CN109522673B - Test method, device, equipment and storage medium - Google Patents
Test method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a testing method, a testing device, testing equipment and a storage medium. The method comprises the following steps: constructing a whole vehicle model according to attribute parameters of the vehicle; calibrating the whole vehicle model according to the relation between the actual running parameters of the vehicle collected in the actual road running process of the vehicle; and testing by adopting the calibrated whole vehicle model. The technical scheme of the embodiment of the invention can fully consider the output value error of the module to be tested caused by parameters such as dynamics and kinematics of the vehicle in the test process, thereby improving the test accuracy.
Description
Technical Field
The embodiment of the invention relates to an automatic driving technology, in particular to a testing method, a testing device, testing equipment and a storage medium.
Background
In the road test process of the automatic driving vehicle, the scenes of the obstacles and the road are non-quantitative and uncontrollable, so that in order to ensure the running safety of the automatic driving vehicle, the test of each module of the automatic driving vehicle is necessary.
Currently, an autonomous vehicle collects sensing data, vehicle motion state data, and vehicle actual track data during actual road travel. And performing simulation test on each module of the vehicle according to the acquired perception data and the vehicle motion state data to obtain offline track data of the automatic driving vehicle in the simulation test process. And performing regression comparison on the actual track data and the offline track data of the automatic driving vehicle, thereby completing the test of each module of the automatic driving vehicle.
However, in the actual running process of the automatic driving vehicle, due to the influence of parameters such as dynamics and kinematics of the vehicle, errors exist between the control data value and the actual running data value of the automatic driving vehicle, so that the accuracy of testing each model of the automatic driving vehicle is influenced.
Disclosure of Invention
The embodiment of the invention provides a testing method, a testing device, testing equipment and a storage medium, which can fully consider the output value error of a module to be tested caused by vehicle dynamics and kinematic parameters in the testing process, thereby improving the testing accuracy.
In a first aspect, an embodiment of the present invention provides a testing method, including:
constructing a whole vehicle model according to attribute parameters of the vehicle;
Calibrating the whole vehicle model according to the relation between the actual running parameters of the vehicle collected in the actual road running process of the vehicle;
And testing by adopting the calibrated whole vehicle model.
In a second aspect, an embodiment of the present invention further provides a testing apparatus, including:
the model construction module is used for constructing a whole vehicle model according to attribute parameters of the vehicle;
the model calibration module is used for calibrating the whole vehicle model according to the relation between the actual running parameters of the vehicle acquired in the actual road running process of the vehicle;
And the test module is used for testing by adopting the calibrated whole vehicle model.
In a third aspect, an embodiment of the present invention further provides an apparatus, including:
One or more processors;
A storage means for storing one or more programs;
The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the test methods as described in any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a test method according to any of the embodiments of the present invention.
According to the scheme provided by the embodiment of the invention, the whole vehicle model is built, the built whole vehicle model is calibrated according to the relation between the actual operation parameters in the vehicle operation process, and then the calibrated whole vehicle model is adopted for testing, so that the output value error of the module to be tested caused by the dynamics and kinematic parameters of the vehicle can be fully considered in the testing process, and the testing accuracy is further improved.
Drawings
FIG. 1 is a flow chart of a testing method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a testing method according to a second embodiment of the present invention;
FIG. 3 is a flow chart of a test method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a testing device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an apparatus according to a fifth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a testing method according to an embodiment of the present invention, where the embodiment is applicable to a situation where performance tests are performed on each module of an autonomous vehicle, for example, a situation where performance tests are performed on a decision-making planning control module of an autonomous vehicle. The method may be performed by a testing apparatus or device provided by an embodiment of the present invention, where the apparatus may be implemented in hardware and/or software. As shown in fig. 1, the method specifically comprises the following steps:
S101, constructing a whole vehicle model according to attribute parameters of the vehicle.
