CN113553508A - Road condition model generation method, device, storage medium and system - Google Patents
Road condition model generation method, device, storage medium and system Download PDFInfo
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- CN113553508A CN113553508A CN202110855909.5A CN202110855909A CN113553508A CN 113553508 A CN113553508 A CN 113553508A CN 202110855909 A CN202110855909 A CN 202110855909A CN 113553508 A CN113553508 A CN 113553508A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
- G06F16/532—Query formulation, e.g. graphical querying
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/53—Querying
- G06F16/535—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Abstract
The embodiment of the invention discloses a road condition model generation method, a road condition model generation device, a storage medium and a road condition model generation system, wherein the method comprises the following steps: acquiring a road image around a vehicle in real time in the driving process of the vehicle; when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server; and generating a road condition model based on the target object model. By the technical scheme provided by the embodiment of the invention, the road condition model around the vehicle can be accurately constructed, and the driving safety of the vehicle is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of vehicle networking, in particular to a road condition model generation method, a road condition model generation device, a road condition model storage medium and a road condition model generation system.
Background
The realization of the automatic driving technology requires real-time construction of a road condition model around the vehicle. In the related art, a model of an object in a road image is constructed mainly based on data in a preset model database, so that a road condition model is constructed based on an identified object model.
However, the model database cannot cover all the object models around the vehicle, and when there is no model corresponding to the object around the vehicle in the model database, the model of the real object around the vehicle can only be replaced by a simple image (such as a rectangle, a square, etc.). Therefore, the road condition model is not accurately constructed, and the automatic driving of the vehicle has great potential safety hazard.
Disclosure of Invention
The embodiment of the invention provides a road condition model generation method, a road condition model generation device, a storage medium and a road condition model generation system, which can accurately construct a road condition model around a vehicle and are beneficial to improving the driving safety of the vehicle.
In a first aspect, an embodiment of the present invention provides a road condition model generating method, including:
acquiring a road image around a vehicle in real time in the driving process of the vehicle;
when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server;
and generating a road condition model based on the target object model.
In a second aspect, an embodiment of the present invention further provides a road condition model generating device, including:
the road image acquisition module is used for acquiring road images around the vehicle in real time in the running process of the vehicle;
an object model obtaining module, configured to obtain, based on a server, a target object model that matches an object in the road image when the object model that matches the object in the road image does not exist in a pre-stored model database;
and the road condition model generating module is used for generating a road condition model based on the target object model.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the road condition model generating method provided in the embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a traffic model generating system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the traffic model generating method provided in the embodiment of the present invention.
According to the road condition model generation scheme provided by the embodiment of the invention, the road image around the vehicle is obtained in real time in the driving process of the vehicle; when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server; and generating a road condition model based on the target object model. By the technical scheme provided by the embodiment of the invention, the road condition model around the vehicle can be accurately constructed, and the driving safety of the vehicle is improved.
Drawings
Fig. 1 is a flowchart of a road condition model generation method according to an embodiment of the present invention;
fig. 2 is a flowchart of a road condition model generation method according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a road condition model generating device according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a road condition model generating system according to another embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present invention are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Fig. 1 is a flowchart of a road condition model generating method according to an embodiment of the present invention, which is applicable to a road condition model building situation, and the method can be executed by a road condition model generating device, which can be composed of hardware and/or software and can be generally integrated in a road condition model generating system. As shown in fig. 1, the method specifically includes the following steps:
and step 110, acquiring a road image around the vehicle in real time in the driving process of the vehicle.
The vehicle may be an autonomous vehicle or a non-autonomous vehicle, and the type of the vehicle is not limited in the embodiment of the present invention.
In the embodiment of the invention, the road image around the vehicle is acquired in real time during the running process of the vehicle, wherein the road image around the vehicle can comprise a road image in front of the vehicle, a road image behind the vehicle, a road image on the left side of the vehicle and a road image on the right side of the vehicle. Specifically, the road image around the vehicle may be acquired by an image acquisition device provided on the vehicle. For example, the image acquisition device installed above the windshield can be used for acquiring image data of a road in front of the vehicle, and meanwhile, the image acquisition device can be arranged on at least one of the left side, the right side and the rear side of the vehicle to acquire the image data of the road around the vehicle from different angles and directions, so that the image acquisition device in front of the vehicle is assisted to monitor the road, and the road around the vehicle can be accurately monitored in all directions.
