A PID adjustment method and device for vehicle adaptive lateral control

Abstract

The present invention relates to a PID adjustment method and device for vehicle adaptive lateral control. The method includes: obtaining the steering wheel angle and driver's grip torque of the vehicle in real time, and determining and / or calculating the dynamic parameter change rate based on them; calculating a dynamic correction value according to the dynamic parameter change rate, and calculating a dynamic correction coefficient based on the dynamic correction value; calculating multiple first correction terms of the PID according to the dynamic correction coefficient; determining a slope coefficient function according to the gradual change time interval and sudden change time interval of the slope experienced by the vehicle; calculating a second correction term of the PID according to the slope coefficient function; and adjusting the lateral control output of the PID based on the multiple first correction terms and the second correction term. The present invention corrects the PID of the vehicle lateral control through the steering wheel angle and driver's grip torque of the vehicle, and combines the slope smoothing strategy, thereby improving its stability and adaptability.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle assisted driving, and particularly relates to a PID adjustment method and device for vehicle adaptive lateral control. Background Art

[0002] In actual driving operations, when the vehicle is in a relatively stable driving state such as high adhesion and low speed, the driver generally mainly perceives the state variables of the vehicle and adjusts the steering wheel to keep the vehicle in a stable driving state. However, when the vehicle is driving in an unstable state such as high speed and sharp turn, it is very difficult for the driver to make a correct steering adjustment based on the perceived vehicle state variables, resulting in danger. At this time, the sideslip angle of the vehicle tire, yaw rate, lateral acceleration, and sideslip angle of the center of mass are selected as stability control parameters, and the yaw moment is generated through vehicle braking, driving, etc. to ensure the stable driving of the vehicle. The vehicle lateral stability control system mainly uses parameters such as yaw rate and sideslip angle of the center of mass as control indicators to maintain the lateral stability of the vehicle. However, in fact, the longitudinal dynamics and lateral dynamics responses of the vehicle exist simultaneously and are coupled with each other. Its dynamic characteristics are mainly determined by the longitudinal and lateral dynamic characteristics of the tires, and its control effect is mainly achieved by the yaw moment control generated by the longitudinal force control of the wheels.

[0003] Among the currently mass-produced ADAS (Advanced Driving Assistance System) functions, the mainstream method is still based on classical PID (Proportional Integral, Differential) control. Classical PID control determines the proportional term, integral term, and differential term coefficients of PID through methods such as calibration and look-up tables, and then sums these three terms to eliminate the lateral deviation and achieve lateral adjustment.

[0004] However, the traditional PID control for lateral control is easily disturbed by the change of adjustment parameters and may even cause the vehicle to move unstably; on the other hand, the PID control for lateral control does not consider the influence of slope and has poor adaptability to slope. Summary of the Invention

[0005] To address the issue of improving the stability and adaptability of the PID lateral control of a vehicle, in the first aspect of the present invention, a PID adjustment method for vehicle adaptive lateral control is provided, including: obtaining the vehicle steering wheel angle and the driver's grip torque in real time, and determining and / or calculating the dynamic parameter change rate based on them; calculating a dynamic correction value according to the dynamic parameter change rate, and calculating a dynamic correction coefficient based on the dynamic correction value; calculating multiple first correction terms of the PID according to the dynamic correction coefficient; determining a slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle; calculating a second correction term of the PID according to the slope coefficient function; and adjusting the lateral control output of the PID based on the multiple first correction terms and the second correction term.

[0006] In some embodiments of the present invention, calculating the dynamic correction value according to the dynamic parameter change rate and calculating the dynamic correction coefficient based on it includes: integrating the dynamic parameter change rate with respect to the time integral parameter of the PID control to obtain a dynamic correction value; and calculating the dynamic correction coefficient according to the dynamic correction value and the maximum driving distance within the PID parameter adjustment time.

[0007] Further, the dynamic correction coefficient is calculated by the following method:

[0008] EffetctiveTuningFactor

[0009] =1 / effectiveRange*e log(effectiveRange)*TuningFactor ,

[0010] where effectiveRange is the maximum driving distance within the parameter adjustment time, and TuningFactor represents the dynamic correction value.

[0011] In some embodiments of the present invention, calculating the slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle: determining the sudden change time interval of the slope coefficient function according to a preset tangent function; determining the gradual change time interval of the slope coefficient function according to a preset polynomial; and calculating the values of the slope coefficient function according to the sudden change time interval and the gradual change time interval of the slope coefficient function, respectively.

