CN114625822A - High-precision map updating method and device and electronic equipment - Google Patents
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Abstract
The disclosure provides a high-precision map updating method and device and electronic equipment, relates to the technical field of artificial intelligence, and particularly relates to an unmanned and high-precision map technology. The method comprises the following steps: acquiring lane line observation information acquired by a vehicle, and establishing a lane line grid graph according to the lane line observation information, wherein the lane line grid graph comprises a plurality of grids and the probability of lane lines existing in the grids; determining an observation lane line according to the probability of the lane line existing in the grid; and matching the observation lane line with the lane line in the high-precision map, and updating the matched lane line according to the observation lane line. The method improves the situation of the high-precision map.
Description
Technical Field
The present disclosure relates to unmanned and high-precision map technologies in the field of artificial intelligence technologies, and in particular, to a method and an apparatus for updating a high-precision map, and an electronic device.
Background
The situation is that the geographical spatial information provided by the map reflects the latest current situation as much as possible, the change of the landform is very frequent under the situation of rapid development of the city, and the map must be continuously updated on the drawn map in order to ensure the situation of the map.
At present, in the related art, the updating process of the high-precision map mainly depends on the high-precision acquisition vehicle to periodically acquire data, and elements in the high-precision map are updated by using the acquired data.
Disclosure of Invention
The present disclosure provides a high-precision map updating method and apparatus, and an electronic device, which improve the present situation of a high-precision map.
According to a first aspect of the present disclosure, there is provided a high-precision map updating method, including:
acquiring lane line observation information acquired by a vehicle, and establishing a lane line grid map according to the lane line observation information, wherein the lane line grid map comprises a plurality of grids and the probability of lane lines existing in the grids;
determining an observation lane line according to the probability of the lane line existing in the grid;
and matching the observation lane line with the lane line in the high-precision map, and updating the matched lane line according to the observation lane line.
According to a second aspect of the present disclosure, there is provided an update apparatus of a high-precision map, including:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for acquiring lane line observation information acquired by a vehicle and establishing a lane line grid map according to the lane line observation information, and the lane line grid map comprises a plurality of grids and the probability of lane lines existing in the grids;
the determining module is used for determining an observation lane line according to the probability of the lane line existing in the grid;
and the updating module is used for matching the observation lane line with the lane line in the high-precision map and updating the matched lane line according to the observation lane line.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of the first aspect described above.
According to a fifth aspect of the present disclosure, there is provided a computer program product, the program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method of the first aspect.
According to the technical scheme disclosed by the invention, the situation of the high-precision map is improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flow chart of a high-precision map updating method according to an embodiment of the present disclosure;
FIG. 2 is a schematic view of a lane line aggregation provided in accordance with an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an updating apparatus for a high-precision map according to an embodiment of the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device used to implement methods of embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The updating method of the high-precision map provided by the embodiment of the disclosure is independent of a high-precision collection vehicle, is based on a common vehicle on a road surface, utilizes a large amount of observation data returned by the common vehicle to construct a lane line grid map, determines an observation lane line based on the lane line grid map, updates the existing high-precision map based on the matching difference between the observation lane line and the lane line in the existing high-precision map, can realize the quick updating of the high-precision map at low cost based on the returned data of the common vehicle, and improves the instantaneity of the high-precision map.
The invention provides a high-precision map updating method, a high-precision map updating device and electronic equipment, which are applied to the fields of unmanned and high-precision maps in the technical field of artificial intelligence, and particularly can be applied to a high-precision map making scene to improve the situation of the high-precision map.
Hereinafter, the update method of the high-precision map provided by the present disclosure will be described in detail by specific embodiments. It is to be understood that the following detailed description may be combined with other embodiments, and that the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a schematic flow chart of an update method of a high-precision map according to an embodiment of the present disclosure. The execution subject of the method is a high-precision map updating device, and the device can be realized in a software and/or hardware mode. As shown in fig. 1, the method includes:
s101, acquiring lane line observation information acquired by a vehicle, and establishing a lane line grid map according to the lane line observation information, wherein the lane line grid map comprises a plurality of grids and the probability of lane lines existing in the grids.
The vehicle in the embodiment of the present disclosure refers to a general vehicle traveling on a road surface, and the lane line observation information collected by the vehicle refers to lane line information observed by a sensor, a radar, a camera, and the like of the vehicle, for example, the lane line observation information includes position information and attribute information of a lane line, where the attribute information may include a type, a color, and the like of the lane line.