And 120, when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server.
The model database comprises object models corresponding to a plurality of objects. In the embodiment of the present invention, it is determined whether an object model matching an object in a road image exists in a pre-stored model database, that is, whether a pattern matching an object in a road image exists in the model database. When it is determined that there is no object model matching the object in the road image in the model database, that is, when it cannot be determined what object is specifically contained in the road image based on the model database, the target object model matching the object in the road image is acquired based on the server. For example, when it is determined that an object model matching an object in a road image does not exist in the model database, an object image corresponding to the object is cut from the road image and sent to the server, so that the server searches for a target object model matching the object in the object image.
Optionally, when there is no object model matching with the object in the road image in the pre-stored model database, obtaining a target object model matching with the object in the road image based on a server, including: when an object model matched with an object in the road image does not exist in a pre-stored model database, sending the road image to a server; obtaining a target object model fed back by the server; wherein the target object model is an object model determined by the server to match an object in the road image. Specifically, when an object model matching with an object in the road image does not exist in the model database, identification information may be directly added to an object area of the road image, and the road image to which the identification information is added is sent to the server, where the identification information is used to instruct the server to determine the object model matching with the object to which the identification is added in the road image. Then, a target object model fed back by the server is obtained. When the server determines the object model matched with the object in the road image, the server can identify what object the object in the road image is specifically based on a machine learning algorithm, so as to determine the object model corresponding to the object.
Optionally, when an object model matching the object in the road image exists in a pre-stored model database, a target object model matching the object in the road image is determined based on the model database. Specifically, when it is determined that an object model matching the object in the road image exists in the model database, the object model matching the object in the road image is searched from the model database, and the searched object model is used as the target object model. The method has the advantages that the target object model corresponding to the object in the road image can be determined quickly and accurately, the real-time property of generating the road condition model is improved, and the driving safety of the vehicle is further ensured.
And step 130, generating a road condition model based on the target object model.
In the embodiment of the present invention, a road condition model corresponding to the current time of the vehicle is generated according to the target object model, where the road condition model includes the presentation conditions of each target object model in the road around the vehicle, such as the position, size, distance from the vehicle, and other related information of the object model in the road condition model. The road condition information around the vehicle can be determined in real time through the road condition model.
According to the road condition model generation method provided by the embodiment of the invention, the road image around the vehicle is obtained in real time in the driving process of the vehicle; when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server; and generating a road condition model based on the target object model. By the technical scheme provided by the embodiment of the invention, the road condition model around the vehicle can be accurately constructed, and the driving safety of the vehicle is improved.
In some embodiments, the road condition model generating method further includes: acquiring running data of the vehicle in real time; correspondingly, after generating the road condition model based on the target object model, the method further includes: judging whether dangerous road conditions exist around the vehicle or not based on the road condition model and the driving data; and when the dangerous road conditions exist around the vehicle, prompting the dangerous road conditions. Specifically, the driving data of the vehicle is obtained in real time during the driving process of the vehicle, wherein the driving data may include related data such as a driving speed, a sudden braking, a sharp turn, an emergency lane change, a bumpy state, and an impact state of the vehicle. Specifically, the data of each sensor in an Electronic Stability Program (ESP) of the vehicle, for example, data of a steering sensor, a wheel speed sensor, a side slip sensor, a lateral acceleration sensor, a steering wheel accelerator pedal sensor, and the like, may be acquired, and the driving state of the vehicle may be further analyzed based on the data of the sensors to obtain the driving data of the vehicle. Of course, the analysis result data in the ESP may be directly obtained as the vehicle travel data. Whether dangerous road conditions exist around the vehicle is judged based on the road condition model and the driving data, and specifically, whether dangerous road conditions exist around the vehicle is judged according to a preset dangerous road condition judgment standard by combining the road condition model and the driving data. For example, if it is determined that there is a stone with a diameter of about 1 m in the center of the road based on the road condition model, and it is known from the driving data of the vehicle that the current driving state of the vehicle is the emergency lane change state, it can be determined that there is a dangerous road condition on the current road and the vehicle is not suitable to drive on the road. And when the dangerous road conditions exist around the vehicle, prompting the dangerous road conditions to avoid the vehicle from approaching the road corresponding to the dangerous road conditions.