[0012] Further, the slope coefficient function is expressed as:

[0013]

[0014] where RampOverTime represents the duration of slope smoothing processing, t start represents the linear processing time, and a, b, c, and d are the coefficients of the gradual change curve of the slope coefficient function.

[0015] In the above embodiments, the real-time acquisition of the vehicle steering wheel angle and the driver's grip torque, and the determination and / or calculation of the dynamic parameter change rate based thereon include: acquiring vehicle steering wheel angle data, and calculating an activity difference based on the same and a preset target activity; judging whether the driver is in an intervention state according to the driver's grip torque: if so, determining the dynamic parameter change rate according to the dynamic parameter value in the previous calculation period and the preset maximum dynamic parameter change rate; if not, determining the dynamic parameter change rate according to the lateral distance offset and the activity difference.

[0016] In a second aspect of the present invention, there is provided a PID adjustment device for vehicle adaptive lateral control, including: an acquisition module for real-time acquiring the vehicle steering wheel angle and the driver's grip torque, and determining and / or calculating a dynamic parameter change rate based thereon; a first correction module for calculating a dynamic correction value according to the dynamic parameter change rate, and calculating a dynamic correction coefficient according to the same; calculating a plurality of first correction terms of the PID according to the dynamic correction coefficient; a second correction module for determining a slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle; calculating a second correction term of the PID according to the slope coefficient function; an output module for adjusting the lateral control output of the PID based on the plurality of first correction terms and the second correction term.

[0017] Further, the first correction module includes: an integration unit for integrating the dynamic parameter change rate with the time integration parameter of the PID control to obtain a dynamic correction value; a calculation unit for calculating a dynamic correction coefficient according to the dynamic correction value and the maximum driving distance within the PID parameter adjustment time.

[0018] In a third aspect of the present invention, there is provided an electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the PID adjustment method for vehicle adaptive lateral control provided by the present invention in the first aspect.

[0019] In a fourth aspect of the present invention, there is provided a computer-readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the PID adjustment method for vehicle adaptive lateral control provided by the present invention in the first aspect.

[0020] The beneficial effects of the present invention are:

[0021] The present disclosure provides a PID adjustment method for vehicle adaptive lateral control, including: obtaining the steering wheel angle and driver's grip torque of the vehicle in real time, and determining and / or calculating the dynamic parameter change rate based on them; calculating a dynamic correction value according to the dynamic parameter change rate, and calculating a dynamic correction coefficient according to the dynamic correction value; calculating a plurality of first correction terms of the PID according to the dynamic correction coefficient; determining a slope coefficient function according to the gradual change time interval and sudden change time interval of the slope experienced by the vehicle; calculating a second correction term of the PID according to the slope coefficient function; and adjusting the lateral control output of the PID based on the plurality of first correction terms and the second correction term. It can be seen that the present disclosure proposes an adaptive PID adjustment method on the basis of the existing PID for vehicle lateral control, and compensates and corrects the original system. This method first calculates the activity of the vehicle steering wheel, combines the driver intervention state, and the lateral offset from the lane line during intervention, calculates the dynamic correction value of the adjustment parameter, and obtains the correction coefficient values of the proportional, integral, and differential of the PID. And combined with the integral term of the error, a slope smoothing strategy is designed to compensate the PID control. This method can solve the disturbances and instability caused by the change of the adjustment parameter in the lateral control, so that the system can reach the target set value more quickly and stably while eliminating the error. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a schematic diagram of the basic process of the PID adjustment method for vehicle adaptive lateral control in some embodiments of the present invention;

[0023] Figure 2 is a schematic diagram of the specific process of the PID adjustment method for vehicle adaptive lateral control in some embodiments of the present invention;

[0024] Figure 3 is a schematic diagram of the optimization of the slope coefficient function in some embodiments of the present invention;

[0025] Figure 4 is a schematic diagram of the structure of the PID adjustment device for vehicle adaptive lateral control in some embodiments of the present invention;

[0026] Figure 5 is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0028] Refer to Figure 1 or Figure 2, in the first aspect of the present invention, a PID adjustment method for vehicle adaptive lateral control is provided, including: S100. Real-time obtain the vehicle steering wheel angle and the driver's grip torque, and determine and / or calculate the dynamic parameter change rate based on them; S200. Calculate the dynamic correction value according to the dynamic parameter change rate, and calculate the dynamic correction coefficient according to it; Calculate multiple first correction terms of the PID according to the dynamic correction coefficient; S300. Determine the slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle; Calculate the second correction term of the PID according to the slope coefficient function; S400. Based on the multiple first correction terms and the second correction term, adjust the lateral control output of the PID.

