A method and system for debugging lateral control parameters of intelligent driving

Abstract

The present invention relates to a method and system for debugging lateral control parameters of intelligent driving, and the method includes: collecting data of a target sensor and its benchmarking sensor under multiple lane conditions at the same time, and estimating the relative delay time between the target sensor and its benchmarking sensor based on the data; building a vehicle simulation environment, the vehicle simulation environment includes a driving simulation environment, a vehicle control simulation debugging environment, and a vehicle mechanics simulation environment; inputting the relative delay time between the target sensor and its benchmarking sensor into the vehicle simulation environment, and debugging the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset interval. The present invention inputs the relative delay of the sensor into the simulation environment of the lateral control parameters, and uses the convergence of multiple sets of deviation values ​​and parameters to determine and debug the optimal lateral control parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle assisted driving, and in particular relates to an intelligent driving lateral control parameter debugging method and system. Background Art

[0002] Intelligent driving vehicles assist or even replace drivers to complete a series of driving actions through sensors, controllers and actuators, and ensure safety, stability, comfort and economy during driving. The entire implementation process generally includes several steps: environmental perception, positioning recognition, behavior prediction, decision planning and control execution. Environmental perception is to identify lane lines, traffic signs, vehicles, etc. through various sensors such as cameras and radars carried by the vehicle; positioning recognition is to determine the current position of the vehicle through map modules or visual SLAM; behavior prediction is to estimate the future position and motion state of pedestrians or vehicles based on their position and motion state over a period of time; decision control is to calculate an optimal execution target based on the current driving environment; control execution controls the throttle, brake and steering according to the execution target to complete intelligent driving.

[0003] In conventional lateral control methods for intelligent driving vehicles, it is assumed that the sensor is ideal or the delay time of sensor recognition is within the allowable range. However, due to reasons such as filtering algorithms, the time for sensors such as actual cameras to output lane lines is delayed by 1 to 2 seconds or even more relative to the detection time. As a result, a large amount of testing and analysis is required during the actual vehicle debugging process to match lateral control parameters, resulting in low debugging efficiency, waste of manpower and financial resources, and even substandard debugging results. Summary of the invention

[0004] In order to solve the problem of low efficiency of real-vehicle debugging of lateral control parameters caused by the large delay time of sensor recognition, a first aspect of the present invention provides a method for debugging lateral control parameters of intelligent driving, including: simultaneously collecting data of a target sensor and its benchmark sensor under multiple lane conditions, and estimating the relative delay time between the target sensor and its benchmark sensor based on the data; building a vehicle simulation environment, the vehicle simulation environment including a driving simulation environment, a vehicle control simulation debugging environment and a vehicle mechanics simulation environment; inputting the relative delay time between the target sensor and its benchmark sensor into the vehicle simulation environment, and debugging the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset range.

[0005] In some embodiments of the present invention, the building of the vehicle simulation environment includes: combining the driving simulation environment, the vehicle control simulation debugging environment and the vehicle mechanics simulation environment through interface matching to form a closed-loop feedback system; the lateral control parameters are feedback-adjusted through the vehicle control simulation debugging environment.

[0006] In some embodiments of the present invention, the debugging of the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset interval includes: debugging and updating the expected lateral speed according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges; performing simulation operations under preset multiple sets of deviation value conditions according to the vehicle lateral control parameters until the lateral speed value falls into a preset interval.

[0007] Furthermore, the debugging and updating of the expected lateral speed according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges includes: setting an initial value of the expected lateral speed, substituting it into the simulation environment for calculation, and using the absolute value of the lateral position deviation as a judgment criterion, and collecting data according to a preset sampling period.

[0008] Furthermore, the simulation operation is performed according to the vehicle lateral control parameter under the condition of preset multiple groups of deviation values ​​until the lateral speed value falls into the preset interval, which includes: estimating a lateral control parameter through the dimensions of the input signal and the output signal of the vehicle lateral control parameter; setting multiple groups of increasing or decreasing deviation values ​​based on the control parameter, recording the lateral position and lateral speed after the simulation operation in sequence, and taking one or more control parameters corresponding to the lateral speed value falling into the preset interval as the vehicle lateral control parameter.

