CN116187101A - Verification method for constructing EHP (Ethernet Passive optical network) based on Prescan - Google Patents
Verification method for constructing EHP (Ethernet Passive optical network) based on Prescan Download PDFInfo
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Abstract
The invention relates to a verification method for constructing an EHP (Ethernet high-speed) based on Prescan, which comprises the following steps: prescan builds a road network verification model and builds a rule-controlled verification road network information base; collecting and packaging road network elements, and storing a road network information file into a road network information base; directly transplanting the control algorithm model of the L2+ autopilot on a visual platform, and then modeling a corresponding positioning trigger module according to a communication protocol; then a cross-platform protocol is adopted to build a communication module, and a communication channel is used for receiving and transmitting signals to match the communication module; and finally, verifying and optimizing the early performance of the regulation algorithm by using the high-precision map information constructed by the EHP, and completing the verification of the model algorithm. The invention discloses a verification method for constructing an EHP (EHP) based on Prescan, which can introduce a high-precision map on a simulation platform to verify and optimize the performance of an initial-stage regulation algorithm, can be matched with a real-vehicle high-precision map for use, and has the advantages of time saving, high efficiency and low cost.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a verification method for constructing an EHP (electro-static road) based on Prescan.
Background
In recent years, as the floor projects of the L2+ level autopilot function are increased, the autopilot technology becomes an important research field. The automatic driving vehicle simulation test is an indispensable flow of the whole system development process, and is more important than the traditional automobile. Especially in the field of regulation, because of the introduction of road networks with complex real scenes and high-precision maps, compared with the traditional road test, problems can be found at the initial stage of functions through a large number of simulation test simulations, the problems are optimized, time and cost are saved, the test efficiency is greatly improved, and the regulation algorithm of the road network can be verified and optimized according to a specific camera case.
The prior art has the following defects:
1. the collection of the high-precision map and the use of the simulation platform consume a great deal of time and money and manufacturing cost, and the early verification of the rule and control algorithm is not a high-cost performance choice;
2. the novel simulation platforms such as apollo and 51sim support access of high-precision maps, but do not receive and transmit information according to an ADAIS V3 protocol, and have the defects of few scenes and immature algorithm transplanting compatibility;
3. classical simulation platforms such as Carsim, prescan and the like are convenient in algorithm transplanting, but due to the lack of support of high-precision maps and ADASIS protocols, high-precision maps of graphic and business companies are required to be purchased at high price, and certain unfriendly requirements on initial verification optimization and low cost of the regular algorithm are met.
Therefore, there is a need to provide a validation method for constructing EHP based on Prescan, so as to solve the above-mentioned problems.
Disclosure of Invention
The invention aims to overcome the defects and the defects of the prior art, and provides a verification method for constructing an EHP based on Prescan, which can solve the problem of richness in scene construction, can introduce a high-precision map on a mature simulation platform to verify and optimize the performance of an initial-stage regulation algorithm, can be matched with a real-vehicle high-precision map for use, and is time-saving, efficient, reusable and low in cost.
The aim of the invention is realized by the following technical scheme:
a verification method for constructing EHP based on Prescan includes the following steps:
firstly, constructing a road network verification model by Prescan, carrying out modeling numbering in the Prescan simulation platform according to a real road network or a plurality of corner cases needing special verification, constructing a rule-controlled verification road network information base, and immediately calling the rule-controlled verification road network information base when in use;
step two, road network element collection and packaging, setting a specific sampling frequency on a sensor provided by Prescan, running on a constructed road network verification model once, finishing information collection of the whole road network simulation model, processing a true value signal provided by the sensor to generate an ADAIS V3 protocol format, packaging in a JSON file form and storing in a road network information base, numbering the road network information base, and matching a road network map when a subsequent engineer uses the road network map;
step three, algorithm model transplanting and corresponding positioning trigger module modeling, namely directly transplanting an L2+ automatic driving control algorithm model on a Simulink visualization platform generated after compiling a corresponding road network verification model, modeling a corresponding positioning trigger module according to an ADAIS V3 protocol, and matching and adjusting parameters of the positioning trigger module in use;
building and adapting a communication module, compiling a protobuf protocol of a high-precision map into a jar packet by adopting a cross-platform communication protocol, introducing the jar packet into a matlab, indexing information of a JSON map file through a code interface, and planning and controlling corresponding signals to communicate among the cross-platforms; receiving and transmitting signals through a special channel under the local area network so as to match the communication modules, and subsequently, adjusting and matching parameters of the communication modules;
and fifthly, verifying a model algorithm, and verifying and optimizing the early performance of the regulation algorithm by using high-precision map information constructed by the EHP.