The attribute parameter of the vehicle may be a fixed parameter of the vehicle itself, which causes an error between the decision-making control value and the actual output value of the vehicle. Static attribute parameters and dynamic attribute parameters may be included. For example, the static attribute parameters may include vehicle empty mass, vehicle track, vehicle wheelbase, and the like. The empty mass of the vehicle may be the mass of the vehicle itself when the vehicle is not carrying any objects; the wheel track of the vehicle can be the distance between the left wheel and the right wheel which are symmetrically arranged on the vehicle; the wheelbase of a vehicle may be the distance between the front and rear wheels on the same side of the vehicle; the dynamic attribute parameters may include: the position of the object in space, velocity, acceleration, etc. The whole vehicle model can be a model which is used for describing the properties of vehicle attribute parameters, running capacity, mechanical properties and the like and is applied to vehicle simulation test. Alternatively, the vehicle model may include, but is not limited to: a whole vehicle dynamics model, a whole vehicle kinematics model, a centroid simplification model and the like. Specifically, the whole vehicle dynamics model can be a model established for researching the relation between the stress condition acted on the vehicle and the motion of the vehicle, and can comprise a 2-degree-of-freedom 1/4 model, a 7-degree-of-freedom 1/2 model, a 15-degree-of-freedom whole vehicle model and the like. The vehicle dynamics model can be a model established in response to the relationship between the vehicle position, speed, acceleration, etc. and time. The centroid simplified model may be a simplified model created in response to the relationship between the centroid of the vehicle and the stress condition.
Optionally, the process of constructing the whole vehicle dynamics model, the whole vehicle kinematics model or the centroid simplification model in the whole vehicle model according to the attribute parameters of the vehicle is similar. The following detailed description will be made by taking the construction of a whole vehicle dynamics model according to attribute parameters of a vehicle as an example.
For example, when the whole vehicle dynamics model is constructed according to the attribute parameters of the vehicle, the method can be used for analyzing the reasons for the errors of the control value and the actual output value of the vehicle, so as to determine which degrees of freedom to start according to the attribute parameters of the vehicle, and constructing the whole vehicle dynamics model which accords with the current automatic driving vehicle; the existing whole vehicle dynamics model (such as a 2-degree-of-freedom 1/4 model, a 7-degree-of-freedom 1/2 model or a 15-degree-of-freedom whole vehicle model) in the existing dynamics theory can be adopted, and the whole vehicle dynamics model of the current automatic driving vehicle can be built by combining the attribute parameters of the current automatic driving vehicle, for example, the obtained attribute parameters of the vehicle can be imported into third-party dynamics software, and the whole vehicle dynamics model of the automatic driving vehicle can be automatically built by the software.
Alternatively, when the whole vehicle model is built according to the attribute parameters of the vehicle, the whole vehicle model can be built for the same model or similar type of automatic driving vehicle.
S102, calibrating the whole vehicle model according to the relation between the actual running parameters of the vehicle collected in the actual road running process.
The actual running parameters of the vehicle may be parameters output by the autopilot vehicle in the actual running process of the road, and may include: acceleration, speed, throttle, brake, steering wheel moment of inertia, torque, friction coefficient, front wheel rotation angle, rear wheel rotation angle, whole vehicle steering error and the like of the automatic driving vehicle.
In order to prevent the condition that parameters of the whole vehicle model are inaccurate due to different measurement errors or running environments of the whole vehicle model constructed in S101, the constructed whole vehicle model needs to be calibrated before performance of the module to be tested in the automatic driving vehicle is tested based on the whole vehicle model, and the condition that the performance test of the module to be tested is inaccurate due to the errors of the whole vehicle model is avoided.
Optionally, there is generally a certain correspondence between the collected actual running parameters of the vehicle, for example, a relationship between acceleration and speed, between throttle, a relationship between rotational inertia of the steering wheel and front wheel rotation angle, rear wheel rotation angle, and so on. In the embodiment of the invention, the constructed whole vehicle model can be calibrated through the relation between the operation parameters output in the actual road running process of the automatic driving vehicle, for example, the model formula parameters related to the acceleration, the speed and the accelerator in the constructed whole vehicle model can be adjusted according to the relation between the acceleration and the speed and the accelerator. The built whole vehicle model can be calibrated through the relation between the operation parameters output in the actual road running process of the automatic driving vehicle and the relation between the operation parameters planned by the whole vehicle model. Specifically, the parameter values in the whole vehicle model can be adjusted according to the relation between the actual operation parameters and the relation between the planned operation parameters, so that the calibration of the constructed whole vehicle model is completed; or constructing a cost function according to the relation between actual operation parameters and the relation between operation parameters obtained by simulation test, and obtaining the formula parameters of the whole vehicle model corresponding to the minimum error, thereby completing the calibration of the constructed whole vehicle model; the method can also be based on a neural network model, the relation between actual operation parameters and the relation between planned operation parameters are input into the neural network model, and the neural network model can analyze and output the optimal parameter value corresponding to the whole vehicle model according to sample data during training and a corresponding algorithm, so that the calibration of the constructed whole vehicle model is completed.
