CN110955159B - Automatic driving simulation example compiling method and device, electronic equipment and storage medium - Google Patents
Automatic driving simulation example compiling method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of automatic driving vehicle control and test verification, in particular to a method and a device for compiling an automatic driving simulation case, electronic equipment and a storage medium. The method comprises the following steps: evaluating the danger level of the functional scene case to obtain the danger level of each functional scene case; selecting a target scene case with a preset danger level from the functional scene cases; classifying the target scene cases according to the target elements; and selecting a target scene category according to the element requirements of the target simulation example, and compiling the target scene case corresponding to the target scene category into the target simulation example. The automatic driving test case is scientifically formulated, so that the implementation of the simulation verification and the site verification of the automatic driving is promoted, and the automatic driving performance detection is more effective and accurate.
Description
Technical Field
The invention relates to the technical field of automatic driving vehicle control and test verification, in particular to a method and a device for compiling an automatic driving simulation case, electronic equipment and a storage medium.
Background
The simulation test and verification of the automatic driving vehicle are indispensable processes in the whole system development process, and are more important compared with the simulation, test and verification means of the traditional automatic driving vehicle. However, how to compile the automatic driving simulation test case is reasonable and effective, how to determine the proportion of relevant elements of the functional definition is important for the effectiveness of the test.
At present, the test scene about automatic driving is mostly based on an accident database and demonstration test (FOT Field operational Tests) data, and a key scene is extracted through the data. However, this method can only form a partial scene, and is only suitable for testing ADAS (Advanced Driving assistance System), which is far from sufficient for an automatic Driving System of level L2 or higher. If the test scene is formed on the basis of the existing ADAS scene, the relative motion relationship is arranged and combined according to the relative position of the parking vehicle and the surrounding vehicles in the scene. And then extracting parameters according to the static traffic environment, the typical road, the traffic accident and the ADAS regulation scene, and then arranging and combining the parameters to generate a test case.
In the ADAS-based test scene, only part of working conditions are considered for selecting parameters in the test scene, the parameters are only simply arranged and combined, and the difference of the proportion of different parameters in different scenes is not considered. The method is only suitable for partial tests of simple ADAS products, and a complete automatic driving test case compiling scheme is not formed.
Disclosure of Invention
The invention mainly aims to provide a method and a device for compiling an automatic driving simulation case, electronic equipment and a storage medium, and aims to realize the compilation of the automatic driving simulation case which better meets the automatic driving requirement.
In order to achieve the above object, the present invention provides a method for compiling an automatic driving simulation example, wherein the method comprises:
evaluating the danger level of the functional scene case to obtain the danger level of each functional scene case;
selecting a target scene case with a preset danger level from the functional scene cases;
classifying the target scene cases according to the target elements;
and selecting a target scene category according to the element requirements of the target simulation example, and compiling the target scene case corresponding to the target scene category into the target simulation example.
Preferably, before the step of performing risk level evaluation on the functional scene cases to obtain the risk level of each functional scene case, the method further includes:
and extracting the functional scene case from the driving traffic database according to the functional extraction algorithm and the simulation case type.
Preferably, before the step of classifying the target scene cases according to the target elements, the method further includes:
setting preset elements according to the type of the simulation case;
and acquiring the existence proportion of the element variable values of the preset elements in the target scene case, and selecting the preset elements with the existence proportion smaller than the preset proportion as the target elements.
Preferably, the step of classifying the target scene cases according to the target elements specifically includes:
acquiring the existence proportion of the target elements in the target scene case;
and carrying out clustering analysis on the target scene cases according to the existence proportion.
Preferably, the step of performing cluster analysis on the target scene cases according to the existence proportion specifically includes:
taking each target case as a class, and calculating the sample space among the classes according to the existence proportion;
when the number of the categories is larger than the preset number of the categories, combining the categories of which the sample intervals are smaller than the preset intervals;
and when the number of the categories is larger than the preset number of the categories, taking the current category as the category to be selected.
Preferably, the step of selecting a target scene category according to the element requirements of the target simulation example and compiling the target scene case corresponding to the target scene category into the target simulation example specifically includes:
selecting a target scene category according to the element requirements of a target simulation example, and determining the required proportion of target elements in the simulation example according to the element requirements;
and compiling the target scene case into a target simulation example according to the required proportion.
