CN116665173A - Lane line identification method, device, equipment and medium - Google Patents
Lane line identification method, device, equipment and medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a lane line identification method, a lane line identification device, lane line identification equipment and a lane line identification medium, wherein the lane line identification method comprises the following steps: acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle, determining first position information of the target vehicle based on the first point cloud data set, determining second position information of the target vehicle based on the second point cloud data set, and fitting according to the first position information and the second position information to obtain target lane line information. According to the technical scheme, the laser radar algorithm and the millimeter wave radar algorithm are fitted, so that lane line information is identified, collision risk of a vehicle in the running process is avoided, the accuracy of lane line identification is guaranteed, and the distance for identifying the lane line is increased.
Description
Technical Field
The embodiment of the invention relates to the technical field of automatic driving, in particular to a lane line identification method, a lane line identification device, lane line identification equipment and lane line identification medium.
Background
With the development of economy, the popularization of home automobiles brings great convenience to the life of people, and meanwhile, dangerous situations accompanied with the use of automobiles are more and more.
In order to solve the above problems, most vehicles identify lane lines by visual sensors to judge vehicles and obstacles in the lane lines, thereby avoiding dangerous situations.
In an actual environment, however, lane lines are complex and various, the lane lines are simply judged by visual information, uncertainty exists, and collision risks are easy to occur.
Disclosure of Invention
The embodiment of the invention provides a lane line identification method, a lane line identification device, lane line identification equipment and a lane line identification medium, which can improve the distance and the accuracy of lane line identification.
In a first aspect, an embodiment of the present invention provides a lane line recognition method, including:
acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle;
determining first location information of the target vehicle based on the first point cloud data set, and determining second location information of the target vehicle based on the second point cloud data set;
and fitting according to the first position information and the second position information to obtain target lane line information.
In a second aspect, an embodiment of the present invention further provides a lane line identifying apparatus, including:
the data acquisition module is used for acquiring a first point cloud data set acquired by the laser radar and a second point cloud data set acquired by the millimeter wave radar on the target vehicle;
the data processing module is used for determining first position information of the target vehicle based on the first point cloud data set and determining second position information of the target vehicle based on the second point cloud data set;
the lane line fitting module is used for fitting according to the first position information and the second position information to obtain target lane line information.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, including:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the lane line identification method as provided by any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a lane line recognition method as provided by any of the embodiments of the present invention.
According to the technical scheme, the first point cloud data set acquired by the laser radar and the second point cloud data set acquired by the millimeter wave radar on the target vehicle are acquired, the millimeter wave radar and the laser radar are combined, the capability of identifying the lane line is guaranteed, the detection distance is improved, the first position information of the target vehicle is determined based on the first point cloud data set, the second position information of the target vehicle is determined based on the second point cloud data set, the vehicle and lane line model can be constructed by determining the first position information and the second position information, the accuracy of lane line identification is guaranteed, the target lane line information is obtained through fitting according to the first position information and the second position information, the target vehicle in front of the vehicle can be judged through the target lane line information, and the collision risk is reduced. According to the technical scheme, the laser radar algorithm and the millimeter wave radar algorithm are fitted, so that lane line information is identified, collision risk of a vehicle in the running process is avoided, the accuracy of lane line identification is guaranteed, and the distance for identifying the lane line is increased.
Drawings
FIG. 1 is a flow chart of a lane line identification method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a lane line recognition method according to an embodiment of the present invention;
FIG. 3 is a flowchart of a lane line recognition method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a lane line recognition device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of a lane line recognition method according to an embodiment of the present invention, where the embodiment is applicable to a scenario of lane line recognition analysis in a vehicle driving process. The method can be executed by a lane line recognition device, which can be realized by software and/or hardware, and is integrated into a computer device with an application development function.
As shown in fig. 1, the lane line recognition method of the present embodiment includes the steps of:
s110, acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle.
The laser radar may be a radar that is disposed in the lane recognition device to emit a laser beam to detect a characteristic amount of a position, a speed, or the like of the target. The millimeter wave radar may be a radar configured to operate in a millimeter wave band detection in the lane line recognition device. A point cloud dataset refers to a set of vectors in a three-dimensional coordinate system.
Specifically, in the driving process of the target vehicle, a laser radar and a millimeter wave radar configured in the lane line identification device start to work, the laser radar collects lane line information on a road, the collected lane line information is used as a first point cloud data set, meanwhile, the millimeter wave radar collects driving vehicle information on the road, and the collected driving vehicle information is used as a second point cloud data set.
S120, determining first position information of the target vehicle based on the first point cloud data set, and determining second position information of the target vehicle based on the second point cloud data set.
