CN111123920A - Method and device for generating automatic driving simulation test scene - Google Patents
Method and device for generating automatic driving simulation test scene Download PDFInfo
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G05D1/02—Control of position or course in two dimensions
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- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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Abstract
The embodiment of the invention provides a method and a device for generating an automatic driving simulation test scene, which are characterized in that data extraction is carried out based on a vehicle-mounted camera and a radar, the signal type is limited according to the requirement of a simulation scene, behavior and action parameters in the existing period of a self vehicle and all target objects in the scene are extracted, the related attribute parameters of the natural environment are extracted, a dynamic traffic flow scene file is constructed, and the dynamic traffic flow scene file is imported into a simulation platform to generate the simulation scene; the method can accurately restore the original data of natural driving, can eliminate the problems of data deviation and efficiency caused by artificial restoration, and improves the reality of a simulation scene.
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 generating an automatic driving simulation test 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 possible scenes, a simulation test scene library is formed by carrying out limited mapping on an infinite driving environment, and the mapping relation of natural driving data is restored in simulation software by a manual construction mode at present, so that software simulation or real automobile test still has great limitation.
Disclosure of Invention
The embodiment of the invention provides a method and a device for generating an automatic driving simulation test scene, which are used for solving the problems of precision loss and high labor time cost of manual restoration of a natural driving scene in the prior art.
In a first aspect, an embodiment of the present invention provides an automatic driving simulation test scenario generation method, including:
acquiring camera data and radar data in real vehicle driving data, and acquiring traffic participation objects in a driving scene based on the camera data and the radar data;
the method comprises the steps of obtaining transverse distance change and longitudinal speed change of each traffic participant relative to a vehicle, dividing all the traffic participants into independent individuals which are not affected with each other based on the transverse distance change and the longitudinal speed change, and fusing to generate dynamic traffic flow data.
Preferably, the method further comprises the following steps:
extracting environmental parameters, and determining the temperature, air humidity, sunlight degree, rainfall, visibility in foggy days and altitude in a driving scene.
Preferably, the acquiring of the lateral distance change and the longitudinal speed change of each traffic participant relative to the own vehicle specifically includes:
obtaining a self-vehicle running track based on the self-vehicle transverse running track and the self-vehicle longitudinal running track;
and determining the target object motion track and the target object motion speed of each traffic participant in the existence period of the traffic participant based on the running track of the vehicle, the longitudinal distance, the transverse distance and the observation angle of each traffic participant and the vehicle.
Preferably, the obtaining of the running track of the vehicle based on the transverse running track and the longitudinal running track of the vehicle specifically includes:
setting initial lane positions of the traffic participation objects by taking the starting point of the own vehicle lane as a central point;
the method comprises the steps of obtaining a transverse driving track of the self-vehicle based on the distance between the center point of the self-vehicle and lane lines on two sides, taking the average value of the speed sum of the self-vehicle in each frame as the longitudinal speed of the self-vehicle, determining the longitudinal driving track of the self-vehicle, and obtaining the driving track of the self-vehicle based on the transverse driving track of the self-vehicle and the longitudinal driving track of the self-vehicle.
Preferably, determining the target object motion trajectory and the target object motion speed of each traffic participant in the existing period thereof specifically includes:
and acquiring the movement speed of the target object of the traffic participation object in the existence period of the traffic participation object based on the longitudinal speed of the vehicle and the relative speed of the vehicle to the target vehicle.
Preferably, the dividing all the traffic participation objects into separate individuals which do not influence each other based on the lateral distance variation and the longitudinal speed variation specifically includes:
counting a transverse distance change curve and a movement speed change curve of each traffic participant, and setting the times of triggers in preset time and a trigger mechanism of transverse distance change and longitudinal speed change;
based on the trigger mechanism, all traffic participation objects are divided into independent individuals which do not influence each other, and the action of each traffic participation object is triggered by taking seconds as the frequency so as to restore the dynamic traffic flow in the natural driving scene.
Preferably, the setting of the number of triggers within a preset time and the trigger mechanism of the lateral distance change and the longitudinal distance change specifically includes:
setting the number of times of a trigger by taking seconds as a unit, and setting a transverse distance change trigger mechanism and a longitudinal speed change trigger mechanism;
a transverse distance change triggering mechanism, which determines a transverse change parameter by taking the central line of the initial lane of the traffic participation object as 0, the left is a positive value, and the right is a negative value;
the longitudinal speed change triggers the mechanism to determine the speed parameter in seconds according to the speed change curve.