Preferably, after the step of selecting the target scene category according to the element requirement of the target simulation example and compiling the target scene case corresponding to the target scene category into the target simulation example, the method further comprises:
importing the target simulation example into an automatic driving test device, and carrying out automatic driving test according to the target simulation example to obtain test data;
and acquiring the prediction data of the simulation example and the absolute value of the test data, and correcting the automatic driving control according to the absolute value.
In addition, in order to achieve the above object, the present invention further provides a simulation example compiling device, including: the system comprises a case extraction module, a target case acquisition module, a cluster analysis module and a simulation case compiling module, wherein the case extraction module, the target case acquisition module, the cluster analysis module and the simulation case compiling module are arranged in the system;
a case extraction module: the system comprises a risk level evaluation module, a risk level evaluation module and a risk level evaluation module, wherein the risk level evaluation module is used for evaluating the risk level of the functional scene cases to obtain the risk level of each functional scene case;
a target case acquisition module: the target scene cases with the danger level as the preset level are selected from the functional scene cases;
a cluster analysis module: the system is used for classifying the target scene cases according to the target elements;
a simulation example compiling module: the method is used for selecting a target scene category according to the element requirements of the target simulation example and compiling the target scene case corresponding to the target scene category into the target simulation example.
In addition, to achieve the above object, the present invention also provides an electronic device, including: the simulation system comprises a memory, a processor and a simulation example programming program stored on the memory and capable of running on the processor, wherein the simulation example programming program is configured to realize the steps of the automatic driving simulation example programming method.
In addition, in order to achieve the above object, the present invention further provides a storage medium, wherein the storage medium stores an automatic driving simulation example programming program, and the simulation example programming program realizes the steps of the automatic driving simulation example programming method when being executed by a processor.
The method comprises the steps of evaluating the danger level of the functional scene case to obtain the danger level of each functional scene case; selecting a target scene case with a preset danger level from the functional scene cases; classifying the target scene cases according to the target elements; and selecting a target scene category according to the element requirements of the target simulation example, and compiling the target scene case corresponding to the target scene category into the target simulation example. The automatic driving test case is scientifically formulated, the simulation verification of automatic driving and the implementation of site verification are promoted, and the automatic driving performance detection is more effective and accurate.
Drawings
FIG. 1 is a schematic diagram of an electronic device in a hardware operating environment according to an embodiment of the present invention
FIG. 2 is a schematic flow chart of a first embodiment of a method for creating an automatic driving simulation example according to the present invention;
FIG. 3 is a flowchart illustrating a second exemplary embodiment of a method for creating an automatic driving simulation according to the present invention;
fig. 4 is a functional block diagram of a first embodiment of an automatic driving simulation example compiling device according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions in the embodiments may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should be considered to be absent and not within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an electronic device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the electronic device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a storage medium 1005, which may include an operating system, a network communication module, a user interface module, and an autopilot simulation routine, may be included.
In the electronic apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the electronic device according to the present invention may be disposed in the electronic device, and the electronic device calls the automatic driving simulation example programming program stored in the memory 1005 through the processor 1001 and executes the automatic driving simulation example programming method according to the present invention.
An embodiment of the present invention provides a method for compiling an automatic driving simulation example, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of a method for compiling an automatic driving simulation example according to the present invention. The method comprises the following steps:
step S100: evaluating the danger level of the functional scene case to obtain the danger level of each functional scene case;
it should be noted that the source of the functional scene case in the scheme is the natural driving traffic database, and before the simulation examples are compiled, which simulation examples need to be compiled are firstly confirmed, samples are obtained from the natural driving traffic database, and the samples are screened. From the practical point of view of the simulation example, the danger level needs to be taken into consideration and graded.
Step S200: selecting a target scene case with a preset danger level from the functional scene cases;
it should be understood that the case with the risk level meeting the preset level has a reference value, the preset level is the basic level for safe driving of the vehicle and is preferably selected, it should be noted that the selection of the risk level also depends on the category of the simulation example, and when the simulation example is a simulation case for a fault condition, the case corresponding to the more dangerous level is considered first, and the method does not limit the case.
Step S300: classifying the target scene cases according to the target elements;
it is easy to understand that the target simulation example is composed of target elements, a part of the elements are selected as alternative elements, and then the elements which have the vivid features and can be used as references are screened from the alternative elements as the target elements. After the target elements are selected, the target scene cases can be classified according to the target elements.
Step S400: and selecting a target scene category according to the element requirements of the target simulation example, and compiling the target scene case corresponding to the target scene category into the target simulation example.