The first position information may be position information of the target vehicle with respect to a lane line on the travel road during travel. The second position information may be position information of the target vehicle with respect to other traveling vehicles on the traveling road during traveling.
Specifically, a first point cloud data set acquired by a laser radar is used for establishing a lane line model, and the position information of a target vehicle relative to a lane line on a running road in the running process is used as first position information of the target vehicle. And establishing a driving vehicle model by using the second point cloud data set acquired by the millimeter wave radar, and taking the position information of the target vehicle relative to other driving vehicles on the driving road in the driving process as second position information of the target vehicle.
S130, fitting according to the first position information and the second position information to obtain target lane line information.
The target lane line information may be position information obtained by combining the first position information and the second position information in the driving process of the target vehicle through fitting operation.
Specifically, in the running process of the target vehicle, fitting operation is performed on position information of the target vehicle relative to a lane line on a running road in the running process and position information of the target vehicle relative to other running vehicles on the running road in the running process, so as to obtain target lane line information.
According to the technical scheme, a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle are acquired, first position information of the target vehicle is determined based on the first point cloud data set, second position information of the target vehicle is determined based on the second point cloud data set, and target lane line information is obtained through fitting according to the first position information and the second position information. According to the technical scheme, the laser radar algorithm and the millimeter wave radar algorithm are fitted, so that lane line information is identified, collision risk of a vehicle in the running process is avoided, the accuracy of lane line identification is guaranteed, and the distance for identifying the lane line is increased.
Example two
Fig. 2 is a flowchart of a lane line recognition method according to an embodiment of the present invention, and the adjustment process of the lane line recognition strategy is further described based on the embodiment and the above embodiments. The method can be executed by a lane line recognition device, which can be realized by software and/or hardware, and is integrated into a computer device with an application development function.
As shown in fig. 2, the lane line recognition method of the present embodiment includes the steps of:
s210, acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle.
S220, performing cluster analysis on each frame of point cloud data in the first point cloud data to obtain a first cluster result, converting the position information of the first cluster result from a laser radar coordinate system to first reference position information under a reference coordinate system of the target vehicle, and taking the first reference position information as the first position information.
Where cluster analysis refers to an analysis process that groups a collection of physical or abstract objects into multiple classes composed of similar objects. The first clustering result may be a clustering result obtained by performing cluster analysis on the first point cloud data by the lane line recognition device. The first reference position information may be position information of the target vehicle with the lane line as a reference coordinate system.
Specifically, during the driving process of the target vehicle, the laser radar configured in the lane line recognition device performs cluster analysis on the collected first point cloud data to obtain a cluster analysis result, establishes a lane line model by using lane line information in the cluster analysis result as a reference coordinate system of the target vehicle, obtains first reference position information, and uses the first reference position information as first position information.
And S230, performing cluster analysis on each frame of point cloud data in the second point cloud data to obtain a second clustering result, converting the position information of the second clustering result from the millimeter wave radar coordinate system into second reference position information under the reference coordinate system of the target vehicle, and taking the second reference position information as second position information.
The second clustering result may be a clustering result obtained by performing cluster analysis on the second point cloud data by the lane line recognition device, and the second reference position information may be position information of the target vehicle by using other running vehicles on the lane line as a reference coordinate system.
Specifically, during the driving process of the target vehicle, the millimeter wave radar configured in the lane line recognition device performs cluster analysis on the collected second point cloud data to obtain a cluster analysis result, establishes a driving vehicle model according to information of other driving vehicles in the cluster analysis result, uses the driving vehicle model as a reference coordinate system of the target vehicle, obtains second reference position information, and uses the second reference position information as second position information.
And S240, performing interpolation sampling between positions corresponding to the first position information and the second position information to obtain a plurality of position point information, and performing curve fitting according to the plurality of position point information to obtain target lane line information.
Specifically, the lane line recognition device performs interpolation sampling between positions corresponding to the first position information and the second position information, so that accuracy of lane line recognition is guaranteed, a plurality of pieces of position point information are obtained through interpolation sampling, the information comprises lane line information and other traveling vehicle information, curve fitting operation is performed on the position point information, and an operation result is used as target lane line information.
According to the technical scheme, a first point cloud data set acquired by a laser radar on a target vehicle and a second point cloud data set acquired by a millimeter wave radar are acquired, clustering analysis is conducted on each frame of point cloud data in the first point cloud data to obtain a first clustering result, position information of the first clustering result is converted into first reference position information under a reference coordinate system of the target vehicle from the laser radar coordinate system, the first reference position information is used as the first position information, clustering analysis is conducted on each frame of point cloud data in the second point cloud data to obtain a second clustering result, position information of the second clustering result is converted into second reference position information under the reference coordinate system of the target vehicle from the millimeter wave radar coordinate system, the second reference position information is used as the second position information, interpolation sampling is conducted between positions corresponding to the first position information and the second position information, a plurality of position point information are obtained, and curve fitting is conducted according to the plurality of position point information to obtain target lane line information. According to the technical scheme, the laser radar algorithm and the millimeter wave radar algorithm are fitted, so that lane line information is identified, collision risk of a vehicle in the running process is avoided, the accuracy of lane line identification is guaranteed, and the distance for identifying the lane line is increased.
