CN117456047A - Method and device for generating electronic horizon data and electronic equipment - Google Patents
Method and device for generating electronic horizon data and electronic equipment Download PDFInfo
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Abstract
The application relates to a method, a device and electronic equipment for generating electronic horizon data, which are used for solving the problems that when a high-precision map has timeliness, the accuracy of the electronic horizon data generated according to the high-precision map is not high and potential safety hazards exist. The method comprises the following steps: acquiring high-precision map and time-lapse map layer data corresponding to the current position of the vehicle; when the version number of the high-precision map is consistent with the version number of the timeliness layer data, reading first road data in a preset distance in front of the vehicle from the high-precision map, and judging whether the first road data has timeliness problems or not based on the first road data and the timeliness layer data; when the first road data has a difference problem, generating electronic horizon data according to the first road data and the timeliness layer data; when the first road data does not have the difference problem, generating electronic horizon data according to the first road data. Based on the method, the accuracy of generating the electronic horizon data can be improved.
Description
Technical Field
The application relates to the technical field of intelligent driving, in particular to a method and device for generating electronic horizon data and electronic equipment.
Background
At present, an on-vehicle high-precision map is mainly provided for an intelligent driving function in an electronic horizon mode, but the high-precision map data is complex to manufacture and long in manufacturing period, and the update frequency of the conventional high-precision map data by industry high-precision map manufacturers is generally more than one quarter, and even more than half a year to one year. This leads to timeliness problems with high-precision maps and real roads, such as: the real road is not opened or a part of road segments are not communicated when drawing for some reasons, so that the road related data is not recorded on the manufactured high-precision map.
When the intelligent driving vehicle generates electronic horizon data according to the high-precision map data, if the high-precision map with timeliness problem is adopted, the generated electronic horizon data is inconsistent with the actual road conditions, and intelligent driving safety problem is easily caused.
Disclosure of Invention
The embodiment of the application provides a method, a device and electronic equipment for generating electronic horizon data, which are used for solving the problems that when a high-precision map has timeliness, the accuracy of the electronic horizon data generated by an electronic horizon module based on the high-precision map is low and potential safety hazards exist.
In a first aspect, the present application provides a method of generating electronic horizon data, the method comprising:
acquiring high-precision map and timeliness map layer data corresponding to a current position of a vehicle, wherein the timeliness map layer data is differential map layer data of a high-precision map road and a real road;
when the version number of the high-precision map is consistent with the version number of the time-lapse layer data, first road data in a preset distance in front of the vehicle is read from the high-precision map, and whether the first road data has a difference problem or not is judged based on the first road data and the time-lapse layer data;
when the first road data has a difference problem, generating electronic horizon data according to the first road data and the timeliness layer data;
and when the first road data does not have the difference problem, generating electronic horizon data according to the first road data.
In one possible design, before the obtaining the high-precision map and the time-lapse map layer data corresponding to the current position of the vehicle, the method further includes: acquiring road environment data perceived by a vehicle at the current position and high-precision map road data corresponding to the vehicle at the current position; matching the road environment data with the high-precision map road data; when first difference data exists between the road environment data and the high-precision map road data, a first difference event is generated and reported to a cloud; and responding to the first differential event audit, generating time-consuming layer data corresponding to the current position of the vehicle according to the first differential data, and configuring a version number for the time-consuming layer data.
In one possible design, the determining whether the first road data has a disparity problem includes: judging whether the first road data has a difference problem of not having the lane data contained in the time-effect layer data; and/or judging whether the first road data has a difference problem that the total number of lanes contained in the first road data is different from the total number of lanes contained in the timeliness layer data; and/or judging whether the first road data has a difference problem that the road attribute is different from the road attribute in the timeliness layer data.
In one possible design, after the generating the time-consuming layer data corresponding to the current position of the vehicle and configuring the version number for the time-consuming layer data, the method further includes: when second differential data exists between the road environment data perceived by the vehicle at the current position and the high-precision map road data corresponding to the current position, generating a second differential event and reporting the second differential event to the cloud, wherein the second differential data is different from the first differential data; and responding to the second differential event audit, updating the timeliness layer data corresponding to the current position of the vehicle according to the second differential data, and updating the version number of the timeliness layer data.