It is easy to understand that, for example, the target simulation example is a case of front vehicle cut-in, in order to enhance the practicability of the case, the required elements are cut-in direction, main vehicle speed and the like, and the target simulation example is compiled by simultaneously performing corresponding proportion combination on the target scene cases according to the proportion of the elements occupied in different types of cases.
The method of the embodiment of the invention realizes the compilation of the automatic driving simulation example based on the cluster analysis, so that the automatic driving simulation example is more in line with the automatic driving requirement and more reasonable and effective, thereby promoting the performance test of the automatic driving vehicle and promoting the research and development efficiency of the automatic driving vehicle.
The automatic driving simulation example compiling method is based on the first embodiment and provides the second embodiment; FIG. 3 is a flowchart illustrating a second exemplary embodiment of a method for creating an automatic driving simulation according to the present invention;
before step S100, the method further includes:
step S101: and extracting the functional scene case from the driving traffic database according to the functional extraction algorithm and the simulation case type.
It should be understood that the present embodiment is explained by taking a preceding vehicle cut-in target case of low-speed traffic congestion as an example. At present, the test scene about automatic driving is mostly based on an accident database and empirical test (FOT) data, and the key scene is extracted through data, so that the requirement of an automatic driving vehicle cannot be met. The invention aims to provide a method for extracting a required functional scene from a Chinese natural driving traffic database.
It should be noted that the extraction is performed with a speed traffic jam scene as a target, for example: 1. extracting a front vehicle cut-in case according to a lane change scene extraction algorithm; 2. continuously screening according to two conditions of high speed and city of the main vehicle speed greater than the target vehicle speed and the driving scene area; 3. and performing subjective danger evaluation and cut-in completion time on the scenes, and finally screening out cases which accord with the type of the target simulation case. Accordingly, cases in which the speed of the target vehicle is greater than that of the host vehicle and cases in which the risk level is not met, such as cases in which a city or highway is cut into quickly, need to be screened out.
Before step S300, the method further includes:
step S201: setting preset elements according to the type of the simulation case;
step S202: and acquiring the existence proportion of the element variable values of the preset elements in the target scene case, and selecting the preset elements with the existence proportion smaller than the preset proportion as the target elements.
Further, for example: firstly, preliminarily selecting scene elements for describing the cut-in of the front vehicle, wherein the scene elements mainly comprise weather, visibility, scene areas, the distance between vehicles at the cut-in time, vehicle speed and the like. And further analyzing the proportion of the variable value of each element, wherein the proportion of the variable value of each element (weather, visibility and road type) is more than 85 percent, which is not beneficial to forming the prominent characteristic of the category, so that the elements are not considered as the clustering elements. Finally, determining variables such as scene areas, the cut-in direction of the target vehicle, the speed of the vehicle and the like as clustering elements.
Step S300 specifically includes:
step S301: acquiring the existence proportion of the target elements in the target scene case;
step S302: and carrying out clustering analysis on the target scene cases according to the existence proportion.
Further, for example: after the variable value is normalized, clustering can be carried out, the distance between samples is calculated circularly, the classes with close distance are merged until the classes are merged into one class, and then the clustering number is determined by the inconsistent coefficient in the clustering process.
Step 302 specifically includes: taking each target case as a class, and calculating the sample space among the classes according to the existence proportion;
when the number of the categories is larger than the preset number of the categories, combining the categories of which the sample intervals are smaller than the preset intervals;
and when the number of the categories is larger than the preset number of the categories, taking the current category as the category to be selected.
Step S400 specifically includes:
step S401: selecting a target scene category according to the element requirements of a target simulation example, and determining the required proportion of target elements in the simulation example according to the element requirements;
step S402: and compiling the target scene case into a target simulation example according to the required proportion.
For example, the following are: after the analysis is completed, 4 large-class target vehicle cut-in scenes are obtained through clustering according to natural driving data. Aiming at the TJP function, according to the traffic fluency distribution probability (traffic jam scene) in the elements, and combining the host vehicle speed (the vehicle speed is 0-60 Km/h). And selecting the category which meets the element requirements of the target simulation example, and designing the test case according to the proportion of the clustering elements.
As shown in table 1, table 1 shows the ratio of each element in the target simulation example in this embodiment.
After step S400, the method further comprises:
step S500: importing the target simulation example into an automatic driving test device, and carrying out automatic driving test according to the target simulation example to obtain test data;
it should be understood that the target simulation example may be compiled for an autopilot test, which is performed to detect the corresponding data of the autopilot vehicle on the one hand and to detect the effect of the simulation example on the other hand. And if the effect of the simulation example is different from the expected value or the simulation example has a fault working condition, timely modifying the simulation example.