As a preferred embodiment of the present invention, the corner case in the first step refers to a corner case, where the operating parameter is out of the normal range of the simulation test of the automatic driving vehicle, and there are cases where several environmental variables or conditions are all in extreme values, even if the extreme values are all within the parameter specification range or boundary, and the corner case also belongs to the corner case.
As a preferable technical scheme of the invention, the true value signal in the second step comprises distance, speed and ID.
As a preferred technical solution of the present invention, the modeling of the corresponding positioning trigger module in the third step includes the following steps:
s1, running a Prescan simulation platform;
s2, judging whether the simulation initial time is reached, if so, sending a Path Control emptying signal to initialize the high-precision map EHR; if not, executing the next step;
s3, the matching model receives the space coordinates (x, y, z) of inertial navigation;
s4, scheduling an EHP time system, and designating a specific period or time;
s5, matching the Path ID and Trigger index of the current road;
s6, indexing the JSON information by using the Path ID and Trigger index;
and S7, transmitting the corresponding high-precision road information to the EHR through LCM communication.
As a preferable technical scheme of the invention, the S3 is specifically that a Prescan platform inertial navigation transmits real-time coordinates of a vehicle to a matching model, and the matching model receives spatial coordinates (x, y, z) of inertial navigation and converts the spatial coordinates into longitude and latitude coordinates.
As a preferred embodiment of the present invention, in S4, a position state of the vehicle is recorded, the position state being displayed in a specific period or at a certain time point.
As a preferable technical scheme of the invention, the cross-platform protocol in the fourth step adopts LCM lightweight communication protocol.
As a preferable technical scheme of the invention, the cross-platform in the step four refers to cross-platform communication between the Ubuntu system and the Windows system.
As a preferable technical scheme of the invention, after the simulation is run, map information is accessed into a rule control algorithm, and the purpose of self simulation verification can be realized by selecting a corresponding road network model and map in a road network library.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, a real road scene is built based on a Prescan simulation platform, an EHP system is built, centimeter-level high-precision map information is encoded through an ADASIV 3 communication protocol, LCM lightweight communication mode is utilized to multicast communication to HERs of different hosts, corresponding map information is analyzed, and then corresponding algorithms such as NOP functions in regulation are verified and optimized, so that a low-cost, high-efficiency and feasible method is provided for early verification regulation algorithm performance, quick algorithm iteration is facilitated, high-cost is not required to use high-precision map services of a manufacturer company, and high-cost investment is provided for collecting the high-precision map.
2. The method is based on the Prescan simulation platform to efficiently acquire the high-precision map to generate the JSON road network information file, and the JSON road network information file is matched with the road network model built by Prescan to form the road network information base, so that the method is convenient for engineers to manage and use, high reusability is achieved, repeated building of scenes such as ramps is not needed, and the practicability is high.
3. The positioning trigger matching model is matched with the JSON road network information, and the position and the lane where the vehicle is positioned are judged in real time according to the control of the EHP time scheduling module, so that the road network information such as the number of lanes, lane attributes, curvature and the like in a space range before and after the current position in the JSON file is indexed, and the verification efficiency of a front-stage algorithm is effectively accelerated.
4. The whole flow of the invention is a novel and innovative method, has good compatibility for the main stream automatic driving model algorithm platform, can realize cross-platform communication, does not even need to be uniformly transplanted on the Simulink platform generated by the road network model for the algorithm of different hosts, and saves a great amount of time for engineers.
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Fig. 1 is an overall flow chart of the present invention.
FIG. 2 is a schematic diagram of a location triggered matching model of the present invention.
Description of the embodiments
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "provided," "connected," and the like are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
As shown in fig. 1-2, the invention is embodied as follows: a verification method for constructing EHP based on Prescan includes the following steps:
firstly, a Prescan builds a road network verification model, models and numbers are carried out in a Prescan simulation platform according to a real road network or a plurality of corner cases needing special verification, a rule-controlled verification road network information base is built, and the rule-controlled verification road network information base can be immediately called when in use.
Specifically, the Prescan simulation platform is mature and abundant in scene library, can provide various models to build environment models, such as lanes, vehicles, pedestrian tracks, positions and the like, can quickly build a road network scene for real verification, and is convenient for engineers to select correspondingly according to different simulation verification tasks. In addition, prescan complete data visualization, simulation data visualization, driving environment visualization and other operations with matlab/Simulink coupling simulation, and the simulation data visualization method is beneficial to follow-up simulation visualization operations.