It should be noted that, in the embodiment of the present invention, the whole vehicle model may be calibrated according to the relationship between the actual running parameters of the vehicle collected during the running process of the actual road in other manners, which is not limited to this embodiment.
S103, testing by adopting the calibrated whole vehicle model.
Optionally, after the whole vehicle model of the automatic driving vehicle is calibrated, when the calibrated whole vehicle model is adopted to test each module to be tested in the vehicle, different testing scenes (such as a two-lane crossroad, a three-lane obstacle-vehicle parallel line crossing, a narrow mountain road turning road and the like) can be set, and each module to be tested in the vehicle is tested, so that the accuracy of a testing result is ensured.
Optionally, the process of testing each model to be tested in the vehicle by using the calibrated whole vehicle model is similar. The embodiment of the invention is described in detail by taking the example of testing the decision-making planning control model in the vehicle by adopting the calibrated whole vehicle model.
When the decision-making planning control module in the vehicle is tested, the automatic driving vehicle is controlled to obtain the sensing data and the current running data of the vehicle, and the planning running data of the automatic driving vehicle is output and controlled by the decision-making planning control module based on the calibrated whole vehicle model, so that the automatic driving vehicle runs in the current scene. At this time, the actual running data output by the automatic driving vehicle can be obtained, and the performance of the decision-making planning control module in the vehicle is judged by comparing the error between the planning running data of the automatic driving vehicle controlled by the decision-making planning control module and the actual running data of the vehicle in the current scene. The actual running track of the automatic driving vehicle can be obtained, and the performance of the decision planning control module in the vehicle can be judged by comparing the deviation degree between the ideal track of the automatic driving vehicle and the actual track.
Optionally, because the attribute parameters of the vehicle may be different in different scenes, when the calibrated whole vehicle model is adopted to test each module to be tested in the vehicle, the test of each module to be tested in the vehicle may be performed aiming at the calibrated whole vehicle model corresponding to different test scenes. Thereby ensuring the accuracy of the test result.
The embodiment provides a testing method, by constructing the whole vehicle model, calibrating the constructed whole vehicle model according to the relation between actual operation parameters in the vehicle operation process, and then testing by adopting the calibrated whole vehicle model, the output value error of the test model caused by the dynamics and kinematic parameters of the vehicle can be fully considered in the testing process, and the testing accuracy is further improved.
Example two
Fig. 2 is a flowchart of a testing method provided by the second embodiment of the present invention, where the method is further optimized based on the foregoing embodiment, and a specific description is specifically given of a specific situation of calibrating an overall vehicle model according to a relationship between actual running parameters of a vehicle collected during an actual road running process. As shown in fig. 2, the method includes:
S201, constructing a whole vehicle model according to attribute parameters of the vehicle.
S202, taking operation parameters of at least one dimension of vehicle actual operation parameters collected in the actual road operation process of the vehicle as input parameters, taking operation parameters of other dimensions as output parameters, and fitting an actual relation curve.
Specifically, the actual running parameters acquired by the vehicle in the actual road running process are numerous, at least one dimension of the actual running parameters can be used as an input parameter, the other dimension is used as an output parameter, and an actual relation curve between the actual running parameters of the vehicle is fitted. Alternatively, a parameter in at least one dimension of the speed, the acceleration, and the control command value among the vehicle running parameters may be used as the input parameter; and taking at least one dimension parameter of transverse compensation, steering, longitudinal compensation, error braking, accelerator, torque, friction coefficient, wheel rotation angle, whole vehicle steering and error in the vehicle operation parameters as an output parameter.
Alternatively, the fitted relation curve may be that one input parameter corresponds to one or more output parameters, or that a plurality of input parameters corresponds to one or more output parameters, and the fitted actual relation curve may be one (i.e. an actual relation curve reflects an actual correspondence between a plurality of input and output parameters), or may be a plurality (i.e. an actual correspondence between a plurality of actual relation curves reflects different input and output parameters). For example, the actual acceleration value in a period of time may be used as an output parameter, and the actual accelerator change value in the period of time may be used as an output parameter to fit a curve of the relationship between the actual acceleration value and the accelerator. Or the curve of relation between the actual turning control instruction and the wheel turning angle and the whole steering error can be fitted by taking the turning control instruction in the actual period of time as an input parameter and taking the actual wheel turning angle and the whole steering error in the period of time as an output parameter.
