CN115687164A - Test case generalization screening method, equipment and storage medium - Google Patents
Test case generalization screening method, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, and discloses a test case generalization screening method, test case generalization screening equipment and a storage medium. The method comprises the steps of displaying parameters of each scene to be configured through a scene template file corresponding to a target scene, obtaining setting information of a user on the parameters of each scene to be configured, determining at least one parameter sampling value under each parameter of the scene to be configured, further determining a driving track of each scene through each parameter sampling value, obtaining test cases to be screened, realizing generalization of the test cases under the target scene, improving the generation efficiency of the test cases, further determining the target test cases in the test cases to be screened according to the scene parameter values and the scene screening rules in the test cases to be screened, realizing screening of the test cases, obtaining the test cases meeting the test requirements, solving the problem of low test efficiency caused by a large number of test cases not meeting the test requirements in the prior art, and improving the actual test efficiency under the target scene.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a test case generalization screening method, test case generalization screening equipment and a storage medium.
Background
With the development of automatic driving, the test requirement for automatic driving is gradually increased, and the automatic driving function is tested through a simulation test case at present.
In the process of constructing the simulated test case, a scene is usually constructed manually, and parameters in the scene are set to obtain the test case. However, this method generates a large number of test cases that do not meet the test requirements, such as not meeting the scene logic, having low test value, and the like, and further resulting in low test efficiency.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a test case generalization screening method, a device and a storage medium, which realize generalization of test cases in various scenes and generalized screening and solve the problem of low test efficiency caused by test cases which do not meet test requirements in the prior art.
The embodiment of the invention provides a test case generalization screening method, which comprises the following steps:
displaying each scene parameter to be configured based on a scene template file corresponding to a target scene, acquiring setting information of a user for each scene parameter to be configured, and determining at least one parameter sampling value under each scene parameter to be configured according to the setting information;
determining the driving track of each scene according to each parameter sampling value to obtain each test case to be screened under the target scene;
and determining a target test case in each test case to be screened according to the scene parameter value in each test case to be screened and the scene screening rule corresponding to the target scene.
An embodiment of the present invention provides an electronic device, including:
a processor and a memory;
the processor is used for executing the steps of the test case generalization screening method according to any embodiment by calling the program or the instruction stored in the memory.
The embodiment of the invention provides a computer-readable storage medium, which stores a program or an instruction, wherein the program or the instruction enables a computer to execute the steps of the test case generalization screening method according to any embodiment.
The embodiment of the invention has the following technical effects:
the method comprises the steps of displaying parameters of each scene to be configured through a scene template file corresponding to a target scene, obtaining setting information of a user on the parameters of each scene to be configured, determining at least one parameter sampling value under each parameter of the scene to be configured through the setting information, further determining a driving track of each scene through each parameter sampling value, obtaining test cases to be screened under the target scene, realizing generalization of the test cases under the target scene, improving the generation efficiency of the test cases, further determining the target test cases in the test cases to be screened according to the scene parameter values and the scene screening rules in the test cases to be screened, realizing screening of all the generalized test cases, obtaining the test cases meeting the test requirements, solving the problem that the test efficiency is low due to a large number of test cases not meeting the test requirements in the prior art, and improving the actual test efficiency under the target scene.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a test case generalization screening method provided by the embodiment of the present invention;
FIG. 2 is a flowchart of another test case generalization screening method provided in the embodiments of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The test case generalization screening method provided by the embodiment of the invention is mainly suitable for generating each test case corresponding to a scene, and screening a target test case meeting the test requirement from each test case. The test case generalization screening method provided by the embodiment of the invention can be executed by electronic equipment integrated in a vehicle-mounted BSD camera or independent of the camera.
Fig. 1 is a flowchart of a test case generalization screening method provided in an embodiment of the present invention. Referring to fig. 1, the test case generalization screening method specifically includes:
s110, displaying each scene parameter to be configured based on a scene template file corresponding to the target scene, acquiring setting information of a user for each scene parameter to be configured, and determining at least one parameter sampling value under each scene parameter to be configured according to the setting information.
The target scenario may be a scenario for testing a function of the automatic driving system. Illustratively, the target scene may be a cut-in scene, a cut-out scene, an auto park scene, or a cut-in scene, among others. For example, the functions of automatic emergency braking, automatic car following, adaptive cruise, speed limit recognition or lane keeping of the automatic driving system can be tested.