[0029] It can be understood that the slope is usually defined as the ratio of the vertical height H and the horizontal width L of the slope surface, denoted by the letter X. Usually expressed as a percentage. That is: X = H / L×100; The most commonly used method for slope is the percentage of the elevation difference between two points and their distance, and its calculation formula is as follows: Slope = (elevation difference / distance) x 100. When expressed as a percentage, that is: i = h / l×100%. For a slope of 3%, it means that for every 100 meters of the road, it rises (descends) 3 meters vertically; 1% means that for every 100 meters of the distance, it rises (descends) 1 meter vertically. Lateral control is mainly used for the control of the vehicle steering wheel, and it mainly performs tracking control according to information such as the path and curvature output by the upper-layer motion planning to reduce the tracking error. At the same time, ensure the stability and comfort of vehicle driving. According to the different vehicle models used for lateral control, it can be divided into two types, including: model-free lateral control method, model-based lateral control method. The PID lateral control method in the present disclosure belongs to the model-free lateral control method.

[0030] In step S200 of some embodiments of the present invention, the calculating the dynamic correction value according to the dynamic parameter change rate and calculating the dynamic correction coefficient according to it include: Integrate the dynamic parameter change rate with respect to the time integral parameter of the PID control to obtain the dynamic correction value; Calculate the dynamic correction coefficient according to the dynamic correction value and the maximum driving distance within the PID parameter adjustment time.

[0031] Further, the dynamic correction coefficient is calculated by the following method:

[0032] EffetctiveTuningFactor

[0033] = 1 / effectiveRange*e log(effectiveRange)*TuningFactor ,

[0034] where effectiveRange is the maximum driving distance within the parameter adjustment time, and TuningFactor represents the dynamic correction value.

[0035] The above steps only involve the correction of the dynamic coefficient. To improve the adaptability to slopes, the present disclosure designs a slope smoothing Rampover strategy to perform dynamic proportional coefficient control on the integral term of the PID. In view of this, in step S300 of some embodiments of the present invention, according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle, a slope coefficient function is calculated: S301. Determine the sudden change time interval of the slope coefficient function according to a preset tangent function; S302. Determine the gradual change time interval of the slope coefficient function according to a preset polynomial; S303. Calculate the numerical values of the slope coefficient function according to the sudden change time interval and the gradual change time interval of the slope coefficient function respectively.

[0036] Specifically, the slope smoothing strategy rampin is a tangent function with a fixed gain. The intermediate process is calculated proportionally, and finally, for the rampout (gradual change output) part, it is output according to the integral term. If the slope smoothing time is RampOverTime and the start linear processing time is t start . Therefore, within t end , the slope coefficient function can be calculated as:

[0037]

[0038] where t start = 0.8 × RampOverTime, t end is a calibrated quantity, k = (f(t start ) - 1) / (t start - RampOverTime), m = 1 - k × RampOverTime.

[0039] Then, optimize the above strategy to perform a transition process on rampout. Assume the curve of rampout is: q(t) = at 3 + bt 2 + ct + d;

[0040] According to , calculate the coefficients a, b, c, d of q(t), that is, obtain q(x).

[0041] The optimized slope coefficient function is:

[0042]

[0043] Referring to Figure 3 , if RampOverTime = 3s, the start linear processing time is:

[0044] t start = 0.8 × RampOverTime = 2.4s. Therefore, within tend Within 5 s, a dynamic proportional coefficient graph with smoothed slope can be drawn.

[0045] Furthermore, the slope coefficient function is expressed as:

[0046]

[0047] wherein, RampOverTime represents the duration of slope smoothing processing, and t start represents the linear processing time, and a, b, c, and d are the coefficients of the gradient curve of the slope coefficient function.

