based on the position deviation and the heading angle deviation.

[0109] In some embodiments, the vehicle control device 400 further includes a reference control parameter determination module, configured to determine the proportional control parameter, integral control parameter, and derivative control parameter corresponding to the current driving speed based on the mapping relationship between the current driving speed and the control parameters, and to use the proportional control parameter, the integral control parameter, and the derivative control parameter as reference control parameters for the vehicle.

[0110] In some embodiments, the control parameter determination module includes: an adjustment submodule, configured to determine an adjustment value for the proportional control parameter based on the high-frequency signal gain and the low-frequency signal gain, and adjust the proportional control parameter based on the adjustment value; and a control parameter submodule, configured to adjust the reference control parameter based on the adjusted proportional control parameter to obtain the control parameters of the vehicle.

[0111] In some embodiments, the control module 440 includes: a first reference angle determination submodule, configured to determine the curvature corresponding to the reference path information and determine a first reference angle of the vehicle's steering wheel based on the curvature; a second reference angle determination submodule, configured to determine a second reference angle of the vehicle's steering wheel based on the control parameters; a target angle determination submodule, configured to determine a target angle of the vehicle's steering wheel based on the first reference angle and the second reference angle; and a control submodule, configured to control the vehicle based on the target angle.

[0112] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform the methods of any of the above embodiments.

[0113] According to one aspect of the embodiments of this application, an electronic device is also provided, such as... Figure 13 As shown, the electronic device 500 includes a processor 510 and one or more memories 520. The one or more memories 520 are used to store program instructions executed by the processor 510. When the processor 510 executes the program instructions, it implements the object recognition method described above.

[0114] Furthermore, the processor 510 may include one or more processing cores. The processor 510 runs or executes instructions, programs, code sets, or instruction sets stored in the memory 520, and retrieves data stored in the memory 520. Optionally, the processor 510 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 510 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor and may be implemented using a separate communication chip.

[0115] According to one aspect of this application, a computer-readable storage medium is also provided, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries computer-readable instructions that, when executed by a processor, implement the methods in any of the above embodiments.

[0116] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0117] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0118] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0119] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0120] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0121] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for controlling a vehicle, characterized in that, The method includes: Obtain the vehicle's current speed, current route information, and reference route information; Based on the current path information and the reference path information, path deviation information is determined; wherein, the path deviation information includes position deviation and heading angle deviation; The method involves determining the high-frequency signal gain corresponding to the current driving speed and the path deviation information, and determining the low-frequency signal gain corresponding to the current driving speed and the path deviation information. The determination of the high-frequency signal gain includes: inputting the current driving speed and the path deviation information into a filter; performing denoising processing on the current driving speed and the path deviation information to obtain denoised current driving speed and path deviation information; wherein the denoised path deviation information includes denoised position deviation and heading angle deviation; determining a high-frequency error value based on the path deviation information and the denoised path deviation information; wherein the high-frequency error value includes high-frequency error of position deviation and high-frequency error of heading angle deviation; determining a first parameter corresponding to the denoised position deviation and the current driving speed based on a first control parameter table; wherein the first control parameter table includes the denoised position deviation, the current driving speed, and the first parameter; and determining the high-frequency signal gain based on the high-frequency error value and the first parameter. Based on the high-frequency signal gain and the low-frequency signal gain, the control parameters of the vehicle are determined, and the vehicle is controlled according to the control parameters.

2. The method according to claim 1, characterized in that, After inputting the current driving speed and the path deviation information into the filter, and performing denoising processing on the current driving speed and the path deviation information to obtain the denoised current driving speed and path deviation information, the method further includes: Based on the second control parameter table, the second parameter and the third parameter corresponding to the current driving speed are determined, wherein the second control parameter table includes the current driving speed, the position deviation after noise reduction processing, the second parameter and the third parameter; If the absolute value of the position deviation after noise reduction is greater than the second parameter, the low-frequency signal gain is determined based on the position deviation after noise reduction, the second parameter, and the third parameter.

