WO2021208812A1

WO2021208812A1 - Parameter configuration method and apparatus, and electronic device and storage medium

Info

Publication number: WO2021208812A1
Application number: PCT/CN2021/086141
Authority: WO
Inventors: 朱欣; 曹晓旭; 刘春晓; 石建萍; 成慧
Original assignee: 上海商汤临港智能科技有限公司
Priority date: 2020-04-14
Filing date: 2021-04-09
Publication date: 2021-10-21
Also published as: CN111487904A; JP2022538275A; CN111487904B

Abstract

Provided are a parameter configuration method and apparatus, and an electronic device and a storage medium. The method is applied to an intelligent device. The intelligent device comprises a controller, and the controller comprises a first controller and a second controller. The method comprises: acquiring control data output by a controller, wherein the control data output by a first controller comprises first control data, and the control data output by a second controller comprises second control data (S11); and configuring a parameter of the second controller according to the first control data and the second control data (S12).

Description

Parameter configuration method and device, electronic equipment and storage medium

Cross-references to related applications

This application claims the priority of the Chinese application entitled "Parameter Configuration Method and Device, Electronic Equipment and Storage Medium", with application number 202010290979.6, filed on April 14, 2020, the entire content of which is incorporated herein by reference.

Technical field

The present disclosure relates to the field of computer technology, and in particular to a parameter configuration method and device, electronic equipment and storage medium.

Background technique

With the rise of autonomous driving technology, more and more academic institutions and technology companies have begun to participate in the research and development of autonomous driving technology. Among them, the controller, as an indispensable module in the automatic driving system, has received more and more attention. How to configure the parameters of the controller accurately, efficiently, at low cost, and safely is a problem that needs to be solved urgently.

Summary of the invention

The present disclosure provides a technical solution for parameter configuration.

According to an aspect of the present disclosure, there is provided a parameter configuration method, the method is applied to a smart device, the smart device includes a plurality of controllers, the plurality of controllers include at least a first controller and a second controller , The method includes: acquiring control data output by each of the plurality of controllers, the control data output by the first controller includes first control data, and the control data output by the second controller includes second control data ; According to the first control data and the second control data, configure the parameters of the second controller.

By acquiring the control data output by each of the multiple controllers of the smart device, where the multiple controllers include a first controller and a second controller, and the control data output by the first controller includes the first control data, The control data output by the second controller includes second control data, and the parameters of the second controller are configured according to the first control data and the second control data, thereby using the first controller as With reference to the controller, the second controller is used as the adjusted controller to find reliable parameters for the second controller accurately, efficiently, and at low cost, so that when the second controller is further adjusted in the subsequent parameters, the intelligence can be reduced. The risk of loss of control of equipment (for example, vehicles) reduces the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller.

In a possible implementation manner, the obtaining the control data output by each of the multiple controllers includes: obtaining first reference trajectory data and first state data of the smart device in the first mode; The multiple controllers generate respective control data according to the first reference trajectory data and the first state data.

In this implementation, the controller generates the control data based on the first reference trajectory data and the first state data, and configures the first control data and second control data based on the first control data and second control data thus generated. The parameters of the second controller can make the configured parameters of the second controller more suitable for real scenes.

In a possible implementation manner, the method further includes: controlling the smart device in the first mode to drive according to the first control data.

According to this implementation manner, by controlling the driving of the smart device in the first mode according to the first control data, the second controller can be configured in the real vehicle, thereby reducing the simulator and actual The cost of switching and debugging the controller back and forth on the car, and can make the second controller after parameter configuration more suitable for real scenes.

In a possible implementation, the method further includes: acquiring second reference trajectory data and second state data of the smart device in the first mode; using the second controller, according to the second Generate third control data with reference to the trajectory data and the second state data;

According to the third control data, the smart device in the first mode is controlled to travel.

According to this implementation manner, by controlling the driving of the smart device in the first mode according to the third control data, the second controller can be adjusted in the real vehicle, thereby reducing the simulator and actual The cost of switching and debugging the controller back and forth on the car, and can make the second controller after parameter adjustment more suitable for real scenes.

In a possible implementation, the method further includes: obtaining actual trajectory data generated by the smart device when driving in the first mode; adjusting according to the second reference trajectory data and the actual trajectory data Parameters of the second controller.

In this implementation manner, the parameters of the second controller are adjusted according to the difference between the actual trajectory data and the second reference trajectory data, thereby enabling the second controller to more accurately adapt to the intelligent Equipment to achieve more precise control and a safer and more comfortable driving experience.

In a possible implementation manner, the first controller is a controller that does not include a vehicle dynamics model, and the second controller is a controller that includes a vehicle dynamics model.

Based on the above implementation method, the controller that does not contain the vehicle dynamics model can be used as the reference controller, and the controller that contains the vehicle dynamics model can be used as the adjusted controller. The controller finds reliable parameters, so that when further parameter adjustments are made to the controller containing the vehicle dynamics model, the risk of loss of control of the smart device (such as a vehicle) can be reduced, and the parameters of the controller containing the vehicle dynamics model can be reduced. The safety risk of potential traffic accidents caused by improper configuration can facilitate on-site commissioning engineers to carry out large-scale performance testing of real vehicle controllers.

In a possible implementation manner, the smart device includes a smart mobile device, and the control data includes at least one of steering wheel steering data, accelerator data, brake data, and indicator light data.

Based on this implementation method, precise automatic control of smart mobile devices can be achieved.

In a possible implementation manner, the controlling the driving of the smart device in the first mode according to the third control data includes: responding to the difference between the first control data and the second control data According to the third control data, the smart device in the first mode is controlled to drive; wherein the preset condition includes the first target control data in the first control data and the The difference between the second target control data in the second control data belongs to the threshold range, and the first target control data and the second target control data are of the same type.

In this implementation manner, the smart device is controlled to drive according to the third control data output by the second controller when the difference between the first control data and the second control data satisfies a preset condition Therefore, it is possible to make the adjusted parameters of the second controller more applicable under the premise of reducing the risk of loss of control of smart devices (such as vehicles) and reducing the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller. In the real scene.