Alternatively, when the input parameters and the output parameters are known and the actual curve is fitted, the input parameters and the output parameters may be input into curve fitting software to obtain a corresponding fitting relation curve, or the input parameters and the output parameters may be brought into a fitting formula to solve specific parameters of the fitting formula, and then the fitting relation curve is drawn.
And S203, performing simulation test on the module to be tested by adopting the operation parameters of at least one dimension and the whole vehicle model.
Optionally, the simulation test may be performed on the module to be tested of the vehicle, where after the operation parameter of at least one dimension is processed by the whole vehicle model, the operation parameter of other dimensions corresponding to the operation parameter of the dimension is determined by the module to be tested according to a related algorithm, for example, if the operation parameter of the acceleration dimension is tested by the whole vehicle model, the operation parameter of the planned accelerator dimension may be output by the decision-making planning control module of the vehicle based on a corresponding decision-making planning control algorithm (such as model predictive control (Model Predictive Control, MPC) algorithm, proportional-Integral-Differential (Proportion-Integral-Differential, PID) algorithm, etc.).
Optionally, a specific process of performing simulation test on the module to be tested by adopting the operation parameter of at least one dimension and the whole vehicle model may be to take the operation parameter of at least one dimension as input of the whole vehicle model, take output of the whole vehicle model as input of the module to be tested, and perform simulation test on the module to be tested to obtain operation parameters of other dimensions of simulation output of the module to be tested. For example, if the decision-making module of the vehicle is tested, the operation parameters of the acceleration dimension may be input into the whole vehicle model, the processed acceleration value output by the whole vehicle model is input into the decision-making control module of the vehicle, and the decision-making control module makes a rule to define the operation parameters of the accelerator dimension for controlling the running of the automatic driving vehicle according to the processed acceleration value output by the vehicle dynamics model and the preset decision-making control rule.
Optionally, the whole vehicle model processes the input operation parameter of at least one dimension, and the operation parameter of at least one dimension may be brought into the whole vehicle model formula, so as to obtain the processed operation parameter value of the dimension. The operating parameter value considers errors caused by vehicle dynamics and kinematic parameters, and the operating parameter value of other dimensionalities planned by the operating parameter decision is more accurate.
S204, taking the operation parameter of at least one dimension as an input parameter, taking a simulation test result as an output parameter, and fitting a simulation relation curve.
Optionally, the manner of fitting the simulation relation curve is similar to the manner of fitting the actual relation curve, except that the corresponding output parameters are different, the test result of the module to be tested in S203 is used as the output parameter when the simulation relation curve is fitted, and the input parameter is an operation parameter of at least one dimension for performing the simulation test on the module to be tested of the vehicle.
It should be noted that, the operation parameters of at least one dimension in S202, S203, and S204 are consistent, because only the simulation relationship curve fitted by the operation parameters of the same dimension is comparable, and the vehicle dynamics model can be calibrated based on the actual relationship curve in S203 and the simulation relationship curve in S204.
S205, comparing the actual relation curve with the simulation relation curve, and calibrating the whole vehicle model according to the comparison result.
Alternatively, the actual relationship curve and the simulation relationship curve may be compared, which may be calculating the overlap ratio of the two curves, or calculating the average error between the two curves, or calculating the overlap ratio and the average error of the two curves simultaneously, so as to determine whether the vehicle dynamics model needs to be calibrated, for example, if the overlap ratio of the two curves is smaller than a preset overlap ratio threshold and/or the average error is smaller than a preset error threshold, the vehicle model is calibrated.