In addition, the corner case in the first step refers to a corner case, where the operating parameter is out of the normal range of the simulation test of the automatic driving vehicle, and there are several situations where the environmental variables or conditions are all in extreme values, even if the extreme values are all within the parameter specification range or boundary, and the corner case also belongs to the corner case. The corner cases or the pathological cases are more favorable for verification and optimization of the self regulation algorithm, the capability of keeping stable running of the vehicle against abnormal events in automatic driving is improved, and the vehicle has better expandability.
Step two, road network element collection and packaging, setting a specific sampling frequency on a sensor provided by Prescan, running on a constructed road network verification model once, completing information collection of the whole road network simulation model, processing a true value signal provided by the sensor to generate an ADAIS V3 protocol format, packaging and storing the format in a road network information base in a JSON file form, numbering the format, and matching a road network map when a subsequent engineer uses the road network map.
Specifically, based on abundant sensors provided by the Prescan simulation platform, the sensor comprises a plurality of detection sensors such as a camera, a laser radar, an IMU (inertial measurement unit) and the like, each sensor can also provide a plurality of truth signals such as distance, speed, ID (identity) and the like, the information acquisition of the whole road network model can be completed by setting a specific sampling frequency on the sensor and running on the constructed road network model once, the truth signals are processed to generate an ADAIS V3 protocol format, and the format is packed and stored in a road network information base in the form of a JSON file and is endowed with numbers, so that road network map matching can be performed when the road network map matching is used by subsequent engineers.
Thirdly, algorithm model transplanting and corresponding positioning trigger module modeling, namely directly transplanting the control algorithm model of L2+ automatic driving on a Simulink visualization platform generated after compiling a corresponding road network verification model, modeling a corresponding positioning trigger module according to an ADAIS V3 protocol, and matching and adjusting parameters of the positioning trigger module in use.
Specifically, the communication protocol adopts the ADAIS V3 protocol which is the same as that of a real vehicle to send high-precision map information constructed by EHP, so that the method has more guiding significance on the simulation result of the algorithm; in addition, the ADAIS V3 protocol is a communication protocol for map data transmission, can cooperate with each manufacturer to provide an effective high-precision map transmission interface, and transmits high-precision map information to each required module through a vehicle-mounted Ethernet, so as to assist a vehicle to complete an automatic driving function and support high-speed automatic driving; and the ADASIS V3 protocol can generate a JSON road network file for rapidly transmitting map data, the JSON file is a lightweight data exchange format, and the data exchange language has a concise and clear hierarchical structure, is easy to read and write by people, is easy to analyze and generate by machines, and effectively improves the network transmission efficiency.
Specifically, the modeling of the corresponding positioning trigger module includes the following steps:
s1, running a Prescan simulation platform;
s2, judging whether the simulation initial time is reached, if so, sending a Path Control emptying signal to initialize the high-precision map EHR; if not, executing the next step;
s3, the matching model receives the space coordinates (x, y, z) of inertial navigation;
s4, scheduling an EHP time system, and designating a specific period or time;
s5, matching the Path ID and Trigger index of the current road;
s6, indexing the JSON information by using the Path ID and Trigger index;
and S7, transmitting the corresponding high-precision road information to the EHR through LCM communication.
First, a Path Control clear signal is transmitted to initialize the high-precision map EHR at the time of initial simulation. When the simulation runs, the Prescan simulation platform inertial navigation transmits real-time coordinates of the self-vehicle to a matching model, the matching model receives spatial coordinates (x, y, z) of the inertial navigation, converts the spatial coordinates into longitude and latitude coordinates, then matches the longitude and latitude coordinates to path id and trigger index of a current lane of the self-vehicle in a specific period or time triggering mode under the action of an EHP time system, the path id and trigger index are used for indexing high-precision road information corresponding to a JSON road network element packet, and finally information obtained by indexing is transmitted to an EHR in a LCM multicast communication mode to provide high-precision map support for planning.
The positioning trigger matching model is matched with the JSON file road network information for use, and the position state of the vehicle is recorded according to a specific period or time designated by the EHP time scheduling module so as to judge the position and the lane of the vehicle in real time, thereby indexing the road network information such as the number of lanes, lane attributes, curvature and the like in a space range before and after the current position in the JSON file, and accelerating the early verification efficiency of an algorithm.
Building and adapting a communication module, compiling a protobuf protocol of a high-precision map into a jar packet by adopting a cross-platform communication protocol, introducing the jar packet into a matlab, indexing JSON map information through a code interface, and planning and controlling corresponding signals to communicate among cross-platforms; and then, the signal is transmitted and received through a special channel under the local area network so as to match the communication module, and the parameters of the communication module can be adjusted and matched later.
