CN111487904A

CN111487904A - Parameter configuration method and device, electronic equipment and storage medium

Info

Publication number: CN111487904A
Application number: CN202010290979.6A
Authority: CN
Inventors: 朱欣; 曹晓旭; 刘春晓; 石建萍; 成慧
Original assignee: Shanghai Sensetime Lingang Intelligent Technology Co Ltd
Current assignee: Shanghai Sensetime Lingang Intelligent Technology Co Ltd
Priority date: 2020-04-14
Filing date: 2020-04-14
Publication date: 2020-08-04
Anticipated expiration: 2040-04-14
Also published as: WO2021208812A1; JP2022538275A; CN111487904B

Abstract

The disclosure relates to a parameter configuration method and apparatus, an electronic device, and a storage medium. The method is applied to a smart device which comprises a controller, wherein the controller comprises a first controller and a second controller, and the method comprises the following steps: acquiring control data output by the controller, wherein the control data output by the first controller comprises first control data, and the control data output by the second controller comprises second control data; and configuring parameters of the second controller according to the first control data and the second control data.

Description

Parameter configuration method and device, electronic equipment and storage medium

Technical Field

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

Background

With the rise of the automatic driving technology, more and more academic institutions and science and technology companies start participating in the research and development work of the automatic driving technology. Among them, the controller has received increasing attention as an indispensable module in an automatic driving system. How to accurately, efficiently, low-cost and safely configure the parameters of the controller is a problem to be solved urgently.

Disclosure of Invention

The present disclosure provides a parameter configuration technical solution.

According to an aspect of the present disclosure, there is provided a parameter configuration method, including:

acquiring control data output by the controller, wherein the control data output by the first controller comprises first control data, and the control data output by the second controller comprises second control data;

and configuring parameters of the second controller according to the first control data and the second control data.

The method comprises the steps of obtaining control data output by a controller of the intelligent device, wherein the controller comprises a first controller and a second controller, the control data output by the first controller comprises first control data, the control data output by the second controller comprises second control data, and parameters of the second controller are configured according to the first control data and the second control data, so that the first controller is used as a reference controller, the second controller is used as a regulated controller, and reliable parameters are accurately, efficiently and locally found for the second controller at low cost, so that when the parameters of the second controller are further regulated subsequently, the risk of losing control of the intelligent 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 is reduced.

In a possible implementation manner, the obtaining the control data output by the controller includes:

acquiring first reference track data and first state data of the intelligent equipment in a first mode;

generating, by the controller, the control data according to the first reference trajectory data and the first state data.

In this implementation manner, the controller generates the control data according to the first reference trajectory data and the first state data, and configures parameters of the second controller based on the generated first control data and second control data, so that the configured parameters of the second controller can be more suitable for a real scene.

In one possible implementation, after the obtaining the control data output by the controller, the method further includes:

and controlling the intelligent equipment in the first mode to run according to the first control data.

According to the implementation mode, the intelligent device in the first mode is controlled to run according to the first control data, so that parameter configuration can be performed on the second controller in the real vehicle, the cost for switching and debugging the controllers on the simulator and the real vehicle back and forth can be reduced, and the second controller after parameter configuration can be more suitable for a real scene.

In one possible implementation, after the configuring the parameter of the second controller, the method further includes:

acquiring second reference track data and second state data of the intelligent equipment in a first mode;

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

and controlling the intelligent equipment in the first mode to run according to the third control data.

According to the implementation mode, the intelligent device in the first mode is controlled to run according to the third control data, so that the second controller can be subjected to parameter adjustment in the real vehicle, the cost for switching and debugging the controllers back and forth on the simulator and the real vehicle can be reduced, and the second controller after parameter adjustment can be more suitable for a real scene.

In one possible implementation, after the controlling the smart device in the first mode to travel according to the third control data, the method further includes:

acquiring actual track data generated when the intelligent equipment runs in the first mode;

and adjusting parameters of the second controller according to the second reference track data and the actual track data.

In this implementation, the parameters of the second controller are adjusted according to the difference between the actual trajectory data and the second reference trajectory data, so that the second controller can be more accurately adapted to the smart device, thereby achieving more accurate control and safer and more comfortable driving experience.

In one possible implementation, 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 implementation mode, the controller without the vehicle dynamics model can be used as a reference controller, the controller with the vehicle dynamics model can be used as a regulated controller, and reliable parameters can be accurately, efficiently and inexpensively found for the controller with the vehicle dynamics model, so that the risk of out-of-control of intelligent equipment (such as a vehicle) can be reduced when the controller with the vehicle dynamics model is further subjected to parameter adjustment in the follow-up process, the safety risk of potential traffic accidents caused by improper parameter configuration of the controller with the vehicle dynamics model can be reduced, and the performance test work of a large-scale real vehicle controller can be conveniently performed by a field debugging engineer.