Optionally, when the whole vehicle model is calibrated according to the comparison result, the whole vehicle model may be calibrated based on acquiring the actual running parameters of the vehicle and the planned running parameters of the to-be-tested module for controlling the automatic driving vehicle, for example, if the decision planning module of the vehicle is tested, the actual multi-dimensional running parameters and the planned running parameters with corresponding relations may be acquired, the running parameters of the accelerator dimension in the actual multi-dimensional running parameters are taken as the output of the decision planning control module, the module input parameter value corresponding to the output parameter value is reversely calculated through the decision planning control algorithm, the calculated input parameter value is taken as the output of the whole vehicle model, the running parameters of the accelerator dimension in the corresponding planned multi-dimensional running parameters are taken as the input of the whole vehicle model, and the running parameters are brought into the whole vehicle model formula to adjust the parameter value in the model formula, thereby completing the calibration of the whole vehicle model. The calibration of the whole vehicle model may also be performed by adopting a neural network model, a cost function constructing method or other methods, which is not limited to this embodiment.
S206, testing by adopting the calibrated whole vehicle model.
The embodiment provides a test method, which is used for calibrating a constructed whole vehicle model by constructing the whole vehicle model, fitting a relation curve of actual running parameters of the vehicle and a relation curve of running parameters of a simulation test, and adopting the calibrated whole vehicle model for testing, so that the accuracy of the whole vehicle model calibration can be improved, and the accuracy of the test is further improved.
Example III
Fig. 3 is a flowchart of a testing method provided by the third embodiment of the present invention, where the method is further optimized on the basis of the foregoing embodiment, and specific description of a specific situation of testing a module to be tested in a vehicle by using a calibrated whole vehicle model is given, and specific description of a specific situation of testing a decision-making planning control module of a vehicle by using a calibrated whole vehicle model is given.
The decision-making planning control module is a core module of the automatic driving vehicle and is used for generating a driving strategy according to the actual road condition and the current operation parameters of the vehicle, planning the operation parameters according to the strategy and then controlling the automatic driving vehicle to safely drive on the road according to the planned operation parameters. For example, the situation of an environmental road is obtained through a camera or radar laser equipment, the situation that the front needs to turn is found, the vehicle runs in a current straight line, a decision instruction of turning is generated, then the running parameters of turning (such as the moment of inertia of a steering wheel, the rotation angle of a front wheel, the rotation angle of a rear wheel, the speed of a vehicle and the like) are planned according to the decision instruction of turning, and finally the automatic driving vehicle is controlled to be perfectly executed according to the planned running parameters. However, due to the influence of vehicle dynamics and kinematic parameters, the planned running parameters are perfectly executed, and errors occur in the actual running parameters and the planning of the vehicle, for example, the front wheel turning angle in the running parameters of the perfectly executed planning is 30 degrees, and the output front wheel turning angle is only 25 degrees due to the influence of friction resistance, and at this time, the front wheel turning angle errors occur. Therefore, the running parameters can be planned based on the whole vehicle model, and then the automatic driving vehicle is controlled to run according to the planned running parameters, so that the error between the decision-making control value and the actual output value is reduced. The test of the vehicle decision planning control module in this embodiment is to evaluate and test the consistency of the running parameters planned based on the whole vehicle model decision and the running parameters finally output by the vehicle.
As shown in fig. 3, the method includes:
s301, constructing a whole vehicle model according to attribute parameters of the vehicle.
S302, taking operation parameters of at least one dimension of vehicle actual operation parameters collected in the actual road operation process of the vehicle as input parameters, taking operation parameters of other dimensions as output parameters, and fitting an actual relation curve.
S303, performing simulation test on the module to be tested by adopting the operation parameters of at least one dimension and the whole vehicle model.
S304, taking the operation parameter of at least one dimension as an input parameter, taking a simulation test result as an output parameter, and fitting a simulation relation curve.
S305, comparing the actual relation curve with the simulation relation curve, and calibrating the whole vehicle model according to the comparison result.
S306, taking the test parameters as the input of the calibrated whole vehicle model to obtain the operation parameters in the vehicle test process.
The test data may be an operation parameter of at least one dimension determined based on the current test purpose, for example, to test the performance of the decision-making control module to control the high-speed running of the autonomous vehicle, and the test data may be an operation parameter of a speed dimension and/or an acceleration dimension as the test parameter. The operation parameters in the vehicle test process can be parameter values corresponding to the test parameters processed by the calibrated whole vehicle model. For example, the test parameters can be brought into a calibrated whole vehicle model formula, and the calculation result of the formula is used as the operation parameters in the vehicle test process.