In this embodiment, the scene template file corresponding to the target scene may be a file for generating a scene parameter template of the target scene. For example, the scene template file corresponding to the target scene may include an adsl file, an ini file, a tadsl file, and a picture file corresponding to the target scene. The scene template file corresponding to the target scene may be generated in advance or may be created in real time.
Specifically, the scene parameter template can be read through the scene template file, and then each scene parameter to be configured in the scene parameter template is displayed to the user. The scene parameter to be configured may be a parameter that can be generalized in the target scene, such as an initial speed of the host vehicle, the number of lanes, an initial speed of the target vehicle, or an initial position of the target vehicle.
It should be noted that the target scene may include a plurality of scene parameters, and a part of scene parameters that allow parameter configuration, that is, each to-be-configured scene parameter, may be presented to the user through a scene template file corresponding to the target scene.
Illustratively, taking a cut-in scenario as an example, the cut-in scenario specifically includes: the main vehicle and the target vehicle respectively run in different lanes in the same direction, the target vehicle exceeds the main vehicle from the rear of the main vehicle, and is changed to the lane where the main vehicle is located after the overtaking is finished, and is braked to the target speed after the change of the lane.
In the cut-in scene, each scene parameter to be configured, which is displayed based on the scene template file, may include: the duration of the initial action of the target vehicle (which may be in units of s), the initial position of the target vehicle (which may be in units of m), the lane change direction of the target vehicle (left/right), the initial speed of the target vehicle (which may be in units of km/h), the lane change duration of the target vehicle (which may be in units of s), the initial speed of the host vehicle (which may be in units of km/h), the number of lanes.
Furthermore, after each scene parameter to be configured is displayed, the setting information of the user for each scene parameter to be configured can be acquired. The setting information may include at least one of a parameter type, a parameter setting value, and a sampling number of each scene parameter to be configured. It should be noted that, in consideration of the situation that the user does not set all the scene parameters to be configured, for the unset scene parameters to be configured, the preset default information of the scene parameters to be configured in the scene parameter template may also be used as the setting information of the scene parameters to be configured.
After the setting information of the user for each scene parameter to be configured is obtained, each parameter sampling value under each scene parameter to be configured can be determined according to the setting information. The parameter sampling value may be a specific value of a scene parameter to be configured.
For example, for each scene parameter to be configured, uniform sampling may be performed in a parameter setting value (parameter setting range) according to the sampling number, so as to obtain each parameter sampling value under the scene parameter to be configured.
And S120, determining the driving track of each scene according to the sampling value of each parameter to obtain each test case to be screened in the target scene.
The scene driving track can be a simulated driving track of each vehicle in the target scene. The scene travel trajectory may include various locations where various vehicles travel over time. A scene driving track can represent a test case to be screened.
Specifically, after the sampling values of each parameter are obtained, the sampling values of each parameter under different scene parameters to be configured can be selected to be combined, so as to obtain the driving track of each scene.
Illustratively, each scene parameter to be configured includes a parameter a, a parameter B, and a parameter C, the parameter sampling value under the parameter a includes A1 and A2, the parameter sampling value under the parameter B includes B1, and the parameter sampling value under the parameter C includes C1 and C2, so that 4 combinations can be obtained by combining the parameter sampling values, (A1, B1, C1), (A1, B1, C2), (A2, B1, C1), and (A2, B1, C2), and further 4 scene driving trajectories are obtained. It should be noted that the scene driving trajectories obtained from the parameter sampling values are not identical to each other.
Moreover, the scene parameters to be configured may be part of the scene parameters in the target scene, so that after the parameter sampling values under different scene parameters to be configured are combined, the preset value corresponding to the scene parameter may be obtained for the non-configured scene parameter, and then the scene driving track is generated according to the preset value and the combined parameter sampling values.
For example, in a cut-in scenario, without configuration, directly obtaining a corresponding value of a scenario parameter may include: the lane change speed of the target vehicle is determined according to the lane change speed of the target vehicle, the lane length of the target vehicle, the lane where the initial state of the host vehicle is located, the initial state distance between the host vehicle and the target vehicle, the initial state lane relationship between the target vehicle and the host vehicle, the lane change time of the target vehicle, the distance between the lane change time point of the target vehicle and the host vehicle, the lane change speed of the lane change time point of the target vehicle and the like.