In a second aspect, an embodiment of the present invention provides an automatic driving simulation test scenario generating apparatus, including:
the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring camera data and radar data in real vehicle running data and acquiring traffic participation objects in a running scene based on the camera data and the radar data;
and the second module is used for acquiring the transverse distance change and the longitudinal speed change of each traffic participant relative to the vehicle, and dividing all the traffic participants into independent individuals which are not influenced by each other on the basis of the transverse distance change and the longitudinal speed change so as to generate dynamic traffic flow data in a fusion manner.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for generating the autopilot simulation test scenario according to the embodiment of the first aspect of the present invention when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for generating the automatic driving simulation test scenario according to the embodiment of the first aspect of the present invention.
According to the method and the device for generating the automatic driving simulation test scene, data extraction is carried out based on a vehicle-mounted camera and a radar, the signal type is limited according to the requirement of a simulation scene, behavior and action parameters in the existing period of a self vehicle and all target objects in the scene are extracted, the related attribute parameters of the natural environment are extracted, a dynamic traffic flow scene file is constructed, and the dynamic traffic flow scene file is imported into a simulation platform to generate the simulation scene; the method can accurately restore the original data of natural driving, can eliminate the problems of data deviation and efficiency caused by artificial restoration, and improves the reality of a simulation scene.
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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 flow chart of a method for generating an autopilot simulation test scenario according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of a method for generating an autopilot simulation test scenario according to an embodiment of the 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.
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 possible scenes, a simulation test scene library is formed by carrying out limited mapping on an infinite driving environment, and the mapping relation of natural driving data is restored in simulation software by a manual construction mode at present, so that software simulation or real automobile test still has great limitation. Therefore, the embodiment of the invention provides a method and a device for generating an automatic driving simulation test scene, which are used for solving the problems of precision loss and high labor time consumption cost of manually restoring a natural driving scene in the prior art. The following description and description will proceed with reference being made to various embodiments.
Fig. 1 and fig. 2 provide a method for generating an automatic driving simulation test scenario according to an embodiment of the present invention, including:
acquiring camera data and radar data in real vehicle driving data, and acquiring traffic participation objects in a driving scene based on the camera data and the radar data;
the method comprises the steps of obtaining transverse distance change and longitudinal speed change of each traffic participant relative to a vehicle, dividing all the traffic participants into independent individuals which are not affected with each other based on the transverse distance change and the longitudinal speed change, and fusing to generate dynamic traffic flow data.
In this embodiment, as a preferred implementation, data extraction is performed based on a vehicle-mounted camera and a radar, signal types are limited according to requirements of a simulation scene, behavior parameters of vehicles and all targets in a scene in a period of existence are extracted, natural environment related attribute parameters are extracted, a dynamic traffic flow scene file is constructed, and the dynamic traffic flow scene file is imported into a simulation platform to generate the simulation scene; the method can accurately restore the original data of natural driving, can eliminate the problems of data deviation and efficiency caused by artificial restoration, and improves the reality of a simulation scene.
On the basis of the above embodiment, the method further includes:
extracting environmental parameters, and determining the temperature, air humidity, sunlight degree, rainfall, visibility in foggy days and altitude in a driving scene.
On the basis of the above embodiments, acquiring the lateral distance change and the longitudinal speed change of each traffic participant relative to the host vehicle specifically includes:
obtaining a self-vehicle running track based on the self-vehicle transverse running track and the self-vehicle longitudinal running track;
and determining the target object motion track and the target object motion speed of each traffic participant in the existence period of the traffic participant based on the running track of the vehicle, the longitudinal distance, the transverse distance and the observation angle of each traffic participant and the vehicle.
In this embodiment, as a preferred embodiment, the self-vehicle transverse travel track is obtained by the distance between the self-vehicle center point and the lane lines on both sides, and the self-vehicle longitudinal travel track is determined, and the self-vehicle travel track is obtained based on the self-vehicle transverse travel track and the self-vehicle longitudinal travel track.
On the basis of the above embodiments, obtaining the own vehicle travel track based on the own vehicle transverse travel track and the own vehicle longitudinal travel track specifically includes:
setting initial lane positions of the traffic participation objects by taking the starting point of the own vehicle lane as a central point;
the method comprises the steps of obtaining a transverse driving track of the self-vehicle based on the distance between the center point of the self-vehicle and lane lines on two sides, taking the average value of the speed sum of the self-vehicle in each frame as the longitudinal speed of the self-vehicle, determining the longitudinal driving track of the self-vehicle, and obtaining the driving track of the self-vehicle based on the transverse driving track of the self-vehicle and the longitudinal driving track of the self-vehicle.
In this embodiment, as a preferred implementation manner, the initial lanes of the own vehicle and all the objects in the initialization scene are set, and the specific positions of the traffic participants in the initial lanes are set with the central points of the starting points of the respective lanes as (0, 0).