In some embodiments, after generating the road condition model based on the target object model, the method further includes: and displaying the road condition model, and planning the driving path of the vehicle based on the road condition model. Specifically, after the road condition model is generated, the road condition model can be displayed on a User Interface (UI), so that for a non-automatically driven vehicle, a driver can conveniently control the vehicle to safely drive according to the road condition model. Optionally, the driving path of the vehicle can be planned based on the road condition model, so that the vehicle can drive according to the planned driving path, and the driving safety of the vehicle can be greatly improved.
Fig. 2 is a flowchart of a road condition model generation method according to another embodiment of the present invention. As shown in fig. 2, the method specifically includes the following steps:
and step 210, acquiring the road image around the vehicle and the driving data of the vehicle in real time in the driving process of the vehicle.
And 250, generating a road condition model based on the target object model.
And step 260, judging whether dangerous road conditions exist around the vehicle or not based on the road condition model and the driving data.
And 270, when the dangerous road conditions exist around the vehicle, prompting the dangerous road conditions.
The road condition model generation method provided by the embodiment of the invention not only can accurately construct the road condition model around the vehicle, but also can judge whether dangerous road conditions exist around the vehicle according to the road condition model and the driving data of the vehicle, thereby being beneficial to improving the driving safety of the vehicle.
Fig. 3 is a schematic structural diagram of a road condition model generating device according to another embodiment of the present invention. As shown in fig. 3, the apparatus includes: a road image acquisition module 310, an object model acquisition module 320 and a road condition model generation module 330. Wherein the content of the first and second substances,
the road image acquisition module 310 is used for acquiring road images around a vehicle in real time in the driving process of the vehicle;
an object model obtaining module 320, configured to obtain, based on a server, a target object model matching an object in the road image when an object model matching the object in the road image does not exist in a pre-stored model database;
a road condition model generating module 330, configured to generate a road condition model based on the target object model.
According to the road condition model generation device provided by the embodiment of the invention, the road image around the vehicle is obtained in real time in the driving process of the vehicle; when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server; and generating a road condition model based on the target object model. By the technical scheme provided by the embodiment of the invention, the road condition model around the vehicle can be accurately constructed, and the driving safety of the vehicle is improved.
Optionally, the object model obtaining module is configured to:
when an object model matched with an object in the road image does not exist in a pre-stored model database, sending the road image to a server;
obtaining a target object model fed back by the server; wherein the target object model is an object model determined by the server to match an object in the road image.
Optionally, the apparatus further comprises:
and the object model determining module is used for determining a target object model matched with the object in the road image based on the model database when the object model matched with the object in the road image exists in the prestored model database.
Optionally, the apparatus further comprises:
the driving data acquisition module is used for acquiring the driving data of the vehicle in real time;
correspondingly, the device further comprises:
the dangerous road condition judging module is used for judging whether dangerous road conditions exist around the vehicle or not based on the road condition model and the driving data after the road condition model is generated based on the target object model;
and the road condition prompting module is used for prompting dangerous road conditions when the dangerous road conditions around the vehicle are determined.
Optionally, the apparatus further comprises:
and the path planning module is used for displaying the road condition model after the road condition model is generated based on the target object model, and planning the driving path of the vehicle based on the road condition model.
The device can execute the methods provided by all the embodiments of the invention, and has corresponding functional modules and beneficial effects for executing the methods. For technical details which are not described in detail in the embodiments of the present invention, reference may be made to the methods provided in all the aforementioned embodiments of the present invention.