In particular, models of different functions may be developed based on different platforms, the control algorithm model for l2+ autopilot is developed by most host factories based on a Simulink visualization platform that provides an interactive graphical environment and a customizable library of modules for designing, simulating, executing, and testing it, while the planning algorithm model may be based on code implementation. Prescan is a simulation platform based on Matlab, a control algorithm can be directly transplanted on a Simulink platform generated after compiling a corresponding road network model, a planning algorithm developed by corresponding C++ can verify the whole regulation system without any transplantation, and the configuration transplantation is not repeated after the transplantation is carried out once only when the system is built, so that the reusability is high; meanwhile, the method is compatible with algorithm models based on Matlab and C/C++, compared with simulation platforms such as apollo,51sim and the like, the method is more demanding in industry, the practicability is stronger, and channel is used as a channel, so that end-to-end data transmission can be realized.
Specifically, the cross-platform communication refers to cross-platform communication between a Ubuntu system and a Windows system, where the Ubuntu system is a Linux operating system mainly based on desktop application, and has various perfect tool chains, and many tools friendly to developers, for example: VIM, emacs, SSH and the like can play a role in achieving half effort in the development process under Linux, and the Ubuntu system can also collect hardware information, position data and use data, so that the use is convenient and quick; the Windows system is an operating system developed based on a graphical user interface, the system performance is perfect, the use is convenient, the display is visual, the two systems are selected for cross-platform operation, the compatibility is better for algorithms of different platforms used in the automatic driving industry, the cross-platform multi-host communication can be realized, and the simulation verification algorithm is more efficient and time-saving due to the support of multi-host parallel verification of the algorithm.
Specifically, the communication module adopts a cross-platform LCM lightweight communication protocol, the LCM is a set of libraries and tools for message transmission and data grouping, and aims to provide high-bandwidth and low-delay message transmission capability for a real-time system, and the transmission speed is higher and the verification efficiency is higher due to the attribute of UDP transmission.
And fifthly, verifying a model algorithm, and verifying and optimizing the early performance of the regulation algorithm by using high-precision map information constructed by the EHP.
Specifically, after the simulation is run, map information is accessed into the regulation algorithm, engineers can verify and optimize the early performance of the regulation algorithm by using high-precision map information constructed by the EHP, and the purpose of self simulation verification can be realized by only selecting a corresponding road network model and a map in the road network library.
The invention discloses a verification method for constructing EHP (Ethernet-based Power line) based on Prescan, which comprises the steps of quickly constructing a road network scene for real verification through a Prescan simulation platform, constructing EHP through creating a plurality of key road network positioning modules and in an ADAIS V3 protocol format, and transmitting information to different hosts in a cross-platform manner through LCM lightweight communication mode to analyze EHR information, so that the performance of an optimization rule and control algorithm is verified, and the cost is low and high efficiency.
The foregoing examples merely illustrate embodiments of the invention and are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (9)
1. The verification method for constructing the EHP based on Prescan is characterized by comprising the following steps of:
firstly, constructing a road network verification model by Prescan, carrying out modeling numbering in the Prescan simulation platform according to a real road network or a plurality of corner cases needing special verification, constructing a rule-controlled verification road network information base, and immediately calling the rule-controlled verification road network information base when in use;
step two, road network element collection and packaging, setting a specific sampling frequency on a sensor provided by Prescan, running on a constructed road network verification model once, finishing information collection of the whole road network simulation model, processing a true value signal provided by the sensor to generate an ADAIS V3 protocol format, packaging in a JSON file form and storing in a road network information base, numbering the road network information base, and matching a road network map when a subsequent engineer uses the road network map;
step three, algorithm model transplanting and corresponding positioning trigger module modeling, namely directly transplanting an L2+ automatic driving control algorithm model on a Simulink visualization platform generated after compiling a corresponding road network verification model, modeling a corresponding positioning trigger module according to an ADAIS V3 protocol, and matching and adjusting parameters of the positioning trigger module in use;
building and adapting a communication module, compiling a protobuf protocol of a high-precision map into a jar packet by adopting a cross-platform communication protocol, introducing the jar packet into a matlab, indexing information of a JSON map file through a code interface, and planning and controlling corresponding signals to communicate among the cross-platforms; receiving and transmitting signals through a special channel under the local area network so as to match the communication modules, and subsequently, adjusting and matching parameters of the communication modules;
and fifthly, verifying a model algorithm, and verifying and optimizing the early performance of the regulation algorithm by using high-precision map information constructed by the EHP.
