CN110579359A - Optimization method and system of automatic driving failure scene library, server and medium - Google Patents
Optimization method and system of automatic driving failure scene library, server and medium Download PDFInfo
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- CN110579359A CN110579359A CN201910854684.4A CN201910854684A CN110579359A CN 110579359 A CN110579359 A CN 110579359A CN 201910854684 A CN201910854684 A CN 201910854684A CN 110579359 A CN110579359 A CN 110579359A
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
Abstract
According to the optimization method and system of the automatic driving failure scene library, the server and the storage medium, failure weight ratios of all factors are determined by analyzing failure event data recorded in the automatic driving drive test process, the complexity of a test scene is comprehensively obtained by using an analytic hierarchy process, the importance degree of different scenes to a test function and the priority of different test scene cases are further determined, the optimization of the test scene library is realized, and the optimization method and system have important significance on the design, test implementation and evaluation after the test of the test scene cases; in addition, the invention can make the failure weight ratio of each factor more accurate than the calculation by carrying out hierarchical classification on each factor.
Description
Technical Field
The invention relates to the technical field of failure testing in automatic driving, in particular to an optimization method and system of an automatic driving failure scene library, a server and a storage medium.
Background
The driving scene data is an important resource for research and development and testing of the intelligent networked automobile and is a key data basis for redefining the intelligent automobile grade. The scene library is used as a case set of the intelligent internet vehicle, and due to the continuity of scene parameter distribution and the diversity of scene factor arrangement combination, the test scene cases are unlimited. Therefore, it is important to evaluate the test scenes, calibrate the complexity of each scene, and determine the test priority.
disclosure of Invention
In view of this, embodiments of the present invention provide an optimization method and system, a server, and a storage medium for an autopilot failure scenario library, so as to solve the technical problems that in the construction of an existing autopilot test scenario, the number of test scenario cases increases in a proportional manner through different permutation and combination of scenario elements, there is no corresponding system method and standard for evaluating the complexity of each scenario case, and the priority and importance of the scenario cases cannot be determined in the test process.
In a first aspect of the embodiments of the present invention, an optimization method for an automatic driving failure scene library is provided, where the optimization method for the automatic driving failure scene library includes the following steps:
Classifying and dividing the factors of each scene to determine the level of each category;
Calculating the failure weight ratio of the quantifiable factors in the whole drive test event according to the drive test event set;
Manufacturing a proportion scale table, constructing a judgment matrix based on the proportion scale table, and calculating the failure weight ratio of unquantized factors according to the characteristic vector of the judgment matrix;
and calculating the complexity of each failure scene according to the failure weight ratio of each level factor, and determining the priority of the test scene case according to the complexity.
In a second aspect of the embodiments of the present invention, an optimization system of an automatic driving failure scene library is provided, where the optimization system of the automatic driving failure scene library includes the following functional modules:
The hierarchy classification module is configured to classify and divide the factors of each scene to determine the hierarchy of each category;
The quantitative calculation module is configured to calculate the failure weight ratio of the quantifiable factors in the whole drive test event according to the drive test event set;
the matrix calculation module is configured to manufacture a proportion scale table, construct a judgment matrix based on the proportion scale table, and calculate the failure weight ratio occupied by the unquantizable factors according to the characteristic vector of the judgment matrix;
And the priority determining module is configured to calculate the complexity of each failure scene according to the failure weight ratio of each layer of factor and determine the priority of the test scene case according to the complexity.
In a third aspect of the embodiments of the present invention, a server is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the optimization method for the automatic driving failure scenario library as described above when executing the computer program.
in a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where a computer program is stored, and the computer program, when executed by a processor, implements the steps of the optimization method for the automatic driving failure scenario library as described above.
The optimization method of the automatic driving failure scene library determines the failure weight ratio of each factor by analyzing the failure event data recorded in the automatic driving drive test process, comprehensively obtains the complexity of the test scene by using an analytic hierarchy process, further determines the importance degree of different scenes on the test function and the priority of different test scene cases, realizes the optimization of the test scene library, and has important significance on the design, test implementation and evaluation after the test of the test scene cases; and the invention makes the failure weight ratio of each factor more accurate by classifying each factor in a hierarchy.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the embodiments or drawings used in the prior art description, and obviously, the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a block flow diagram of a method for optimizing an automatic driving failure scene library according to an embodiment of the present invention;
FIG. 2 is a hierarchical classification diagram of factors in the optimization method for an automatic driving failure scene library according to the embodiment of the present invention;
FIG. 3 is a block diagram of functional modules of an optimization system for an autopilot failure scenario library according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server according to an embodiment of the present invention.