The method includes that a calibrated whole vehicle model is connected to a vehicle simulation tool, namely an output port of the calibrated whole vehicle model is connected to an input port of a decision-making planning control module, test data are input into the calibrated whole vehicle model, and the whole vehicle model processes the test parameters to obtain operation parameters in a vehicle test process.
S307, taking the operation parameters in the vehicle test process as the input of the module to be tested in the vehicle, performing the vehicle simulation test, and obtaining the simulation track data in the vehicle simulation test process.
For example, the operation parameters in the vehicle testing process obtained in S306 are used as input parameters of the decision-making control module to perform the simulation test of the vehicle, and the specific testing process may be: the decision programming control module issues a decision instruction to the automatic driving vehicle according to the input parameters, then programs the running parameters of the vehicle according to the instruction, controls the automatic driving vehicle to execute the running parameter value to run on a road, and simultaneously carries out track simulation according to the planned running parameters to obtain simulation track data.
S308, determining a test result of the module to be tested according to the actual track data and the simulation track data associated with the test parameters.
The actual track related to the test parameters can be track data of the automatic driving vehicle actually running on the road based on the control of the decision-making planning control module. The simulation track data are track data simulated by simulation software according to the running parameters planned by the decision planning control module. The test result of the control module may be a test result for indicating whether the actual running track of the autonomous vehicle and the planned simulated running track controlled by the decision-making control module are consistent.
Specifically, there are many methods for determining the test result of the control module according to the actual track data and the simulated track data, which is not limited in this embodiment. Calculating the coincidence ratio of actual track data and simulated track data, wherein the larger the coincidence ratio is, the better the performance of the decision planning control module is; the average error value of the actual track data and the simulated track data can be calculated, and the smaller the error value is, the better the performance of the decision planning control module is, and the like.
It should be noted that, in the embodiment of the present invention, the decision-making planning control module for testing the automatic driving vehicle is described as an example, but the present invention is not limited thereto, and other modules of the automatic driving vehicle may be tested, and the specific execution process is similar to the execution process of the decision-making planning control module, and will not be described again.
The embodiment provides a testing method, which comprises the steps of constructing a vehicle dynamics model, calibrating the constructed whole vehicle model by fitting a relation curve of actual running parameters of a vehicle and a relation curve of running parameters of a simulation test, connecting the output of the whole vehicle model with the input of a module to be tested, inputting the testing parameters into the whole vehicle model, and carrying out test evaluation on the module to be tested according to simulation track data and actual track data output by a final module to be tested. The accuracy of the test is improved.
Example IV
Fig. 4 is a schematic structural diagram of a test device according to a fourth embodiment of the present invention, where the test device may execute the test method according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 4, the apparatus includes:
The model construction module 401 is configured to construct a whole vehicle model according to attribute parameters of a vehicle;
The model calibration module 402 is configured to calibrate the whole vehicle model according to a relationship between actual running parameters of the vehicle collected during an actual road running process of the vehicle;
and the test module 403 is configured to perform a test by using the calibrated whole vehicle model.
The embodiment provides a testing device, by constructing a whole vehicle model, after the whole vehicle model is calibrated according to the relation between actual operation parameters in the vehicle operation process, the calibrated whole vehicle model is adopted for testing, and the output value error of a module to be tested caused by the dynamics and kinematic parameters of the vehicle can be fully considered in the testing process, so that the testing accuracy is improved.
Further, the model calibration module 402 includes:
the curve fitting unit is used for taking the operation parameter of at least one dimension of the actual operation parameters of the vehicle collected in the actual road operation process as an input parameter, taking the operation parameters of other dimensions as output parameters, and fitting an actual relation curve;
The simulation test unit is used for performing simulation test on the module to be tested by adopting the operation parameters of at least one dimension and the whole vehicle model;
The curve fitting unit is further used for taking the operation parameter of at least one dimension as an input parameter, taking a simulation test result as an output parameter, and fitting a simulation relation curve;
And the model calibration unit is used for comparing the actual relation curve with the simulation relation curve and calibrating the whole vehicle model according to a comparison result.
Further, the simulation test unit is specifically configured to:
And taking the operation parameter of at least one dimension as the input of the whole vehicle model, taking the output of the whole vehicle model as the input of a module to be tested, and performing simulation test on the module to be tested to obtain operation parameters of other dimensions of the simulation output of the module to be tested.