The size of the grid included in the lane line grid map may be set as needed, and for example, the size of the grid is 0.2m × 0.2 m. When the lane line grid graph is established, the area is divided according to the size of the grid to obtain a plurality of grids, and the probability that the lane line exists in each grid is determined based on the lane line observation information because the lane line observation information may have certain errors or deviations and the data accuracy of different types of vehicles may be different.
S102, determining an observation lane line according to the probability of the lane line existing in the grid.
It can be understood that, when determining the probability of a lane line existing in a grid based on the lane line observation information, the greater the number of times a lane line is observed in a grid, the higher the probability of a lane line existing in the grid, and therefore, after obtaining a lane line grid map, the observed lane line, that is, the lane line determined based on the lane line observation information of the vehicle, can be determined based on the probability of a lane line existing in the grid.
S103, matching the observation lane line with the lane line in the high-precision map, and updating the matched lane line according to the observation lane line.
The high-precision map in the step is an existing drawn high-precision map, and the method in the embodiment of the disclosure is used for updating the high-precision map, so that the observation lane line is matched with the lane line in the high-precision map, the matched lane line is determined, and the matched lane line is updated based on the observation lane line, so that the lane line in the high-precision map keeps the latest information.
According to the method, the lane line grid graph is constructed by utilizing the lane line observation information returned by the common vehicle, the observation lane line is determined based on the probability of the lane line existing in the grid in the lane line grid graph, the existing high-precision map is updated based on the matching difference between the observation lane line and the lane line in the existing high-precision map, the high-precision map can be rapidly updated at low cost, and the instantaneity of the high-precision map is improved.
The respective steps are explained on the basis of the above-described embodiment.
Optionally, the establishing of the lane line grid map according to the lane line observation information includes:
establishing an initial lane line grid graph, wherein the probability of lane lines existing in a grid in the initial lane line grid graph is a first preset probability; determining the observation state of the grid according to the observation information of the lane lines, wherein the observation state comprises the observation of the lane lines and the observation of the lane lines; and updating the first preset probability according to the observation state of the grid to obtain a lane line grid map.
The method comprises the steps of dividing regions according to the size of a preset grid to obtain an initial lane line grid graph, and setting the given times of lane lines existing in each grid as a first preset probability in the initial lane line grid graph because the probability that each grid has lane lines or does not have lane lines is the same before any other processing is carried out. Illustratively, the first preset probability is 0.5, i.e., the probability of the grid having lane lines is 50%.
Then, according to the acquired lane line observation information, whether each grid observes the existence of the lane line or does not observe the existence of the lane line is determined, and based on the observation state, the first preset probability is updated, so that the probability that the lane line exists in the grid corresponding to the grid where the lane line exists is increased, the probability that the lane line exists in the grid corresponding to the grid where the lane line does not exist is reduced, the lane line grid graph is obtained, and the lane line grid graph can accurately feed back the lane line.
How the first preset probability is updated is further described below.
Optionally, determining an initial probability of the grid having the lane line according to the first preset probability and the observation state of the grid; and updating the initial probability of the grid within a preset range around the grid according to the observation state of the grid to obtain the lane line grid map.
Optionally, according to the first preset probability and the observation state of the grid, an initial probability that the grid has the lane line is determined based on a markov random field theory, and a Belief Propagation algorithm (Loopy Belief Propagation) is used to calculate probability Propagation of the grid currently observing the lane line to other grids observing the lane line within a peripheral preset range, so as to increase the initial probability of the other grids observing the lane line, and reduce the probability of the lane line for the grids not observing the lane line. Therefore, the probability of observing the grids of the lane lines is higher and higher, the probability of not observing the grids of the lane lines is lower and lower, and the accuracy of the lane line grid graph is improved.
Optionally, when the initial probability of the grid within the preset range around the grid is updated according to the observation state of the grid, the lane line angle may be determined according to the lane line observation information corresponding to the grid, and the grid may be divided into different levels according to the lane line angle; and updating the initial probability of the grids at the same level in the preset range around the grids according to the observation state of the grids.
Optionally, the grid is divided into 4 different levels according to the lane line angle, each level corresponds to an angle range, and when the initial probabilities of other grids around the grid are updated according to the observation state of the grid, the grids of the different levels do not affect each other, so that the probability of lane lines in different directions is prevented from being worn away, and the accuracy of the lane lines in all directions is ensured.
The levels corresponding to different lane line angles can be pre-defined or can be dynamically allocated, optionally, in the process of updating the initial probability, whether the two grids are in the same layer or not is determined dynamically according to the lane line angles of the adjacent grids, and then whether probability propagation occurs when the peripheral grids are updated is determined, so that probability mutual wearing of lane lines in different directions is avoided, and accuracy of lane lines in all directions is guaranteed.