[0009] In the above embodiment, it also includes: taking the debugged expected lateral speed and lateral control parameters as basic parameters, performing actual vehicle calibration and parameter adjustment, and selecting the lateral control parameters with the highest matching degree.

[0010] The second aspect of the present invention provides an intelligent driving lateral control parameter debugging system, including: an acquisition module, used to simultaneously collect data of a target sensor and its benchmark sensor under multiple lane conditions, and estimate the relative delay time between the target sensor and its benchmark sensor based on the data; a simulation module, used to build a vehicle simulation environment, the vehicle simulation environment includes a driving simulation environment, a vehicle control simulation debugging environment and a vehicle mechanics simulation environment; a debugging module, used to input the relative delay time between the target sensor and its benchmark sensor into the vehicle simulation environment, and debug the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset range.

[0011] Furthermore, the debugging module includes a first debugging unit and a second debugging unit. The first debugging unit is used to debug and update the expected lateral speed according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges; the second debugging unit is used to perform simulation operations according to the vehicle lateral control parameters under the preset multiple sets of deviation value conditions until the lateral speed value falls into a preset range.

[0012] According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent driving lateral control parameter debugging method provided in the first aspect of the present invention.

[0013] According to a fourth aspect of the present invention, a computer-readable medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, the intelligent driving lateral control parameter debugging method provided in the first aspect of the present invention is implemented.

[0014] The beneficial effects of the present invention are:

[0015] 1. The present invention provides reliable data simulation for simulating the intelligent driving lateral control parameter debugging environment by statistically processing the relative time delay between the target sensor and its reference sensor;

[0016] 2. By adopting a feedback system, a simulation environment for lateral control parameters is simulated; multiple sets of deviation values ​​and the convergence of parameters are used to determine and debug the optimal lateral control parameters; that is, a large amount of testing and analysis for lateral control parameter matching is reduced, thereby improving debugging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A basic flow chart of a method for debugging lateral control parameters of intelligent driving in some embodiments of the present invention;

[0018] Figure 2 A schematic diagram of the framework principle of the intelligent driving lateral control parameter debugging method in some embodiments of the present invention;

[0019] Figure 3 It is a schematic diagram of the effect of the desired lateral speed adjustment in some embodiments of the present invention;

[0020] Figure 4 It is a schematic diagram of the effect of lateral control parameter debugging in some embodiments of the present invention;

[0021] Figure 5 It is a structural schematic diagram of an intelligent driving lateral control parameter debugging system in some embodiments of the present invention;

[0022] Figure 6 It is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION

[0023] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0024] refer to Figure 1 In a first aspect of the present invention, a method for debugging lateral control parameters of intelligent driving is provided, comprising: S100. Simultaneously collecting data of a target sensor and its benchmark sensor under multiple lane conditions, and estimating the relative delay time between the target sensor and its benchmark sensor based on the data; S200. Building a vehicle simulation environment, the vehicle simulation environment includes a driving simulation environment, a vehicle control simulation debugging environment, and a vehicle mechanics simulation environment; S300. Inputting the relative delay time between the target sensor and its benchmark sensor into the vehicle simulation environment, debugging the expected lateral speed and lateral control parameters, until the expected lateral position deviation converges and the lateral speed value falls into a preset range.

[0025] It can be understood that the target sensor and its reference sensor represent visual sensors or optical sensors with different precision or sampling frequencies, which are used to obtain visual information of the vehicle during driving and assist in intelligent driving of the vehicle. For example, the target sensor usually samples a camera with a maximum sampling delay of 300ms, while the reference sensor has a maximum sampling delay of 50ms.

[0026] In step S100 of some embodiments of the present invention, two different sensor devices are mounted on the vehicle, one is a known accurate sensor as a benchmark, and the other is a sensor device to be estimated. A data acquisition device is used to simultaneously record the lane line recognition information of the two sensors and record the corresponding timestamps. Data acquisition is performed for different lane conditions (including but not limited to, straight road cutting into curve, curve cutting into straight road, S curve, etc.).

[0027] Time alignment is performed through timestamps, and the recognition time of the two sensors for the same road information at different times and under different working conditions is analyzed and counted, and the recognition delay time of the sensor to be estimated relative to the benchmark sensor is calculated using statistical methods. For example, the validity of the error is determined based on 3 sigma, and the final relative delay (relative delay time) is determined through the probability density function and expectation of the error.