In one possible implementation, 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.

Based on the implementation mode, accurate automatic control can be achieved for the intelligent mobile device.

In one possible implementation manner, the controlling the smart device in the first mode to travel according to the third control data includes:

in response to the difference between the first control data and the second control data meeting a preset condition, controlling the intelligent device in a first mode to run according to the third control data; the preset condition includes that a difference value between first target control data in the first control data and second target control data in the second control data belongs to a threshold range, and the types of the first target control data and the second target control data are the same.

In this implementation manner, when the difference between the first control data and the second control data meets the preset condition, the intelligent device is controlled to run according to the third control data output by the second controller, so that the parameters adjusted by the second controller are more suitable for the real scene on the premise of reducing the risk of losing control of the intelligent device (for example, a vehicle) and reducing the safety risk of a potential traffic accident caused by improper configuration of the parameters of the second controller.

According to an aspect of the present disclosure, there is provided a parameter configuration apparatus, the apparatus being applied to a smart device, the smart device including a controller including a first controller and a second controller, the apparatus including:

the first acquisition module is used for acquiring the control data output by the controller, the control data output by the first controller comprises first control data, and the control data output by the second controller comprises second control data;

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

In one possible implementation manner, the first obtaining module is configured to:

In one possible implementation, the apparatus further includes:

and the first control module is used for controlling the intelligent equipment in the first mode to run according to the first control data.

In one possible implementation, the apparatus further includes:

the second acquisition module is used for acquiring second reference track data and second state data of the intelligent equipment in the first mode;

a generating module, configured to generate, by the second controller, third control data according to the second reference trajectory data and the second state data;

and the second control module is used for controlling the intelligent equipment in the first mode to run according to the third control data.

In one possible implementation, the apparatus further includes:

the third acquisition module is used for acquiring actual track data generated when the intelligent equipment runs in the first mode;

and the adjusting module is used for adjusting the parameters of the second controller according to the second reference track data and the actual track data.

In one possible implementation, the second control module is configured to:

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 invoke the memory-stored executable instructions to perform the above-described method.

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

In the embodiment of the disclosure, control data output by a controller of an intelligent device is acquired, wherein the controller includes a first controller and a second controller, the control data output by the first controller includes first control data, the control data output by the second controller includes second control data, and a parameter of the second controller is configured according to the first control data and the second control data. Therefore, the first controller is used as a reference controller, the second controller is used as a regulated controller, and reliable parameters are accurately, efficiently and inexpensively found for the second controller. And, since the configuration of the second controller is implemented based on the first control data actually output by the first controller, and the smart device can be generally controlled using the first control data before the second controller completes the configuration, the parameters of the second controller can be more adapted to the application scenario where the smart device is currently located. In addition, in the subsequent process of further adjusting the parameters of the second controller, the risk of the intelligent device (such as a vehicle) losing control can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.

Fig. 1 shows a flowchart of a parameter configuration method provided in 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 the core components of a PID-based controller in an embodiment of the disclosure.

FIG. 4 shows a schematic diagram of the control effect after 3 parameter adjustments by a PID based controller in an embodiment of the disclosure.

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

FIG. 6 illustrates a schematic diagram of the 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.

FIG. 8 illustrates a schematic diagram of objective function optimization of core components in an MPC based controller, in accordance with an embodiment of the present disclosure.

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

Fig. 10 is a schematic diagram illustrating the general architecture of an autopilot system provided by an embodiment of the present disclosure.

FIG. 11 is a schematic diagram illustrating an embodiment of the present disclosure in which a reference controller is utilized to direct a tuned controller to perform parameter configuration.

Fig. 12 is a schematic diagram illustrating a parameter configuration flow of a controller provided in an embodiment of the present disclosure.

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

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

FIG. 15 is a schematic diagram showing lateral trajectory error, heading error, and longitudinal speed error over time under control of an MPC based controller after parameter adjustment to the MPC based controller in an embodiment of the present disclosure.

Fig. 16 shows a block diagram of a parameter configuration apparatus provided in an embodiment of the present disclosure.

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

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

Detailed Description

Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.

Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.

It should be noted that the smart device may further include other controllers besides the first controller and the second controller, and in the embodiment of the present disclosure, the type of the controller included in the smart device, the number of the controllers of each type, and the like are not limited. In the embodiment of the present disclosure, an example is taken in which an intelligent device includes a first controller and a second controller, and a technical solution provided by the embodiment of the present disclosure is explained.