According to an aspect of the present disclosure, there is provided a parameter configuration device, the device is applied to a smart device, the smart device includes a plurality of controllers, the plurality of controllers include a first controller and a second controller, The device includes: a first acquisition module configured to acquire control data output by each of the multiple controllers, the control data output by the first controller includes first control data, and the control output by the second controller The data includes second control data;

The configuration module is used to configure the parameters of the second controller according to the first control data and the second control data.

In a possible implementation manner, the first acquisition module is configured to: acquire first reference trajectory data and first state data of the smart device in the first mode; through the multiple controllers, according to all The first reference trajectory data and the first state data are used to generate the respective control data.

In a possible implementation manner, the device further includes: a first control module, configured to control the smart device in the first mode to drive according to the first control data.

In a possible implementation manner, the device further includes: a second acquisition module, configured to acquire second reference trajectory data, and second state data of the smart device in the first mode; and a generating module, configured to pass The second controller generates third control data according to the second reference trajectory data and the second status data; the second control module is configured to control the first mode according to the third control data The smart device travels.

In a possible implementation manner, the apparatus further includes: a third acquisition module, configured to acquire actual trajectory data generated by the smart device when driving in the first mode; and an adjustment module, configured according to the first mode Second, adjust the parameters of the second controller by referring to the trajectory data and the actual trajectory data.

In a possible implementation manner, the second control module is configured to: in response to the difference between the first control data and the second control data satisfying a preset condition, according to the third control data, Control the driving of the smart device in the first mode; wherein the preset condition includes the difference between the first target control data in the first control data and the second target control data in the second control data Belonging to a threshold value range, the first target control data and the second target control data are of the same type.

According to an aspect of the present disclosure, there is provided an electronic device including: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to call the memory to store The executable instructions to perform the above method.

According to an aspect of the present disclosure, there is provided a computer-readable storage medium having computer program instructions stored thereon, and the computer program instructions implement the above-mentioned method when executed by a processor.

According to an aspect of the present disclosure, there is provided a computer program product for storing computer-readable instructions, which when executed, cause a computer to execute the foregoing method.

In the embodiment of the present disclosure, the control data output by each of the multiple controllers of the smart device is acquired, wherein the multiple controllers include a first controller and a second controller, and the control output by the first controller is The data includes first control data, the control data output by the second controller includes second control data, and the parameters of the second controller are configured according to the first control data and the second control data. Therefore, the first controller is used as the reference controller, and the second controller is used as the adjusted controller to find reliable parameters for the second controller accurately, efficiently, and at low cost. Moreover, because the configuration of the second controller is implemented based on the first control data actually output by the first controller, the first control data can usually be used to control the smart device before the second controller completes the configuration. Therefore, The parameters of the second controller are more adapted to the current application scenario of the smart device. In addition, in the subsequent process of further adjusting the parameters of the second controller, the risk of loss of control of the smart device (for example, a vehicle) can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

It should be understood that the above general description and the following detailed description are only exemplary and explanatory, rather than limiting the present disclosure.

According to the following detailed description of exemplary embodiments with reference to the accompanying drawings, other features and aspects of the present disclosure will become clear.

Description of the drawings

The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments that conform to the present disclosure, and are used together with the specification to explain the technical solutions of the present disclosure.

Fig. 1 shows a flowchart of a parameter configuration method provided by an embodiment of the present disclosure.

Fig. 2 shows a schematic diagram of a PID-based controller (PID controller) in an embodiment of the present disclosure.

Fig. 3 shows a schematic diagram of core components of a PID-based controller in an embodiment of the present disclosure.

FIG. 4 shows a schematic diagram of the control effect after adjusting the three parameters of the PID-based controller in the embodiment of the present disclosure.

FIG. 5 shows a schematic diagram of an MPC-based controller (MPC controller) in an embodiment of the present disclosure.

Fig. 6 shows a schematic diagram of core components of an MPC-based controller in an embodiment of the present disclosure.

FIG. 7 shows a schematic diagram of a vehicle dynamics model in an MPC-based controller of an embodiment of the present disclosure.

8A-8C show schematic diagrams of objective function optimization of core components in an MPC-based controller in an embodiment of the present disclosure.

Fig. 9 shows a schematic diagram of an optimization process of an MPC-based controller in an embodiment of the present disclosure.

FIG. 10 shows a schematic diagram of the overall architecture of an automatic driving system provided by an embodiment of the present disclosure.

FIG. 11 shows a schematic diagram of using a reference controller to guide the adjusted controller to perform parameter configuration in an embodiment of the present disclosure.

FIG. 12 shows a schematic diagram of a parameter configuration process of a controller provided by an embodiment of the present disclosure.

13a to 13d show schematic diagrams of control results of the MPC-based controller in an embodiment of the present disclosure.

FIG. 14 shows a schematic diagram of the lateral trajectory error, the orientation error, and the longitudinal velocity error over time under the control of the MPC-based controller in the embodiment of the present disclosure.

FIG. 15 shows a schematic diagram of the lateral trajectory error, the heading error, and the longitudinal speed error changing with time under the control of the MPC-based controller after parameter adjustment of the MPC-based controller in an embodiment of the present disclosure.

FIG. 16 shows a block diagram of a parameter configuration device provided by an embodiment of the present disclosure.

FIG. 17 shows a block diagram of an electronic device 800 provided by an embodiment of the present disclosure.

FIG. 18 shows a block diagram of an electronic device 1900 provided by an embodiment of the present disclosure.

Detailed ways

Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the drawings. The same reference numerals in the drawings indicate elements with the same or similar functions. Although various aspects of the embodiments are shown in the drawings, unless otherwise noted, the drawings are not necessarily drawn to scale.

The dedicated word "exemplary" here means "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" need not be construed as being superior or better than other embodiments.