By obtaining each parameter sampling value under each scene parameter to be configured and determining each scene driving track according to each parameter sampling value, generalization of the test case under the target scene is realized. By the method, batch test cases can be generated in a target scene, a user does not need to manually configure each test case, and the generation efficiency of the test cases is improved.
Specifically, the user may save the setting information of each scene parameter to be configured as an ini file corresponding to the target scene, for example, an ini file in the updated scene template file. Further, generalization is carried out based on the ini file, a corresponding ini file required by the generation of the xosc file is generalized, and the ini file, the xosc file and the esi file of each test case to be screened are further obtained.
S130, determining a target test case in each test case to be screened according to the scene parameter value in each test case to be screened and the scene screening rule corresponding to the target scene.
The scene parameter value may be a value of each scene parameter in a scene driving track of the test case to be screened. The scene screening rule corresponding to the target scene may be a rule for removing test cases that do not meet actual test requirements. The scene filtering rule can be set by a user or can be pre-entered into software.
In this embodiment, the scene screening rule may be determined according to the implementation logic of the target scene, and is used to screen out the target test case that meets the implementation logic of the target scene from the test cases to be screened.
For example, in the cut-in scenario or the passing scenario, the target vehicle needs to exceed the host vehicle, and if the vehicle speed of the target vehicle is less than the vehicle speed of the host vehicle, the target vehicle cannot complete the passing action, so the implementation logic of the target scenario is that the initial speed of the target vehicle needs to be greater than the initial speed of the host vehicle, and further, the scenario filtering rule may be that the initial speed of the target vehicle is greater than the initial speed of the host vehicle.
Alternatively, in order to avoid the speed difference between the target vehicle and the host vehicle being too small, which may result in the target vehicle and the host vehicle not completing the overtaking action when the target vehicle and the host vehicle travel to the end of the lane, the scene screening rule may further define a speed difference between the initial vehicle speed of the target vehicle and the initial vehicle speed of the host vehicle, e.g., the initial vehicle speed of the target vehicle and the initial vehicle speed of the host vehicle is greater than the initial vehicle speed of the host vehicle, and the speed difference between the initial vehicle speed of the target vehicle and the initial vehicle speed of the host vehicle is not less than the set value.
In addition to the above-mentioned determination of the scene screening rule according to the implementation logic of the target scene to screen out the target test cases which meet the logic, the scene screening rule may also be determined according to the test value of each test case to be screened to screen out the target test case with higher test value among the test cases to be screened.
Specifically, when actually testing the functions in the automatic driving control system, the functions in the automatic driving control system usually need to be triggered through each test case to detect information such as whether the functions are successfully triggered or not, and whether the functions are triggered in time or not. Therefore, if in one test case, the function in the automatic driving control system needs to be triggered successfully and needs to be triggered timely, the test case is high in test value.
For example, the higher the risk of collision between the host vehicle and the target vehicle in the test case to be screened is, the more successfully the automatic emergency braking function of the automatic driving system needs to be triggered, and the more timely the automatic emergency braking function needs to be triggered to avoid collision, the higher the test value of the test case to be screened is.
Therefore, in the embodiment, the collision risk can be described by the collision time of the test case to be tested, the smaller the collision time is, the higher the collision risk is, and then the target test case is screened out by the collision time.
Of course, the target test case can be screened out by combining the implementation logic and the test value of the target scene.
In a specific implementation manner, determining a target test case in each test case to be screened according to a scene parameter value in each test case to be screened and a scene screening rule corresponding to a target scene may be:
determining collision time under at least two time points in a scene driving track of the test case to be screened according to scene parameter values in the test case to be screened aiming at each test case to be screened, and determining a screening reference value corresponding to the test case to be screened according to each collision time; and sequencing the screening reference values of the test cases to be screened, and determining a target test case according to a sequencing result.
The Time to collision may be TTC (Time to collision). Specifically, for each time point, the collision time between the host vehicle and the target vehicle at the time point may be determined according to the distance between the host vehicle and the target vehicle at the time point in the scene driving trajectory and the speed difference between the host vehicle and the target vehicle at the time point.