[0048] It can be understood that the above slope coefficient function processes the slope through a piecewise function, and each piecewise function is characterized by a different smoothing curve (continuous function). Optionally, the Sigmoid function, tanh function, Relu function, and softmax function are used to replace the slope smoothing strategy implemented by the above slope coefficient function.

[0049] In step S100 of the above embodiment, the real-time acquisition of the vehicle steering wheel angle and the driver's grip torque, and the determination and / or calculation of the dynamic parameter change rate based thereon include: S101. Acquire the vehicle steering wheel angle data, and calculate the activity difference according to it and the preset target activity; specifically, first pass the steering wheel angle through a high-pass filter, take the mean square deviation value within the Δt period for processing, and then through look-up table mapping, take out the dynamic activity SteelActivity of the steering wheel, and subtract it from the target activity AngleActivityTarget to obtain the activity difference, that is: ΔSteelActivity = AngleActivityTarget - SteerActivity, where AngleActivityTa-rget is a calibrated value.

[0050] S102. Determine whether the driver is in an intervention state according to the driver's grip torque: if so, determine the dynamic parameter change rate according to the dynamic parameter value of the previous calculation period and the preset maximum dynamic parameter change rate; if not, determine the dynamic parameter change rate according to the lateral distance offset and the activity difference.

[0051] Specifically, it is determined whether the driver is in an intervention state according to whether the driver's hand torque value is greater than the intervention threshold and lasts for a period of time.

[0052] When the driver intervenes, the dynamic parameter change rate value is fixed. The value is determined according to whether the dynamic parameter value TuningFactorFeedBack in the previous calculation cycle is greater than the maximum dynamic parameter change rate MaxTuningRate. If TuningFactorFeedBack > MaxTuningRate, the current dynamic parameter change rate TuningRate = -MaxTuningRate; otherwise, the dynamic parameter change rate TuningRate = MaxTuningRate. Among them, MaxTuningRate is a calibrated quantity.

[0053] When the driver does not intervene, different processing needs to be performed according to the lateral distance Offset. When |Offset| > OffsetThreshold, the dynamic parameter change rate TuningRate = 0.1 * |Offset|;

[0054] Otherwise, the dynamic parameter change rate is: TuningRate = GActivity * ΔSteerActivity.

[0055] Based on steps S100 - S300 of the above embodiment, in step S400 of some embodiments of the present invention, based on the plurality of first correction terms and the second correction term, the lateral control output of the PID is adjusted. Specifically, the PID parameters before correction are KP, KI, KD, and are multiplied by the dynamic correction coefficient TuningFactor respectively to obtain the PID parameters after correction. Then the lateral control process after correction is:

[0056] A lat = IRamp × IPart + TuningFactor × PID controller

[0057] where IPart is obtained by looking up a one-dimensional table according to the product of the error and the integral term, and the PID controller is a common classical controller. A lat represents the lateral control output of the PID, which can be represented by one or more parameters related to lateral displacement such as lateral displacement, speed, acceleration, torque, etc.

[0058] Embodiment 2

[0059] Reference Figure 4, the second aspect of the present invention provides a PID adjustment device 1 for vehicle adaptive lateral control, comprising: an acquisition module 11, configured to acquire the vehicle steering wheel angle and the driver's grip torque in real time, and determine and / or calculate the dynamic parameter change rate based thereon; a first correction module 12, configured to calculate a dynamic correction value according to the dynamic parameter change rate, and calculate a dynamic correction coefficient according thereto; calculate a plurality of first correction terms of the PID according to the dynamic correction coefficient; a second correction module 13, configured to determine a slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle; calculate a second correction term of the PID according to the slope coefficient function; an output module, configured to adjust the lateral control output of the PID based on the plurality of first correction terms and the second correction term.

[0060] Further, the first correction module 12 includes: an integration unit, configured to integrate the dynamic parameter change rate with the time integration parameter of the PID control duration to obtain a dynamic correction value; a calculation unit, configured to calculate a dynamic correction coefficient according to the dynamic correction value and the maximum driving distance within the PID parameter adjustment time.