2. The method of claim 1, wherein the corner case in the step one refers to a corner case, wherein the operating parameter is out of the normal range of the simulation test of the automatic driving vehicle, and wherein there are several environmental variables or conditions in extreme values, even if the extreme values are within the parameter specification range or boundary, which are corner cases.
3. The method of claim 1, wherein the true value signal in the second step includes distance, speed, and ID.
4. The method for verifying EHP based on Prescan of claim 1, wherein the modeling of the corresponding positioning trigger module in the third step comprises the following steps:
s1, running a Prescan simulation platform;
s2, judging whether the simulation initial time is reached, if so, sending a Path Control emptying signal to initialize the high-precision map EHR; if not, executing the next step;
s3, the real-time coordinates of the vehicle are converted into longitude and latitude coordinates by the matching model;
s4, scheduling an EHP time system, and designating a specific period or time;
s5, matching the Path ID and Trigger index of the current road;
s6, indexing the JSON information by using the Path ID and Trigger index;
and S7, transmitting the corresponding high-precision road information to the EHR through LCM communication.
5. The method for verifying EHP according to claim 4, wherein S3 is specifically that the Prescan platform inertial navigation transmits real-time coordinates of the vehicle to a matching model, and the matching model receives spatial coordinates (x, y, z) of the inertial navigation and converts the spatial coordinates into longitude and latitude coordinates.
6. The method according to claim 4, wherein the S4 records the position status of the vehicle in a specific period or at a certain time point.
7. The authentication method for constructing EHP based on Prescan according to claim 1, wherein the cross-platform protocol in the fourth step adopts LCM lightweight communication protocol.
8. The authentication method for constructing an EHP based on Prescan according to claim 1, wherein the cross-platform communication in the fourth step is cross-platform communication between Ubuntu system and Windows system.
9. The method for constructing EHP according to claim 1, wherein after running simulation, the map information is accessed into a rule control algorithm, and the purpose of self simulation verification can be achieved by selecting a corresponding road network model and map in the road network information base.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120032676A1 (en) * | 2010-08-06 | 2012-02-09 | Toshiba Medical Systems Corporation | Spatial intensity correction for RF shading non-uniformities in MRI |
| CN113110392A (en) * | 2021-04-28 | 2021-07-13 | 吉林大学 | In-loop testing method for camera hardware of automatic driving automobile based on map import |
| CN113378413A (en) * | 2021-08-12 | 2021-09-10 | 深圳市城市交通规划设计研究中心股份有限公司 | Simulation road network platform building system, method, equipment and storage medium based on Vissim |
| CN115309074A (en) * | 2022-08-31 | 2022-11-08 | 重庆长安汽车股份有限公司 | Automatic driving simulation test method and device, simulation equipment and storage medium |
| DE102022112059B3 (en) * | 2022-05-13 | 2023-04-20 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method, system and computer program product for calibrating and validating a driver assistance system (ADAS) and/or an automated driving system (ADS) |
-
2023
- 2023-04-26 CN CN202310459892.0A patent/CN116187101B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120032676A1 (en) * | 2010-08-06 | 2012-02-09 | Toshiba Medical Systems Corporation | Spatial intensity correction for RF shading non-uniformities in MRI |
| CN113110392A (en) * | 2021-04-28 | 2021-07-13 | 吉林大学 | In-loop testing method for camera hardware of automatic driving automobile based on map import |
| CN113378413A (en) * | 2021-08-12 | 2021-09-10 | 深圳市城市交通规划设计研究中心股份有限公司 | Simulation road network platform building system, method, equipment and storage medium based on Vissim |
| DE102022112059B3 (en) * | 2022-05-13 | 2023-04-20 | Dr. Ing. H.C. F. Porsche Aktiengesellschaft | Method, system and computer program product for calibrating and validating a driver assistance system (ADAS) and/or an automated driving system (ADS) |
| CN115309074A (en) * | 2022-08-31 | 2022-11-08 | 重庆长安汽车股份有限公司 | Automatic driving simulation test method and device, simulation equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| ]W. RESSEL等: "Dynamic measurements and simulation of road surface characteristics", SPRINGER FACHMEDIEN WIESBADEN, pages 1 - 8 * |
| 贺勇;王春香;董永坤;: "基于PRESCAN的汽车自动驾驶仿真", 电脑知识与技术, vol. 8, no. 33, pages 8047 - 8049 * |
| 闻龙: "面向智能网联汽车的高性能计算仿真平台", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 7, pages 035 - 292 * |
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