The smaller the collision time is, the higher the collision risk of the test case to be screened at the time point is; and, the smaller the collision time, the more likely it is that the target vehicle is more likely to pass the host vehicle, the more in line with the scene logic of the target scene.
In this embodiment, under each test case to be screened, the collision time at two time points can be randomly calculated, so as to avoid calculating all the time points and reduce the calculation amount; of course, the collision time at all time points can also be calculated to improve the accuracy of the screened target test case. For example, taking a cut-in scene as an example, considering that the collision risk existing at the initial time and the lane change time is high, the collision time at the initial time and the collision time in the lane change process can be calculated.
Optionally, determining collision times of at least two time points in a scene driving track of the test case to be screened includes: if the target scene is a cut-in scene, determining an initial distance, a main vehicle initial speed, a target vehicle initial speed, an initial state duration and a target vehicle lane changing speed in a scene driving track of the test case to be screened; determining a collision time at an initial state time point based on the initial distance, the initial velocity of the host vehicle and the initial velocity of the target vehicle; and determining the collision time at the lane change state time point based on the initial distance, the initial speed of the host vehicle, the initial speed of the target vehicle and the lane change speed of the target vehicle.
The initial distance may be a distance between the host vehicle and the target vehicle at an initial time (i.e., an initial time point). The host vehicle initial speed may be a vehicle speed of the host vehicle at an initial time, the target vehicle initial speed may be a vehicle speed of the target vehicle at the initial time, the initial state duration may be a duration of an initial state cut into the scene, and the target vehicle lane change speed may be a lane change vehicle speed at a time point of a lane change of the target vehicle cut into the scene.
The collision time at the initial state time point can reflect the degree of the test case to be screened according with the implementation logic of the target scene. Specifically, since the initial velocity of the target vehicle at the initial state time point should be greater than the initial velocity of the host vehicle in order to complete the overtaking action, the greater the velocity difference between the initial velocity of the target vehicle and the initial velocity of the host vehicle, the more successful the overtaking action can be completed. Therefore, the smaller the collision time at the initial state time point is, the higher the degree of the initial speed of the target vehicle exceeding the initial speed of the host vehicle is, and the higher the realizability of the overtaking action of the target vehicle is, the more the test case to be screened conforms to the realization logic of the target scene.
Illustratively, the time of collision at the initial-state time point = initial distance/(host initial velocity — target vehicle initial velocity).
The collision time under the lane changing state time point can reflect the collision risk of the test case to be screened, namely the test value. Specifically, in the process of changing to the lane where the main vehicle is located after the target vehicle finishes overtaking work, the main vehicle and the target vehicle have collision risks; the smaller the collision time is, the higher the collision risk is, the more the functions in the automatic driving control system need to be triggered to execute corresponding decisions, so that the occurrence of collision is avoided, and the higher the test value of the test case to be screened is indicated.
The collision time at the lane change state time point may be the collision time at any time point in the lane change process, or the collision time at all time points in the lane change process. Exemplary, the collision time at the lane change state time point = (initial distance + (primary vehicle initial speed-target vehicle initial speed) × (initial state duration)/(primary vehicle initial speed-target vehicle lane change speed).
By the method, the determination of the collision time of each test case to be screened under the initial state time point and the determination of the collision time of each test case to be screened under the lane change state time point is realized, the determination of the degree of the test cases to be screened conforming to the implementation logic of the target scene is further realized, the determination of the test value of each test case to be screened is also realized, the target test cases which simultaneously satisfy the scene implementation logic and have high test value are conveniently screened, and the test efficiency of the scene is further improved.
Further, for each test case to be screened, after determining collision times at least two time points in the test case to be screened, a minimum value of the collision times at all the time points may be used as a screening reference value, or an average value of the collision times at all the time points may be used as the screening reference value.
For example, for the cut-in scenario, after determining the collision time at the initial state time point and the collision time at the lane change state time point of each test case to be screened, the minimum collision time in the test cases to be screened may be used as the screening reference value of the test case to be screened.
Optionally, determining a screening reference value corresponding to the test case to be screened according to each collision time includes: taking the minimum value in the collision time as a screening reference value corresponding to the test case to be screened; or determining the time headway of at least two time points in the scene driving track of the test case to be screened, and determining the screening reference value corresponding to the test case to be screened according to the collision time and the time headway of each time point.