How to determine the observation lane lines from the probability of the presence of lane lines in the grid is described further below.
Optionally, determining a grid with a lane line existence probability greater than a second preset probability as a target grid; and aggregating the lane line observation information corresponding to the target grid to obtain the observation lane line.
For a grid in the lane line grid graph, the greater the probability that the grid has the lane line, the more likely the grid actually has the lane line, so the grid with the probability that the grid has the lane line greater than the second preset probability is determined as a target grid with the lane line, and the observed lane line is determined based on aggregation of the lane line observation information corresponding to the target grid. The aggregating of the lane line information corresponding to the target grid includes aggregating of the position information and the attribute information of the lane line. By aggregating the grids, the accuracy of observing the lane lines is improved. For example, the positions of the lane lines are aggregated as shown in fig. 2 to obtain an observed lane line.
Optionally, for the observation lane line obtained by aggregation, the observation lane line may be divided into a plurality of lane lines by determining a lane bifurcation or a bifurcation point therein.
After the observation lane line is obtained, the geometric points of the observation lane line and the lane line in the high altitude map are matched by Hidden Markov Model (HMM) matching, and the connection relation in the high altitude map is fully trusted because the HMM is updated based on the high-precision map, and the transition probability of the HMM is set to be 1. Optionally, geometric points of the lane lines of the high-precision map may be subjected to a dense processing in advance to prevent matching of a wrong lane line.
And after the lane line of the high-precision map is matched, updating the matched lane line according to the position and the attribute of the observed lane line.
Optionally, if the distance between the observation lane line and the matched lane line is less than or equal to the preset distance, the confidence of the position information of the matched lane line is increased.
The distance between the observation lane line and the matched lane line is smaller than or equal to the preset distance, and the observation lane line and the matched lane line are the same lane line, so that the confidence of the position information of the matched lane line can be increased, and the confidence of the high-precision map is improved by updating the observation information based on the lane line.
Optionally, if the distance between the observation lane line and the matched lane line is greater than the preset distance, and the matched lane line is not matched with other observation lane lines, the position information of the matched lane line is updated to the position information of the observation lane line in the high-precision map.
The distance between the observation lane line and the matched lane line is greater than the preset distance, and the matched lane line is not matched with other observation lane lines, so that the position of the matched lane line is changed, and the position of the matched lane line is moved to the position where the observation lane line is located, therefore, the position information of the matched lane line is updated to the position information of the observation lane line, and the situation of the high-precision map is ensured by updating the observation information based on the lane line.
Optionally, if the distance between the observation lane line and the matched lane line is greater than the preset distance, and the matched lane line is also matched with another observation lane line closer to the preset distance, it indicates that the observation lane line is a newly added lane line, and therefore, a lane line can be newly added in the high-precision map according to the position information and the attribute information of the observation lane line, and the situation of the high-precision map is ensured by updating the observation information based on the lane line.
Optionally, if the lane line attribute information of the observed lane line is different from the lane line attribute information of the matched lane line, the lane line attribute information of the matched lane line is updated to the lane line attribute information of the observed lane line in the high-precision map, and the attribute information may be the type, the color and the like of the lane line, so that the situation of the high-precision map is ensured by updating the observation information based on the lane line.
Based on the above embodiment, it can be understood that after the observation lane line is determined based on the lane line grid map and the high-precision map is updated, the lane line grid map can be continuously updated according to the new lane line observation information returned by the vehicle, so as to update the high-precision map again in time.
Optionally, acquiring new lane line observation information acquired by the vehicle; and updating the probability of the lane lines existing in the grids according to the new lane line observation information. The process of establishing the lane line grid diagram according to the lane line observation information in the previous embodiment is similar to the process of establishing the lane line grid diagram according to the lane line observation information in the previous embodiment, namely the initial probability of the lane line existing in the grid is determined according to the first preset probability and the observation state of the grid; and updating the initial probability of the grids in the preset range around the grids according to the observation state of the grids to obtain the lane line grid map, wherein the probability that each grid in the obtained lane line grid map of the previous version has the lane lines is taken as the first preset probability in the calculation process. According to the lane line observation information returned by the vehicle, the method disclosed by the embodiment of the disclosure can be repeatedly executed, so that the situation of the high-precision map is ensured.
Optionally, after the lane line grid map is updated each time, the updated version may be stored, for example, the updated version may be stored in the cloud, so that the version may be rolled back. Optionally, since the lane line grid map is a sparse matrix, it can be stored in the cloud in a minimized manner by using a hybrid (hyb) ELL + COO manner.