In a second aspect, the present application provides an apparatus for generating electronic horizon data, the apparatus comprising:
the system comprises a data acquisition module, a display module and a display module, wherein the data acquisition module acquires high-precision map and time-efficiency map layer data corresponding to a current position of a vehicle, and the time-efficiency map layer data is differential map layer data of a high-precision map road and a real road;
the data judging module is used for reading first road data in a preset distance in front of the vehicle from the high-precision map when the version number of the high-precision map is consistent with the version number of the time-lapse layer data, and judging whether the first road data has a difference problem or not based on the first road data and the time-lapse layer data;
the first generation module is used for generating electronic horizon data according to the first road data and the timeliness layer data when the first road data has a difference problem;
and the second generation module is used for generating electronic horizon data according to the first road data when the first road data has no difference problem.
In one possible design, the device is further configured to: acquiring road environment data perceived by a vehicle at the current position and high-precision map road data corresponding to the vehicle at the current position; matching the road environment data with the high-precision map road data; when first difference data exists between the road environment data and the high-precision map road data, a first difference event is generated and reported to a cloud; and responding to the first differential event audit, generating time-consuming layer data corresponding to the current position of the vehicle according to the first differential data, and configuring a version number for the time-consuming layer data.
In one possible design, the determining module is specifically configured to: judging whether the first road data has a difference problem of not having the lane data contained in the time-effect layer data; and/or judging whether the first road data has a difference problem that the total number of lanes contained in the first road data is different from the total number of lanes contained in the timeliness layer data; and/or judging whether the first road data has a difference problem that the road attribute is different from the road attribute in the timeliness layer data.
In one possible design, the device is further configured to: when second differential data exists between the road environment data perceived by the vehicle at the current position and the high-precision map road data corresponding to the current position, generating a second differential event and reporting the second differential event to the cloud, wherein the second differential data is different from the first differential data; and responding to the second differential event audit, updating the timeliness layer data corresponding to the current position of the vehicle according to the second differential data, and updating the version number of the timeliness layer data.
In a third aspect, the present application provides an electronic device, including:
a memory for storing a computer program;
and the processor is used for realizing the method steps for generating the electronic horizon data when executing the computer program stored in the memory.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored therein, which when executed by a processor, implements a method step of generating electronic horizon data as described above.
The technical effects of each of the second to fourth aspects and the technical effects that may be achieved by each aspect are referred to above for the technical effects that may be achieved by the first aspect or each possible aspect in the first aspect, and the detailed description is not repeated here.
Drawings
FIG. 1 is a flow chart of a method of generating electronic horizon data provided herein;
fig. 2 is a schematic diagram of one possible application scenario provided in the present application;
fig. 3 is a schematic diagram of one possible application scenario provided in the present application;
fig. 4 is a schematic diagram of one possible application scenario provided in the present application;
fig. 5 is a schematic diagram of one possible application scenario provided in the present application;
FIG. 6 is a flow chart for generating time-dependent layer data provided herein;
fig. 7 is a schematic diagram of an apparatus for generating electronic horizon data according to the present application;
fig. 8 is a schematic diagram of a structure of an electronic device provided in the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings. The specific method of operation in the method embodiment may also be applied to the device embodiment or the system embodiment.
In the description of the present application "a plurality of" is understood as "at least two". "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. A is connected with B, and can be represented as follows: both cases of direct connection of A and B and connection of A and B through C. In addition, in the description of the present application, the words "first," "second," and the like are used merely for distinguishing between the descriptions and not be construed as indicating or implying a relative importance or order.
Referring to fig. 1, a flow chart of a method for generating electronic horizon data according to an embodiment of the present application is shown, and a specific implementation flow of the method is as follows:
step 101: acquiring high-precision map and time-lapse map layer data corresponding to the current position of the vehicle;
in the embodiment of the application, the time-efficient layer data is the difference layer data of the high-precision map road and the real road, and the time-efficient layer data is stored in the cloud.