The Headway may be THW (Time Headway). Specifically, for each time point, the headway between the host vehicle and the target vehicle at the time point may be determined according to the distance between the host vehicle and the target vehicle at the time point in the scene driving track and the speed of the following vehicle at the time point.
Taking the cut-in scene as an example, the headway time at the initial state time point and the collision time at the lane change state time point can be determined according to the initial distance of the test case to be screened, the initial speed of the main vehicle, the initial speed of the target vehicle and the lane change speed of the target vehicle.
Specifically, for each test case to be screened, the minimum value can be selected from all collision times and all headway time distances in the test case to be screened as the screening reference value corresponding to the test case to be screened.
By determining the screening reference value according to the collision time and the headway at each time point, the logic conformity degree of the scene realization of each test case to be screened is further improved, and the judgment accuracy of the test value is further improved.
After the screening reference values of all the test cases to be screened are determined, further, sorting is performed according to the screening reference values of all the test cases to be screened. Specifically, if the test cases are sorted from large to small, the test cases to be screened corresponding to the screening reference values with the preset number can be selected as the target test cases; if the test cases are sorted from small to large, the test cases to be screened corresponding to the screening reference values with the preset number can be selected as target test cases. The preset number may be the number of the preset selected target test cases.
By the aid of the method, the screening of the test cases based on the scene realization logic conformity degree and the test value of each test case to be screened is realized, the screened test cases can be ensured to realize the scene, and whether the function of the automatic driving control system is successfully triggered or not is effectively tested, so that the screened test cases are more in line with actual test requirements, and the test efficiency of the automatic driving control system in each scene is improved.
Specifically, after each target test case is obtained, rendering simulation can be performed on each target test case to display each target test case in real time, so that a user can conveniently remove the target test cases which do not meet the test requirements.
Furthermore, each target test case can also be accessed to the simulation software, so that each test case can be tested through the simulation software. For example, each target Test case to be tested may be selected in the Simulation software, and then connected to the Simulation environment, where the target Test case includes a configuration of a remote IP (Internet Protocol) address, an SCP (Simulation Control Protocol) port, an RDB (Runtime Data Bus) port, a user name and a password, and a selection of a directory address of a remote VTD (Virtual Test Drive) project script file, and further, each target Test case is sequentially simulated.
The test case generalization screening method provided by this embodiment may be used to perform test case generalization and test case screening in each target scene in sequence according to the task execution sequence of each target scene, so as to obtain the target test case in each target scene.
The embodiment has the following technical effects: the method comprises the steps of displaying parameters of each scene to be configured through a scene template file corresponding to a target scene, obtaining setting information of each scene parameter to be configured of a user, determining at least one parameter sampling value under each scene parameter to be configured through the setting information, further determining a driving track of each scene through each parameter sampling value, obtaining test cases to be screened under the target scene, realizing generalization of the test cases under the target scene, improving the generation efficiency of the test cases, further determining the target test cases in the test cases to be screened according to scene parameter values and scene screening rules in the test cases to be screened, realizing screening of all the generalized test cases, obtaining the test cases meeting testing requirements, solving the problem of low testing efficiency caused by a large number of test cases not meeting the testing requirements in the prior art, and improving the actual testing efficiency under the target scene.
Fig. 2 is a flowchart of another test case generalization screening method provided in the embodiment of the present invention. On the basis of the foregoing embodiments, an exemplary description is given of a process of determining at least one parameter value in each scene to be configured according to the setting information. Referring to fig. 2, the test case generalization screening method specifically includes:
s210, displaying each scene parameter to be configured based on the scene template file corresponding to the target scene, and acquiring the setting information of the user aiming at each scene parameter to be configured.
And S220, determining the parameter type, the parameter set value and the sampling number of each scene parameter to be configured based on the setting information.
The parameter type may be a value type of a scene parameter to be configured, such as a fixed type, a discrete type, an arithmetic type, or a distributed type. The parameter setting value may include a value range or each fixed value of the scene parameter to be configured. The number of samples may be the number of parameter sample values sampled in the parameter set point.
Illustratively, referring to table 1, table 1 shows various parameter types, and describes meanings of the various parameter types and filling rules of parameter setting values under the various parameter types.