Fig. 3 is a schematic structural diagram of an updating apparatus for a high-precision map according to an embodiment of the present disclosure. As shown in fig. 3, the high-precision map updating apparatus 300 includes:
the establishing module 301 is configured to obtain lane line observation information acquired by a vehicle, and establish a lane line grid map according to the lane line observation information, where the lane line grid map includes a plurality of grids and a probability that a lane line exists in the grids;
a determining module 302, configured to determine an observation lane line according to a probability that the grid has the lane line;
and the updating module 303 is configured to match the observation lane line with a lane line in the high-precision map, and update the matched lane line according to the observation lane line.
In one embodiment, the establishing module 301 includes:
the device comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing an initial lane line grid graph, and the probability that a lane line exists in a grid in the initial lane line grid graph is a first preset probability;
the first determining unit is used for determining the observation state of the grid according to the observation information of the lane lines, wherein the observation state comprises the observation of the lane lines and the observation of the lane lines;
and the first updating unit is used for updating the first preset probability according to the observation state of the grid so as to obtain a lane line grid map.
In one embodiment, a first update unit includes:
the first determining subunit is used for determining the initial probability of the grid having the lane line according to the first preset probability and the observation state of the grid;
and the first updating subunit is used for updating the initial probability of the grid within the preset range around the grid according to the observation state of the grid to obtain the lane line grid map.
In one embodiment, the determining module 302 includes:
the second determining unit is used for determining the grid with the lane line probability larger than a second preset probability as the target grid;
and the aggregation unit is used for aggregating the lane line observation information corresponding to the target grid to obtain the observation lane line.
In one embodiment, the update module 303 includes:
and the second updating unit is used for increasing the confidence of the position information of the matched lane line if the distance between the observation lane line and the matched lane line is less than or equal to the preset distance.
In one embodiment, the update module 303 includes:
and the third updating unit is used for updating the position information of the matched lane line into the position information of the observed lane line in the high-precision map if the distance between the observed lane line and the matched lane line is greater than the preset distance and the matched lane line is not matched with other observed lane lines.
In one embodiment, the update module 303 includes:
and a fourth updating unit configured to update the lane line attribute information of the matched lane line to the lane line attribute information of the observed lane line in the high-precision map, if the lane line attribute information of the observed lane line is different from the lane line attribute information of the matched lane line.
In one embodiment, the establishing module 301 is further configured to:
acquiring new lane line observation information acquired by a vehicle;
and updating the probability of the lane lines existing in the grids according to the new lane line observation information.
In one embodiment, the first update subunit is configured to:
determining lane line angles according to lane line observation information corresponding to the grids, and dividing the grids into different levels according to the lane line angles;
and updating the initial probability of the grids at the same level in the preset range around the grids according to the observation state of the grids.
The apparatus in the embodiment of the present disclosure may be configured to execute the method for updating a high-precision map in the above method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium storing computer instructions, according to embodiments of the present disclosure.
According to an embodiment of the present disclosure, the present disclosure also provides a computer program product comprising: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.
FIG. 4 is a schematic block diagram of an electronic device used to implement methods of embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 4, the electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
A number of components in the electronic device 400 are connected to the I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS"). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Claims (21)
1. A high-precision map updating method comprises the following steps:
acquiring lane line observation information acquired by a vehicle, and establishing a lane line grid map according to the lane line observation information, wherein the lane line grid map comprises a plurality of grids and the probability of lane lines existing in the grids;
determining an observation lane line according to the probability of the lane line existing in the grid;
and matching the observation lane line with the lane line in the high-precision map, and updating the matched lane line according to the observation lane line.
2. The method of claim 1, wherein the building a lane line grid map from the lane line observation information comprises:
establishing an initial lane line grid graph, wherein the probability of lane lines existing in grids in the initial lane line grid graph is a first preset probability;
determining the observation state of the grid according to the lane line observation information, wherein the observation state comprises the observation of the lane line and the observation of the lane line;
and updating the first preset probability according to the observation state of the grid so as to obtain the lane line grid map.
3. The method according to claim 2, wherein the updating the first preset probability according to the observation state of the grid to obtain the lane line grid map comprises:
determining the initial probability of the grid having the lane line according to the first preset probability and the observation state of the grid;
and updating the initial probability of the grid within the preset range around the grid according to the observation state of the grid to obtain the lane line grid map.
4. The method of any one of claims 1-3, wherein the determining an observed lane line from the probability of a lane line existing in the grid comprises:
determining the grids with the probability of the lane lines existing in the grids greater than a second preset probability as target grids;
and aggregating the lane line observation information corresponding to the target grid to obtain the observation lane line.
