CN115470122A - Automatic driving test method, device, medium and equipment based on simulation scene - Google Patents
Automatic driving test method, device, medium and equipment based on simulation scene Download PDFInfo
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Abstract
The application discloses an automatic driving test method, device, medium and equipment based on a simulation scene, and relates to the technical field of automatic driving, wherein the method comprises the following steps: acquiring at least one dynamic object set, wherein the dynamic object set comprises at least one target dynamic object, and the target dynamic object represents a virtual traffic dynamic object; generating at least one simulation scene from at least one set of dynamic objects; performing a test on the automatic driving algorithm based on at least one simulation scene to obtain a test result; determining an analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object. According to the technical scheme, the traffic dynamic object influencing the automatic driving vehicle is introduced in the simulation test process, and the test result is analyzed from the attribute dimension of the traffic dynamic object, so that the automatic driving algorithm can be tested more comprehensively and accurately, and a more detailed algorithm evaluation result can be obtained.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to an automatic driving test method, device, medium and equipment based on a simulation scene.
Background
The test of the automatic driving algorithm is divided into a simulation test and an actual road test, wherein the simulation test can comprise a software in-loop test, a model in-loop test, a driver in-loop test and a vehicle in-loop test. If the automatic driving algorithm after the simulation test is not mature enough, the actual road test has a great amount of uncertainty and danger, so that a great amount of manpower, material resources and financial resources are required to be invested for testing, and the personal safety of testers can be endangered.
In the prior art, a test scenario mainly aims at a single functional scenario, such as Automatic Emergency Braking (AEB), adaptive Cruise Control (ACC), and the like. The test evaluation of these single-function scenes also mainly depends on standard regulations, such as international standards, chinese New Car Assessment Program (C-NCAP), etc., and the evaluation result only represents whether the scene passes or not. The test and evaluation method ignores dynamic traffic participants influencing the automatic driving vehicle, lacks evaluation on the whole automatic driving algorithm, and is difficult to meet the requirements of high-level automatic driving on test comprehensiveness and accuracy.
Disclosure of Invention
In order to improve the integrity, comprehensiveness and accuracy of simulation tests, the application provides an automatic driving test method, an automatic driving test device, an automatic driving test medium and automatic driving test equipment based on a simulation scene. The technical scheme is as follows:
in a first aspect, the present application provides an automatic driving test method based on a simulation scenario, where the method includes:
acquiring at least one dynamic object set, wherein the dynamic object set comprises at least one target dynamic object, and the target dynamic object represents a virtual traffic dynamic object;
generating at least one simulation scene according to the at least one dynamic object set;
based on the at least one simulation scene, testing an automatic driving algorithm to obtain a test result;
determining an analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object.
Optionally, the determining an analysis result of the automatic driving algorithm according to the test result includes:
determining an attribute dimension of the target dynamic object, wherein the attribute dimension corresponds to at least one attribute value;
determining at least one simulation scene set, wherein the simulation scene sets correspond to the attribute values one by one;
determining the test passing proportion information of the automatic driving algorithm in the at least one simulation scene set according to the test result;
determining attribute weight corresponding to the at least one attribute value;
obtaining analysis information corresponding to the at least one attribute value according to the test passing proportion information of the automatic driving algorithm in the at least one simulation scene set and the attribute weight corresponding to the at least one attribute value;
and obtaining the analysis information of the automatic driving algorithm in the attribute dimension according to the analysis information corresponding to the at least one attribute value.
Optionally, the method further includes:
and adding the analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object to obtain target analysis information of the automatic driving algorithm, wherein the target analysis information indicates the comprehensive performance of the automatic driving algorithm.
Optionally, the obtaining at least one dynamic object set includes:
acquiring a dynamic object library, wherein the dynamic object library comprises at least one dynamic object;
and selecting at least one dynamic object from the dynamic object library as at least one target dynamic object, and obtaining the dynamic object set from the at least one target dynamic object.
Optionally, the generating at least one simulation scene according to the at least one dynamic object set includes:
acquiring an initial static simulation scene; the static simulation scene comprises at least one target static object;
and adding at least one target dynamic object in the dynamic object set into the static simulation scene to obtain the simulation scene corresponding to the dynamic object set.
Optionally, the method further includes:
configuring object attribute information of the at least one target dynamic object in the dynamic object set based on real distribution information corresponding to the virtual traffic dynamic object; the object attribute information includes quantity information, position information, or behavior information.