TABLE 1 various parameter types
| Type of parameter | Means of | Rules for filling parameter settings |
| Fixed type | The fixed value is a determined value and is a non-random variable, and the fixed value does not change along with time in the simulation process. | Fill in individual values, such as: v =60 |
| Discrete type | A discrete value is a value having a finite or infinite number of values, which may or may not be represented by an integer. | Filling in multiple values with commas in English spaced as V = [60,70,80 =] |
| Equal difference type | And the equal difference value is an equal difference number sequence, an upper limit and a lower limit are given, and equal difference calculation is carried out on a given interval according to the sampling number. | Fill interval values, upper and lower limits, mid-way "." signs are spaced as: v = [60..80] |
| Distribution pattern | The probability distribution of the scene parameters is described as a normal distribution. | The data is displayed in a parameter list (mean, variance) after being configured in a distribution setting dialog box |
And S230, determining at least one parameter sampling value under each scene parameter to be configured based on the parameter type, the parameter set value and the sampling number of the scene parameter to be configured.
Specifically, after the parameter type, the parameter setting value, and the sampling number of each scene parameter to be configured are obtained, at least one parameter sampling value may be determined in the parameter setting value according to the parameter type and the sampling number for each scene parameter to be configured.
In a specific embodiment, determining at least one parameter sampling value under a scene parameter to be configured based on a parameter type, a parameter setting value and a sampling number of the scene parameter to be configured includes:
if the parameter type of the scene parameter to be configured is fixed, taking the parameter set value as a parameter sampling value;
if the parameter type of the scene parameter to be configured is discrete, selecting at least one parameter sampling value from the parameter set value according to the sampling number;
if the parameter type of the scene parameter to be configured is an equal difference type, determining a sampling step length according to an upper limit and a lower limit in a parameter set value and the sampling quantity, and determining at least one parameter sampling value in the parameter set value based on the sampling step length;
and if the parameter type of the scene parameter to be configured is a distribution type, determining at least one parameter sampling value according to the sampling number, the distribution mean value in the parameter setting value and the distribution variance in the parameter setting value.
Specifically, for a fixed scene parameter to be configured, a parameter set value can be directly used as a parameter sampling value; for discrete scene parameters to be configured, sequentially selecting sampling number values from parameter set values as parameter sampling values according to the sampling number; for the equal difference type scene parameters to be configured, the difference between the upper limit and the lower limit in the parameter set value can be calculated, the sampling step length is determined according to the ratio of the difference to the sampling quantity, and then the sampling parameter values are sequentially determined according to the sampling step length and the upper limit; and determining normal distribution of the scene parameters to be configured according to the distribution mean and the distribution variance in the parameter set values aiming at the distributed scene parameters to be configured, and further determining sampling values of all the parameters in the normal distribution according to the sampling number.
Through the method, the determination of the sampling values of the parameters under the fixed, discrete, equal difference and distributed scene parameters to be configured is realized, so that various generalization modes of the scene parameters to be configured are supported, and the scene generalization requirements are met.
S240, determining the driving track of each scene according to the sampling value of each parameter to obtain each test case to be screened in the target scene.
In a specific embodiment, determining the driving trajectory of each scene according to the parameter sampling values may be: acquiring generalization configuration information of a user for a target scene, and determining a generalization type and a generalization number based on the generalization configuration information; and determining the driving track of each scene according to the generalization type, the generalization number and the sampling value of each parameter.
Specifically, the generalized configuration interface can be displayed according to a scene template file corresponding to the target scene, and the generalized configuration information set in the user and the generalized configuration interface can be acquired. The generalization configuration information may include a generalization type and a generalization number. The generalization type is traversal or array, and the generalization number is the number of generalized test cases to be screened.
Specifically, if the generalization type of the target scene is traversal, all parameter sampling values under all scene parameters to be configured can be combined to obtain a generalized number of scene driving tracks; if the generalization type of the target scene is an array, the parameter sampling values under all scene parameters to be configured can be combined without being put back, so that a generalization number of scene driving tracks are obtained.
Optionally, determining the driving track of each scene according to the generalization type, the generalization number and the sampling value of each parameter, including: if the generalization type is traversal, traversing and combining the parameter sampling values under the scene parameters to be configured based on the generalization number to obtain parameter combinations, and generating a scene driving track corresponding to each parameter combination; and if the generalization type is an array, carrying out array combination processing on each parameter sampling value under each scene parameter to be configured based on the generalization number to obtain each parameter value combination, and generating a scene driving track corresponding to each parameter value combination.