The motion trail of road traffic participants except the self vehicle in the existence period is determined through the running trail of the self vehicle, the longitudinal distance LonD between the target vehicle and the self vehicle, the transverse distance LatD and the observation angle alpha.
On the basis of the above embodiments, determining the target object motion trajectory and the target object motion speed of each traffic participant in the existing period thereof specifically includes:
and acquiring the movement speed of the target object of the traffic participation object in the existence period of the traffic participation object based on the longitudinal speed of the vehicle and the relative speed of the vehicle to the target vehicle.
In the present embodiment, as a preferred embodiment, the speed of the target vehicle in its existence period can be obtained by using the vehicle longitudinal speed V and the relative speed V' with respect to the target vehicle.
On the basis of the foregoing embodiments, segmenting all the traffic participation objects into separate individuals that do not affect each other based on the lateral distance variation and the longitudinal speed variation specifically includes:
counting a transverse distance change curve and a movement speed change curve of each traffic participant, and setting the times of triggers in preset time and a trigger mechanism of transverse distance change and longitudinal speed change;
based on the trigger mechanism, all traffic participation objects are divided into independent individuals which do not influence each other, and the action of each traffic participation object is triggered by taking seconds as the frequency so as to restore the dynamic traffic flow in the natural driving scene.
On the basis of the above embodiments, the setting of the number of times of the trigger within the preset time and the trigger mechanism of the lateral distance change and the longitudinal distance change specifically includes:
setting the number of times of a trigger by taking seconds as a unit, and setting a transverse distance change trigger mechanism and a longitudinal speed change trigger mechanism;
a transverse distance change triggering mechanism, which determines a transverse change parameter by taking the central line of the initial lane of the traffic participation object as 0, the left is a positive value, and the right is a negative value;
the longitudinal speed change triggers the mechanism to determine the speed parameter in seconds according to the speed change curve.
In this embodiment, as a preferred implementation, the speed variation curve and the transverse distance variation curve are counted, the number of triggers is set in seconds,
the trigger mechanism is divided into the following two types:
A. and determining a transverse change parameter by taking the central line of the initial lane of the target vehicle as 0, taking the left as a positive value and taking the right as a negative value (LaneChange).
B. The speed parameters were determined from the speed profile in seconds, from the longitudinal speed change (SpeedChange).
With the trigger mechanism, all road participants are segmented into separate individuals that do not affect each other. And triggering the target vehicle to act by taking seconds as frequency to accurately restore the dynamic traffic flow in the natural driving scene.
The embodiment of the invention also provides a device for generating the automatic driving simulation test scene, which is based on the automatic driving simulation test scene generation method in the embodiments and comprises the following steps:
the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring camera data and radar data in real vehicle running data and acquiring traffic participation objects in a running scene based on the camera data and the radar data;
and the second module is used for acquiring the transverse distance change and the longitudinal speed change of each traffic participant relative to the vehicle, and dividing all the traffic participants into independent individuals which are not influenced by each other on the basis of the transverse distance change and the longitudinal speed change so as to generate dynamic traffic flow data in a fusion manner.
An embodiment of the present invention provides an electronic device, as shown in fig. 3, including: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a communication bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the communication bus 504. The processor 501 may call a computer program running on the memory 503 and on the processor 501 to execute the automatic driving simulation test scenario generation method provided by the above embodiments, for example, including:
acquiring camera data and radar data in real vehicle driving data, and acquiring traffic participation objects in a driving scene based on the camera data and the radar data;
the method comprises the steps of obtaining transverse distance change and longitudinal speed change of each traffic participant relative to a vehicle, dividing all the traffic participants into independent individuals which are not affected with each other based on the transverse distance change and the longitudinal speed change, and fusing to generate dynamic traffic flow data.
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.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to, when executed by a processor, perform the method for generating an autopilot simulation test scenario provided in the foregoing embodiments, for example, the method includes:
acquiring camera data and radar data in real vehicle driving data, and acquiring traffic participation objects in a driving scene based on the camera data and the radar data;
the method comprises the steps of obtaining transverse distance change and longitudinal speed change of each traffic participant relative to a vehicle, dividing all the traffic participants into independent individuals which are not affected with each other based on the transverse distance change and the longitudinal speed change, and fusing to generate dynamic traffic flow data.