Fig. 1 shows a flowchart of a parameter configuration method provided in an embodiment of the present disclosure. The execution subject of the parameter configuration method may be a parameter configuration apparatus. For example, the parameter configuration method may be performed by a terminal device or a server or other processing device. The terminal device may be a vehicle-mounted device, a User Equipment (UE), a mobile device, a User terminal, a Personal Digital Assistant (PDA), a handheld device, a computing device, or a wearable device. In some possible implementations, the parameter configuration method may be implemented by a processor calling computer readable instructions stored in a memory. The parameter configuration method is applied to intelligent equipment, and the intelligent equipment comprises a controller, wherein the controller comprises a first controller and a second controller. The smart device may include a smart mobile device, such as a vehicle or a mobile robot, etc. The following describes an embodiment of the present disclosure by taking an intelligent device as an example. As shown in fig. 1, the parameter configuration method includes steps S11 to S12.

In step S11, the control data output by the controller 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 for tracking a trajectory in automatic driving, and may also be a controller having other functions, which is not limited in the embodiment of the present disclosure. The control data output by the controllers (first control data output by the first controller and/or second control data output by the second controller) may be used to control the smart device. The first controller and the second controller may 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 may be acquired multiple times. For example, for any one of the plurality of times, the first control data output by the first controller and the second control data output by the second controller may be acquired simultaneously to compare the two. Of course, the first control data and the second control data may be acquired in sequence, or the second control data and the first control data may be acquired in sequence. It should be noted that, in the process of acquiring the control data, the order of the first control data and the second control data is not limited.

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

In an embodiment of the disclosure, the parameter of the second controller may be configured according to a difference between the first control data and the second control data. For example, a parameter of the second controller may be initialized and/or adjusted based on a difference between the first control data and the second control data.

Initializing parameters of the second controller, namely performing initial configuration on the parameters of the second controller; and adjusting the parameters of the second controller means that the parameters of the second controller which completes initialization are adjusted. In a possible implementation manner, the parameter of the second controller that completes initialization may be a default second controller parameter, and specifically may be a parameter of the second controller configured by combining historical experience values, or a factory parameter configured before the second controller is shipped, and the like, which is not limited herein.

In a possible implementation manner, the obtaining the control data output by the controller includes: acquiring first reference track data and first state data of the intelligent equipment in a first mode; generating, by the controller, the control data according to the first reference trajectory data and the first state data.

In this implementation, the first reference trajectory data may represent reference trajectory data acquired in configuring parameters of the second controller according to the first control data and the second control data. The first reference trajectory data may include a reference trajectory and target speeds for a plurality of waypoints on the reference trajectory, and the first reference trajectory data may be output by a trajectory planning module in the autonomous driving system.

In this implementation, the first status data may represent status data of the smart device obtained during configuration of parameters of the second controller according to the first control data and the second control data. Wherein the first status data may be acquired in real time. For example, the first state data may include one or a combination of a plurality of items of position, velocity, acceleration, and the like of the smart device.

In this implementation, the smart device has at least a first mode, which may be, for example, a fully autonomous driving mode or a semi-autonomous driving mode. The smart device may also be provided with a second mode, which may be, for example, a full manual driving mode. The smart device may further have a third mode, for example, the third mode may be a semi-autonomous driving mode if the first mode is a fully-autonomous driving mode, or the third mode may be a fully-autonomous driving mode if the first mode is a semi-autonomous driving mode. Each mode may further include a sub-mode to more finely divide each mode, and the mode type of the smart device and specific parameters of the sub-mode are not limited herein.

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

In one possible implementation, after the obtaining the control data output by the controller, the method further includes: and controlling the intelligent equipment in the first mode to run according to the first control data.

In this implementation, the smart device in the first mode may be controlled to travel according to the first control data by setting a first controller as a current controller of the smart device. For example, the first controller may be set as a current controller of the smart device through a switch (Switcher) of the smart device.

In one possible implementation, after the configuring the parameter of the second controller, the method further includes: acquiring second reference track data and second state data of the intelligent equipment in a first mode; generating, by the second controller, third control data according to the second reference trajectory data and the second state data; and controlling the intelligent equipment in the first mode to run according to the third control data.

In this implementation, 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 a reference trajectory and target speeds for a plurality of waypoints on the reference trajectory, and the second reference trajectory data may be output by a trajectory planning module in the autonomous driving system. The first reference trajectory data may include content in the second reference trajectory data, that is, the second reference trajectory data may be reference number trajectory data of a subsequent section of the first reference trajectory data, for example, the second reference trajectory data is acquired after the intelligent device travels for a period of time according to the first reference trajectory data. Alternatively, in the case where the smart device changes the travel route or the like, the second reference trajectory data may be different from the first reference trajectory data.