The traversing combination processing can be replaced combination processing, and all permutation and combination modes under each parameter sampling value under each scene parameter to be configured are obtained. The array combination processing may be combination processing without putting back, that is, a partial combination mode under each parameter sampling value under each scene parameter to be configured is obtained, and specifically, the parameter sampling values at the same position in each scene parameter to be configured may be taken as a combination.
Illustratively, the parameter sampling values of the scene parameter a to be configured include 30 and 50, and the parameter sampling values of the scene parameter B to be configured include 20 and 30; if the generalization type is traversal, four parameter value combinations of (30, 20), (30, 30), (50, 20), and (50, 30) can be obtained without considering the number of generalization, and if the generalization type is array, two parameter value combinations of (30, 20), (50, 30) can be obtained without considering the number of generalization.
Of course, in consideration of the generalization number, if the generalization number is less than the number of traversal combinations or exceeds the number of array combinations, the parameter value combinations of the generalization number may be directly selected, and if the generalization number is greater than or equal to the number of traversal combinations or exceeds the number of array combinations, the parameter value combinations of the traversal combinations or the number of array combinations may be directly obtained.
Further, after each parameter value combination is obtained, the generalized solver can calculate each parameter value combination to obtain the value of each scene parameter under each parameter value combination, and generate the scene driving track.
By the method, test case generalization under traversal and group generalization types is realized, so that multiple generalization modes are supported, and generalization requirements of different scenes are met. Moreover, a generalization quantity of test cases to be screened, which are in accordance with the user setting, can be obtained, and the generalization requirement of the user on the scene is further met.
And S250, determining the target test case in the test cases to be screened according to the scene parameter values in the test cases to be screened and the scene screening rule corresponding to the target scene.
The embodiment has the following technical effects: after setting information of a user for each scene parameter to be configured is obtained, the parameter type, the parameter setting value and the sampling number of each scene parameter to be configured are determined according to the setting information, and then for each scene parameter to be configured, each parameter sampling value under the scene parameter to be configured is determined according to the parameter type, the parameter setting value and the sampling number, so that generalization of test cases is realized according to the information such as the type and the number set by the user, the information such as the parameter type, the parameter setting value and the sampling number set by the user is supported, the generalization requirements of the user on different scenes are met, moreover, each sampling value under each scene parameter to be configured does not need to be set manually, the required number of test cases to be screened does not need to be selected manually, and the scene generalization efficiency is further improved.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 3, the electronic device 300 includes one or more processors 301 and memory 302.
The processor 301 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 300 to perform desired functions.
In one example, the electronic device 300 may further include: an input device 303 and an output device 304, which are interconnected by a bus system and/or other form of connection mechanism (not shown). The input means 303 may comprise, for example, a keyboard, a mouse, etc. The output device 304 can output various information to the outside, including warning prompt information, braking force, etc. The output devices 304 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 300 relevant to the present invention are shown in fig. 3, omitting components such as buses, input/output interfaces, and the like. In addition, electronic device 300 may include any other suitable components depending on the particular application.
In addition to the above methods and apparatus, embodiments of the present invention may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps of the test case generalization screening method provided by any of the embodiments of the present invention.
The computer program product may write program code for carrying out operations for embodiments of the present invention in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, an embodiment of the present invention may also be a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the processor is caused to execute the steps of the test case generalization screening method provided by any embodiment of the present invention.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but is not limited to, an electronic, 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 readable storage medium include: an electrical connection having one or more wires, a portable 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present application. As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not to be taken in a singular sense, but rather are intended to include a plural sense unless the context clearly dictates otherwise. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, or apparatus that comprises the element.
It is further noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the invention. Unless expressly stated or limited otherwise, the terms "mounted," "connected," "coupled," and the like are to be construed broadly and encompass, for example, both fixed and removable coupling or integral coupling; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the technical solutions of the embodiments of the present invention.