The term "and/or" in this article is only an association relationship that describes associated objects, which means that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, exist alone B these three situations. In addition, the term "at least one" in this document means any one of a plurality of or any combination of at least two of the plurality, for example, including at least one of A, B, and C, may mean including A, Any one or more elements selected in the set formed by B and C.

In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. Those skilled in the art should understand that the present disclosure can also be implemented without certain specific details. In some instances, the methods, means, elements, and circuits well known to those skilled in the art have not been described in detail, so as to highlight the gist of the present disclosure.

In the embodiment of the present disclosure, the control data output by the controller of the smart device is acquired, wherein the controller includes a first controller and a second controller, and the control data output by the first controller includes a first control Data, the control data output by the second controller includes second control data, and the parameters of the second controller are configured according to the first control data and the second control data. Therefore, the first controller is used as the reference controller, and the second controller is used as the adjusted controller to find reliable parameters for the second controller accurately, efficiently, and at low cost. Moreover, because the configuration of the second controller is implemented based on the first control data actually output by the first controller, the first control data can usually be used to control the smart device before the second controller completes the configuration. Therefore, The parameters of the second controller are more adapted to the current application scenario of the smart device. In addition, in the subsequent process of further adjusting the parameters of the second controller, the risk of loss of control of the smart device (for example, a vehicle) can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

It should be noted that the smart device may also include other controllers besides the first controller and the second controller. In the embodiments of the present disclosure, the types of controllers included in the smart device and the number of controllers of various types Etc. are not limited. In the embodiments of the present disclosure, a smart device including a first controller and a second controller is taken as an example to illustrate the technical solutions provided by the embodiments of the present disclosure.

Fig. 1 shows a flowchart of a parameter configuration method provided by an embodiment of the present disclosure. The parameter configuration method can be applied to a parameter configuration device. For example, the parameter configuration method can be executed by a terminal device or a server or other processing device. Among them, the terminal device may be a vehicle-mounted device, a user equipment (UE), a mobile device, a user terminal, a terminal, a personal digital assistant (PDA), a handheld device, a computing device, or a wearable device. In some possible implementation manners, the parameter configuration method may be implemented by a processor invoking computer-readable instructions stored in a memory. The parameter configuration method is applied to a smart device, the smart device includes a plurality of controllers, and the plurality of controllers include at least a first controller and a second controller. The smart device may include a smart mobile device, such as a vehicle or a mobile robot. The following uses a smart device as a vehicle as an example to describe the embodiments of the present disclosure. As shown in Fig. 1, the parameter configuration method includes step S11 to step S12.

In step S11, the control data output by each of the plurality of controllers is acquired, the control data output by the first controller includes first control data, and the control data output by the second controller includes second control data.

The controller in the embodiment of the present disclosure may be a controller used for trajectory tracking in automatic driving, or may be a controller with other functions, which is not limited in the embodiment of the present disclosure. The control data output by the controller (the first control data output by the first controller and/or the second control data output by the second controller) can be used to control the smart device. The first controller and the second controller can obtain the first control data and the second control data respectively according to the same input data. In the process of parameter configuration of the second controller, the first control data output by the first controller and the second control data output by the second controller can be acquired multiple times. For example, for any one of the multiple times, the first control data output by the first controller and the second control data output by the second controller can be simultaneously acquired to compare the two. Of course, it is also possible to obtain the first control data and the second control data in sequence according to a certain sequence, or to obtain the second control data and the first control data in sequence. It should be noted that in the process of acquiring the control data, the sequence of the first control data and the second control data is not limited.

In step S12, the parameters of the second controller are configured according to the first control data and the second control data.

In the embodiment of the present disclosure, the parameters of the second controller may be configured according to the difference between the first control data and the second control data. For example, the parameters of the second controller may be initialized and/or adjusted according to the difference between the first control data and the second control data.

Wherein, initializing the parameters of the second controller refers to initial configuration of the parameters of the second controller; adjusting the parameters of the second controller refers to adjusting the parameters of the second controller that has been initialized. In a possible implementation manner, the parameter of the second controller that is initialized may be the default second controller parameter, specifically it may be the parameter of the second controller configured in combination with historical experience values, or the second controller The factory parameters configured before leaving the factory are not limited here.

In the embodiments of the present disclosure, since the configuration of the second controller is implemented based on the first control data actually output by the first controller, the first control data can usually be used to control the intelligent control data before the second controller completes the configuration. Device, therefore, the parameters of the second controller can be more adapted to the current application scenario of the smart device. In addition, in the subsequent process of further adjusting the parameters of the second controller, the risk of loss of control of the smart device (for example, a vehicle) can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

In this implementation manner, the first reference trajectory data may represent reference trajectory data acquired during the process of configuring the parameters of the second controller according to the first control data and the second control data. The first reference trajectory data may include the reference trajectory and target speeds of multiple passing points on the reference trajectory, and the first reference trajectory data may be output by the trajectory planning module in the automatic driving system.

In this implementation manner, the first state data may represent the state data of the smart device acquired during the process of configuring the parameters of the second controller according to the first control data and the second control data. Among them, the first state data may be acquired in real time. For example, the first state data may include one or a combination of multiple items of the position, speed, and acceleration of the smart device.

In this implementation, the smart device has at least a first mode. For example, the first mode may be a fully automatic driving mode or a semi-automatic driving mode. The smart device may also have a second mode, for example, the second mode may be a fully manual driving mode. The smart device may also have a third mode. For example, if the first mode is a fully automatic driving mode, the third mode may be a semi-automatic driving mode, or if the first mode is a semi-automatic driving mode, the third mode may be Fully automatic driving mode. Among them, each mode may also include sub-modes to more finely divide each mode. The mode type of the smart device and the specific parameters of the sub-modes are not limited herein.

In this implementation manner, the first controller may generate first control data according to the first reference trajectory data and the first state data, where the first control data represents the data generated by the first controller and the The first reference trajectory data and the control data corresponding to the first state data. The second controller may generate second control data according to the first reference trajectory data and the first state data, where the second control data represents the data generated by the second controller and the first reference trajectory data Control data corresponding to the first state data. The second control data may refer to the control data generated by the second controller in the process of configuring the parameters of the second controller according to the first control data and the second control data.