Step S600: and acquiring the prediction data of the simulation example and the absolute value of the test data, and correcting the automatic driving control according to the absolute value.
It is easy to understand that, in the case that the simulation example has a reference value, the prediction data of the vehicle running in the simulation example is referred, and when the absolute value between the test data and the referenceable data is large, the problem of the test vehicle should be found according to the absolute value and corrected in time.
The method provided by the embodiment of the invention realizes effective compilation of the automatic driving simulation example, so that the automatic driving simulation example has reference, the research and development efficiency of the automatic driving automobile is promoted, the accuracy of the test experiment of the automatic driving automobile is improved, and the automatic driving simulation example is more in line with the automatic driving characteristics.
The invention also provides an automatic driving simulation example compiling device, and referring to fig. 4 and fig. 4, the invention is a functional module diagram of the first embodiment of the automatic driving simulation example compiling device of the invention. The device comprises: the system comprises a case extraction module 10, a target case acquisition module 20, a cluster analysis module 30 and a simulation case compiling module 40, wherein the simulation case compiling module is used for compiling a simulation case;
case extraction module 10: the system comprises a risk level evaluation module, a risk level evaluation module and a risk level evaluation module, wherein the risk level evaluation module is used for evaluating the risk level of the functional scene cases to obtain the risk level of each functional scene case;
it should be noted that the source of the functional scene case in the scheme is the natural driving traffic database, and before the simulation examples are compiled, which simulation examples need to be compiled are firstly confirmed, samples are obtained from the natural driving traffic database, and the samples are screened. From the practical point of view of the simulation example, the danger level needs to be taken into consideration and graded.
Target case acquisition module 20: the target scene cases with the danger level as the preset level are selected from the functional scene cases;
it should be understood that the case with the risk level meeting the preset level has a reference value, the preset level is the basic level for safe driving of the vehicle and is preferably selected, it should be noted that the selection of the risk level also depends on the category of the simulation example, and when the simulation example is a simulation case for a fault condition, the case corresponding to the more dangerous level is considered first, and the method does not limit the case.
The cluster analysis module 30: the system is used for classifying the target scene cases according to the target elements;
it is easy to understand that the target simulation example is composed of target elements, a part of the elements are selected as alternative elements, and then the elements which have the vivid features and can be used as references are screened from the alternative elements as the target elements. After the target elements are selected, the target scene cases can be classified according to the target elements.
Simulation example compilation module 40: the method is used for selecting a target scene category according to the element requirements of the target simulation example and compiling the target scene case corresponding to the target scene category into the target simulation example.
It is easy to understand that, for example, the target simulation example is a case of front vehicle cut-in, in order to enhance the practicability of the case, the required elements are cut-in direction, main vehicle speed and the like, and the target simulation example is compiled by simultaneously performing corresponding proportion combination on the target scene cases according to the proportion of the elements occupied in different types of cases.
The device provided by the embodiment of the invention realizes the compilation of the automatic driving simulation example based on cluster analysis, so that the automatic driving simulation example is more in line with the automatic driving requirement and more reasonable and effective, thereby promoting the performance test of the automatic driving vehicle and promoting the research and development efficiency of the automatic driving vehicle.
The invention also provides a storage medium, wherein the storage medium is stored with an automatic driving simulation example programming program, and the steps of the automatic driving simulation example programming method are realized when the simulation example programming program is executed by a processor.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the method for compiling the automatic driving simulation example provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. An automatic driving simulation example compiling method is characterized by comprising the following steps:
evaluating the danger level of the functional scene case to obtain the danger level of each functional scene case;
selecting a target scene case with a preset danger level from the functional scene cases;
classifying the target scene cases according to the target elements;
selecting a target scene category according to the element requirements of a target simulation example, and compiling a target scene case corresponding to the target scene category into the target simulation example;
the step of classifying the target scene cases according to the target elements specifically includes:
acquiring the existence proportion of the target elements in the target scene case;
performing clustering analysis on the target scene cases according to the existence proportion;
before the step of classifying the target scene cases according to the target elements, the method further includes:
setting preset elements according to the type of the simulation case;
and acquiring the existence proportion of the element variable values of the preset elements in the target scene case, and selecting the preset elements with the existence proportion smaller than the preset proportion as the target elements.
2. The automated driving simulation example compilation method of claim 1, wherein prior to the step of performing risk rating evaluation on the functional scenario cases to obtain a risk rating for each functional scenario case, the method further comprises:
and extracting the functional scene case from the driving traffic database according to the functional extraction algorithm and the simulation case type.
