CN111177887A - Method and device for constructing simulation track data based on real driving scene - Google Patents
Method and device for constructing simulation track data based on real driving scene Download PDFInfo
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- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
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- G07C5/0841—Registering performance data
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Abstract
The embodiment of the invention provides a method and a device for constructing simulation track data based on a real driving scene, wherein the method comprises the following steps: extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data when a real driving scene is reproduced; and constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters, and inserting the sampling field data into an original field data set with a black box problem. When the sampling field data are used for reproducing a real driving scene, and when the problem of black box data in the reproduced real driving scene occurs, the missing field data are automatically filled, so that the data of each second in the simulation scene becomes complete, and the constructed simulation scene can reproduce the real driving scene in a certain period of time by using the field data.
Description
Technical Field
The embodiment of the invention relates to the technical field of automatic driving, in particular to a method and a device for constructing simulation track data based on a real driving scene.
Background
With the gradual trend of the flow ceiling of the mobile internet, the digital fusion of the internet and the traditional industries such as agriculture, industry, building industry and service industry becomes a new trend, and the technology of combining the industrial internet with 5G, cloud computing and the like can accelerate the economic transformation of the entity. The automobile serves as an indispensable intelligent mobile device in an industrial internet scene, and with the innovation of new-generation automobile technical revolution such as new energy, intelligent internet and automatic driving, a reproducible and circular business mode closed loop is created by combining different landing scenes.
The automatic driving means that the intelligent automobile senses the driving environment around the automobile by installing sensor equipment (including 2D (two-dimensional) photographing visual sensing, laser radar, millimeter wave radar and the like) arranged on the automobile, fast operation and analysis are carried out by combining a navigation high-precision map, potential road condition environments are continuously simulated and deeply learned and judged, the optimal or most suitable driving route and mode of the automobile are further planned by means of an algorithm, and then the optimal or most suitable driving route and mode are fed back to a control system through a chip to carry out actual operation actions such as braking and steering wheel control.
At present, automatic driving is in a high-speed development stage, and the development and testing of the corresponding system are rapidly developed, but the industry has not agreed how to perform safety testing in the real world. In a real road, because unknown scenes are difficult to exhaust, the number of scenes in a test scene in a limited range is extremely large because of a plurality of combinations of roads, environments and traffic participants, and investigation finds that existing software or platforms in the industry at present do not optimize the use cases of the test scene and output a relatively intuitive scene description. For an automatic driving automobile, a test environment is an important ring in an evaluation system, because the environment has the characteristics of high uncertainty, non-repeatability, unpredictability, inexhaustibility and the like, a tested object can be positioned in all scenes which are possibly met, a simulation test scene library is formed by carrying out limited mapping on an infinite driving environment, and data and standards on which simulation scenes are constructed are various.
Disclosure of Invention
The embodiment of the invention provides a method and a device for constructing simulation track data based on a real driving scene, which are used for solving the problem that a black box problem occurs in the real driving scene in the prior art.
In a first aspect, an embodiment of the present invention provides a method for constructing simulated trajectory data based on a real driving scene, including:
extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data when a real driving scene is reproduced;
and constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters, and inserting the sampling field data into an original field data set with a black box problem.
Preferably, the scene description parameters include the number of target objects, the type of target objects, the action behavior parameters of the own vehicle and the target objects, the distance of the target objects, road factors and weather factors.
Preferably, the vehicle and target object action behavior parameters include vehicle and target object action behaviors, and occurrence time, occurrence number and duration of the vehicle and target object action behaviors, and the vehicle and target object action behaviors include acceleration, turning, lane changing and overtaking.
Preferably, before extracting the driving scene description parameters from the real driving scene video based on the time period of black box problem of the data when the real driving scene is reproduced, the method further includes:
and filtering and detecting the data of the reproduced real driving scene, and if the data values in a certain period are all 0 or no data is detected, judging that the data in the period has the black box problem.
Preferably, before extracting the driving scene description parameters from the real driving scene video based on the time period of black box problem of the data when the real driving scene is reproduced, the method further includes:
the method comprises the steps of obtaining an actual motion state of a target object, obtaining a recurring motion state of the target object in the same time period based on sampling field data when a real driving scene is reproduced, and judging that the sampling field data in the time period has a black box problem if the actual motion state is different from the recurring motion state.
In a second aspect, an embodiment of the present invention provides an apparatus for constructing simulated trajectory data based on a real driving scene, including:
the driving scene description module is used for extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data during reproduction of a real driving scene;
and the second module is used for constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters and inserting the sampling field data into an original field data set with a black box problem.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the method for constructing simulated trajectory data based on real driving scenarios according to the embodiment of the first aspect of the present invention when executing the program.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for constructing simulated trajectory data based on real driving scenarios as described in embodiments of the first aspect of the present invention.