In this implementation, the second state data may represent state data of the smart device acquired during the process of controlling the smart device to travel according to the third control data. Wherein the second status data may be acquired in real time. For example, the second state data may include one or a combination of a plurality of items of position, velocity, acceleration, etc. of the smart device.

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

In one possible implementation, after the controlling the smart device in the first mode to travel according to the third control data, the method further includes: acquiring actual track data generated when the intelligent equipment runs in the first mode; and adjusting parameters of the second controller according to the second reference track data and the actual track data.

The actual trajectory data may include, among other things, the actual trajectory and the velocities of the plurality of points on the actual trajectory. Based on the second reference trajectory data and the actual trajectory data, an orientation error, a lateral trajectory error, and a longitudinal velocity error therebetween may be determined, such that parameters of the second controller may be adjusted based on the orientation error, the lateral trajectory error, and the longitudinal velocity error therebetween.

In this implementation, the steering wheel steering data may include one or a combination of more of steering wheel rotation angle, number of rotations, direction of rotation, and the like. The throttle data may include one or a combination of throttle size, throttle speed, etc., where throttle size may be expressed as a percentage of maximum throttle amount, etc. The braking data may include one or a combination of braking force, braking speed, etc., wherein the braking force may be expressed in percentage of the maximum braking force, etc. The indicator light data may include one or a combination of indicator light types, durations, etc. Based on the implementation mode, accurate automatic control can be achieved for the intelligent mobile device.

In one possible implementation manner, the controlling the smart device in the first mode to travel according to the third control data includes: in response to the difference between the first control data and the second control data meeting a preset condition, controlling the intelligent device in a first mode to run according to the third control data; the preset condition includes that a difference value between first target control data in the first control data and second target control data in the second control data belongs to a threshold range, and the types of the first target control data and the second target control data are the same.

In this implementation, the first target control data may include one or more types of data in the first control data, and accordingly, the second target control data may also include one or more types of data in the second control data. The first target control data and the second target control data comprise the same data type. For example, the first target control data includes steering wheel data, throttle data, and brake data in the first control data, and the second target control data includes steering wheel data, throttle data, and brake data in the second control data; for another example, the first target control data includes steering wheel data, throttle data, brake data, and indicator light data in the first control data, and the second target control data includes steering wheel data, throttle data, brake data, and indicator light data in the second control data.

In this implementation manner, the threshold range may be a threshold value based on a parameter, and if a difference between first target control data in the first control data and second target control data in the second control data is less than or equal to the threshold value, a preset condition is satisfied; or, the threshold range may use two thresholds as upper and lower limits, and if a difference between first target control data in the first control data and second target control data in the second control data belongs to the threshold range, the preset condition is satisfied.

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

In this implementation, the smart device in the first mode may be controlled to travel according to the third control data by setting the second controller as a current controller of the smart device. For example, the second controller may be set as the current controller of the smart device by switching a switch.

In this implementation, the type, duration, etc. of the indicator light in the indicator light data may be quantized, so that the difference between the indicator light data in the first control data and the indicator light data in the second control data may be obtained.

In this implementation, if the difference between the first control data and the second control data satisfies the predetermined condition, it may be determined that the parameter of the second controller is more reliable. In other possible implementations, the difference between the first control data and the second control data may be observed manually, and whether the second controller performs the same control action as the first controller may be determined manually, in other words, whether the second controller can drive the vehicle correctly and complete the tracking of the first reference trajectory data as the first controller may be determined manually.

For example, the first controller may be a PI (Proportional, Integral) based controller or a PID (Proportional, Integral, Derivative) based controller, etc., and the second controller may be a MPC (Model Predictive Control) based controller or a L QR (L initial Quadratic Regulator), linear Quadratic Regulator) based controller, etc.

In this implementation, the second controller is based on a vehicle dynamics Model, namely a Dynamic Bicycle Model (Dynamic Bicycle Model). The vehicle dynamics model is capable of dynamically modeling vehicle motion behavior, simplifying the non-linear model into a linear model that is used to predict the vehicle's behavior or travel path over a limited time in the future, with a reasonable set of assumptions (e.g., small angle assumptions, error-based modeling of the model, and more model linearization benefit than modeling based directly on reference angles and reference positions).