Claims (10)
1. A test case generalization screening method is characterized by comprising the following steps:
displaying each scene parameter to be configured based on a scene template file corresponding to a target scene, acquiring setting information of a user for each scene parameter to be configured, and determining at least one parameter sampling value under each scene parameter to be configured according to the setting information;
determining the driving track of each scene according to each parameter sampling value to obtain each test case to be screened under the target scene;
and determining a target test case in each test case to be screened according to the scene parameter value in each test case to be screened and the scene screening rule corresponding to the target scene.
2. The method according to claim 1, wherein the determining at least one parameter sample value under each of the scene parameters to be configured according to the setting information includes:
determining the parameter type, the parameter set value and the sampling number of each scene parameter to be configured based on the setting information;
and for each scene parameter to be configured, determining at least one parameter sampling value under the scene parameter to be configured based on the parameter type, the parameter set value and the sampling number of the scene parameter to be configured.
3. The method according to claim 2, wherein the determining at least one parameter sampling value under the scene parameter to be configured based on the parameter type, the parameter setting value and the sampling number of the scene parameter to be configured comprises:
if the parameter type of the scene parameter to be configured is fixed, taking the parameter set value as a parameter sampling value;
if the parameter type of the scene parameter to be configured is a discrete type, selecting at least one parameter sampling value from the parameter set value according to the sampling number;
if the parameter type of the scene parameter to be configured is an equal difference type, determining a sampling step length according to an upper limit and a lower limit in the parameter set value and the sampling quantity, and determining at least one parameter sampling value in the parameter set value based on the sampling step length;
and if the parameter type of the scene parameter to be configured is a distribution type, determining at least one parameter sampling value according to the sampling number, the distribution mean value in the parameter setting value and the distribution variance in the parameter setting value.
4. The method of claim 1, wherein determining each scene driving trajectory from each of the parameter sample values comprises:
acquiring generalization configuration information of a user aiming at the target scene, and determining a generalization type and a generalization number based on the generalization configuration information;
and determining the driving track of each scene according to the generalization type, the generalization number and each parameter sampling value.
5. The method of claim 4, wherein determining each scene driving trajectory from the generalization type, the generalization number, and each of the parameter sample values comprises:
if the generalization type is traversal, traversing and combining the parameter sampling values under the scene parameters to be configured based on the generalization number to obtain parameter value combinations, and generating a scene driving track corresponding to each parameter value combination;
and if the generalization type is an array, performing array combination processing on each parameter sampling value under each scene parameter to be configured based on the generalization number to obtain each parameter value combination, and generating a scene driving track corresponding to each parameter value combination.
6. The method according to claim 1, wherein the determining a target test case in each test case to be screened according to a scene parameter value in each test case to be screened and a scene screening rule corresponding to the target scene comprises:
for each test case to be screened, determining collision time under at least two time points in a scene driving track of the test case to be screened according to scene parameter values in the test case to be screened, and determining a screening reference value corresponding to the test case to be screened according to each collision time;
and sequencing the screening reference values of the test cases to be screened, and determining a target test case according to a sequencing result.
7. The method according to claim 6, wherein the determining, according to each collision time, a screening reference value corresponding to the test case to be screened includes:
taking the minimum value in the collision time as a screening reference value corresponding to the test case to be screened; alternatively, the first and second electrodes may be,
and determining the time headway of at least two time points in the scene driving track of the test case to be screened, and determining the screening reference value corresponding to the test case to be screened according to the collision time and the time headway of each time point.
8. The method according to claim 6, wherein the determining collision times at least two time points in the scene driving track of the test case to be screened comprises:
if the target scene is a cut-in scene, determining an initial distance, a main vehicle initial speed, a target vehicle initial speed, an initial state duration and a target vehicle lane changing speed in a scene driving track of the test case to be screened;
determining a collision time at an initial state time point based on the initial distance, the initial velocity of the host vehicle, and the initial velocity of the target vehicle;
determining a collision time at a lane change state time point based on the initial distance, the initial velocity of the host vehicle, the initial velocity of the target vehicle, and the lane change velocity of the target vehicle.
9. An electronic device, characterized in that the electronic device comprises:
a processor and a memory;
the processor is used for executing the steps of the test case generalization screening method according to any one of claims 1 to 8 by calling the program or the instruction stored in the memory.
10. A computer-readable storage medium storing a program or instructions for causing a computer to perform the steps of the test case generalization screening method according to any one of claims 1 to 8.
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