Example III
Fig. 3 is a flowchart of a lane line recognition method according to an embodiment of the present invention, and the adjustment process of the lane line recognition strategy is further described based on the embodiment and the above embodiments. The method can be executed by a lane line recognition device, which can be realized by software and/or hardware, and is integrated into a computer device with an application development function.
As shown in fig. 3, the lane line recognition method of the present embodiment includes the steps of:
s310, determining whether the information acquisition states of various vehicles in the driving process of the target vehicle are normal.
The acquisition state is the working state of a laser radar and a millimeter wave radar which are configured in the lane line identification device.
Specifically, in the driving process of the target vehicle, before collecting data, the lane line identification device needs to determine whether the laser radar and the millimeter wave radar can work normally, if so, the follow-up work is performed, and if not, the system stops the follow-up work and records error information.
S320, acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle.
S330, determining first position information of the target vehicle based on the first point cloud data set, and determining second position information of the target vehicle based on the second point cloud data set.
S340, fitting according to the first position information and the second position information to obtain target lane line information.
S350, controlling the target vehicle according to the target lane line information.
Specifically, in the running process of the target vehicle, the running track of the target vehicle is controlled according to the target lane line information, so that collision risk is avoided.
Optionally, the target vehicle is controlled according to the target lane line information, and is specifically used for determining an obstacle in the range of the target lane line, and controlling the target vehicle according to the current position information of the target vehicle and the distance between the obstacle.
Specifically, the target lane line information may include obstacle information within a range of the target lane line, and the target vehicle recognizes obstacles on the lane line according to the target lane line information and avoids the obstacles during traveling.
According to the technical scheme, whether the information acquisition states of various vehicles in the driving process of the target vehicle are normal or not is determined, a first point cloud data set acquired by a laser radar on the target vehicle and a second point cloud data set acquired by a millimeter wave radar are acquired, first position information of the target vehicle is determined based on the first point cloud data set, second position information of the target vehicle is determined based on the second point cloud data set, target lane line information is obtained through fitting according to the first position information and the second position information, and the target vehicle is controlled according to the target lane line information. According to the technical scheme, the laser radar algorithm and the millimeter wave radar algorithm are fitted, so that lane line information is identified, collision risk of a vehicle in the running process is avoided, the accuracy of lane line identification is guaranteed, and the distance for identifying the lane line is increased.
Example IV
Fig. 4 is a schematic structural diagram of a lane line recognition device according to an embodiment of the present invention, where the embodiment is applicable to a scenario of lane line recognition analysis during a vehicle driving process, and the device may be implemented by software and/or hardware, and integrated into a computer terminal device with an application development function.
As shown in fig. 4, the lane line recognition device includes: a data acquisition module 410, a data processing module 420, and a lane line fitting module 430.
The data acquisition module 410 is configured to acquire a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle; a data processing module 420 for determining first location information of the target vehicle based on the first point cloud data set and determining second location information of the target vehicle based on the second point cloud data set; the lane line fitting module 430 is configured to fit the target lane line information according to the first location information and the second location information.
According to the technical scheme, the first point cloud data set acquired by the laser radar and the second point cloud data set acquired by the millimeter wave radar on the target vehicle are acquired, the first position information of the target vehicle is determined based on the first point cloud data set, the second position information of the target vehicle is determined based on the second point cloud data set, and the target lane line information is obtained through fitting according to the first position information and the second position information. According to the technical scheme, through mutual matching among the modules, the laser radar algorithm and the millimeter wave radar algorithm are fitted, so that lane line information is identified, collision risk of a vehicle in the driving process is avoided, the accuracy of lane line identification is ensured, and the distance for identifying the lane line is increased.
Based on the above aspects, optionally, the data processing module 420 includes:
performing cluster analysis on each frame of point cloud data in the first point cloud data to obtain a first cluster result;
converting the position information of the first clustering result from a laser radar coordinate system to first reference position information under a reference coordinate system of the target vehicle;
the first reference position information is taken as first position information.
Based on the above aspects, optionally, the data processing module 420 includes:
performing cluster analysis on each frame of point cloud data in the second point cloud data to obtain a second clustering result;
converting the position information of the second classification result from the millimeter wave radar coordinate system to second reference position information in a reference coordinate system of the target vehicle;
the second reference position information is taken as second position information.