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

FIG. 3 shows a schematic diagram of the core components of a PID-based controller in an embodiment of the disclosure. As shown in FIG. 3, a PID-based controller can accomplish the control task by adjusting only 3 parameters, each of which is K_p、K_iAnd K_d. In autonomous driving systems, PI-based controllers often meet the control requirements of the system, i.e., via K_pMake the automatic driving system respond immediately, pass K_iSteady state errors in an autonomous driving system are eliminated.

FIG. 4 shows a schematic diagram of the control effect after 3 parameter adjustments by a PID based controller in an embodiment of the disclosure. In fig. 4, a straight line 41 is a reference trajectory, 42 is an actual trajectory without control, and 43 is an actual trajectory under control of the PID-based controller. Since the PID-based controller only needs to adjust K_pAnd K_iThe two parameters can complete automatic control of the unmanned vehicle, and the debugging is easy, therefore, the embodiment of the disclosure can use the PI-based controller as a reference controller for guiding the second controller containing the vehicle dynamics model to perform parameter configuration. In the embodiment of the present disclosure, a suitable parameter may be selected for the PI-based controller through manual parameter adjustment, or a suitable parameter may be selected for the PI-based controller through an automatic parameter adjustment method in the related art.

FIG. 5 illustrates a schematic diagram of an MPC based controller (MPC controller) in an embodiment of the disclosure. The controller based on the MPC can realize closed-loop control of longitudinal speed and transverse position, thereby realizing automatic driving of the unmanned vehicle. In addition to the feedback control shown in FIG. 5, the MPC based controller also contains a feed forward control. As shown in FIG. 5, the MPC based controller may output second control data based on an error (error) between the first reference trajectory data output by the trajectory planning module and the first state data of the vehicle, or may output third control data based on an error between the second reference trajectory data output by the trajectory planning module and the second state data of the vehicle to control the vehicle.

FIG. 6 illustrates a schematic diagram of the core components of an MPC based controller in an embodiment of the present disclosure. As shown in fig. 6, an MPC-based controller includes two core components, a vehicle dynamics model and an optimizer. The vehicle dynamics model is used for predicting a possible future driving track of the vehicle; the optimizer solves an optimization problem to find the trajectory from the plurality of candidate predicted trajectories that minimizes the objective function J to obtain the second control data or the third control data to ensure that the MPC-based controller is most likely to drive the vehicle close to the first reference trajectory data or the second reference trajectory data.

FIG. 7 shows a schematic diagram of a vehicle dynamics model in an MPC based controller of an embodiment of the present disclosure, in FIG. 7, 71 represents a vehicle, above a vehicle head and below a vehicle tail, an orientation error e1 may be derived from a difference between an orientation of a reference point on a reference track in first or second reference track data and a current orientation of the vehicle head, a lateral track error e2 may be derived from a minimum distance between a current position of the vehicle (e.g., a geometric center point of the vehicle) and a tangent to the reference point, a lateral track error e2 may be equal to a minimum distance between the current position of the vehicle and a tangent to the reference point, a longitudinal Velocity (L on Velocity) error e3. may be derived from a difference between a reference Velocity in the first or second reference track data and an actual Velocity of the vehicle in FIG. 7,

the differential of the e1 is shown,

indicating the second derivative of e1, e2 and e3 are similar. The MPC-based controller in the disclosed embodiment is capable of dynamically modeling vehicle motion behavior based on a vehicle dynamics model, which reduces a non-linear model to a linear model as shown in fig. 7 for predicting the behavior or driving trajectory of the vehicle for a limited time in the future, with a reasonable set of assumptions. In fig. 7, steer represents steering wheel steering data (e.g., steering wheel rotation angle), throttle represents throttle data (e.g., percentage of maximum throttle amount), and brake represents braking data (e.g., percentage of maximum braking effort).

FIG. 8 illustrates a schematic diagram of objective function optimization of core components in an MPC based controller, in accordance with an embodiment of the present disclosure. In fig. 8, Δ steer represents the amount of change in steer (steering wheel steering data) between two adjacent times, Δ throttle represents the amount of change in throttle (throttle data) between two adjacent times, and Δ break represents the amount of change in break (braking data) between two adjacent times. FIG. 8 shows an objective function

