CN111797001A - Method for constructing automatic driving simulation test model based on SCANeR - Google Patents
Method for constructing automatic driving simulation test model based on SCANeR Download PDFInfo
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Abstract
The invention provides a method for constructing an automatic driving simulation test model based on SCANeR, which comprises the following steps: s1, deconstruction and reconstruction of scene data, and extracting scene characteristic elements; s2, editing a static scene according to the scene characteristic elements; s3, editing a dynamic scene according to the scene characteristic elements; s4, interacting the contents in the steps S2 and S3 in the SCANeR to construct a virtual driving test scene; s5, judging whether the test conditions are met, if not, returning to the step S2; if yes, the test is started. The method for constructing the automatic driving simulation test model based on the SCANeR combines the technologies of cloud computing, high-precision maps, virtual reality and the like, the driving scene virtual simulation can expand the test into more application scenes, and the method has a great acceleration and propulsion effect on the research and development of the automatic driving function of L2 and above.
Description
Technical Field
The invention belongs to the technical field of intelligent driving, and particularly relates to a construction method of an automatic driving simulation test model based on SCANeR.
Background
At present, the research on intelligent networked automobiles at home and abroad is in an outbreak period, a plurality of new technical and novel methods promote the industry to rapidly advance, and the research on automatic driving is also shifted from the L2 level to the L3\ L4 level or even the L5 level. Since the intelligent networked automobile basically does not depend on people to drive from the level L3, the safety becomes an important consideration for the development of the industry. In the process of researching the automatic driving technology, virtual simulation scene data are basic data resources for research and development and testing of the intelligent networked automobile, are important case libraries and exercise problem sets for evaluating the functional safety of the intelligent networked automobile and are key data bases for redefining the level of the intelligent automobile. However, many simulation test software aiming at the automatic driving technology exist at present, and in the scene building process, the reasons of various scene formats, poor scene rendering results, road information distortion, mismatching of scene interface communication and the like exist, so that the accuracy and the safety of the automatic driving simulation test are seriously influenced.
Disclosure of Invention
In view of this, the present invention provides a method for constructing an automatic driving simulation test model based on the scanner, which aims to overcome the above-mentioned defects in the prior art.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a construction method of an automatic driving simulation test model based on SCANeR comprises the following steps:
s1, deconstruction and reconstruction of scene data, and extracting scene characteristic elements;
s2, editing a static scene according to the scene characteristic elements;
s3, editing a dynamic scene according to the scene characteristic elements;
s4, interacting the contents in the steps S2 and S3 in the SCANeR to construct a virtual driving test scene;
s5, judging whether the test conditions are met, if not, returning to the step S2; if yes, the test is started.
Further, the specific steps of step S1 are as follows:
s101, extracting scene characteristic elements according to scene element analysis to realize scene deconstruction;
s102, marking different scene elements and stripping the scene elements from the whole scene;
s103, analyzing the internal relation among different elements, selecting proper scene elements according to the test requirements, and reconstructing according to the coupling relation of the scene elements.
Further, the static scene editing in step S2 includes, but is not limited to, collecting a road classification and traffic sign library, and establishing a general road virtual scene library; the road virtual scene library comprises but is not limited to an urban road sub-library, an urban supporting facility sub-library, an expressway sub-library, a road facility sub-library and a parking lot sub-library.
Further, the specific implementation steps of step S2 are as follows:
s201, preprocessing data, and restoring a three-dimensional environment according to imported data;
s202, editing data, namely editing the data, modeling a road according to a road building rule, and building a three-dimensional environment;
and S203, issuing the created three-dimensional environment as data or service which can be identified by the three-dimensional simulation platform.
Further, the three-dimensional environment adopts a data format of, but not limited to, openrive.
Further, the dynamic scene editing in step S3 includes, but is not limited to, creating a traffic scene, a traffic condition simulation, and a pedestrian simulation.
Further, the specific process of step S4 is as follows: static traffic information is interacted with dynamic traffic information in the SCANeR, wherein the static traffic information and the dynamic traffic information comprise but are not limited to working conditions, environments, ranges and scene combinations.
Further, the working conditions include, but are not limited to, high speed, city, countryside, parking lot; the environment includes but is not limited to sunny, rainy, snowy, haze; the range includes but is not limited to typical scenes, corner scenes, accident scenes; the scene combination comprises but is not limited to ADAS functions AEB/ACC/LKA/FCW/LDW/TSR, high-speed self-driving, automatic parking, traffic jam and automatic driving functions.