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform a road condition model generation method, and the method includes:
acquiring a road image around a vehicle in real time in the driving process of the vehicle;
when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server;
and generating a road condition model based on the target object model.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDRRAM, SRAM, EDORAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium containing the computer-executable instructions provided in the embodiments of the present invention is not limited to the above-described road condition model generation operation, and may also execute the relevant operations in the road condition model generation method provided in any embodiments of the present invention.
The embodiment of the invention provides a vehicle, wherein the road condition model generation device provided by the embodiment of the invention can be integrated in the vehicle. Fig. 4 is a block diagram of a vehicle according to an embodiment of the present invention. The vehicle 400 may include: the road condition model generating method comprises a memory 401, a processor 402 and a computer program stored on the memory 401 and executable by the processor, wherein the processor 402 implements the road condition model generating method according to the embodiment of the invention when executing the computer program.
The road condition model generation system provided in the embodiment of the invention,
acquiring a road image around a vehicle in real time in the driving process of the vehicle; when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server; and generating a road condition model based on the target object model. By the technical scheme provided by the embodiment of the invention, the road condition model around the vehicle can be accurately constructed, and the driving safety of the vehicle is improved.
The traffic condition model generation device, the storage medium and the traffic condition model generation system provided in the above embodiments may execute the traffic condition model generation method provided in any embodiment of the present invention, and have functional modules and beneficial effects corresponding to the execution of the method. For details of the road condition model generation method provided in any embodiment of the present invention, reference may be made to the above-mentioned embodiments.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A road condition model generation method is characterized by comprising the following steps:
acquiring a road image around a vehicle in real time in the driving process of the vehicle;
when an object model matched with the object in the road image does not exist in a pre-stored model database, acquiring a target object model matched with the object in the road image based on a server;
and generating a road condition model based on the target object model.
2. The method of claim 1, wherein when there is no object model matching the object in the road image in the pre-stored model database, obtaining a target object model matching the object in the road image based on a server, comprises:
when an object model matched with an object in the road image does not exist in a pre-stored model database, sending the road image to a server;
obtaining a target object model fed back by the server; wherein the target object model is an object model determined by the server to match an object in the road image.
3. The method of claim 1, further comprising: when an object model matching the object in the road image exists in a pre-stored model database, a target object model matching the object in the road image is determined based on the model database.
4. The method of claim 1, further comprising:
acquiring running data of the vehicle in real time;
correspondingly, after generating the road condition model based on the target object model, the method further includes:
judging whether dangerous road conditions exist around the vehicle or not based on the road condition model and the driving data;
and when the dangerous road conditions exist around the vehicle, prompting the dangerous road conditions.
5. The method of claim 1, after generating the road condition model based on the target object model, further comprising:
and displaying the road condition model, and planning the driving path of the vehicle based on the road condition model.
6. A road condition model generation device, comprising:
the road image acquisition module is used for acquiring road images around the vehicle in real time in the running process of the vehicle;
an object model obtaining module, configured to obtain, based on a server, a target object model that matches an object in the road image when the object model that matches the object in the road image does not exist in a pre-stored model database;
and the road condition model generating module is used for generating a road condition model based on the target object model.
7. The apparatus of claim 6, wherein the object model obtaining module is configured to:
when an object model matched with an object in the road image does not exist in a pre-stored model database, sending the road image to a server;
obtaining a target object model fed back by the server; wherein the target object model is an object model determined by the server to match an object in the road image.
8. The apparatus of claim 6, further comprising:
the driving data acquisition module is used for acquiring the driving data of the vehicle in real time;
correspondingly, the device further comprises:
the dangerous road condition judging module is used for judging whether dangerous road conditions exist around the vehicle or not based on the road condition model and the driving data after the road condition model is generated based on the target object model;
and the road condition prompting module is used for prompting dangerous road conditions when the dangerous road conditions around the vehicle are determined.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processing device, implements the road condition model generating method according to any one of claims 1 to 5.
10. A traffic model generating system, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the traffic model generating method according to any one of claims 1 to 5 when executing the computer program.
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Application publication date: 20211026 |