In a possible implementation manner, after the acquisition of the control data output by the controller, the method further includes: controlling the smart device in the first mode to drive according to the first control data.

In this implementation manner, the first controller may be set as the current controller of the smart device to control the smart device in the first mode to drive according to the first control data. For example, the first controller can be set as the current controller of the smart device through a switcher of the smart device.

According to this implementation mode, by controlling the driving of the smart device in the first mode according to the first control data, the second controller can be parameterized in the real vehicle, thereby reducing the number of operating conditions in the simulator (such as The cost of switching and debugging the controller back and forth between the equipment that simulates the operation of the vehicle and the real vehicle, and can make the second controller after parameter configuration more suitable for real scenes.

In a possible implementation manner, after the configuration of the parameters of the second controller, the method further includes: acquiring second reference trajectory data, and second state data of the smart device in the first mode ; Through the second controller, according to the second reference trajectory data and the second state data, generate third control data; according to the third control data, control the smart device in the first mode to drive .

In this implementation manner, the second reference trajectory data may represent reference trajectory data acquired during the process of controlling the smart device to travel according to the third control data. The second reference trajectory data may include the reference trajectory and the target speeds of multiple passing points on the reference trajectory, and the second reference trajectory data may be output by the trajectory planning module in the automatic driving system. The first reference trajectory data may include the content in the second reference trajectory data, that is, the second reference trajectory data may be the reference trajectory data of the later section of the first reference trajectory data. For example, the smart device drives for a period of time according to the first reference trajectory data. After time, the second reference trajectory data is obtained. Or, in the case where the smart device changes the driving route, etc., the second reference trajectory data may be different from the first reference trajectory data.

In this implementation manner, the second state data may represent state data of the smart device acquired during the process of controlling the smart device to drive according to the third control data. Wherein, the second state data may be acquired in real time. For example, the second state data may include one or a combination of multiple items of the position, speed, and acceleration of the smart device.

In this implementation manner, the third control data may represent control data generated by the second controller and corresponding to the second reference trajectory data and the second state data. The third control data may refer to the control data generated by the second controller in the process of controlling the smart device to travel according to the third control data.

In a possible implementation manner, after the control of the smart device in the first mode to drive according to the third control data, the method further includes: acquiring that the smart device is in the first mode Driving, actual trajectory data generated; adjusting the parameters of the second controller according to the second reference trajectory data and the actual trajectory data.

The actual trajectory data may include the actual trajectory and the speed of multiple points on the actual trajectory. According to the second reference trajectory data and the actual trajectory data, the heading error, lateral trajectory error and longitudinal velocity error between the two can be determined, so that the first can be adjusted according to the heading error, lateral trajectory error and longitudinal velocity error between the two. 2. Parameters of the controller.

In this implementation manner, the steering wheel steering data may include one or a combination of multiple of the steering wheel rotation angle, the number of rotations, and the rotation direction. The throttle data may include one or a combination of multiple of throttle size, throttle speed, etc., wherein the throttle size can be expressed as a percentage of the maximum throttle amount. The braking data may include one or a combination of multiple of braking force, braking speed, etc., wherein the braking force can be expressed as a percentage of the maximum braking force. The indicator light data may include one or a combination of multiple items, such as indicator type and duration. Based on this implementation method, precise automatic control of smart mobile devices can be achieved.

In this implementation manner, the first target control data may include one or more types of data in the first control data, and correspondingly, the second target control data may also include one or more types of data in the second control data. data. Wherein, the first target control data and the second target control data include the same data type. For example, the first target control data includes steering wheel steering data, throttle data, and brake data in the first control data, and the second target control data includes steering wheel steering data, throttle data, and brake data in the second control data; another example is the first control data. One target control data includes steering wheel steering data, throttle data, brake data, and indicator data in the first control data, and the second target control data includes steering wheel steering data, throttle data, brake data, and indicator data in the second control data. .

In this implementation manner, the threshold range may be a parameter as the threshold, if the difference between the first target control data in the first control data and the second target control data in the second control data is less than or equal to The threshold value satisfies the preset condition; or, the threshold value range may use two threshold values as upper and lower limits, if the first target control data in the first control data is between the first target control data in the second control data and the second target control data in the second control data. If the difference of is within the threshold range, the preset condition is met.

As an example of this implementation, the first target control data includes at least one of steering wheel steering data, throttle data, brake data, and indicator light data in the first control data, and the second target control data includes at least one of the second control data. At least one of the steering wheel steering data, throttle data, brake data, and indicator light data. The preset condition includes at least one of the following: the difference between the steering wheel steering data in the first control data and the steering wheel steering data in the second control data is less than or equal to a first threshold, the first control data The difference between the throttle data in the second control data and the throttle data in the second control data is less than or equal to the second threshold, and the difference between the brake data in the first control data and the brake data in the second control data The difference between is less than or equal to the third threshold, and the difference between the indicator data in the first control data and the indicator data in the second control data is less than or equal to the fourth threshold.

In this implementation manner, the second controller may be set as the current controller of the smart device to control the smart device in the first mode to drive according to the third control data. For example, the second controller can be set as the current controller of the smart device through a switch.

In this implementation manner, the indicator type, duration, etc. in the indicator data can be quantified, so that the difference between the indicator data in the first control data and the indicator data in the second control data can be obtained. Difference.

In this implementation, if the difference between the first control data and the second control data satisfies the preset condition, it can be determined that the parameters of the second controller are relatively reliable.

For example, the first controller may be a controller based on PI (P: Proportional; I: Integral) or a controller based on PID (P: Proportional; I: Integral; D: Derivative). The second controller may be a controller based on MPC (Model Predictive Control, model predictive control) or a controller based on LQR (Linear Quadratic Regulator, linear quadratic regulator).