The vectorized target function is J-xTQx + Δ u^TR Δ u. Wherein the content of the first and second substances,

u＝[steer throttle/brake]^T，Δu＝[Δsteer Δthrottle/brake]^T，Q＝[q1 q2 q3 q4 q5q6]^T，R＝[r1 r2]^T. From the vectorized objective function, it can be seen that the parameters that need to be configured by the MPC-based controller include 8, where the Q vector contains 6 parameters to be configured, Q1, Q2, Q3, Q4, Q5 and Q6, and the R vector contains 2 parameters to be configured, R1 and R2.The upper left diagram of fig. 8 shows a schematic diagram of the position of the predicted trajectory of the vehicle in the lane, and the upper left diagram of fig. 8 also shows the position of the center line of the lane and the prediction range. 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, throttle data, brake data, and indicator light data). The upper right hand graph of FIG. 8 illustrates lateral trajectory error cte from time t +1 to time t +7_t+1To cte_t+7(i.e., e 2). Accordingly, the orientation error e1 from time t +1 to time t +7 can be expressed as he respectively_t+1To he_t+7The longitudinal speed error e3 from time t +1 to time t +7 can be respectively expressed as ve_t+1To ve_t+7。

FIG. 9 shows a schematic diagram of an optimization process for an MPC based controller in an embodiment of the present disclosure. As shown in fig. 9, the goal of parameter configuration of an MPC-based controller is to minimize J. After Q and R are configured, the MPC-based controller can perform trajectory prediction and evaluation (calculate the objective function J) on the potential control action sequence in the manner shown in fig. 9 through parameter adjustment, so as to obtain an 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 J50, J30 and J10. As shown in fig. 9, when J is 10, the predicted trajectory of the vehicle is closest to the center line of the lane, and therefore, the control operation sequence corresponding to J10 is the optimal control operation sequence.

In general, the number of controlled variables of a controller containing a vehicle dynamics model is greater than the number of controlled variables of a controller not containing a vehicle dynamics model. For example, the PI-based controller has 2 parameters to be adjusted, each being a proportional term (K)_p) And integral term (K)_i). Although the model-free PI controller is simple and easy to debug, the control is not accurate enough, and controllers such as MPC-based controllers and the like including vehicle dynamics models model vehicle dynamics behaviors and can accurately depict the future motion trail of the vehicle. The disclosed embodiment can adopt a controller (second controller) containing a vehicle dynamic model to conduct behavior modeling on the unmanned vehicle, and then obtainMore accurate automatic control effect is obtained. However, controllers containing vehicle dynamics models typically contain a plurality of parameters, for example, MPC-based controllers contain 8 parameters to be adjusted, 6 parameters to be adjusted for adjusting the system state (heading error, lateral trajectory error, longitudinal speed error) and 2 parameters to be adjusted for adjusting the system input (steering angle, throttle amount or brake amount). The debugging difficulty of 8 parameters is often higher than that of 2 parameters, and the actual adjusting process is low in efficiency and high in cost. Meanwhile, the unreliable parameters may bring out the out-of-control of the unmanned vehicle, which leads to the traffic accidents to cause casualties, thereby bringing potential life risks to research and development personnel, field debugging engineers and other field shunting.

Fig. 10 is a schematic diagram illustrating the general architecture of an autopilot system provided by an embodiment of the present disclosure. As shown in fig. 10, the trajectory planning module outputs reference trajectory data (e.g., first reference trajectory data, second reference trajectory data) and reads status data (e.g., first status data, second status data) of the vehicle through the bus (read bus); the controller (the first controller and/or the second controller) outputs control data (the first controller outputs first control data, the second controller outputs second control data and/or third control data) according to the difference between the reference track data and the state data of the vehicle, and writes the control data into the vehicle through a bus (write bus), so that closed-loop control over the vehicle is realized, and the vehicle is driven to run.

FIG. 11 shows a schematic diagram of a reference controller used to direct a regulated controller to perform parameter configuration in the disclosed embodiment, as shown in FIG. 11, the reference controller may be a PID-based controller or the like, the regulated controller may be an L QR-based controller or an MPC-based controller or the like, and the reference controller may direct the regulated controller to configure parameters (e.g., Q vectors, R vectors, etc.). for example, R1 may be adjusted according to a difference between steering wheel steering data in first control data and second control data, R2 may be adjusted according to a difference between throttle data or brake data in the first control data and the second control data, Q1 and Q2 may be adjusted according to an orientation error e1, Q3 and Q4 may be adjusted according to a lateral trajectory error e2, and Q5 and Q6 may be adjusted according to a longitudinal velocity error e 3.

The first controller may be enabled to perform accurate control of the unmanned vehicle in straight and curved roads by adjusting values of the proportional term and the term, i.e., the control data output by the first controller may be enabled to drive the unmanned vehicle to properly track the reference trajectory, once the first controller is successfully adapted to the automatic driving system (i.e., the unmanned vehicle is controlled by the first controller), the control data may be considered to be the control data that is properly output by the first controller, which may be considered to be the control data that is properly output by the first controller).