Detailed Description
in order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the embodiments described below are only a part of the embodiments of the present invention, but not all of the embodiments. 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 embodiment of the invention provides an optimization method of an automatic driving failure scene library, which comprises the following steps of:
And S1, classifying and dividing the factors of each scene to determine the levels of all the categories.
In the hierarchical classification of the factors, between two adjacent hierarchical levels, the next hierarchical factor is the specific classification of the previous hierarchical factor; as shown in fig. 2, the first layer (decision layer) includes: dynamic scenes, static scenes; the second layer (middle layer) includes a refined classification of static scenes: weather, road, light intensity, etc., and a refined classification of dynamic scenes: the speed of the vehicle, the speed of the target vehicle, etc.; the third layer (factor layer) contains specific classes of factors: for example, weather includes sunny days, rainy days, snowy days, foggy days and the like, and the failure weight ratio of each factor can be calculated more accurately through the hierarchical classification.
And S2, calculating the failure weight ratio of the quantifiable factors in the whole drive test event according to the drive test event set.
the quantifiable factors include, but are not limited to: in sunny days, rainy days, snowy days and foggy days, the failure weight ratio of quantifiable factors in the whole road test event set is specifically as follows: with the probability of a failure event occurring for that factor. For example, to calculate the failure weight ratio in rainy days: the actual vehicle drive test accumulation days are n1 days, wherein the rainy days are n2 days, and the recorded failure event m2 is that the occurrence frequency of the rainy days is m1, namely: the failure weight ratio of the rainy day is C1 ═ m2/m1)/(n2/n1, and by combining other weather (sunny day C2, snow day C3 and fog day C4) in the same type, the failure weight ratio of the rainy day in the drive test event set is obtained as follows: C1/(C1+ C2+ C3+ C4).
S3, making a scale table, constructing a judgment matrix based on the scale table, and calculating the failure weight ratio occupied by unquantized factors according to the feature vector of the judgment matrix, wherein the scale table is used for setting the weight quantization value of factor comparison, and the factors contained in the judgment matrix are the same type of factors.
A scale table was made empirically as follows:
| factor i to factor j | quantized value |
| Of equal importance | 1 |
| Of slight importance | 3 |
| of greater importance | 5 |
| Of strong importance | 7 |
| of extreme importance | 9 |
| Intermediate values of two adjacent judgments | 2，4，6，8 |
Quantizing a in the decision matrix based on a scale tableijAs a result of comparing the importance of the element i with the importance of the element j, for example: in the camera LDW (lane departure warning), for the influence of camera lane identification, the importance of the fog days is set more than that of the fine days according to the experience, the quantitative value is 5 by checking the scale table, otherwise, the quantitative value is 1/5 after the fine days are compared with the fog days.
And comparing the weather factors pairwise to obtain the whole judgment matrix. As shown in the following table:
| in sunny days | rainy day | Snow sky | in fog weather | |
| In sunny days | 1 | 3 | 3 | 5 |
| Rainy day | 1/3 | 1 | 1 | 3 |
| Snow sky | 1/3 | 1 | 1 | 3 |
| In fog weather | 1/5 | 1/3 | 1/3 | 1 |
Calculating the characteristic vector of the judgment matrix to obtain:
| In sunny days | rainy day | snow sky | In fog weather | Feature vector (weight) | |
| In sunny days | 1 | 3 | 3 | 5 | 0.0781 |
| rainy day | 1/3 | 1 | 1 | 3 | 0.1998 |
| Snow sky | 1/3 | 1 | 1 | 3 | 0.1998 |
| In fog weather | 1/5 | 1/3 | 1/3 | 1 | 0.5222 |
For the case of more matrix levels, the feature vectors can be encoded by software such as MATLAB and the like, and can be obtained by automatic calculation.
and S4, calculating the complexity of each failure scene according to the failure weight ratio of each level factor, and determining the priority of the test scene case according to the complexity.
Calculating the complexity of the whole scene according to the weight ratios A1-An of the decision layer, the weight ratios B1-Bn of the middle layer and the weight ratios C1-Cn of the factor layer: f-a 1 × B1 × C1+ … + An × Bn Cn.
In order to enable the complexity to be more visualized and facilitate statistical analysis, the scene complexity is mapped into a percentile system according to the upper extreme scene and the lower extreme scene.
the complexity mapping conversion formula is as follows:
Fbai＝(F-Fmin)*100/(Fmax-Fmin)
Fbai is the percentage complexity of conversion, Fmin is the lower extreme limit scene, and Fmax is the upper extreme limit scene.