Optionally, based on the at least one simulation scenario, performing a test on an automatic driving algorithm to obtain a test result, where the test result includes:
acquiring a vehicle power model, a vehicle-mounted sensor model and an automatic driving algorithm;
integrating the at least one simulation scene, the vehicle dynamic model, the vehicle-mounted sensor model and the automatic driving algorithm to obtain at least one test case; the test cases correspond to the simulation scenes one by one;
executing the at least one test case to obtain a test result; the test result indicates test pass information of the autopilot algorithm in the at least one test case.
In a second aspect, the present application provides an automatic driving test device based on a simulation scenario, the device including:
the system comprises a dynamic object acquisition module, a virtual traffic dynamic object acquisition module and a virtual traffic dynamic object acquisition module, wherein the dynamic object acquisition module is used for acquiring at least one dynamic object set, the dynamic object set comprises at least one target dynamic object, and the target dynamic object represents a virtual traffic dynamic object;
the simulation scene generation module is used for generating at least one simulation scene according to the at least one dynamic object set;
the test module is used for executing a test on the automatic driving algorithm based on the at least one simulation scene to obtain a test result;
the analysis module is used for determining the analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object.
In a third aspect, the present application provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the automatic driving test method based on the simulation scenario according to the first aspect.
In a fourth aspect, the present application provides a computer device, which includes a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the automatic driving test method based on simulation scenario according to the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when executed by a processor, implement a method of automatic driving test based on simulation scenarios as described in the first aspect.
The automatic driving test method, the device, the medium and the equipment based on the simulation scene have the following technical effects:
according to the scheme, at least one dynamic object set is firstly obtained, wherein the dynamic object set comprises at least one target dynamic object, the target dynamic objects in different dynamic object sets can be different, and the target dynamic object is used for representing a virtual traffic dynamic object; secondly, generating at least one simulation scene according to at least one dynamic object set, wherein the dynamic object sets correspond to the simulation scenes one by one, and at least one target dynamic object in the corresponding dynamic object set is integrated in one simulation scene; performing a test on an automatic driving algorithm in at least one simulation scene to obtain a test result; and finally, determining an analysis result according to the test result, wherein the analysis result indicates the analysis information of the automatic driving algorithm in the attribute dimension of the target dynamic object. By utilizing the scheme provided by the application, the automatic driving algorithm can be tested more comprehensively and closer to a real scene through the simulation scene integrated with the virtual traffic dynamic object, and the simulation scene in the application is not limited to the automatic driving function which can only be tested singly, but can be used for testing the whole process of high-level automatic driving, so that the requirements of the high-level automatic driving test on the integrity, the comprehensiveness and the accuracy of the simulation test are met. In addition, according to the scheme provided by the application, the test result of the automatic driving algorithm is analyzed from the attribute dimension of the virtual traffic dynamic object, the processing performance of the automatic driving algorithm on each virtual traffic dynamic object in the simulation test can be known, so that the automatic driving algorithm can be evaluated more carefully and accurately, and the optimization iteration of the algorithm is promoted.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of an automatic driving test method based on a simulation scenario according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a composition of a dynamic object set provided by an embodiment of the present application;
FIG. 3 is a schematic flowchart of a process for performing a test on an autopilot algorithm based on a simulation scenario according to an embodiment of the application;
FIG. 4 is a schematic flowchart illustrating analyzing a test result from an attribute dimension of a target dynamic object according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an automatic driving test device based on a simulation scenario according to an embodiment of the present application;
fig. 6 is a schematic hardware structure diagram of an apparatus for implementing an automatic driving test method based on a simulation scenario according to an embodiment of the present application.
Detailed Description
In order to improve the integrity, comprehensiveness and accuracy of a simulation test, the embodiment of the application provides an automatic driving test method, an automatic driving test device, an automatic driving test medium and automatic driving test equipment based on a simulation scene. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application. Examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The following introduces an automatic driving test method based on a simulation scenario provided by the present application. Fig. 1 is a flowchart of an automated driving test method based on a simulation scenario provided by an embodiment of the present application, which provides the operation steps of the method according to the embodiment or the flowchart, but may include more or less operation steps based on conventional or non-creative labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. In practice, the system or server product may be implemented in a sequential or parallel manner (e.g., parallel processor or multi-threaded environment) according to the embodiments or methods shown in the figures. Referring to fig. 1, an automatic driving test method based on a simulation scenario provided in an embodiment of the present application may include the following steps:
s100: and acquiring at least one dynamic object set, wherein the dynamic object set comprises at least one target dynamic object, and the target dynamic object represents the virtual traffic dynamic object.