[0061] In some embodiments, in an intelligent driving vehicle equipped with the PID adjustment device for vehicle adaptive lateral control provided by the second aspect of the present invention, any intelligent driving vehicle may include sensors, an intelligent driving domain controller, vehicle-mounted communication devices, high-precision positioning devices, other vehicle controllers, and a human-machine interaction system. Among them, the sensors include one or more of the following devices: at least one millimeter-wave radar, at least one lidar, and at least one camera. The functions of the above modules included in the intelligent driving vehicle are explained in detail below:

[0062] Millimeter-wave radar: A radar that operates in the millimeter wave band, used to collect the beam transmission time and beam speed to an obstacle, and send the collected data to the intelligent driving domain controller; or used to calculate data such as the distance and speed of surrounding obstacles after collecting the beam transmission time and beam speed, and send the calculated data to the intelligent driving domain controller.

[0063] LiDAR: A radar system that detects the position, speed and other characteristic quantities of a target by emitting laser beams. Its working principle is to emit a detection signal (laser beam) to the target, and then compare the received signal (target echo) reflected from the target with the emitted signal. After appropriate processing, relevant data of the target can be obtained, such as parameters of the target distance, azimuth, altitude, speed, attitude, and even shape. In this application, the LiDAR is used to collect the signal reflected from the obstacle and send the reflected signal and the emitted signal to the intelligent driving domain controller; or after collecting the signal reflected from the obstacle, compare it with the emitted signal, process to obtain data such as the distance and speed of the surrounding obstacles, and send the processed data to the intelligent driving domain controller.

[0064] Camera: Used to collect surrounding images or videos and send the collected images or videos to the intelligent driving domain controller; among them, when the camera is an intelligent camera, the camera can analyze the speed and distance of the surrounding obstacles after collecting the images or videos, and send the analyzed data to the intelligent driving domain controller.

[0065] High-precision positioning device: Collects the accurate position information of the current vehicle (with an error less than 20 cm) and the global positioning system (GPS) time information corresponding to the accurate position information, and sends the collected information to the intelligent driving domain controller. Among them, the high-precision positioning device can be a combined positioning system or a combined positioning module. The high-precision positioning device can include devices and sensors such as a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). The global navigation satellite system can output global positioning information with a certain accuracy (for example, 5 - 10 Hz), and the frequency of the inertial measurement unit is generally relatively high (for example, 1000 Hz). The high-precision positioning device can output high-frequency accurate positioning information (generally required to be above 200 Hz) by fusing the information of the inertial measurement unit and the global navigation satellite system.

[0066] Other vehicle controllers: Execute the control commands of the intelligent driving domain controller and send relevant information such as vehicle steering, gear, acceleration, and deceleration to the intelligent driving domain controller.

[0067] Human-machine interaction system: Provides an audio-visual method for message interaction between the intelligent vehicle and the driver, and can use the display screen to display the trajectories of this vehicle and other vehicles.

[0068] Intelligent Driving Domain Controller: It can be installed in a vehicle. The intelligent driving domain controller is specifically implemented by a processor, which includes a central processing unit (CPU) or a device or module with processing capabilities. For example, the intelligent driving domain controller can be a mobile data center (MDC) in the vehicle. When executing the autonomous driving function, that is, in the autonomous driving mode, the intelligent driving domain controller sends the trajectory planning information to the vehicle-mounted communication device, and sends its own position information and the predicted trajectories of other surrounding vehicles to the human-machine interaction system; when driving the vehicle manually, that is, in the manual driving mode, it sends the sensor information, the actual trajectory of the vehicle, and its own predicted trajectory (predicted by a neural network or other artificial intelligence (AI) algorithms based on the sensor information and the information transmitted from other vehicle controllers) to the vehicle-mounted communication device, and sends its own position information and the predicted trajectories of other surrounding vehicles to the human-machine interaction system.

[0069] Vehicle-mounted Communication Device: A device for communicating with other vehicles, which receives the predicted trajectory information of other vehicles (which can also be described as predicted trajectory, trajectory information, etc.) and sends it to the intelligent driving domain controller, and sends its own trajectory to other surrounding vehicles; it communicates with the cloud, sends the sensor information, positioning information, and information of other controllers on the vehicle to the cloud, and receives the trained model parameters from the cloud. For example, the vehicle-mounted communication device can be a telematics BOX (TBOX).

[0070] Embodiment 3

[0071] Reference Figure 5 , In the third aspect of the present invention, an electronic device is provided, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the PID adjustment method for vehicle adaptive lateral control in the first aspect of the present invention.