According to the method and the device for constructing the simulation track data based on the real driving scene, provided by the embodiment of the invention, when the real driving scene is reproduced by using the sampling field data, and when the problem of black box data in the reproduced real driving scene occurs, the missing field data is automatically filled, so that the data of each second in the simulation scene becomes complete, and the constructed simulation scene can reproduce the real driving scene in a certain period of time by using the field data.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a block flow diagram of a method for constructing simulated trajectory data based on a real driving scenario according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for constructing simulated trajectory data based on a real driving scene according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first" and "second" in the embodiments of the present application are used 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 the description of the present application, the terms "comprise" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a system, product or apparatus that comprises a list of elements or components is not limited to only those elements or components but may alternatively include other elements or components not expressly listed or inherent to such product or apparatus. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
With the rapid development of the automatic driving technology, people need to construct a large number of simulation driving scenes to assist the research of the automatic driving technology. The data and standards on which the simulation scene is constructed are various, and when the drive test is carried out to collect the video, the field data which is derived by the sampling sensor and describes each action behavior of the driving scene is also included in addition to the video file. The device inevitably has the problem of data acquisition loss of a certain section, and the subsequent reproduction simulation scene is influenced. According to the embodiment of the invention, when the real driving scene is reproduced by using the sampling field data, and the problem of black box data in the reproduced real driving scene is solved, the missing field data is automatically filled, so that the data of each second in the simulation scene becomes complete, and the simulation scene can be constructed to reproduce the real driving scene in a certain period of time by using the field data. The following description and description will proceed with reference being made to various embodiments.
Fig. 1 and fig. 2 provide a method for constructing simulated trajectory data based on a real driving scene in an embodiment of the present invention, including:
s11, extracting driving scene description parameters from the real driving scene video based on the time period of black box problem of data when the real driving scene is reproduced;
s12, constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters, and inserting the sampling field data into an original field data set with a black box problem.
In this embodiment, as a preferred implementation, when a certain segment of real driving scene is reproduced by using simulation software, parameters of the segment of driving scene, such as driving behavior of a target object, distance of the target object, weather factors, road factors, and the like within a certain segment of black box data time period (in which driving scene data is missing) are obtained by referring to real driving video data, and then a writing tool automatically fills up missing data in sampling field data to obtain complete sampling data, so that the simulation software can reproduce the real driving scene according to the complete sampling field data.
Specifically, in this embodiment, based on the real driving scene video, the driving scene description parameters are extracted from the time period in which the black box data problem occurs, such as the type of the target object, the type of the road, the width of the lane, whether the target object accelerates, whether the target object is parallel to the line, whether the target object turns a corner, and the time when the target object acts; and a tool is used for constructing field data and automatically inserting the constructed field data into the data set with black box data problems, so that the completeness of the data is ensured when the driving scene is reproduced.
Analyzing the original field data set with data missing and the extracted scene description, compiling a tool, and automatically constructing field data to be inserted into the original field data set so as to enable the data to become complete. For example, data of 5 th to tenth second are missing, the original data set is analyzed to obtain the speed of the fifth second and the speed of the tenth second, the scene is described as the accelerated driving and overtaking of a trolley, the target object in the missing data is the trolley according to the scene description, the acceleration is calculated according to the behavior of acceleration, the overtaking behavior is set as the target overtaking time point, and field data of each second is automatically generated by using a tool, so that the missing driving scene data can be obtained.
On the basis of the above embodiment, the scene description parameters include the number of target objects, the type of target objects, the motion behavior parameters of the vehicle and the target objects, the distance of the target objects, road factors and weather factors.
In this embodiment, as a preferred implementation, the extracted scene description dimensions include: the type of road, the weather type, the number of target objects, the type of target object (person, trolley, truck, etc.), the initial speed and acceleration of the host vehicle and the target, the action behavior of the host vehicle and the target object (lane change, overtaking, acceleration, etc.), the time when the action behavior of the host vehicle and the target object occurs, the occurrence number and duration, and so on.
On the basis of the above embodiments, the vehicle and target object motion behavior parameters include vehicle and target object motion behaviors, and occurrence time, occurrence number, and duration of the vehicle and target object motion behaviors, and the vehicle and target object motion behaviors include acceleration, turning, lane change, and passing.
On the basis of the above embodiments, before extracting the driving scene description parameters from the real driving scene video based on the time period when the black box problem occurs in the data when the real driving scene is reproduced, the method further includes:
and filtering and detecting the data of the reproduced real driving scene, and if the data values in a certain period are all 0 or no data is detected, judging that the data in the period has the black box problem.
In this embodiment, as a preferred embodiment, when constructing a file of a simulation scene using collected data, preprocessing is performed on the data once, for example, if the rule for determining that the data is missing is to determine that a large number of data fields are zero or empty, it is determined that the data has a black box problem.