Further, the curve fitting unit is specifically configured to:
taking at least one dimension parameter of the speed, the acceleration and the control command value in the vehicle running parameters as an input parameter; and taking at least one dimension parameter of transverse compensation, steering, longitudinal compensation, error braking, accelerator, torque, friction coefficient, wheel rotation angle, whole vehicle steering and error in the vehicle operation parameters as an output parameter.
Further, the test module 403 is specifically configured to:
taking the test parameters as the input of the calibrated whole vehicle model to obtain the operation parameters in the vehicle test process;
Taking the operation parameters in the vehicle test process as the input of a module to be tested in the vehicle, performing vehicle simulation test, and obtaining simulation track data in the vehicle simulation test process;
And determining a test result of the module to be tested according to the actual track data and the simulation track data associated with the test parameters.
Example five
Fig. 5 is a schematic structural diagram of a device according to a fifth embodiment of the present invention. Fig. 5 shows a block diagram of an exemplary device 50 suitable for use in implementing embodiments of the present invention. The device 50 shown in fig. 5 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. As shown in fig. 5, the device 50 is in the form of a general purpose computing device. The components of the device 50 may include, but are not limited to: one or more processors or processing units 501, a system memory 502, and a bus 503 that connects the various system components (including the system memory 502 and processing units 501).
Bus 503 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Device 50 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 50 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 502 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 504 and/or cache memory 505. The device 50 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 506 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 503 through one or more data medium interfaces. The system memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 508 having a set (at least one) of program modules 507 may be stored in, for example, system memory 502, such program modules 507 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 507 typically perform the functions and/or methods of the described embodiments of the invention.
The device 50 may also communicate with one or more external devices 509 (e.g., keyboard, pointing device, display 510, etc.), one or more devices that enable a user to interact with the device, and/or any device (e.g., network card, modem, etc.) that enables the device 50 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 511. Also, the device 50 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 512. As shown in fig. 5, the network adapter 512 communicates with other modules of the device 50 via the bus 503. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with device 50, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 501 executes various functional applications and data processing by running programs stored in the system memory 502, for example, implementing the test method provided by the embodiment of the present invention.
Example six
The sixth embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the test method described in the above embodiment.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The foregoing embodiment numbers are merely for the purpose of description and do not represent the advantages or disadvantages of the embodiments.
It will be appreciated by those of ordinary skill in the art that the modules or operations of embodiments of the invention described above may be implemented in a general-purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or operations within them may be implemented as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in terms of differences from other embodiments, so that identical or similar parts between the embodiments are mutually referred to.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method of testing, comprising:
constructing a whole vehicle model according to attribute parameters of the vehicle; the whole vehicle model comprises a whole vehicle dynamics model and a whole vehicle kinematics model, and also comprises a centroid simplified model, wherein the centroid simplified model is a simplified model established by reflecting the relation between the centroid of the vehicle and the stress condition;
Calibrating the whole vehicle model according to the relation between the actual running parameters of the vehicle collected in the actual road running process of the vehicle;
testing by adopting the calibrated whole vehicle model;
The method for testing the vehicle model by using the calibrated vehicle model comprises the following steps:
taking the test parameters as the input of the calibrated whole vehicle model to obtain the operation parameters in the vehicle test process; taking the operation parameters in the vehicle test process as the input of a module to be tested in the vehicle, and performing vehicle simulation test;
According to the relation between the actual running parameters of the vehicle collected in the actual road running process, the whole vehicle model is calibrated, and the method comprises the following steps:
Taking the operation parameter of at least one dimension of the vehicle actual operation parameters collected in the actual road operation process of the vehicle as an input parameter, taking the operation parameters of other dimensions as output parameters, and fitting an actual relation curve;
performing simulation test on the module to be tested by adopting the operation parameters of at least one dimension and the whole vehicle model;
Taking the operation parameter of at least one dimension as an input parameter, taking a simulation test result as an output parameter, and fitting a simulation relation curve;
comparing the actual relation curve with the simulation relation curve, and calibrating the whole vehicle model according to a comparison result;
And performing simulation test on the module to be tested by adopting the operation parameters of at least one dimension and the whole vehicle model, wherein the simulation test comprises the following steps:
And taking the operation parameter of at least one dimension as the input of the whole vehicle model, taking the output of the whole vehicle model as the input of a module to be tested, and performing simulation test on the module to be tested to obtain operation parameters of other dimensions of the simulation output of the module to be tested.