For example: the lower extreme scene is a scene formed by … at low speed of the vehicle in sunny days, common roads and in the daytime, and the complexity of the scene is Fmin-0.4; the upper extreme scene is a scene composed of a foggy day, an expressway, a night, a vehicle speed, and …, and the scene complexity Fmax is 0.8. Then the complexity of the total scene complexity F ═ 0.6 map is 50 in this upper and lower limit scenario.
The higher the complexity of the scene is, the higher the test priority thereof is, the lower the complexity thereof is, the lower the test priority thereof is, even the scene case with lower complexity can be regarded as an invalid case to be eliminated, in a word, based on the complexity of each scene case, the importance degree of different scenes on the test function and the priority of different test scene cases are determined, and the optimization of the test scene library is realized.
in addition, when the scene library is constructed through parameter recombination, factors with high weight can be recombined according to the occupied weight ratio of each factor, and a typical and representative scene case is constructed.
The optimization method of the automatic driving failure scene library determines the failure weight ratio of each factor by analyzing the failure event data recorded in the automatic driving drive test process, comprehensively obtains the complexity of the test scene by using an analytic hierarchy process, further determines the importance degree of different scenes on the test function and the priority of different test scene cases, realizes the optimization of the test scene library, and has important significance on the design, test implementation and evaluation after the test of the test scene cases; and the invention makes the failure weight ratio of each factor more accurate by classifying each factor in a hierarchy.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The above mainly describes an optimization method of an automatic driving failure scene library, and a detailed description will be given below of an optimization system of an automatic driving failure scene library.
Fig. 3 shows a functional block diagram of an optimization system of an automatic driving failure scenario library according to an embodiment of the present invention. As shown in fig. 3, the optimization system of the automatic driving failure scenario library includes the following functional modules:
The hierarchical classification module 10 is configured to classify and divide the factors of each scene to determine the hierarchy of each category;
A quantitative calculation module 20 configured to calculate a failure weight ratio of the quantifiable factors in the entire drive test event according to the set of drive test events;
The matrix calculation module 30 is configured to make a scale table, construct a judgment matrix based on the scale table, and calculate the failure weight ratio of the unquantizable factors according to the eigenvector of the judgment matrix;
And the priority determining module 40 is configured to calculate the complexity of each failure scenario according to the failure weight ratio of each level factor, and determine the priority of the test scenario case according to the complexity.
Fig. 4 is a schematic diagram of an optimized server structure of an automatic driving failure scenario library according to an embodiment of the present invention. The server is a device for providing computing services, and generally refers to a computer with high computing power, which is provided to a plurality of users through a network. As shown in fig. 4, the server 5 of this embodiment includes: a memory 51, a processor 52 and a system bus 53, said memory 51 comprising an executable program 511 stored thereon, it being understood by a person skilled in the art that the terminal device structure shown in fig. 4 does not constitute a limitation of the terminal device, and may comprise more or less components than those shown, or may combine certain components, or a different arrangement of components.
The following specifically describes each constituent component of the terminal device with reference to fig. 4:
The memory 51 may be used to store software programs and modules, and the processor 52 executes various functional applications of the terminal and data processing by operating the software programs and modules stored in the memory 51. The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the stored data area may store data (such as audio data, a phonebook, etc.) created according to the use of the terminal, etc. Further, the memory 51 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
An executable program 511 of the optimization method for the automatic driving failure scenario library is contained in the memory 51, the executable program 511 may be divided into one or more modules/units, the one or more modules/units are stored in the memory 51 and executed by the processor 52 to complete the transmission of the notification and obtain the notification implementation process, and the one or more modules/units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used for describing the execution process of the computer program 511 in the server 5. For example, the computer program 511 may be divided into an acquisition module, a comparison module, a concatenation module and a sending module.
the processor 52 is a control center of the server, connects various parts of the entire terminal device using various interfaces and lines, performs various functions of the terminal and processes data by running or executing software programs and/or modules stored in the memory 51 and calling data stored in the memory 51, thereby performing overall monitoring of the terminal. Alternatively, processor 52 may include one or more processing units; preferably, the processor 52 may integrate an application processor, which primarily handles operating systems, applications, etc., and a modem processor, which primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 52.
the system bus 53 is used to connect functional units inside the computer, and can transmit data information, address information, and control information, and may be, for example, a PCI bus, an ISA bus, a VESA bus, or the like. The instructions of the processor 52 are transmitted to the memory 51 through the bus, the memory 51 feeds data back to the processor 52, and the system bus 53 is responsible for data and instruction interaction between the processor 52 and the memory 51. Of course, the system bus 53 may also access other devices, such as network interfaces, display devices, etc.