In an embodiment of the present application, the set of dynamic objects is a set of one or more target traffic dynamic objects, which are used to characterize virtual traffic dynamic objects, such as pedestrians, automobiles, animals, road surface dynamic obstacles, air dynamic obstacles, changing traffic lights, and the like. The target dynamic object is configured with object attribute information, which may include, but is not limited to, object feature information, quantity information, location information, behavior information, and the like. In the case of acquiring a plurality of dynamic object sets, different dynamic object sets may include different numbers or different types of target dynamic objects, or target dynamic objects of the same type may have different object attribute information.
In one embodiment of the present application, specifically, step S100 may include:
s110: and acquiring a dynamic object library, wherein the dynamic object library comprises at least one dynamic object.
In one possible implementation, a dynamic object library is pre-constructed, and each dynamic object in the dynamic object library represents a different category of virtual traffic dynamic object. When a dynamic object library is constructed, dynamic elements which affect the behavior of the autonomous vehicle are expressed into different dynamic objects according to categories, and object attribute variables such as shape, volume, position, speed, motion trajectory, collision recovery coefficient and the like which are common to the dynamic elements of the type are configured for the dynamic objects, that is, in the dynamic object library, the dynamic objects are abstract expressions and packages of the dynamic elements of the same type.
S120: and selecting at least one dynamic object from the dynamic object library as at least one target dynamic object, and obtaining the dynamic object set by the at least one target dynamic object.
In one possible implementation, as shown in fig. 2, a limited number of dynamic objects are randomly extracted from n dynamic objects in the dynamic object library multiple times, and the extracted dynamic objects are used as target dynamic objects, so as to form different sets of dynamic objects, for example, a dynamic object set 1 may include a dynamic object 1 and a dynamic object 3; the dynamic object set 2 may include a dynamic object 2.... The dynamic object n; the dynamic object set m may include a dynamic object 1 and a dynamic object 3. That is, in the embodiment of the present application, the number of dynamic objects (which may also be referred to as target dynamic objects) in each dynamic object set obtained after extraction and the types of the related dynamic objects may be different.
In the embodiment, by constructing the dynamic object library in advance, the dynamic elements which affect the behavior of the automatic driving vehicle are abstractly expressed and packaged according to the types, so that the dynamic object sets with different combination conditions can be quickly obtained, the simulation scene with higher coverage and closer to the practical condition is generated, the constructed dynamic object library can be repeatedly used, the development amount in the simulation test link is reduced, the repeated development and configuration of the dynamic objects with the same type are avoided, and the test efficiency is effectively improved.
Further, based on the real distribution information corresponding to the virtual traffic dynamic object, configuring the object attribute information of at least one target dynamic object in the dynamic object set; the object attribute information includes quantity information, position information, or behavior information.
The real distribution information corresponding to the virtual traffic dynamic object represents the representation distribution condition of the object attribute information of the virtual traffic dynamic object in the real scene. Illustratively, taking pedestrians as an example, the number and the positions of the target dynamic objects representing the pedestrian category in the dynamic object set are configured according to the pedestrian density distribution at the road intersection in different time periods. Illustratively, taking vehicles as an example, according to the actual road traffic flow, the driving track or driving behavior of the target dynamic object which characterizes the vehicle category in the dynamic object set is configured, such as acceleration, steering, lane change and the like.
In the embodiment, the object attribute information of the target dynamic object is subjected to generalized configuration according to the real distribution condition, so that the method can be closer to a real scene, can meet the requirements of various simulation tests, and enables the result of the simulation test to be more effective and have reference value.
S200: at least one simulation scene is generated based on the at least one set of dynamic objects.
In the embodiment of the application, one or more target dynamic objects included in one dynamic object set are integrated into one simulation scene, and under the condition that a plurality of dynamic object sets exist, a plurality of simulation scenes are respectively and correspondingly generated, that is, one dynamic object set corresponds to one simulation scene, and the simulation scenes form a concrete situation simulating reality, so that the time characteristics, road characteristics, traffic flow characteristics and the like of road traffic under the concrete situation are reflected.
In one embodiment of the application, a corresponding simulation scene is generated based on the dynamic object set, and the perception of the automatic driving algorithm on the dynamic object and the decision and control on the automatic driving are realized by utilizing the simulation scene, so that the performance of the automatic driving algorithm in the simulation scene is measured.