In this implementation, the second controller is based on a vehicle dynamics model, that is, a dynamic bicycle model (Dynamic Bicycle Model). The vehicle dynamics model can dynamically model the vehicle motion behavior, through a series of reasonable assumptions (for example: small angle assumptions, the model is based on error (error) modeling, which is more conducive to model linearization than directly based on the reference angle and reference position. ), the nonlinear model is simplified to a linear model, which is used to predict the behavior or trajectory of the vehicle in a limited time in the future.

Fig. 2 shows a schematic diagram of a PID-based controller (PID controller) in an embodiment of the present disclosure. The PID-based controller can realize the longitudinal speed closed-loop control and the lateral position closed-loop control, so as to realize the automatic driving of the unmanned vehicle. In addition to the feedback control shown in Figure 2, the PID-based controller also includes feedforward control. As shown in FIG. 2, the PID-based controller may output first control data based on the error between the first reference trajectory data output by the trajectory planning module and the first state data of the car to control the driving of the car.

Fig. 3 shows a schematic diagram of core components of a PID-based controller in an embodiment of the present disclosure. As shown in FIG. 3, only three parameters need to be adjusted based on the PID control task controller can be completed, the three parameters are proportional term K _p, K _i of the integral term and differential term K _d. In an automatic driving system, PI-based controllers can often meet the control requirements of the system, that is, K _p makes the automatic driving system respond immediately, and K _i eliminates the steady-state error in the automatic driving system.

FIG. 4 shows a schematic diagram of the control effect after adjusting the three parameters of the PID-based controller in the embodiment of the present disclosure. Among them, the straight line 41 in FIG. 4 is the reference trajectory, 42 is the actual trajectory under no control, and 43 is the actual trajectory under the control of the PID-based controller. Since the PID-based controller only needs to adjust the _{two parameters of K p} and K _i to complete the automatic control of the unmanned vehicle, and is easy to debug, the embodiment of the present disclosure can use the PI-based controller as a reference control The device is used to guide the second controller including the vehicle dynamics model to configure the parameters. In the embodiments of the present disclosure, appropriate parameters can be selected for the PI-based controller through manual parameter adjustment, or the automatic parameter adjustment method in the related art can be used to select appropriate parameters for the PI-based controller.

FIG. 5 shows a schematic diagram of an MPC-based controller (MPC controller) in an embodiment of the present disclosure. The MPC-based controller can realize the longitudinal speed closed-loop control and the lateral position closed-loop control, so as to realize the automatic driving of the unmanned vehicle. In addition to the feedback control shown in Figure 5, the MPC-based controller also includes feedforward control. As shown in FIG. 5, the MPC-based controller may output second control data based on the error between the first reference trajectory data output by the trajectory planning module and the first state data of the vehicle, or may be based on the trajectory planning The error between the second reference trajectory data output by the module and the second state data of the car, and the third control data is output to control the car.

Fig. 6 shows a schematic diagram of core components of an MPC-based controller in an embodiment of the present disclosure. As shown in Figure 6, the MPC-based controller includes two core components: a vehicle dynamics model and an optimizer. Among them, the vehicle dynamics model is used to predict the possible future trajectory of the vehicle; the optimizer solves an optimization problem, finds the trajectory that minimizes the objective function J from multiple candidate predicted trajectories, and obtains the second control data or the third control data , So as to ensure that the MPC-based controller can drive the vehicle close to the first reference trajectory data or the second reference trajectory data as much as possible.

FIG. 7 shows a schematic diagram of a vehicle dynamics model in an MPC-based controller of an embodiment of the present disclosure. In Figure 7, 71 represents a car, with the front of the car on the top and the rear of the car on the bottom. According to the difference between the orientation of the reference point on the reference trajectory in the first reference trajectory data or the second reference trajectory data and the current heading of the vehicle, the steering error e1 can be obtained; according to the current position of the vehicle (for example, the geometric center point of the vehicle) and The minimum distance between the tangents of the reference points can be used to obtain the lateral trajectory error e2. For example, the lateral trajectory error e2 can be equal to the minimum distance between the current position of the car and the tangent of the reference point; according to the first reference trajectory data or the second reference The difference between the reference speed in the trajectory data and the actual speed of the vehicle, the longitudinal speed (Lon Velocity) error e3 can be obtained. In Figure 7,

Represents the differential of e1,

Represents the second derivative of e1, e2 and e3 are similar. The MPC-based controller in the embodiments of the present disclosure is based on the vehicle dynamics model, which can dynamically model the vehicle motion behavior. Through a series of reasonable assumptions, the nonlinear model is simplified to the linear model shown in FIG. Predict the behavior or trajectory of the vehicle within a limited time in the future. In Figure 7, ST (steer) represents steering wheel steering data (such as steering wheel rotation angle), TH (throttle) represents throttle data (such as percentage of maximum throttle), BR (brake) represents braking data (such as maximum braking force) %).

FIG. 8C shows a schematic diagram of the objective function optimization of the core components in the MPC-based controller according to an embodiment of the present disclosure. In Figure 8C, ΔST represents the amount of change in ST (steering wheel steering data) between two adjacent moments, ΔTH represents the amount of change in TH (throttle data) between two adjacent moments, and ΔBR represents the amount of change between two adjacent times. The amount of change in BR (brake data) between times. Figure 8C shows the objective function

The objective function after vectorization is J=x ^T Qx+Δu ^T RΔu. in,

u=[steer throttle/brake] ^T , Δu=[Δsteer Δthrottle/brake] ^T , Q=[q1 q2 q3 q4 q5 q6] ^T , R=[r1 r2] ^T. It can be seen from the vectorized objective function that the MPC-based controller needs to configure 8 parameters, of which the Q vector contains 6 parameters to be configured, namely q1, q2, q3, q4, q5 and q6, R vector Contains 2 parameters to be configured, namely r1 and r2. FIG. 8A shows a schematic diagram of the position of the predicted trajectory of the vehicle in the lane, and FIG. 8A also shows the position and the predicted range of the center line of the lane. The predicted trajectory is a trajectory generated by executing a predicted control action sequence (the control action data may include at least one of steering wheel steering data, accelerator data, brake data, and indicator light data). Fig. 8B shows a schematic diagram of the lateral trajectory error cte _t+1 to cte _t+7 (ie, e2) from time t+1 to time t+7, where the abscissa is time and the ordinate is distance. Correspondingly, the orientation error e1 from time t+1 to t+7 can be expressed as he _t+1 to he _t+7 , and the longitudinal velocity error e3 from time t+1 to t+7 can be expressed as ve _{t +1} to ve _t+7 .