[0072] The electronic device 500 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage device 508 into the random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0073] Typically, the following devices can be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 can allow the electronic device 500 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 5 the electronic device 500 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices can be alternatively implemented or had. Figure 5 Each block shown in

[0074] In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by a processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The 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 of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment of the present disclosure, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the embodiment of the present disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0075] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more computer programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device is caused to:

[0076] Computer program code for performing the operations of the embodiments of the present disclosure may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, Python, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0077] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0078] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A PID adjustment method for vehicle adaptive lateral control, characterized in that, including: Obtain the vehicle steering wheel angle and the driver's grip torque in real time, and determine and / or calculate the change rate of dynamic parameters based on them; Calculate the dynamic correction value according to the change rate of the dynamic parameter, and calculate the dynamic correction coefficient according to it; calculate multiple first correction terms of the PID according to the dynamic correction coefficient; Determine the slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle; Calculate the second correction term of the PID according to the slope coefficient function; Adjust the lateral control output of the PID based on the multiple first correction terms and the second correction term; The slope coefficient function is expressed as: Among them, RampOverTime represents the duration of ramp smoothing processing, and t start represents the linear processing time, and a, b, c, and d are the coefficients of the gradient curve of the ramp coefficient function.

2. The PID adjustment method for vehicle adaptive lateral control according to claim 1, characterized in that The calculating the dynamic correction value according to the change rate of the dynamic parameter, and calculating the dynamic correction coefficient according to it includes: Integrate the time integral parameter of the PID control duration with respect to the change rate of the dynamic parameter to obtain the dynamic correction value; Calculate the dynamic correction coefficient according to the dynamic correction value and the maximum driving distance within the PID parameter adjustment time.

3. The PID adjustment method for vehicle adaptive lateral control according to claim 2, wherein The dynamic correction coefficient is calculated by the following method: ， where effectiveRange is the maximum driving distance within the parameter adjustment time, and TuningFactor represents the dynamic correction value.

4. The PID adjustment method for vehicle adaptive lateral control according to claim 1, wherein The calculating the slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle: Determine the sudden change time interval of the slope coefficient function according to the preset tangent function; Determine the gradual change time interval of the slope coefficient function according to the preset polynomial; Calculate the numerical values of the slope coefficient function according to the sudden change time interval and the gradual change time interval of the slope coefficient function respectively.

5. The PID adjustment method for vehicle adaptive lateral control according to any one of claims 1 to 4, characterized in that The obtaining the vehicle steering wheel angle and the driver's grip torque in real time, and determining and / or calculating the change rate of dynamic parameters based on them includes: Obtain the vehicle steering wheel angle data, and calculate the activity difference according to it and the preset target activity; Judge whether the driver is in an intervention state according to the driver's grip torque: if so, determine the change rate of the dynamic parameter according to the dynamic parameter value of the previous calculation period and the preset maximum dynamic parameter change rate; if not, determine the change rate of the dynamic parameter according to the lateral distance offset and the activity difference.

6. A PID adjustment device for vehicle adaptive lateral control, characterized in that, including: An acquisition module for obtaining the vehicle steering wheel angle and the driver's grip torque in real time, and determining and / or calculating the change rate of dynamic parameters based on them; A first correction module for calculating the dynamic correction value according to the change rate of the dynamic parameter, and calculating the dynamic correction coefficient according to it; calculating multiple first correction terms of the PID according to the dynamic correction coefficient; A second correction module for determining the slope coefficient function according to the gradual change time interval and the sudden change time interval of the slope experienced by the vehicle; Calculating the second correction term of the PID according to the slope coefficient function; An output module for adjusting the lateral control output of the PID based on the multiple first correction terms and the second correction term; The slope coefficient function is expressed as: Among them, RampOverTime represents the duration of slope smoothing processing, and t start represents the linear processing time, and a, b, c, and d are the coefficients of the gradient curve of the slope coefficient function.

7. The PID adjustment device for vehicle adaptive lateral control according to claim 6, characterized in that, The first correction module includes: An integration unit for integrating the time integral parameter of the PID control duration with respect to the change rate of the dynamic parameter to obtain the dynamic correction value; A calculation unit for calculating the dynamic correction coefficient according to the dynamic correction value and the maximum driving distance within the PID parameter adjustment time.

8. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the PID adjustment method for vehicle adaptive lateral control according to any one of claims 1 to 5.

9. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the PID adjustment method for vehicle adaptive lateral control according to any one of claims 1 to 5.

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