After the first controller is successfully adapted to the automatic driving system, the first controller can be selected as a current controller of the unmanned vehicle through the selector switch, a comparator (comparator) is turned on, the comparator compares the difference between first control data output by the first controller and second control data output by the second controller, and parameters of the second controller are configured according to the difference between the first control data and the second control data, so that the difference between the second control data output by the second controller after configuration and the first control data output by the first controller is minimum. If the difference between the first control data and the second control data satisfies the predetermined condition, it can be determined that the second controller has obtained more reliable parameters. It was found experimentally that the parameter Q in MPC based controllers was [0.0,0.0,1,0,0,1]^T，R＝[0.4,0.8]^TFirst of controller output based on MPCThe two control data are substantially identical to the first control data output by the PI-based controller, i.e., we can use the MPC-based controller of the parameter to achieve the same trajectory tracking effect as the PI-based controller.

13 a-13 d show schematic diagrams of the control results of an MPC based controller in an embodiment of the disclosure. Wherein figure 13a shows a schematic of a reference trajectory and an actual trajectory of an unmanned vehicle under control of an MPC-based controller. In fig. 13a, a UTM (Universal Transverse Mercator grid system) coordinate system is used. Figure 13b shows a schematic of the reference longitudinal speed versus the actual longitudinal speed of the unmanned vehicle under control of the MPC-based controller versus distance. Fig. 13c shows a schematic diagram of the reference orientation of the vehicle head and the actual orientation of the vehicle head of the unmanned vehicle under control of the MPC-based controller. Fig. 13d shows a schematic of the reference steering wheel steering angle and the actual steering wheel steering angle of the unmanned vehicle under control of the MPC-based controller. Here, the distance in fig. 13b to 13d may represent a distance with respect to a start point of the autonomous driving task. As shown in fig. 13 a-13 d, MPC-based controllers are based on a parameter (Q ═ 0.0,0.0,1,0,0,1]^T，R＝[0.4,0.8]^T) Autonomous driving is completed, which parameter, from the evaluation data, can ensure that the MPC-based controller is safely performing the basic autonomous driving task, but performance is to be further improved.

Fig. 14 shows a graphical representation of the lateral trajectory Error (CTE), heading Error, and longitudinal velocity Error over time under control of an MPC-based controller in an embodiment of the disclosure. In fig. 14, the abscissa is time in units of seconds. As shown in fig. 14, the lateral trajectory error is large, that is, lateral control of the MPC-based controller is not accurate enough, which may cause the unmanned vehicle to deviate from the lane center line for driving. To make the control of the MPC-based controller more accurate, we can make further parameter adjustments to the MPC-based controller. In the disclosed embodiment, after configuring the parameters of the second controller according to the first control data and the second control data, the second controller may be regarded as the current control of the unmanned vehicle through the switchAnd the controller controls the unmanned vehicle through the second controller, and further adjusts parameters of the second controller so that the second controller is more accurately adapted to the automatic driving system. It was found experimentally that the MPC based controller parameters (Q ═ 0.0,0.0,1,0,0, 1) can be tuned to the problem of large lateral trajectory errors]^T，R＝[0.4,0.8]^T) The adjustment was made so that Q is [0.03,0.0,1,0,0,1]^T,R＝[0.4,0.8]^TThe penalty degree of the transverse track error is increased, so that the MPC-based controller can control the unmanned vehicle to run closer to the center line of the lane.

FIG. 15 is a schematic diagram showing lateral trajectory error, heading error, and longitudinal speed error over time under control of an MPC based controller after parameter adjustment to the MPC based controller in an embodiment of the present disclosure. In the example shown in FIG. 15, after parameter adjustments are made to the MPC based controller, the lateral trajectory error drops by about 50% relative to before parameter adjustments. As shown in fig. 13d, the actual steering wheel steering angle output by the MPC-based controller fluctuates more frequently than the reference steering wheel steering angle, and the steering direction is not smooth, which may cause an uncomfortable driving experience. Similarly, we can further adjust the parameters of the MPC-based controller to make the steering angle change of the steering wheel smoother in the control data outputted from the MPC-based controller.

In one example, an automatic transmission car can be used as an experimental platform, the average speed is 20km/h, and the test path is that the car moves straight first, then the car moves straight by turning left, and then the car moves straight by turning right.