On the basis of the above embodiments, before extracting the driving scene description parameters from the real driving scene video based on the time period when the black box problem occurs in the data when the real driving scene is reproduced, the method further includes:
the method comprises the steps of obtaining an actual motion state of a target object, obtaining a recurring motion state of the target object in the same time period based on sampling field data when a real driving scene is reproduced, and judging that the sampling field data in the time period has a black box problem if the actual motion state is different from the recurring motion state.
In this embodiment, as a preferred embodiment, abnormal data is determined based on a common motion rule, and if a large deviation is found between the abnormal data and a traffic motion and a motion trajectory of a real scene, it is determined that the black box problem exists in the data of the sampling field in the time period; if the real driving scene is reproduced, the scene is clearly accelerated, the field also indicates acceleration, the speed is found to be reduced during processing, or the lane is clearly changed but the data is not displayed, namely the actual motion state is different from the reproduced motion state, the problem that the sampled field data in the period has a black box is judged.
The embodiment of the invention also provides a device for constructing simulation track data based on the real driving scene, and the method for constructing the simulation track data based on the real driving scene in the embodiments comprises the following steps:
the driving scene description module is used for extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data during reproduction of a real driving scene;
and the second module is used for constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters and inserting the sampling field data into an original field data set with a black box problem.
An embodiment of the present invention provides an electronic device, and as shown in fig. 3, the server may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may invoke logic instructions in the memory 830 to perform the methods of constructing simulated trajectory data based on real driving scenarios provided by the various embodiments described above, including, for example:
extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data when a real driving scene is reproduced;
and constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters, and inserting the sampling field data into an original field data set with a black box problem.
In addition, the logic instructions in the memory 503 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method for constructing simulated trajectory data based on real driving scenes, provided by the foregoing embodiments, when executed by a processor, for example, the method includes:
extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data when a real driving scene is reproduced;
and constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters, and inserting the sampling field data into an original field data set with a black box problem.
In summary, according to the method and the device for constructing the simulated trajectory data based on the real driving scene provided by the embodiments of the present invention, when the real driving scene is reproduced by using the sampled field data, and when a black box data problem occurs in the reproduced real driving scene, the missing field data is automatically filled, so that the data of each second in the simulated scene becomes complete, and the constructed simulated scene can reproduce the real driving scene in a certain period of time by using the field data.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A method for constructing simulation track data based on a real driving scene is characterized by comprising the following steps:
extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data when a real driving scene is reproduced;
and constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters, and inserting the sampling field data into an original field data set with a black box problem.
2. The method for constructing simulated trajectory data based on real driving scenarios as claimed in claim 1, wherein said scenario description parameters comprise number of target objects, type of target objects, own vehicle and target object action behavior parameters, distance of target objects, road factors and weather factors.
3. The method for constructing the simulated trajectory data based on the real driving scenario as claimed in claim 2, wherein the parameters of the motion behaviors of the host vehicle and the target object comprise the motion behaviors of the host vehicle and the target object, and the occurrence time, the occurrence number and the duration of the motion behaviors of the host vehicle and the target object, and the motion behaviors of the host vehicle and the target object comprise acceleration, turning, lane changing and overtaking.
4. The method for constructing the simulation trajectory data based on the real driving scene as claimed in claim 1, wherein before extracting the driving scene description parameters from the real driving scene video based on the time period of black box problem of the data when the real driving scene is reproduced, further comprising:
and filtering and detecting the data of the reproduced real driving scene, and if the data values in a certain period are all 0 or no data is detected, judging that the data in the period has the black box problem.
5. The method for constructing the simulation trajectory data based on the real driving scene as claimed in claim 1, wherein before extracting the driving scene description parameters from the real driving scene video based on the time period of black box problem of the data when the real driving scene is reproduced, further comprising:
the method comprises the steps of obtaining an actual motion state of a target object, obtaining a recurring motion state of the target object in the same time period based on sampling field data when a real driving scene is reproduced, and judging that the sampling field data in the time period has a black box problem if the actual motion state is different from the recurring motion state.
6. An apparatus for constructing simulated trajectory data based on a real driving scenario, comprising:
the driving scene description module is used for extracting driving scene description parameters from a real driving scene video based on a time period when a black box problem occurs in data during reproduction of a real driving scene;
and the second module is used for constructing sampling field data for reproducing a real driving scene based on the driving scene description parameters and inserting the sampling field data into an original field data set with a black box problem.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method of constructing simulated trajectory data based on real driving scenarios as claimed in any one of claims 1 to 5.
8. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of constructing simulated trajectory data based on real driving scenarios as claimed in any one of claims 1 to 5.
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| CN115440028B (en) * | 2022-07-22 | 2024-01-30 | 中智行(苏州)科技有限公司 | Traffic road scene classification method based on labeling |
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Application publication date: 20200519 |