5. The method of any one of claims 1-4, wherein the updating the matched lane line according to the observed lane line comprises:
and if the distance between the observation lane line and the matched lane line is less than or equal to a preset distance, increasing the confidence coefficient of the position information of the matched lane line.
6. The method of any of claims 1-4, wherein the updating the matched lane line according to the observed lane line comprises:
and if the distance between the observation lane line and the matched lane line is greater than the preset distance and the matched lane line is not matched with other observation lane lines, updating the position information of the matched lane line into the position information of the observation lane line in the high-precision map.
7. The method of any of claims 1-6, wherein the updating the matched lane line according to the observed lane line comprises:
and if the lane line attribute information of the observed lane line is different from the lane line attribute information of the matched lane line, updating the lane line attribute information of the matched lane line into the lane line attribute information of the observed lane line in the high-precision map.
8. The method of any of claims 1-7, further comprising:
acquiring new lane line observation information acquired by a vehicle;
and updating the probability of the lane lines existing in the grid according to the new lane line observation information.
9. The method of claim 3, wherein updating the initial probability of the grid within the predetermined range around the grid based on the observed state of the grid comprises:
determining lane line angles according to lane line observation information corresponding to the grids, and dividing the grids into different levels according to the lane line angles;
and updating the initial probability of the grids at the same level in the preset range around the grids according to the observation state of the grids.
10. An updating device of a high-precision map comprises:
the system comprises an establishing module, a judging module and a judging module, wherein the establishing module is used for acquiring lane line observation information acquired by a vehicle and establishing a lane line grid map according to the lane line observation information, and the lane line grid map comprises a plurality of grids and the probability of lane lines existing in the grids;
the determining module is used for determining an observation lane line according to the probability of the lane line existing in the grid;
and the updating module is used for matching the observation lane line with the lane line in the high-precision map and updating the matched lane line according to the observation lane line.
11. The apparatus of claim 10, wherein the establishing means comprises:
the device comprises an establishing unit, a calculating unit and a calculating unit, wherein the establishing unit is used for establishing an initial lane line grid map, and the probability that a lane line exists in a grid in the initial lane line grid map is a first preset probability;
a first determining unit, configured to determine an observation state of the grid according to the lane line observation information, where the observation state includes an observation of a lane line and a non-observation of a lane line;
and the first updating unit is used for updating the first preset probability according to the observation state of the grid so as to obtain the lane line grid map.
12. The apparatus of claim 11, wherein the first updating unit comprises:
the first determining subunit is used for determining the initial probability of the grid having the lane line according to the first preset probability and the observation state of the grid;
and the first updating subunit is used for updating the initial probability of the grid within the preset range around the grid according to the observation state of the grid to obtain the lane line grid map.
13. The apparatus of any of claims 10-12, wherein the means for determining comprises:
the second determining unit is used for determining the grid with the lane line probability larger than a second preset probability as the target grid;
and the aggregation unit is used for aggregating the lane line observation information corresponding to the target grid to obtain the observation lane line.
14. The apparatus of any of claims 10-13, wherein the update module comprises:
and the second updating unit is used for increasing the confidence of the position information of the matched lane line if the distance between the observation lane line and the matched lane line is less than or equal to the preset distance.
15. The apparatus of any of claims 10-13, wherein the update module comprises:
and the third updating unit is used for updating the position information of the matched lane line into the position information of the observation lane line in the high-precision map if the distance between the observation lane line and the matched lane line is greater than the preset distance and the matched lane line is not matched with other observation lane lines.
16. The apparatus of any of claims 10-15, wherein the update module comprises:
and a fourth updating unit, configured to update, in the high-precision map, the lane line attribute information of the matched lane line to the lane line attribute information of the observed lane line if the lane line attribute information of the observed lane line is different from the lane line attribute information of the matched lane line.
17. The apparatus of any of claims 10-16, wherein the establishing means is further configured to:
acquiring new lane line observation information acquired by a vehicle;
and updating the probability of the lane lines existing in the grid according to the new lane line observation information.
18. The apparatus of claim 12, wherein the first update subunit is to:
determining lane line angles according to lane line observation information corresponding to the grids, and dividing the grids into different levels according to the lane line angles;
and updating the initial probability of the grids at the same level in the preset range around the grids according to the observation state of the grids.
19. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.
21. A computer program product comprising a computer program which, when executed by a processor, implements the method of any one of claims 1-9.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116168173A (en) * | 2023-04-24 | 2023-05-26 | 之江实验室 | Lane line map generation method, device, electronic device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116168173A (en) * | 2023-04-24 | 2023-05-26 | 之江实验室 | Lane line map generation method, device, electronic device and storage medium |
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