In another embodiment of the present application, the simulation scene generated based on the dynamic object set further includes a static object, and the simulation scene is utilized to realize perception of the dynamic object and the static object, and decision and control of automatic driving by an automatic driving algorithm. Specifically, the step S200 may include the steps of:
s210: acquiring an initial static simulation scene; the static simulation scene includes at least one target static object.
The static simulation scene is a simulation of static objects in a real road scene, and the target static objects in the static simulation scene can be road topological structures, lane lines, fixed traffic signboards and the like, and can also be static environment objects with constant wind speed, light intensity and the like.
S220: and adding at least one target dynamic object in the dynamic object set into the static simulation scene to obtain the simulation scene corresponding to the dynamic object set.
Further, a constraint relationship may also exist between the target static object and the target dynamic object, for example, a road topology structure limits a driving path of the target dynamic object, and a lane line limits behaviors of the target dynamic object, such as turning, lane changing, and the like.
In the embodiment, the combination of the static object and the dynamic object enables the simulation scene to be closer to the real scene, enhances the scene reality of the simulation test, and simultaneously improves the scene coverage of the simulation test.
S300: and executing a test on the automatic driving algorithm based on at least one simulation scene to obtain a test result.
In the embodiment of the application, a simulation scene is established by using a simulation test platform, and an automatic driving algorithm is operated, so that the automatic driving condition of the whole process of the simulated automatic driving vehicle in the simulation scene under the guidance of the automatic driving algorithm is obtained and is used as a test result. In the embodiment of the application, the constructed simulation scene is not used for testing a single automatic driving function, but is used for testing the whole automatic driving process from the starting point to the end point, so that the requirement of high-level automatic driving test can be met.
In an embodiment of the present application, specifically, as shown in fig. 3, the step S300 may include the following steps:
s310: and acquiring a vehicle dynamic model, a vehicle-mounted sensor model and an automatic driving algorithm.
In one possible embodiment, the autonomous vehicle power model is pre-constructed according to vehicle type, function, etc. and is modeled based on dynamics, including full vehicle, body, engine, steering, braking, front and rear suspensions, tires, aerodynamic effects, etc.
In one possible embodiment, the sensors onboard the autonomous vehicle to be tested are modeled, including cameras, lidar, millimeter wave radar, ultrasonic radar, global positioning systems, inertial measurement units, and the like.
In one embodiment of the present application, the autonomous driving algorithm includes a perception algorithm, a decision algorithm, and a control algorithm.
S320: and integrating at least one simulation scene, a vehicle dynamic model, a vehicle-mounted sensor model and an automatic driving algorithm to obtain at least one test case.
The simulation test platform is used for integrating a simulation scene, a vehicle dynamic model, a vehicle-mounted sensor model and an automatic driving algorithm into a test case, and the test cases correspond to the simulation scene one by one and are equivalent to the test cases corresponding to a dynamic object set one by one.
And integrating a simulation scene, a vehicle power model, a vehicle-mounted sensor model and an automatic driving algorithm to form a closed-loop simulation test system so as to carry out combined simulation test.
S330: executing at least one test case to obtain a test result; the test result indicates test pass information of the autopilot algorithm in the at least one test case.
Specifically, in the process of executing a test case by a simulation test platform, a vehicle-mounted sensor model sends sensing information of a simulation scene in the current test case to a sensing algorithm in an automatic driving algorithm, and the sensing algorithm is subjected to detection and fusion processing to obtain target information, for example, position information of a pedestrian is detected from image information acquired by an image sensor; then, a decision algorithm obtains corresponding decision information according to the target information, for example, the running path of the automatic driving vehicle is re-planned according to the road congestion information; and the control algorithm determines control information according to the decision information so as to control the vehicle to execute corresponding instructions.
If the test case is not in compliance with the standard regulation or is abnormal during the execution of the test case, the test passing information of the automatic driving algorithm is determined, and the test passing information can be represented as passing or failing.
Further, the state of the autonomous vehicle is updated in real time in the simulation scenario.
In the embodiment, a closed-loop simulation test system is formed by integrating the simulation scene, the vehicle power model, the vehicle-mounted sensor model and the automatic driving algorithm, so that the joint simulation test is performed, and the completeness, the authenticity and the accuracy of the automatic driving simulation test are improved.
S400: determining an analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object.
In the embodiment of the application, the test result is further analyzed from the attribute dimension of the target dynamic object, so that the influence degree of each dimension attribute of the target dynamic object on the automatic driving algorithm can be known, the performance of the automatic driving algorithm can be evaluated more carefully, the automatic driving algorithm can be subjected to targeted iterative optimization, and the risk and the cost of road test are reduced.