[0028] refer to Figure 2 In order to more closely simulate real vehicle driving, in step S200 of some embodiments of the present invention, building a vehicle simulation environment includes: combining a driving simulation environment, a vehicle control simulation debugging environment, and a vehicle mechanics simulation environment through interface matching to form a closed-loop feedback system; and the lateral control parameters are feedback-adjusted through the vehicle control simulation debugging environment.

[0029] The vehicle control algorithm simulation environment is built, and the sensor delay simulation algorithm is built using the signal system processing method. According to the relative delay time of the sensor estimated in step S100, the corresponding data value is filled in the vehicle control algorithm simulation environment.

[0030] In step S300 of some embodiments of the present invention, the debugging of the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset interval includes: S301. Debugging and updating the expected lateral speed according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges; S302. The lateral control parameters are simulated under the conditions of preset multiple sets of deviation values ​​until the lateral speed value falls into a preset interval.

[0031] Specifically, a vehicle control algorithm simulation environment is built, and a vehicle lateral control parameter debugging module is built. The input signals of the module include: a) the lateral position deviation value of the vehicle relative to the target trajectory; b) the heading angle deviation value of the vehicle relative to the target trajectory; c) the lateral speed of the vehicle. The output signals of the module include: a) the expected lateral speed of the vehicle; b) the lateral control parameters of the vehicle; and this is used as the debugging environment for debugging the expected lateral speed and lateral control parameters.

[0032] Furthermore, in step S301, the debugging and updating of the expected lateral velocity according to the convergence of the lateral position deviation and its absolute value until the expected lateral velocity converges includes: setting an initial value of the expected lateral velocity, substituting it into the simulation environment for calculation, and using the absolute value of the lateral position deviation as a judgment criterion, and collecting data according to a preset sampling period.

[0033] Specifically, the desired lateral speed is adjusted first. An initial value is given for the desired lateral speed, and simulation calculations are performed. The absolute value of the lateral position deviation is used as the judgment standard, and data is collected according to a given sampling period. An increasing input deviation value is given, and the desired lateral speed is updated, and simulation calculations and data sampling are performed again. The operation is repeated many times. Until the absolute value of the lateral position deviation gradually converges. If the absolute value of the lateral position deviation gradually diverges, a decreasing deviation value is given, and the operation is repeated many times. Until the absolute value of the lateral position deviation gradually converges. Finally, the appropriate desired lateral speed parameters are selected based on the overshoot and convergence time of the lateral position.

[0034] Furthermore, in step S302, the simulation operation is performed according to the vehicle lateral control parameter under the condition of preset multiple groups of deviation values ​​until the lateral speed value falls into the preset interval, including: estimating a lateral control parameter through the dimensions of the input signal and the output signal of the vehicle lateral control parameter; setting multiple groups of increasing or decreasing deviation values ​​based on the control parameter, recording the lateral position and lateral speed after the simulation operation in turn, and taking the one or more control parameters corresponding to the lateral speed value falling into the preset interval as the vehicle lateral control parameter.

[0035] Specifically, the lateral control parameters are adjusted. First, a lateral control parameter is estimated through the dimensions of the input signal and output signal of the control parameter. A set of increasing deviation values ​​and a set of decreasing deviation values ​​are set respectively, with several parameters in each set, and the deviation values ​​are added to the estimated lateral control parameters for simulation calculation, and the simulation calculation results are recorded in sequence. Finally, the optimal lateral control parameter is calculated based on the lateral position and lateral speed.

[0036] In the above embodiment, it also includes: taking the debugged expected lateral speed and lateral control parameters as basic parameters, performing actual vehicle calibration and parameter adjustment, and selecting the lateral control parameters with the highest matching degree.

[0037] Example 2

[0038] refer to Figure 5 According to a second aspect of the present invention, an intelligent driving lateral control parameter debugging system 1 is provided, comprising: an acquisition module 11, for simultaneously acquiring data of a target sensor and its benchmark sensor under multiple lane conditions, and estimating the relative delay time between the target sensor and its benchmark sensor based on the data; a simulation module 12, for building a vehicle simulation environment, wherein the vehicle simulation environment comprises a driving simulation environment, a vehicle control simulation debugging environment, and a vehicle mechanics simulation environment; a debugging module 13, for inputting the relative delay time between the target sensor and its benchmark sensor into the vehicle simulation environment, and debugging the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset interval.