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

In the disclosed embodiment, the control data output by the controller of the smart device is obtained, wherein, the controller comprises a first controller and a second controller, the control data output by the first controller comprises first control data, the control data output by the second controller comprises second control data, and configuring parameters of the second controller according to the first control data and the second control data, therefore, the first controller is used as a reference controller, the second controller is used as a regulated controller, reliable parameters are accurately, efficiently and inexpensively found for the second controller, therefore, when the parameters of the second controller are further adjusted subsequently, the risk of the intelligent device (such as a vehicle) being out of control can be reduced, and the safety risk of potential traffic accidents caused by improper parameter configuration of the second controller can be reduced.

It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted.

It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.

In addition, the present disclosure also provides a parameter configuration apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the parameter configuration methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the method section are not repeated.

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

In a possible implementation manner, the first obtaining module 21 is configured to: acquiring first reference track data and first state data of the intelligent equipment in a first mode; generating, by the controller, the control data according to the first reference trajectory data and the first state data.

In one possible implementation, the apparatus further includes: and the first control module is used for controlling the intelligent equipment in the first mode to run according to the first control data.

In one possible implementation, the apparatus further includes: the second acquisition module is used for acquiring second reference track data and second state data of the intelligent equipment in the first mode; a generating module, configured to generate, by the second controller, third control data according to the second reference trajectory data and the second state data; and the second control module is used for controlling the intelligent equipment in the first mode to run according to the third control data.

In one possible implementation, the apparatus further includes: the third acquisition module is used for acquiring actual track data generated when the intelligent equipment runs in the first mode; and the adjusting module is used for adjusting the parameters of the second controller according to the second reference track data and the actual track data.

In one possible implementation, the second control module is configured to: in response to the difference between the first control data and the second control data meeting a preset condition, controlling the intelligent device in a first mode to run according to the third control data; the preset condition includes that a difference value between first target control data in the first control data and second target control data in the second control data belongs to a threshold range, and the types of the first target control data and the second target control data are the same.

In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.

Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-described method. 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, which includes computer readable code, and when the computer readable code runs on a device, a processor in the device executes instructions for implementing the parameter configuration method provided in any of the above embodiments.

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

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

The electronic device may 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 disclosure. For example, the electronic device 800 may be a vehicle-mounted device, a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like terminal.

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

The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate 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 at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

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

The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user, in some embodiments, the screen may include a liquid crystal display (L CD) 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 a user.

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

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 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 assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 may be configured to facilitate wired or wireless communication between the electronic device 800 and other devices, the electronic device 800 may access a wireless network based on a communication standard, such as Wi-Fi, 2G, 3G, 4G/L TE, 5G, or a combination thereof, in one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel, in one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communication.

In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), programmable logic devices (P L D), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.

Fig. 18 shows a block diagram of an electronic device 1900 provided by an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server. Referring to fig. 18, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.

The electronic device 1900 may also include a power 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 a network, and an input/output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as Windows, stored in memory 1932

Mac OS

Or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.

The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing 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 the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or 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, fiber optic 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 a computer-readable storage medium in the respective computing/processing device.

Computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including AN object oriented programming language such as Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations 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 diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.

Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A parameter configuration method, applied to a smart device including a controller including a first controller and a second controller, the method comprising:

2. The method of claim 1, wherein the obtaining control data output by the controller comprises:

3. The method of claim 1 or 2, wherein after said obtaining control data output by said controller, said method further comprises:

4. The method of any of claims 1 to 3, wherein after said configuring the parameters of the second controller, the method further comprises:

5. The method of claim 4, wherein after said controlling said smart device in a first mode to travel according to said third control data, said method further comprises:

6. The method of 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 controller that includes a vehicle dynamics model.

7. The method of any one of claims 1 to 6, wherein the smart device comprises a smart mobile device and the control data comprises at least one of steering wheel steering data, throttle data, brake data, indicator light data.

8. The method of claim 4, wherein controlling the smart device in a first mode to travel according to the third control data comprises:

in response to the difference between the first control data and the second control data meeting a preset condition, controlling the intelligent device in a first mode to run according to the third control data;

the preset condition includes that a difference value between first target control data in the first control data and second target control data in the second control data belongs to a threshold range, and the types of the first target control data and the second target control data are the same.

9. A parameter configuration apparatus, applied to a smart device, the smart device including a controller, the controller including a first controller and a second controller, the apparatus comprising:

10. The apparatus of claim 9, wherein the first obtaining module is configured to:

11. The apparatus of claim 9 or 10, further comprising:

12. The apparatus of claim 11, further comprising:

13. An electronic device, comprising:

one or more processors;

a memory for storing executable instructions;

wherein the one or more processors are configured to invoke the memory-stored executable instructions to perform the method of any one of claims 1 to 8.

14. A computer readable storage medium having computer program instructions stored thereon, which when executed by a processor implement the method of any one of claims 1 to 8.