Fig. 9 shows a schematic diagram of an optimization process of an MPC-based controller in an embodiment of the present disclosure. As shown in Figure 9, the goal of the parameter configuration of the MPC-based controller is to minimize J. After configuring Q and R, through parameter adjustment, the MPC-based controller can predict and evaluate the potential control action sequence (calculate the objective function J) according to the method shown in Figure 9 to obtain the optimal control action sequence As the second control data or the third control data. Fig. 9 shows a schematic diagram of the predicted trajectory of the vehicle when J=50, J=30, and J=10. As shown in Fig. 9, when J=10, the predicted trajectory of the vehicle is closest to the center line of the lane. Therefore, the control action sequence corresponding to J=10 is the optimal control action sequence.

Generally, the amount of parameters to be adjusted for a controller that includes a vehicle dynamics model is larger than that of a controller that does not include a vehicle dynamics model. For example, the PI-based controller has two parameters to be adjusted, namely the proportional term (K _p ) and the integral term (K _i ). Although model-free PI controllers are relatively simple and easy to debug, the control is not precise enough, while controllers containing vehicle dynamics models such as MPC-based controllers model vehicle dynamics behavior and can more accurately describe the vehicle's future motion trajectory . In the embodiments of the present disclosure, a controller (a second controller) containing a vehicle dynamics model may be used to model the behavior of the unmanned vehicle, thereby obtaining a more accurate automatic control effect. However, the controller containing the vehicle dynamics model usually contains multiple parameters. For example, the MPC-based controller contains 8 parameters to be adjusted, of which 6 parameters are used to adjust the system state (heading error, lateral trajectory error, longitudinal Speed error), 2 parameters to be adjusted are used to adjust the system input (steering wheel steering angle, throttle amount or brake amount). The debugging difficulty of 8 parameters is often higher than that of 2 parameters. Therefore, the method provided in this application uses the PID controller as the reference controller, and configures the MPC controller according to the first control data and the second control data. Parameters to be adjusted. Before the parameters to be adjusted of the MPC controller are adjusted, the PID controller is still used as the current controller to control the driving of the unmanned vehicle, which can reduce the potential traffic accidents that may be caused by using the unadjusted MPC controller.

FIG. 10 shows a schematic diagram of the overall architecture of an automatic driving system provided by an embodiment of the present disclosure. As shown in Figure 10, the trajectory planning module outputs reference trajectory data (such as the first reference trajectory data, the second reference trajectory data), and reads the state data of the car (such as the first state data, the first state data, and the first state data) through the bus (read bus). Two status data); the controller (the first controller and/or the second controller) outputs the control data according to the difference between the reference trajectory data and the state data of the car (the first controller outputs the first control data, the second The controller outputs the second control data and/or the third control data), and writes the control data into the car through the bus (write bus), so as to realize the closed-loop control of the car and drive the car to travel.

FIG. 11 shows a schematic diagram of using a reference controller to guide the adjusted controller to perform parameter configuration in an embodiment of the present disclosure. As shown in Figure 11, the reference controller can be a PID-based controller, etc., the adjusted controller can be an LQR-based controller or an MPC-based controller, etc., and the reference controller can guide the adjusted controller to configure parameters (such as Q vector, R vector, etc.). For example, r1 can be adjusted according to the difference between the steering wheel steering data in the first control data and the second control data; r2 can be adjusted according to the difference between the throttle data or the brake data in the first control data and the second control data; according to the heading error e1, adjust q1 and q2; adjust q3 and q4 according to lateral trajectory error e2; adjust q5 and q6 according to longitudinal velocity error e3.

FIG. 12 shows a schematic diagram of a parameter configuration process of a controller provided by an embodiment of the present disclosure. As shown in Figure 12, after the autopilot system is started, the first controller (controllers that do not include vehicle dynamics models, such as PI-based controllers) and the second controller (controllers that include vehicle dynamics models, such as LQR-based controllers or MPC-based controllers) can start working at the same time. Turn on the switch, select the first controller as the current controller of the unmanned vehicle, that is, send the first control data output by the first controller to the vehicle actuator to drive the unmanned vehicle to drive. In the process of using the first controller as the current controller of the unmanned vehicle, the first controller can complete the accurate control of the unmanned vehicle in straight lines and curves by adjusting the values of the proportional term and the integral term, that is, The control data output by the first controller can drive the unmanned vehicle to correctly track the reference trajectory. Once the first controller is successfully adapted to the automatic driving system (that is, the unmanned vehicle can correctly track the reference trajectory under the control of the first controller), the first control data output by it can be regarded as the reference control data, and the reference control The data can enable unmanned vehicles to complete routine autonomous driving tasks more accurately.

After the first controller is successfully adapted to the automatic driving system, the comparator can be turned on, and the comparator can be used to compare the first control data output by the first controller and the second control data output by the second controller. According to the difference between the first control data and the second control data, configure the parameters of the second controller so that the second control data output by the second controller after the configuration is the same as the first control data output by the first controller. The difference between the control data is minimal. If the difference between the first control data and the second control data satisfies the preset condition, it can be determined that the second controller has obtained more reliable parameters. As an example, when the parameters of the MPC-based controller are Q=[0.0,0.0,1,0,0,1] ^T and R=[0.4,0.8] ^T , the second control data output by the MPC-based controller It is basically the same as the first control data output by the PI-based controller, that is, the MPC-based controller with this parameter can be used to achieve the same trajectory tracking effect as the PI-based controller.