2. The method according to claim 1, wherein taking as input parameters operation parameters of at least one dimension of actual operation parameters of the vehicle collected during actual road operation and taking as output parameters operation parameters of other dimensions, comprises:
taking at least one dimension parameter of the speed, the acceleration and the control command value in the vehicle running parameters as an input parameter; and taking at least one dimension parameter of transverse compensation, steering, longitudinal compensation, error braking, accelerator, torque, friction coefficient, wheel rotation angle, whole vehicle steering and error in the vehicle operation parameters as an output parameter.
3. The method of claim 1, wherein testing using the calibrated whole vehicle model comprises:
taking the test parameters as the input of the calibrated whole vehicle model to obtain the operation parameters in the vehicle test process;
Taking the operation parameters in the vehicle test process as the input of a module to be tested in the vehicle, performing vehicle simulation test, and obtaining simulation track data in the vehicle simulation test process;
And determining a test result of the module to be tested according to the actual track data and the simulation track data associated with the test parameters.
4. A test device, comprising:
The model construction module is used for constructing a whole vehicle model according to attribute parameters of the vehicle; the whole vehicle model comprises a whole vehicle dynamics model and a whole vehicle kinematics model, and also comprises a centroid simplified model, wherein the centroid simplified model is a simplified model established by reflecting the relation between the centroid of the vehicle and the stress condition;
the model calibration module is used for calibrating the whole vehicle model according to the relation between the actual running parameters of the vehicle acquired in the actual road running process of the vehicle;
the test module is used for testing by adopting the calibrated whole vehicle model;
the testing module is specifically used for:
taking the test parameters as the input of the calibrated whole vehicle model to obtain the operation parameters in the vehicle test process; taking the operation parameters in the vehicle test process as the input of a module to be tested in the vehicle, and performing vehicle simulation test;
the model calibration module includes:
the curve fitting unit is used for taking the operation parameter of at least one dimension of the actual operation parameters of the vehicle collected in the actual road operation process as an input parameter, taking the operation parameters of other dimensions as output parameters, and fitting an actual relation curve;
The simulation test unit is used for performing simulation test on the module to be tested by adopting the operation parameters of at least one dimension and the whole vehicle model;
The curve fitting unit is further used for taking the operation parameter of at least one dimension as an input parameter, taking a simulation test result as an output parameter, and fitting a simulation relation curve;
The model calibration unit is used for comparing the actual relation curve with the simulation relation curve and calibrating the whole vehicle model according to a comparison result; the simulation test unit is specifically used for:
And taking the operation parameter of at least one dimension as the input of the whole vehicle model, taking the output of the whole vehicle model as the input of a module to be tested, and performing simulation test on the module to be tested to obtain operation parameters of other dimensions of the simulation output of the module to be tested.
5. The apparatus of claim 4, wherein the curve fitting unit has means for:
taking at least one dimension parameter of the speed, the acceleration and the control command value in the vehicle running parameters as an input parameter; and taking at least one dimension parameter of transverse compensation, steering, longitudinal compensation, error braking, accelerator, torque, friction coefficient, wheel rotation angle, whole vehicle steering and error in the vehicle operation parameters as an output parameter.
6. The apparatus of claim 4, wherein the test module is specifically configured to:
taking the test parameters as the input of the calibrated whole vehicle model to obtain the operation parameters in the vehicle test process;
Taking the operation parameters in the vehicle test process as the input of a module to be tested in the vehicle, performing vehicle simulation test, and obtaining simulation track data in the vehicle simulation test process;
And determining a test result of the module to be tested according to the actual track data and the simulation track data associated with the test parameters.
7. A test apparatus, comprising:
One or more processors;
A storage means for storing one or more programs;
When executed by the one or more processors, causes the one or more processors to implement the test method of any of claims 1-3.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the test method according to any of claims 1-3.
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| CN110807238B (en) * | 2019-08-26 | 2022-11-18 | 腾讯科技（深圳）有限公司 | Simulation model calibration method and related equipment |
| CN111897305B (en) * | 2020-06-02 | 2022-08-02 | 浙江吉利汽车研究院有限公司 | Data processing method, device, equipment and medium based on automatic driving |
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