Compared with the prior art, the invention has the following advantages:
because the real scene is not limited, the reconstruction and the scene parameterization recombination of the complex scene can be realized more easily than the real vehicle test, the limitation that the road test cannot realize the scene coverage and the repeatability is made up, the customization and the parameterization simulation of the scene, the traffic flow, the road information, the vehicle dynamics and the driver model can be realized, the expandability and the transportability are better, meanwhile, the more targeted test can be carried out, the more subdivided business model is supported, and the long-term promotion effect is also realized on the industry development. By combining technologies such as cloud computing, high-precision maps and virtual reality, the driving scene virtual simulation can expand the test into more application scenes, and has a great acceleration and propulsion effect on the research and development of the automatic driving function of L2 and above.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of imported high-precision map data according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a restored three-dimensional scene according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a three-dimensional environment for data editing according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of editing a crossing connection according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of editing a connection between lanes at an intersection according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating the post-editing effects of the intersection according to the embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating the editing of a road sign according to an embodiment of the present invention;
FIG. 9 is a diagram illustrating dynamic scene editing according to an embodiment of the present invention;
FIG. 10 is a schematic view of a traffic scene according to an embodiment of the present invention;
fig. 11 is a schematic diagram of a vehicle trajectory setting according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
According to high-precision map information, building a virtual scene in the SCANeR, wherein the virtual scene comprises road positions (accurate GPS information) and shapes (accurate road course and curvature), related indication marks (such as lane line types, colors and the like), related road marks (such as speed limit boards and indicator lamps) and necessary related infrastructure (such as parking lots, ramps and the like), and adding six dynamic communication to realize the construction of an automatic driving simulation test model.
(1) Scene deconstruction and automatic reconstruction technology
The real traffic scene is complex and changeable, the scene data source is wide, and the data volume is huge, so the scene feature element extraction is carried out according to the scene element analysis, and the scene deconstruction is realized. Meanwhile, the scene elements are complex and various, and the required scene element types are different when different automatic driving functions are tested. How to automatically reconstruct a test scene according to test requirements is a key problem to be solved urgently at present. Marking different scene elements and stripping the scene elements from the whole scene, analyzing the internal relation among the different elements, selecting proper scene elements according to the test requirement and automatically reconstructing according to the coupling relation of the scene elements are important points for solving the problem of automatic generation of the test scene.
(2) Static scene editing
The method comprises the steps of collecting Chinese road classification and traffic sign libraries, establishing a general and Chinese characteristic road virtual scene library, covering static virtual scene sub-libraries such as urban roads, urban supporting facilities, expressways, road facilities, parking lot sub-libraries and the like, and laying a foundation for realizing virtual scene construction.
(3) Dynamic scene editing
The dynamic scene editing comprises the test functions of complex dynamic driving tasks such as starting, parking, following, lane changing, left turning at an intersection, right turning at the intersection, straight going through a pedestrian crossing line, side parking, meeting, roundabout, driving through main and auxiliary roads of an overpass, passing through a school area, a tunnel, a bridge, a muddy road, a sharp turning road, overtaking, driving at night, backing and warehousing, side parking, passing through a road in a rain area, passing through a road in a fog area, passing through a wet and slippery road surface, avoiding emergency vehicles and the like.
(4) Virtual driving test scenario construction
Static traffic information and dynamic traffic information are interacted in the SCANeR, the working condition covers key fields such as high speed, city, countryside and parking lot, the environment covers various types such as typical scenes, corner scenes and accident scenes in various weather, range coverage such as sunny days, rainy days, snow days and haze. The scene combination covers all ADAS functions AEB/ACC/LKA/FCW/LDW/TSR and the like, and also has the functions of high-speed self-driving, automatic parking, automatic traffic jam driving and the like. And the function definition of the watching tested object is divided into perception type, control type, decision type test and the like by combining with the test requirement.
The specific implementation process of each step is as follows:
1.1. data processing and management
1.1.1. Input/output module
The SCANeR supports the high-precision map format of the current process, and simultaneously supports reading images, radars, digital elevation models and the like. And the import of user-defined modules, such as sensor models, real traffic flow data, accident data and the like, is supported, and a user can create a user-defined function module based on the Framework.
1.2. Static scene editing
1.2.1. Data pre-processing
Restoring the three-dimensional environment according to the imported data, as shown in fig. 2 and 3;
1.2.2. editing of data
The data can be edited, the operations comprise translation and elevation of basic three-dimensional points, lines and planes, and typical elements such as road networks, lane networks and roadside facilities and the like of road related elements can be created and modified in a three-dimensional environment, such as road lines, lane markings, traffic signs, traffic lights, guardrails, road shoulders and the like, and the types of roads comprise express, high-speed and urban general roads. As shown in fig. 4.
Road modeling is performed following road building rules, multilane multiple lane types (motor vehicle traffic lanes, pedestrian lanes, bicycle lanes), road surface undulation (longitudinal and transverse), buildings (independent three-dimensional houses, trees, and the like) on two sides of a road, an expandable traffic element library (signal lamps and the like), editing of high-precision intersection maps, and reconstructing of a virtual road environment world are shown in fig. 5-8.