13a to 13d show schematic diagrams of control results of the MPC-based controller in an embodiment of the present disclosure. Wherein, FIG. 13a shows a schematic diagram of the reference trajectory and the actual trajectory of the unmanned vehicle under the control of the MPC-based controller. In Figure 13a, the UTM (Universal Transverse Mercator grid system) coordinate system is used. Fig. 13b shows a schematic diagram of the reference longitudinal speed and the actual longitudinal speed of the unmanned vehicle with respect to the distance under the control of the MPC-based controller. Fig. 13c shows a schematic diagram of the reference heading of the vehicle head and the actual heading of the unmanned vehicle under the control of the MPC-based controller. Fig. 13d shows a schematic diagram of the reference steering wheel steering angle and the actual steering wheel steering angle of the unmanned vehicle under the control of the MPC-based controller. Wherein, the distance in FIG. 13b to FIG. 13d may represent the distance relative to the starting point of the automatic driving task. As shown in Figure 13a to Figure 13d, the MPC-based controller has completed the automatic driving ^{based on the parameters (Q=[0.0,0.0,1,0,0,1] T} , R=[0.4,0.8] ^T). From the data point of view, this parameter can ensure that the MPC-based controller safely completes the basic autonomous driving tasks, but the performance needs to be further improved.

FIG. 14 shows a schematic diagram of the cross track error (CTE, Cross Track Error), heading error, and longitudinal speed error over time under the control of an MPC-based controller in an embodiment of the present disclosure. In Figure 14, the abscissa is time and the unit is seconds. As shown in Figure 14, the lateral trajectory error is large, that is, the lateral control of the MPC-based controller is not accurate enough, which will cause the unmanned vehicle to deviate from the center line of the lane. In order to make the control of the MPC-based controller more precise, further parameter adjustments can be made to the MPC-based controller. In the embodiment of the present disclosure, after the parameters of the second controller are configured according to the first control data and the second control data, the second controller can be used as the current controller of the unmanned vehicle through a switch, and the second controller The unmanned vehicle is controlled, and the parameters of the second controller are further adjusted according to the second reference trajectory data and the actual trajectory data, so that the second controller is more accurately adapted to the automatic driving system. For the problem of large lateral trajectory error, the parameters of the MPC-based controller (Q=[0.0,0.0,1,0,0,1] ^T , R=[0.4,0.8] ^T ) can be adjusted. Yes, adjusted to Q=[0.03,0.0,1,0,0,1] ^T , R=[0.4,0.8] ^T , which increases the penalty for lateral trajectory errors, so that MPC-based controllers can Control the unmanned vehicle to drive closer to the center line of the lane.

FIG. 15 shows a schematic diagram of the lateral trajectory error, the heading error, and the longitudinal speed error changing with time under the control of the MPC-based controller after parameter adjustment of the MPC-based controller in an embodiment of the present disclosure. In the example shown in FIG. 15, after the parameter adjustment of the MPC-based controller, the lateral trajectory error is reduced by about 50% from before the parameter adjustment. As shown in Figure 13d, the actual steering angle of the steering wheel output by the MPC-based controller fluctuates more frequently than the reference steering angle of the steering wheel, and the direction steering is not smooth, which will bring an uncomfortable driving experience. The parameters of the MPC-based controller can be further adjusted to make the change of the steering angle of the steering wheel in the output control data more smooth.

In one example, an automatic transmission car can be used as the experimental platform. The average speed is 20km/h. The test path is to go straight first, then turn left and go straight, then turn right and go straight.

The embodiments of the present disclosure may be applied to application scenarios such as automatic driving systems, driving assistance systems, and automatic parking systems, which are not limited in the embodiments of the present disclosure.

In the embodiment of the present disclosure, the control data output by each of the multiple controllers of the smart device is acquired, wherein the multiple controllers include a first controller and a second controller, and the control output by the first controller is The data includes first control data, the control data output by the second controller includes second control data, and the parameters of the second controller are configured according to the first control data and the second control data, and the parameters of the second controller are configured by This uses the first controller as the reference controller and the second controller as the adjusted controller to find reliable parameters for the second controller accurately, efficiently, and at low cost, so that the second controller will be further performed in the follow-up. When the parameters are adjusted, the risk of loss of control of smart devices (for example, vehicles) can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

It can be understood that, without violating the principle and logic, the various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment, which is limited in length and will not be repeated in this disclosure.

Those skilled in the art can understand that in the above-mentioned methods of the specific implementation, the writing order of the steps does not mean a strict execution order but constitutes any limitation on the implementation process. The specific execution order of each step should be based on its function and possibility. The inner logic is determined.

In addition, the present disclosure also provides parameter configuration devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any parameter configuration method provided in the present disclosure. For the corresponding technical solutions and descriptions, refer to the corresponding records in the method section. ,No longer.

FIG. 16 shows a block diagram of a parameter configuration device provided by an embodiment of the present disclosure. The device is applied to a smart device, the smart device includes a plurality of controllers, and the plurality of controllers includes a first controller and a second controller. As shown in FIG. 16, the parameter configuration device includes: a first obtaining module 21, configured to obtain control data output by each of the multiple controllers, and the control data output by the first controller includes first control data, The control data output by the second controller includes second control data; the configuration module 22 is configured to configure the parameters of the second controller according to the first control data and the second control data.

In a possible implementation manner, the first acquisition module 21 is configured to: acquire first reference trajectory data and first state data of the smart device in the first mode; through the multiple controllers, according to The first reference trajectory data and the first state data generate the control data.

In the embodiment of the present disclosure, the control data output by the controller of the smart device is acquired, wherein the controller includes a first controller and a second controller, and the control data output by the first controller includes a first control Data, the control data output by the second controller includes second control data, and the parameters of the second controller are configured according to the first control data and the second control data. Therefore, the first controller is used as the reference controller, and the second controller is used as the adjusted controller to find reliable parameters for the second controller accurately, efficiently, and at low cost. Moreover, because the configuration of the second controller is implemented based on the first control data actually output by the first controller, the first control data can usually be used to control the smart device before the second controller completes the configuration. Therefore, The parameters of the second controller are more adapted to the current application scenario of the smart device. In addition, in the subsequent process of further parameter adjustment of the second controller, the risk of loss of control of the smart device (such as a vehicle) can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

In some embodiments, the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments. For specific implementation, refer to the description of the above method embodiments. For brevity, here No longer.

The embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the foregoing method when executed by a processor. Wherein, the computer-readable storage medium may be a non-volatile computer-readable storage medium, or may be a volatile computer-readable storage medium.

The embodiments of the present disclosure also provide a computer program product, including computer-readable code. When the computer-readable code runs on the device, the processor in the device executes the method for realizing the parameter configuration method provided by any of the above embodiments. instruction.

The embodiments of the present disclosure also provide another computer program product for storing computer-readable instructions, which when executed, cause the computer to perform the operation of the parameter configuration method provided in any of the foregoing embodiments.

An embodiment of the present disclosure also provides an electronic device, including: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to call the executable stored in the memory Instructions to perform the above method.

The electronic device can be provided as a terminal, server or other form of device.

FIG. 17 shows a block diagram of an electronic device 800 provided by an embodiment of the present disclosure. For example, the electronic device 800 may be a vehicle-mounted device, a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.

17, the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.

The processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method to operate on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.

The power supply component 806 provides power for various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with the generation, management, and distribution of power for the electronic device 800.

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor can not only sense the boundary of the touch or slide action, but also detect the duration and pressure related to the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module. The above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: home button, volume button, start button, and lock button.

The sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation. For example, the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components. For example, the component is the display and the keypad of the electronic device 800. The sensor component 814 can also detect the electronic device 800 or the electronic device 800. The position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact. The sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as Wi-Fi, 2G, 3G, 4G/LTE, 5G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.

In an exemplary embodiment, there is also provided a non-volatile computer-readable storage medium, such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.

FIG. 18 shows a block diagram of an electronic device 1900 provided by an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server. 18, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions executable by the processing component 1922, such as application programs. The application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above-described methods.

The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 . The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as

Or similar.

In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.

The present disclosure may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.

The computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon The protruding structure in the hole card or the groove, and any suitable combination of the above. The computer-readable storage medium used here is not interpreted as the instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .

The computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages. Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server implement. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network-including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect to the user's computer) connect). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions. The computer-readable program instructions are executed to realize various aspects of the present disclosure.

Here, various aspects of the present disclosure are described with reference to flowcharts and/or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and/or block diagrams, and combinations of blocks in the flowcharts and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner, so that the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

It is also possible to load computer-readable program instructions on a computer, other programmable data processing device, or other equipment, so that a series of operation steps are executed on the computer, other programmable data processing device, or other equipment to produce a computer-implemented process , So that the instructions executed on the computer, other programmable data processing apparatus, or other equipment realize the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.

The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions, and operations of the system, method, and computer program product according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function. Executable instructions. In some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.

The computer program product can be specifically implemented by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium. In another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (SDK), etc. Wait.

The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Without departing from the scope and spirit of the illustrated embodiments, many modifications and changes are obvious to those of ordinary skill in the art. The choice of terms used herein is intended to best explain the principles, practical applications, or improvements to technologies in the market of the embodiments, or to enable other ordinary skilled in the art to understand the embodiments disclosed herein.

Claims

A parameter configuration method, characterized in that the method is applied to a smart device, the smart device includes a plurality of controllers, the plurality of controllers include at least a first controller and a second controller, and the method includes :

Acquiring control data output by each of the multiple controllers, where the control data output by the first controller includes first control data, and the control data output by the second controller includes second control data;

According to the first control data and the second control data, the parameters of the second controller are configured.
The method according to claim 1, wherein the acquiring control data output by each of the multiple controllers comprises:

Acquiring first reference trajectory data and first state data of the smart device in the first mode;

Through the plurality of controllers, the respective control data are generated according to the first reference trajectory data and the first state data.
The method according to claim 1 or 2, wherein the method further comprises:

According to the first control data, the smart device in the first mode is controlled to drive.
The method according to any one of claims 1 to 3, wherein the method further comprises:

Acquiring second reference trajectory data and second state data of the smart device in the first mode;

Generating third control data by the second controller according to the second reference trajectory data and the second state data;

According to the third control data, the smart device in the first mode is controlled to travel.
The method according to claim 4, wherein the method further comprises:

Acquiring actual trajectory data generated by the smart device driving in the first mode;

Adjusting the parameters of the second controller according to the second reference trajectory data and the actual trajectory data.
The method according to any one of claims 1 to 5, wherein the first controller is a controller that does not include a vehicle dynamics model, and the second controller is a control that includes a vehicle dynamics model Device.
The method according to any one of claims 1 to 6, wherein the smart device includes a smart mobile device, and the control data includes at least one of steering wheel steering data, throttle data, brake data, and indicator light data. item.
The method according to claim 4, wherein the controlling the smart device in the first mode to drive according to the third control data comprises:

In response to the difference between the first control data and the second control data satisfying a preset condition, controlling the smart device in the first mode to drive according to the third control data;

Wherein, the preset condition includes that the difference between the first target control data in the first control data and the second target control data in the second control data belongs to a threshold range, and the first target control data is equal to The types of the second target control data are the same.
A parameter configuration device, characterized in that the device is applied to a smart device, the smart device includes a plurality of controllers, the plurality of controllers include at least a first controller and a second controller, and the device includes :

The first acquisition module is configured to acquire the control data output by each of the plurality of controllers, the control data output by the first controller includes first control data, and the control data output by the second controller includes second control data;

The configuration module is used to configure the parameters of the second controller according to the first control data and the second control data.
An electronic device, characterized by comprising: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to call the executable instructions stored in the memory, To implement the method described in any one of claims 1 to 8.
A computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the method according to any one of claims 1 to 8 when the computer program instructions are executed by a processor.
A computer program product for storing computer-readable instructions, which when executed, cause a computer to execute the method described in any one of claims 1 to 8.