The server at least includes a CPU, a chipset, a memory, a disk system, and the like, and other components are not described herein again.
in the embodiment of the present invention, the executable program executed by the processor 52 included in the terminal specifically includes: a method for optimizing a library of automated driving failure scenarios, comprising:
Classifying and dividing the factors of each scene to determine the level of each category;
calculating the failure weight ratio of the quantifiable factors in the whole drive test event according to the drive test event set;
Manufacturing a proportion scale table, constructing a judgment matrix based on the proportion scale table, and calculating the failure weight ratio of unquantized factors according to the characteristic vector of the judgment matrix;
And calculating the complexity of each failure scene according to the failure weight ratio of each level factor, and determining the priority of the test scene case according to the complexity.
it is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described or recited in detail in a certain embodiment, reference may be made to the descriptions of other embodiments.
Those of ordinary skill in the art would appreciate that the modules, elements, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; while the invention has been described in detail and with reference to the foregoing examples, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. the optimization method of the automatic driving failure scene library is characterized by comprising the following steps of:
Classifying and dividing the factors of each scene to determine the level of each category;
Calculating the failure weight ratio of the quantifiable factors in the whole drive test event according to the drive test event set;
manufacturing a proportion scale table, constructing a judgment matrix based on the proportion scale table, and calculating the failure weight ratio of unquantized factors according to the eigenvector of the judgment matrix;
And calculating the complexity of each failure scene according to the failure weight ratio of each level factor, and determining the priority of the test scene case according to the complexity.
2. The method for optimizing an automatic driving failure scene library according to claim 1, wherein in the hierarchical classification of the factors, between two adjacent hierarchical levels, the factor of the next hierarchical level is a specific classification of the factor of the previous hierarchical level.
3. The optimization method of the automatic driving failure scenario library according to claim 1, wherein the failure weight ratio of the quantifiable factors in the entire drive test event set is specifically: with the probability of a failure event occurring for that factor.
4. the method for optimizing an automatic driving failure scene library according to claim 1, wherein the scale table is used for setting a weight quantization value of factor comparison.
5. The method for optimizing an automatic driving failure scene library according to claim 1, wherein the factors included in the judgment matrix are the same type of factors.
6. The method of optimizing an autopilot failure scenario library of claim 1 wherein the scenario complexity is mapped to percentiles based on upper and lower extreme scenarios prior to complexity comparison.
7. the optimization method of the automated driving failure scenario library of claim 6, wherein the complexity mapping transformation formula is as follows:
Fbai＝(F-Fmin)*100/(Fmax-Fmin)
Fbai is the percentage complexity of conversion, Fmin is the lower extreme limit scene, and Fmax is the upper extreme limit scene.
8. the optimization system of the automatic driving failure scene library is characterized by comprising the following functional modules:
The hierarchy classification module is configured to classify and divide the factors of each scene to determine the hierarchy of each category;
The quantitative calculation module is configured to calculate the failure weight ratio of the quantifiable factors in the whole drive test event according to the drive test event set;
The matrix calculation module is configured to make a scale table, construct a judgment matrix based on the scale table, and calculate the failure weight ratio occupied by the unquantizable factors according to the eigenvector of the judgment matrix;
And the priority determining module is configured to calculate the complexity of each failure scene according to the failure weight ratio of each layer of factor and determine the priority of the test scene case according to the complexity.
9. a server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method for optimizing a library of automated driving failure scenarios according to any one of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for optimizing a library of automated driving failure scenarios according to any one of claims 1 to 7.
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| CN112629881A (en) * | 2020-12-16 | 2021-04-09 | 北京航空航天大学 | Method for extracting automatic driving simulation test element |
| CN113326210A (en) * | 2021-08-03 | 2021-08-31 | 北京赛目科技有限公司 | Method and device for determining automatic driving test scene |
| CN113326210B (en) * | 2021-08-03 | 2021-10-12 | 北京赛目科技有限公司 | Method and device for determining automatic driving test scene |
| CN113589798A (en) * | 2021-08-12 | 2021-11-02 | 上海裹动科技有限公司 | Automatic test behavior generation method and server |
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| CN110579359B (en) | 2021-11-09 |
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