Optionally, before performing analysis and evaluation, screening and filtering are performed on the obtained test result to obtain an effective test result.
In one embodiment of the present application, as shown in fig. 4, specifically, the step S400 may include the following steps:
s410: and determining the attribute dimension of the target dynamic object, wherein the attribute dimension corresponds to at least one attribute value.
It is feasible that the attribute dimensions of the target dynamic object include number, position, behavior, and the like, as shown in table 1, the attribute values of the number dimension may be 0, 1, 2, 3, and the like, the position dimension may have multiple levels of attributes, and under the condition that the one-level attribute value is right ahead, the position dimension may be further divided into three types according to distance, that is, in table 1, there are 24 types of attribute values of the position dimension in total. The possible situation of the attribute value needs to be determined according to the used dynamic object set, and at least one used dynamic object set can be counted to determine the attribute value of each attribute dimension.
TABLE 1 Attribute dimension and Attribute value
S420: and determining at least one simulation scene set, wherein the simulation scene sets correspond to the attribute values one to one.
Specifically, simulation scenes meeting the attribute values are extracted from at least one simulation scene used according to the attribute values to form a simulation scene set corresponding to the attribute values. Illustratively, for the attribute value-number 3, the simulation scenes with the total number of 3 are taken as a set according to the total number of the target dynamic objects in each simulation scene.
In another possible implementation manner, corresponding test case sets are extracted and formed according to the attribute values, and the test cases are in one-to-one correspondence with the simulation scenes.
S430: and determining the test passing proportion information of the automatic driving algorithm in at least one simulation scene set according to the test result.
In the foregoing embodiment, the test result indicates that the test pass information of the autopilot algorithm in each test case may be represented as pass or fail. The simulation scenes correspond to the test cases one by one, and the test result can also indicate the test passing information of the automatic driving algorithm in each simulation scene. For example, for the simulation scene set corresponding to the attribute value-number 3, the test passing proportion information may be obtained by dividing the number of simulation scenes in the simulation scene set whose test result is passing by the total number of simulation scenes in the simulation scene set corresponding to the attribute value-number 3.
S440: and determining attribute weight corresponding to at least one attribute value.
The ratio of the attribute weights corresponding to the attribute values may be determined according to the ratio of the number of simulation scenes corresponding to the attribute values in the simulation scene set.
It is feasible that, for an attribute dimension, the sum of the attribute weights corresponding to all attribute values involved is 1.
S450: and obtaining analysis information corresponding to at least one attribute value according to the test passing proportion information of the automatic driving algorithm in at least one simulation scene set and the attribute weight corresponding to at least one attribute value.
The method can be feasible, the attribute weight corresponding to the attribute value is multiplied by the test pass proportion information of the simulation scene corresponding to the attribute value, so as to obtain the analysis information of the automatic driving algorithm under the condition of the attribute value, and the performance of the automatic driving algorithm under the condition of the attribute value can be described.
S460: and obtaining analysis information of the automatic driving algorithm in the attribute dimension according to the analysis information corresponding to the at least one attribute value.
The analysis information corresponding to at least one attribute value in the same attribute dimension can be added to obtain the analysis information of the automatic driving algorithm in the attribute dimension. The analysis information may be expressed as a numerical value.
Illustratively, in the numerical dimension, assume that the numerical attribute values are zero, single, multiple (more than 2), and traffic flow (more than 30). Take a single target dynamic object as an example. The analysis information corresponding to the attribute value may be expressed as: score (single target dynamic object) = (number of simulation scenes that contain single target dynamic object and pass test/number of simulation scenes that contain single target dynamic object) × weight corresponding to single target dynamic object. The weights can also be designed according to the normal distribution condition of the number, and the weight accumulation result corresponding to the attribute values of different numbers is 1.