3. The method according to claim 1, characterized in that, The filter includes a notch filter and a low-pass filter. The current driving speed and path deviation information are input into the filter, and noise reduction processing is performed on the current driving speed and path deviation information to obtain the noise-reduced current driving speed and path deviation information, including: The current driving speed and the path deviation information are input into the notch filter, and the current driving speed and the path deviation information are subjected to fixed-frequency noise reduction processing to obtain reference information; The reference information is input into the low-pass filter, and high-frequency signal denoising processing is performed on the reference information to obtain the current driving speed and the path deviation information after denoising processing.

4. The method according to claim 1, characterized in that, The current path information includes current position information and current heading angle; the reference path information includes the target trajectory; and determining path deviation information based on the current path information and the reference path information includes: Based on the current location information, a target tracking point is determined from the target trajectory route, wherein the target tracking point is the point on the target trajectory route that is closest to the current location information; The position deviation is determined based on the current position information and the target tracking point, and the heading angle deviation is determined based on the current heading angle and the target heading angle corresponding to the target tracking point; The path deviation information is determined based on the position deviation and the heading angle deviation.

5. The method according to claim 1, characterized in that, Before determining the control parameters of the vehicle based on the high-frequency signal gain and the low-frequency signal gain, the method further includes: Based on the mapping relationship between the current driving speed and the control parameters, the proportional control parameter, integral control parameter, and derivative control parameter corresponding to the current driving speed are determined, and the proportional control parameter, integral control parameter, and derivative control parameter are used as the reference control parameters of the vehicle.

6. The method according to claim 5, characterized in that, Determining the vehicle's control parameters based on the high-frequency signal gain and the low-frequency signal gain includes: The adjustment value of the proportional control parameter is determined based on the high-frequency signal gain and the low-frequency signal gain, and the proportional control parameter is adjusted based on the adjustment value. The reference control parameters are adjusted according to the adjusted proportional control parameters to obtain the control parameters of the vehicle.

7. The method according to any one of claims 1-6, characterized in that, Controlling the vehicle according to the control parameters includes: Determine the curvature corresponding to the reference path information, and determine the first reference angle of the vehicle's steering wheel based on the curvature; Based on the control parameters, a second reference angle for the vehicle's steering wheel is determined; The target angle of the vehicle's steering wheel is determined based on the first reference angle and the second reference angle; The vehicle is controlled according to the target angle.

8. A vehicle control device, characterized in that, The device includes: The acquisition module is used to acquire the vehicle's current speed, current path information, and reference path information; The path deviation information determination module is used to determine path deviation information based on the current path information and the reference path information; wherein, the path deviation information includes position deviation and heading angle deviation; A signal gain determination module is used to determine the high-frequency signal gain corresponding to the current driving speed and the path deviation information, and to determine the low-frequency signal gain corresponding to the current driving speed and the path deviation information; wherein, the signal gain determination module includes a target information determination submodule, used to input the current driving speed and the path deviation information into a filter, and to perform denoising processing on the current driving speed and the path deviation information to obtain the denoised current driving speed and the path deviation information; wherein, the denoised path deviation information includes the denoised position deviation and heading angle deviation; A high-frequency error value determination submodule is used to determine a high-frequency error value based on the path deviation information and the path deviation information after denoising processing; wherein, the high-frequency error value includes position deviation high-frequency error and heading angle deviation high-frequency error; a first determination submodule is used to determine a first parameter corresponding to the position deviation after denoising processing and the current driving speed based on a first control parameter table; wherein, the first control parameter table includes the position deviation after denoising processing, the current driving speed, and the first parameter; a high-frequency signal gain determination submodule is used to determine the high-frequency signal gain based on the high-frequency error value and the first parameter; The control module is used to determine the control parameters of the vehicle based on the high-frequency signal gain and the low-frequency signal gain, and to control the vehicle based on the control parameters.

9. An electronic device, characterized in that, The electronic device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1-7.

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