Based on the above technical solutions, the lane line fitting module 430 may optionally include:
interpolation sampling is carried out between positions corresponding to the first position information and the second position information, so that a plurality of position point information is obtained;
and performing curve fitting according to the plurality of position point information to obtain target lane line information.
On the basis of the above technical solutions, optionally, the lane line recognition device further includes:
and determining whether the acquisition states of various vehicle information in the driving process of the target vehicle are normal.
On the basis of the above technical solutions, optionally, the lane line recognition device further includes:
and the vehicle control module is used for controlling the target vehicle according to the target lane line information.
On the basis of the above technical solutions, optionally, the vehicle control module includes:
determining an obstacle within the target lane line;
the target vehicle is controlled according to the distance between the current position information of the target vehicle and the obstacle.
The lane line identification device provided by the embodiment of the invention can execute the lane line identification method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention. Fig. 5 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 5 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. The computer device 12 may be any terminal device with computing power, such as an intelligent controller, a server, a mobile phone, and the like.
As shown in FIG. 5, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown in fig. 5, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running a program stored in the system memory 28, for example, implements the lane line recognition method provided by the present embodiment, and includes:
acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle;
determining first location information of the target vehicle based on the first point cloud data set, and determining second location information of the target vehicle based on the second point cloud data set;
and fitting according to the first position information and the second position information to obtain target lane line information.
The embodiment of the invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the lane line identification method as provided by any embodiment of the invention, the method comprising:
acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle;
determining first location information of the target vehicle based on the first point cloud data set, and determining second location information of the target vehicle based on the second point cloud data set;
and fitting according to the first position information and the second position information to obtain target lane line information.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It will be appreciated by those of ordinary skill in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. 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, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A lane line recognition method, comprising:
acquiring a first point cloud data set acquired by a laser radar and a second point cloud data set acquired by a millimeter wave radar on a target vehicle;
determining first location information of the target vehicle based on the first point cloud data set, and determining second location information of the target vehicle based on the second point cloud data set;
and fitting according to the first position information and the second position information to obtain target lane line information.
2. The method of claim 1, wherein the determining first location information of the target vehicle based on the first point cloud data set comprises:
performing cluster analysis on each frame of point cloud data in the first point cloud data to obtain a first cluster result;
converting the position information of the first clustering result from a laser radar coordinate system to first reference position information under a reference coordinate system of the target vehicle;
the first reference position information is taken as the first position information.
3. The method of claim 1, wherein the determining second location information of the target vehicle based on the second point cloud data set comprises:
performing cluster analysis on each frame of point cloud data in the second point cloud data to obtain a second clustering result;
converting the position information of the second aggregation result from a millimeter wave radar coordinate system to second reference position information under a reference coordinate system of the target vehicle;
and taking the second reference position information as the second position information.
4. The method of claim 1, wherein the fitting the target lane line information from the first location information and the second location information comprises:
interpolation sampling is carried out between the positions corresponding to the first position information and the second position information, so that a plurality of position point information are obtained;
and performing curve fitting according to the plurality of position point information to obtain the target lane line information.
5. The method of claim 1, wherein prior to acquiring the first and second point cloud data sets, the method further comprises:
and determining whether the information acquisition states of various vehicles in the driving process of the target vehicle are normal.
6. The method of claim 1, wherein at the time, the method further comprises:
and controlling the target vehicle according to the target lane line information.
7. The method of claim 6, wherein the controlling the target vehicle according to the target lane line information comprises:
determining an obstacle within the target lane line range;
and controlling the target vehicle according to the distance between the current position information of the target vehicle and the obstacle.
8. A lane line recognition device, characterized by comprising:
the data acquisition module is used for acquiring a first point cloud data set acquired by the laser radar and a second point cloud data set acquired by the millimeter wave radar on the target vehicle;
a data processing module for determining first location information of the target vehicle based on the first point cloud data set and determining second location information of the target vehicle based on the second point cloud data set;
and the lane line fitting module is used for fitting according to the first position information and the second position information to obtain target lane line information.
9. A computer device, the computer device comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the lane line identification method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the lane line identification method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310693221.0A CN116665173A (en) | 2023-06-12 | 2023-06-12 | Lane line identification method, device, equipment and medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310693221.0A CN116665173A (en) | 2023-06-12 | 2023-06-12 | Lane line identification method, device, equipment and medium |
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| CN116665173A true CN116665173A (en) | 2023-08-29 |
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| CN202310693221.0A Pending CN116665173A (en) | 2023-06-12 | 2023-06-12 | Lane line identification method, device, equipment and medium |
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| Country | Link |
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| CN (1) | CN116665173A (en) |
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