For example, in the position dimension, the vehicle can be divided into eight positions, namely, a front position, a rear position, a front left position, a rear left position, a front right position, a rear right position, a side-by-side left position and a side-by-side right position, according to lane position division, the relative distance between the target dynamic object and the autonomous vehicle is designed to be near (within 50 m), medium (50 m-100 m) and far (beyond 100 m), the attribute weights corresponding to the near, medium and far can also be designed according to test requirements, and the accumulation result of all the attribute value weights is 1. Taking the straight ahead as an example, score (straight ahead) = (number of simulation scenes in which the target dynamic object is directly in front of the autonomous vehicle at a short distance and the test passes/number of simulation scenes in which the target dynamic object is directly in front of the autonomous vehicle at a short distance) × near weight + number of simulation scenes in which the target dynamic object is directly in front of the autonomous vehicle at a middle distance and the test passes/number of simulation scenes in which the target dynamic object is directly in front of the autonomous vehicle at a middle distance) × middle distance weight + number of simulation scenes in which the target dynamic object is directly in front of the autonomous vehicle at a long distance and the test passes/number of simulation scenes in which the target dynamic object is directly in front of the autonomous vehicle at a long distance) × long distance weight. The scores of other positions and relative distances can be designed by referring to the score right in front, and the scores of different positions are weighted and summed according to the corresponding weights of the eight positions to form the score in the position dimension. The presence of at least one target dynamic object directly in front of the autonomous vehicle at close range, i.e. a simulated scene in which the target dynamic object is directly in front of the autonomous vehicle at close range, does not require that all target dynamic objects are directly in front of the autonomous vehicle at close range.
Illustratively, in the behavioral dimension, the behavioral characteristics can be represented by speed, acceleration and steering, for example, the speed can be divided into low speed (below 30 km/h), medium speed (between 30km/h and 60 km/h) and high speed (above 60 km/h), the weights of the low speed, the medium speed and the high speed can be designed according to the test requirements, and the cumulative result of all the weights is 1. The acceleration is divided into acceleration and deceleration, and the weights are each 1/2. The steering can be divided into left steering and right steering with weights of 1/2 each. Here, taking the vehicle speed of the target dynamic object as an example, score (vehicle speed of the target dynamic object) = (the number of simulation scenes in which the target dynamic object is in a low-speed state and the test passes/the number of simulation scenes in which the target dynamic object is in a low-speed state) × low-speed weight + (the number of simulation scenes in which the target dynamic object is in a medium-speed state and the test passes/the number of simulation scenes in which the target dynamic object is in a medium-speed state) × medium-speed weight + (the number of simulation scenes in which the target dynamic object is in a high-speed state and the test passes/the number of simulation scenes in which the target dynamic object is in a high-speed state) × high-speed weight. The scores for acceleration and steering are referenced to the design of the velocity score.
In the above embodiment, the test result of the automatic driving algorithm is further analyzed from the attribute dimension and the attribute value of the dynamic object, and the processing performance of the automatic driving algorithm on the dynamic object in the simulation test can be known according to the Score, for example, if Score (a single target dynamic object) is higher than Score (multiple target dynamic objects), it can be shown that the automatic driving performance becomes worse when the number of the dynamic objects is increased, and further the influence degree of each dimension attribute of the target dynamic object on the automatic driving algorithm can be known, and the performance of the automatic driving algorithm can be evaluated more finely, so that the automatic driving algorithm is optimized iteratively more specifically, and the risk and the cost of the road test are reduced.
In an embodiment of the present application, further, the method may further include:
and adding the analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object to obtain the target analysis information of the automatic driving algorithm, wherein the target analysis information indicates the comprehensive performance of the automatic driving algorithm.
Furthermore, weights corresponding to different attribute dimensions can be designed according to functional requirements or test requirements, and the weights represent the importance of the attribute dimensions. And carrying out weighted summation according to the weight corresponding to the attribute dimension and the analysis information of the attribute dimension to obtain target analysis information capable of measuring the comprehensive performance of the automatic driving algorithm.
In the embodiment, the quality of the automatic driving algorithm can be analyzed and evaluated in detail from different attribute dimensions, the overall measurement can be carried out, and meanwhile, the method can also be used as a comparison basis before and after iterative optimization of the algorithm.
According to the embodiment, the automatic driving test method based on the simulation scene can test the automatic driving algorithm more comprehensively and closer to the real scene through the simulation scene integrated with the virtual traffic dynamic object, the simulation scene in the method is not limited to test only a single automatic driving function, but can test the whole process of high-level automatic driving, and the requirements of the high-level automatic driving test on the integrity, comprehensiveness and accuracy of the simulation test are met. In addition, according to the scheme provided by the application, the test result of the automatic driving algorithm is analyzed from the attribute dimension of the virtual traffic dynamic object, the processing performance of the automatic driving algorithm on each virtual traffic dynamic object in the simulation test can be known, so that the automatic driving algorithm can be evaluated more carefully and accurately, and the optimization iteration of the algorithm is promoted.