1.2.3. Distribution of data
The created three-dimensional environment is released as data or service which can be identified by a three-dimensional simulation platform, and the release as data format popular now such as option and the like can be supported. As shown in fig. 9.
1.3. Dynamic scene editing
The dynamic scene editing is mainly used for adding various dynamic factors which may be encountered in actual driving into the scene, such as a running vehicle, a person, a traffic control device, V2X and the like. As shown in fig. 10.
The module can establish traffic scenes, automatically operated traffic condition simulation, manual intervention traffic conditions, pedestrian simulation, setting of different trigger modes, support of path planning and trajectory planning, support of simulation of various traffic targets, and support of external triggering of various traffic behaviors without limitation in quantity. As shown in fig. 11.
2. Scene output module
The established scenes are output, and natural driving scenes, regulation scenes, accident scenes and recombination scenes can be output according to classification.
2.1. Scene operation control center
The objects supporting the introduction comprise a vehicle body, a road, a lane line, other vehicles on the road, scenery along the road, light rays and the like. The three-dimensional real-time rendering engine can generate a real-time light and shadow effect according to the road traffic scene defined in the past and the behaviors of vehicles and pedestrians, and can render the reflection of rain, snow, fog, rain pavement and ice and snow pavement.
The functions of the operation control center comprise real-time shadow effect, road surface reflection and wet and slippery road surface special effect, sun glare, high-quality vehicle body rendering, rain, snow, fog weather rendering and the like.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A construction method of an automatic driving simulation test model based on SCANeR is characterized in that: the method comprises the following steps:
s1, deconstruction and reconstruction of scene data, and extracting scene characteristic elements;
s2, editing a static scene according to the scene characteristic elements;
s3, editing a dynamic scene according to the scene characteristic elements;
s4, interacting the contents in the steps S2 and S3 in the SCANeR to construct a virtual driving test scene;
s5, judging whether the test conditions are met, if not, returning to the step S2; if yes, the test is started.
2. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 1, wherein the method comprises the following steps: the specific steps of step S1 are as follows:
s101, extracting scene characteristic elements according to scene element analysis to realize scene deconstruction;
s102, marking different scene elements and stripping the scene elements from the whole scene;
s103, analyzing the internal relation among different elements, selecting proper scene elements according to the test requirements, and reconstructing according to the coupling relation of the scene elements.
3. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 1, wherein the method comprises the following steps: the static scene editing in the step S2 includes, but is not limited to, collecting a road classification and traffic sign library, and establishing a general road virtual scene library; the road virtual scene library comprises but is not limited to an urban road sub-library, an urban supporting facility sub-library, an expressway sub-library, a road facility sub-library and a parking lot sub-library.
4. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 3, wherein the method comprises the following steps: the specific implementation steps of step S2 are as follows:
s201, preprocessing data, and restoring a three-dimensional environment according to imported data;
s202, editing data, namely editing the data, modeling a road according to a road building rule, and building a three-dimensional environment;
and S203, issuing the created three-dimensional environment as data or service which can be identified by the three-dimensional simulation platform.
5. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 4, wherein the method comprises the following steps: the three-dimensional environment is in a data format of, but not limited to, openrive.
6. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 1, wherein the method comprises the following steps: the dynamic scene editing in step S3 includes, but is not limited to, creating traffic scenes, traffic condition simulation, and pedestrian simulation.
7. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 1, wherein the method comprises the following steps: the specific process of step S4 is as follows: static traffic information is interacted with dynamic traffic information in the SCANeR, wherein the static traffic information and the dynamic traffic information comprise but are not limited to working conditions, environments, ranges and scene combinations.
8. The method for constructing the SCANeR-based automatic driving simulation test model according to claim 7, wherein the method comprises the following steps: the working conditions include, but are not limited to, high speed, city, country, parking lot; the environment includes but is not limited to sunny, rainy, snowy, haze; the range includes but is not limited to typical scenes, corner scenes, accident scenes; the scene combination comprises but is not limited to ADAS functions AEB/ACC/LKA/FCW/LDW/TSR, high-speed self-driving, automatic parking, traffic jam and automatic driving functions.
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| CN112527633A (en) * | 2020-11-20 | 2021-03-19 | 北京赛目科技有限公司 | Automatic driving simulation test method and device for scene library |
| CN112597061A (en) * | 2021-01-20 | 2021-04-02 | 中国汽车技术研究中心有限公司 | ACC system performance test method and related equipment |
| CN112668194A (en) * | 2020-12-31 | 2021-04-16 | 禾多科技（北京）有限公司 | Automatic driving scene library information display method, device and equipment based on page |
| CN112685289A (en) * | 2020-12-11 | 2021-04-20 | 中国汽车技术研究中心有限公司 | Scene generation method, and scene-based model in-loop test method and system |
| CN112989568A (en) * | 2021-02-06 | 2021-06-18 | 武汉光庭信息技术股份有限公司 | Simulation scene three-dimensional road automatic construction method and device |
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