3. The method for formulating the automatic driving simulation example according to claim 1, wherein the step of performing cluster analysis on the target scene cases according to the existence proportion specifically comprises:
taking each target case as a class, and calculating the sample space among the classes according to the existence proportion;
when the number of the categories is larger than the preset number of the categories, combining the categories of which the sample intervals are smaller than the preset intervals;
and when the number of the categories is larger than the preset number of the categories, taking the current category as the category to be selected.
4. The method for creating the automatic driving simulation example according to claim 3, wherein the step of selecting the target scene category according to the element requirements of the target simulation example and creating the target scene case corresponding to the target scene category into the target simulation example specifically comprises:
selecting a target scene category according to the element requirements of a target simulation example, and determining the required proportion of target elements in the simulation example according to the element requirements;
and compiling the target scene case into a target simulation example according to the required proportion.
5. The method for creating the automatic driving simulation example according to claim 1, wherein after the step of selecting the target scene category according to the element requirements of the target simulation example and creating the target scene case corresponding to the target scene category into the target simulation example, the method further comprises:
importing the target simulation example into an automatic driving test device, and carrying out automatic driving test according to the target simulation example to obtain test data;
and acquiring the prediction data of the simulation example and the absolute value of the test data, and correcting the automatic driving control according to the absolute value.
6. A simulation case compilation apparatus, comprising: the system comprises a case extraction module, a target case acquisition module, a cluster analysis module and a simulation case compiling module, wherein the case extraction module, the target case acquisition module, the cluster analysis module and the simulation case compiling module are arranged in the system;
a case extraction module: the system comprises a risk level evaluation module, a risk level evaluation module and a risk level evaluation module, wherein the risk level evaluation module is used for evaluating the risk level of the functional scene cases to obtain the risk level of each functional scene case;
a target case acquisition module: the target scene cases with the danger level as the preset level are selected from the functional scene cases;
a cluster analysis module: the system is used for classifying the target scene cases according to the target elements;
a simulation example compiling module: the system comprises a target simulation case, a target scene classification and a target simulation case, wherein the target scene classification is selected according to the element requirements of the target simulation case, and the target scene case corresponding to the target scene classification is compiled into the target simulation case;
the cluster analysis module is specifically used for acquiring the existence proportion of the target elements in the target scene case;
performing clustering analysis on the target scene cases according to the existence proportion;
the cluster analysis module is specifically used for setting preset elements according to the types of the simulation cases;
and acquiring the existence proportion of the element variable values of the preset elements in the target scene case, and selecting the preset elements with the existence proportion smaller than the preset proportion as the target elements.
7. An electronic device, characterized in that the device comprises: a memory, a processor, and a simulation routine stored on the memory and executable on the processor, the simulation routine configured to implement the steps of the autopilot simulation routine method of any one of claims 1-5.
8. A storage medium having stored thereon an automatic driving simulation example preparation program which, when executed by a processor, realizes the steps of the automatic driving simulation example preparation method according to any one of claims 1 to 5.
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| CN111680362B (en) * | 2020-05-29 | 2023-08-11 | 北京百度网讯科技有限公司 | Automatic driving simulation scene acquisition method, device, equipment and storage medium |
| CN111967123B (en) * | 2020-06-30 | 2023-10-27 | 中汽数据有限公司 | Method for generating simulation test cases in simulation test |
| CN112017462B (en) * | 2020-08-25 | 2021-08-31 | 禾多科技（北京）有限公司 | Method, apparatus, electronic device, and medium for generating scene information |
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| CN112328477B (en) * | 2020-11-02 | 2023-08-15 | 东软睿驰汽车技术（沈阳）有限公司 | Method and device for generating test case of automatic driving algorithm and electronic equipment |
| CN112765812B (en) * | 2021-01-19 | 2021-09-07 | 中国科学院软件研究所 | Autonomous ability rapid evaluation method and system for unmanned system decision strategy |
| CN113157563B (en) * | 2021-03-17 | 2023-08-01 | 中国人民解放军32801部队 | Test case generation method and device of unmanned system |
| CN113326639B (en) * | 2021-08-03 | 2021-11-02 | 北京赛目科技有限公司 | Method and device for determining automatic driving test scene and electronic equipment |
| CN113688042B (en) * | 2021-08-25 | 2023-06-27 | 北京赛目科技有限公司 | Determination method and device of test scene, electronic equipment and readable storage medium |
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