The embodiment of the present application further provides an automatic driving test device 500 based on a simulation scenario, as shown in fig. 5, the device 500 may include:
a dynamic object obtaining module 510, configured to obtain at least one dynamic object set, where the dynamic object set includes at least one target dynamic object, and the target dynamic object represents a virtual traffic dynamic object;
a simulation scene generating module 520, configured to generate at least one simulation scene according to the at least one dynamic object set;
a testing module 530, configured to perform a test on an automatic driving algorithm based on the at least one simulation scenario to obtain a test result;
the analysis module 540 is used for determining an analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object.
In one embodiment of the present application, the analysis module 540 may include:
an attribute dimension determination unit, configured to determine an attribute dimension of the at least one set of dynamic objects, where the attribute dimension includes at least one attribute value;
a simulation scene set determining unit, configured to determine at least one simulation scene set, where the simulation scene sets correspond to the attribute values one to one;
the proportion information determining unit is used for determining the test passing proportion information of the automatic driving algorithm in the at least one simulation scene set according to the test result;
an attribute weight determining unit, configured to determine an attribute weight corresponding to the at least one attribute value;
the first analysis unit is used for obtaining analysis information corresponding to at least one attribute value according to the test passing proportion information of the automatic driving algorithm in at least one simulation scene set and the attribute weight corresponding to the at least one attribute value;
and the second analysis unit is used for obtaining the analysis information of the automatic driving algorithm in the attribute dimension according to the analysis information corresponding to the at least one attribute value.
In one embodiment of the present application, the apparatus 500 may further include:
and the comprehensive analysis module is used for adding the analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object to obtain target analysis information of the automatic driving algorithm, and the target analysis information indicates the comprehensive performance of the automatic driving algorithm.
In an embodiment of the present application, the dynamic object obtaining module 510 may include:
a first obtaining unit, configured to obtain a dynamic object library, where the dynamic object library includes at least one dynamic object;
and the second acquisition unit is used for selecting at least one dynamic object from the dynamic object library as at least one target dynamic object and obtaining the dynamic object set from the at least one target dynamic object.
In an embodiment of the present application, the simulation scenario generating module 520 may include:
a third obtaining unit, configured to obtain an initial static simulation scene; the static simulation scene comprises at least one target static object;
and the object adding unit is used for adding at least one target dynamic object in the dynamic object set into the static simulation scene to obtain the simulation scene corresponding to the dynamic object set.
In one embodiment of the present application, the apparatus 500 may further include:
configuring object attribute information of the at least one target dynamic object in the dynamic object set based on real distribution information corresponding to the virtual traffic dynamic object; the object attribute information includes quantity information, position information, or behavior information.
In one embodiment of the present application, the test module 530 may include:
the fourth acquisition unit is used for acquiring a vehicle power model, a vehicle-mounted sensor model and an automatic driving algorithm;
the integration unit is used for integrating the at least one simulation scene, the vehicle dynamic model, the vehicle-mounted sensor model and the automatic driving algorithm to obtain at least one test case; the test cases correspond to the simulation scenes one by one;
the test unit is used for executing the at least one test case to obtain a test result; the test result indicates test pass information of the autopilot algorithm in the at least one test case.
It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
The embodiment of the present application provides a computer device, which includes a processor and a memory, where at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded and executed by the processor to implement an automatic driving test method based on a simulation scenario, which is provided by the above method embodiment.
Fig. 6 is a schematic hardware structure diagram of an apparatus for implementing an automatic driving test method based on a simulation scenario according to an embodiment of the present application, where the apparatus may participate in forming or including the device or system according to the embodiment of the present application. As shown in fig. 6, the apparatus 10 may include one or more (shown with 1002a, 1002b, … …,1002 n) processors 1002 (the processors 1002 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1004 for storing data, and a transmission device 1006 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 6 is only an illustration and is not intended to limit the structure of the electronic device. For example, device 10 may also include more or fewer components than shown in FIG. 6, or have a different configuration than shown in FIG. 6.
It should be noted that the one or more processors 1002 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the device 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).
The memory 1004 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the methods described in the embodiments of the present application, and the processor 1002 executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, so as to implement the above-described automatic driving test method based on simulation scenarios. The memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 1004 may further include memory located remotely from the processor 1002, which may be connected to the device 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 1006 is used for receiving or sending data via a network. Specific examples of such networks may include wireless networks provided by the communication provider of the device 10. In one example, the transmission device 1006 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 1006 can be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the device 10 (or mobile device).