[0039] Furthermore, the debugging module 13 includes a first debugging unit and a second debugging unit. The first debugging unit is used to debug and update the expected lateral speed according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges; the second debugging unit is used to perform simulation operations according to the vehicle lateral control parameters under the preset multiple sets of deviation value conditions until the lateral speed value falls into a preset range.

[0040] Example 3

[0041] refer to Figure 6 According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the first aspect of the present invention.

[0042] The electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0043] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 6 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0044] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above functions defined in the method of the embodiment of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0045] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more computer programs. When the one or more programs are executed by the electronic device, the electronic device:

[0046] Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, Python, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0047] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for debugging lateral control parameters of intelligent driving, characterized in that: include: Simultaneously collecting data of the target sensor and its reference sensor under multiple lane conditions, and estimating a relative delay time between the target sensor and its reference sensor based on the data; Building a vehicle simulation environment, which includes a driving simulation environment, a vehicle control simulation debugging environment, and a vehicle mechanics simulation environment; The relative delay time between the target sensor and its reference sensor is input into the vehicle simulation environment, and the expected lateral speed and lateral control parameters are debugged until the expected lateral position deviation converges and the lateral speed value falls into a preset interval: the expected lateral speed is debugged and updated according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges; and simulation calculations are performed under the preset multiple groups of deviation value conditions according to the vehicle lateral control parameters until the lateral speed value falls into a preset interval.

2. The intelligent driving lateral control parameter debugging method according to claim 1 is characterized in that: The vehicle simulation environment construction includes: The driving simulation environment, the vehicle control simulation debugging environment and the vehicle mechanics simulation environment are combined through interface matching to form a closed-loop feedback system; the lateral control parameters are feedback-adjusted through the vehicle control simulation debugging environment.

3. The intelligent driving lateral control parameter debugging method according to claim 2 is characterized in that: The step of debugging and updating the expected lateral velocity according to the convergence of the lateral position deviation and its absolute value until the expected lateral velocity converges includes: An initial value of the expected lateral velocity is set, substituted into the simulation environment for calculation, and the absolute value of the lateral position deviation is used as a judgment standard, and data is collected according to a preset sampling period.

4. The intelligent driving lateral control parameter debugging method according to claim 2 is characterized in that: The step of performing simulation calculations according to the vehicle lateral control parameters under the preset multiple groups of deviation value conditions until the lateral speed value falls into the preset interval includes: A lateral control parameter is estimated through the dimensions of the input signal and the output signal of the vehicle lateral control parameter; A plurality of increasing or decreasing deviation values ​​are set based on the control parameters, the lateral position and lateral velocity after the simulation operation are recorded in sequence, and one or more control parameters corresponding to the lateral velocity value falling into the preset interval are used as the vehicle lateral control parameters.

5. The intelligent driving lateral control parameter debugging method according to any one of claims 1 to 4, characterized in that: Also includes: The expected lateral speed and lateral control parameters are used as basic parameters for real vehicle calibration and parameter adjustment, and the lateral control parameters with the highest matching degree are selected.

6. An intelligent driving lateral control parameter debugging system, characterized in that: include: A collection module, used for simultaneously collecting data of a target sensor and its reference sensor under multiple lane conditions, and estimating a relative delay time between the target sensor and its reference sensor based on the data; A simulation module is used to build a vehicle simulation environment, which includes a driving simulation environment, a vehicle control simulation debugging environment, and a vehicle mechanics simulation environment; The debugging module is used to input the relative delay time between the target sensor and its benchmark sensor into the vehicle simulation environment, and debug the expected lateral speed and lateral control parameters until the expected lateral position deviation converges and the lateral speed value falls into a preset interval: debug and update the expected lateral speed according to the convergence of the lateral position deviation and its absolute value until the expected lateral speed converges; perform simulation operations under the preset multiple groups of deviation value conditions according to the vehicle lateral control parameters until the lateral speed value falls into the preset interval.

7. An electronic device comprising: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent driving lateral control parameter debugging method as described in any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the intelligent driving lateral control parameter debugging method as described in any one of claims 1 to 5 is implemented.

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