In summary, the method and the device for generating the automatic driving simulation test scene provided by the embodiment of the invention extract data based on the vehicle-mounted camera and the radar, limit the signal type according to the requirement of the simulation scene, extract behavior parameters of the self vehicle and all targets in the scene in the existing period, extract natural environment related attribute parameters, construct a dynamic traffic flow scene file, and import the dynamic traffic flow scene file into the simulation platform to generate the simulation scene; the method can accurately restore the original data of natural driving, can eliminate the problems of data deviation and efficiency caused by artificial restoration, and improves the reality of a simulation scene.
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 (10)
1. A method for generating an automatic driving simulation test scene is characterized by comprising the following steps:
acquiring camera data and radar data in real vehicle driving data, and acquiring traffic participation objects in a driving scene based on the camera data and the radar data;
the method comprises the steps of obtaining transverse distance change and longitudinal speed change of each traffic participant relative to a vehicle, dividing all the traffic participants into independent individuals which are not affected with each other based on the transverse distance change and the longitudinal speed change, and fusing to generate dynamic traffic flow data.
2. The method of generating an autopilot simulation test scenario of claim 1, further comprising:
extracting environmental parameters, and determining the temperature, air humidity, sunlight degree, rainfall, visibility in foggy days and altitude in a driving scene.
3. The method for generating the automatic driving simulation test scenario according to claim 1, wherein obtaining lateral distance changes and longitudinal speed changes of each traffic participant relative to the own vehicle specifically comprises:
obtaining a self-vehicle running track based on the self-vehicle transverse running track and the self-vehicle longitudinal running track;
and determining the target object motion track and the target object motion speed of each traffic participant in the existence period of the traffic participant based on the running track of the vehicle, the longitudinal distance, the transverse distance and the observation angle of each traffic participant and the vehicle.
4. The method for generating the automatic driving simulation test scenario according to claim 3, wherein the obtaining of the driving trajectory of the vehicle based on the lateral driving trajectory of the vehicle and the longitudinal driving trajectory of the vehicle specifically comprises:
setting initial lane positions of the traffic participation objects by taking the starting point of the own vehicle lane as a central point;
the method comprises the steps of obtaining a transverse driving track of the self-vehicle based on the distance between the center point of the self-vehicle and lane lines on two sides, taking the average value of the speed sum of the self-vehicle in each frame as the longitudinal speed of the self-vehicle, determining the longitudinal driving track of the self-vehicle, and obtaining the driving track of the self-vehicle based on the transverse driving track of the self-vehicle and the longitudinal driving track of the self-vehicle.
5. The method for generating the automatic driving simulation test scenario according to claim 4, wherein determining the target object motion trajectory and the target object motion speed of each traffic participant in the existing period specifically comprises:
and acquiring the movement speed of the target object of the traffic participation object in the existence period of the traffic participation object based on the longitudinal speed of the vehicle and the relative speed of the vehicle to the target vehicle.
6. The method for generating the automated driving simulation test scenario according to claim 3, wherein the segmenting all the traffic participant objects into separate individuals that do not affect each other based on the lateral distance variation and the longitudinal speed variation specifically comprises:
counting a transverse distance change curve and a movement speed change curve of each traffic participant, and setting the times of triggers in preset time and a trigger mechanism of transverse distance change and longitudinal speed change;
based on the trigger mechanism, all traffic participation objects are divided into independent individuals which do not influence each other, and the action of each traffic participation object is triggered by taking seconds as the frequency so as to restore the dynamic traffic flow in the natural driving scene.
7. The method for generating the automated driving simulation test scenario of claim 6, wherein the setting of the number of triggers within a preset time and the trigger mechanism of the lateral distance change and the longitudinal distance change specifically includes:
setting the number of times of a trigger by taking seconds as a unit, and setting a transverse distance change trigger mechanism and a longitudinal speed change trigger mechanism;
a transverse distance change triggering mechanism, which determines a transverse change parameter by taking the central line of the initial lane of the traffic participation object as 0, the left is a positive value, and the right is a negative value;
the longitudinal speed change triggers the mechanism to determine the speed parameter in seconds according to the speed change curve.
8. An automatic driving simulation test scenario generation device, comprising:
the system comprises a first module, a second module and a third module, wherein the first module is used for acquiring camera data and radar data in real vehicle running data and acquiring traffic participation objects in a running scene based on the camera data and the radar data;
and the second module is used for acquiring the transverse distance change and the longitudinal speed change of each traffic participant relative to the vehicle, and dividing all the traffic participants into independent individuals which are not influenced by each other on the basis of the transverse distance change and the longitudinal speed change so as to generate dynamic traffic flow data in a fusion manner.
9. 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 generating an autopilot simulation test scenario of any of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the automated driving simulation test scenario generation method of any of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911261261.8A CN111123920A (en) | 2019-12-10 | 2019-12-10 | Method and device for generating automatic driving simulation test scene |
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