The present application further provides a computer-readable storage medium, where the computer-readable storage medium may be disposed in a server to store at least one instruction or at least one program for implementing an automatic driving test method based on a simulation scenario in the method embodiment, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the automatic driving test method based on a simulation scenario provided in the method embodiment.
Optionally, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Embodiments of the present invention also provide a computer program product or a computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the automatic driving test method based on the simulation scenario provided in the above-mentioned various optional embodiments.
It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device and storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple and reference may be made to the partial description of the method embodiments for relevant points.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. An automatic driving test method based on a simulation scene is characterized by comprising the following steps:
acquiring at least one dynamic object set, wherein the dynamic object set comprises at least one target dynamic object, and the target dynamic object represents a virtual traffic dynamic object;
generating at least one simulation scene according to the at least one dynamic object set;
based on the at least one simulation scene, testing an automatic driving algorithm to obtain a test result;
determining an analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object.
2. The method of claim 1, wherein determining the analysis result of the autonomous driving algorithm based on the test result comprises:
determining an attribute dimension of the target dynamic object, wherein the attribute dimension corresponds to at least one attribute value;
determining at least one simulation scene set, wherein the simulation scene sets correspond to the attribute values one to one;
determining the test passing proportion information of the automatic driving algorithm in the at least one simulation scene set according to the test result;
determining attribute weight corresponding to the at least one attribute value;
obtaining analysis information corresponding to the at least one attribute value according to the test passing proportion information of the automatic driving algorithm in the at least one simulation scene set and the attribute weight corresponding to the at least one attribute value;
and obtaining the analysis information of the automatic driving algorithm in the attribute dimension according to the analysis information corresponding to the at least one attribute value.
3. The method of claim 1, further comprising:
and adding the analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object to obtain target analysis information of the automatic driving algorithm, wherein the target analysis information indicates the comprehensive performance of the automatic driving algorithm.
4. The method of claim 1, wherein obtaining at least one set of dynamic objects comprises:
acquiring a dynamic object library, wherein the dynamic object library comprises at least one dynamic object;
and selecting at least one dynamic object from the dynamic object library as at least one target dynamic object, and obtaining the dynamic object set from the at least one target dynamic object.
5. The method of claim 1, wherein the generating at least one simulation scene from the at least one set of dynamic objects comprises:
acquiring an initial static simulation scene; the static simulation scene comprises at least one target static object;
and adding at least one target dynamic object in the dynamic object set into the static simulation scene to obtain the simulation scene corresponding to the dynamic object set.
6. The method of claim 4, further comprising:
configuring object attribute information of the at least one target dynamic object in the dynamic object set based on real distribution information corresponding to the virtual traffic dynamic object; the object attribute information includes quantity information, position information, or behavior information.
7. The method of claim 1, wherein performing a test on an autonomous driving algorithm based on the at least one simulation scenario, resulting in a test result, comprises:
obtaining a vehicle power model, a vehicle-mounted sensor model and an automatic driving algorithm;
integrating the at least one simulation scene, the vehicle dynamic model, the vehicle-mounted sensor model and the automatic driving algorithm to obtain at least one test case; the test cases correspond to the simulation scenes one by one;
executing the at least one test case to obtain a test result; the test result indicates test pass information of the autopilot algorithm in the at least one test case.
8. An autopilot testing apparatus based on a simulation scenario, the apparatus comprising:
the system comprises a dynamic object acquisition module, a virtual traffic dynamic object acquisition module and a virtual traffic dynamic object acquisition module, wherein the dynamic object acquisition module is used for acquiring at least one dynamic object set, the dynamic object set comprises at least one target dynamic object, and the target dynamic object represents a virtual traffic dynamic object;
the simulation scene generation module is used for generating at least one simulation scene according to the at least one dynamic object set;
the test module is used for executing a test on the automatic driving algorithm based on the at least one simulation scene to obtain a test result;
the analysis module is used for determining the analysis result of the automatic driving algorithm according to the test result; the analysis result indicates analysis information of the automatic driving algorithm in each attribute dimension of the target dynamic object.
9. A computer-readable storage medium, wherein at least one instruction or at least one program is stored in the computer-readable storage medium, and the at least one instruction or the at least one program is loaded by a processor and executed to implement a method for automatic driving test based on simulation scenario as claimed in any one of claims 1 to 7.
10. A computer device comprising a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement a method for simulation scenario-based autopilot testing as claimed in any one of claims 1 to 7.
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