Specifically, when the intelligent driving vehicle is started by ignition, the vehicle-mounted electronic horizon module loads a high-precision map of a corresponding city and time-dependent layer data of the corresponding city from the cloud based on GPS positioning information. And acquiring a high-precision map corresponding to the current position of the vehicle and time-efficiency map data corresponding to the current position. For example, in fig. 2, a1 is a high-precision map within a preset length distance in front of the current position of the vehicle, and a portion of the square frame P in a2 is time-dependent layer data within a preset length distance in front of the current position of the vehicle. When the a1 high-precision map is constructed by only the paths (Link 1- > Link2- > Link 3) from a to B of the paths that the vehicle can travel, the path C is not opened for some practical reasons, and thus is not recorded in the a1 high-precision map. The part of the square frame P in a2 is a path C (Link 4- > Link 5) which can be driven currently, and a path (Link 1- > Link4- > Link 5) which can be driven from A to C by the vehicle exists in the real path. The portion of the box P in a2 is also the differential layer data between the a1 high-precision map data and the a2 real road data.
Step 102: when the version number of the high-precision map is consistent with the version number of the timeliness layer data, reading first road data in a preset distance in front of the vehicle from the high-precision map, and judging whether the first road data has a difference problem or not based on the first road data and the timeliness layer data;
specifically, the version number of the acquired high-precision map is compared with the version number of the time-lapse map data.
If the version number of the high-precision map is inconsistent with the version number of the time-effect layer data, the data with the latest version number is selected from the high-precision map and the time-effect layer data, and the electronic horizon data is generated based on the data with the latest version number. For example, the version number of the obtained high-precision map is 1.0, the version number of the timeliness layer data is 2.0, and compared with the update of the version number of the timeliness layer data, the electronic horizon data is generated based on the timeliness layer data. In contrast, if the version number of the high-precision map is 2.0 and the version number of the time-efficient layer data is 1.0, the electronic horizon data is generated based on the road data of the high-precision map.
If the version number of the high-precision map is consistent with the version number of the timeliness layer data, first road data in a preset distance in front of the vehicle is read from the high-precision map, and whether the first road data has a difference problem or not is judged based on the first road data and the timeliness layer data.
Because the vehicle-mounted electronic horizon module only generates electronic horizon data within a preset distance at a time, first road data within the preset distance in front of the vehicle is read from the high-precision map, the preset distance can be 2km, 3km and the like according to requirements, and the preset distance is not particularly limited in the embodiment of the application.
After first road data in a preset distance in front of a vehicle are read, whether the first road data have a difference problem or not is judged based on the first road data and the timeliness layer data. The difference problem refers to that the first road data and the road data in the time-lapse layer data have difference data, and specifically may include, but not limited to, the following three difference problems:
1. the first road data does not have lane data included in the time-lapse layer data.
A new lane path exists in the time-dependent layer data and the first road data does not have the new lane data. For example, as shown in fig. 2, a1 is the first road data in the high-precision map, the first road data includes only the lane path from a to B, the time-efficient layer data P includes the lane path from a to C, and the first road data does not include the data of the lane path from a to C included in the time-efficient layer data P. There is a difference problem in the first-pass data.
2. The first road data contains a different total number of lanes than the time-lapse layer data.
The same path segment in the first road data and the time-efficient layer data, such as the path segment Link2 in fig. 3, b1 is a Link2 path segment in the first road data, and b2 is a Link2 path segment in the time-efficient layer data. The Link2 path segment in the first road data includes two lanes lane1 and lane2, and the remaining lanes are blocked for some reason and cannot be used, so that the blocked lanes are not recorded in the high-precision map data when the high-precision map is constructed. The closed lanes in the Link2 path segment in the time-efficient layer data acquired now are already in use, so that the Link2 path segment now contains four lanes lane1, lane2, lane3 and lane4. The total number of lanes contained in the first road data is different from the total number of lanes contained in the timeliness layer data, so that the first road data has a difference problem.
3. The road attribute in the first road data is different from the road attribute in the time-lapse map layer data.
Specifically, the road attribute may be different between the lane attribute of the same lane in the first lane data and the time-efficient layer data, for example, in fig. 4, c1 is the first lane data, c2 is the time-efficient layer data, lane1 in the first lane data is a common vehicle lane, and lane1 in the time-efficient layer data is an emergency lane; the road attribute may also be different between the first lane data and the same path segment in the timeliness layer data, for example, in fig. 5, d1 is the first lane data, d2 is the timeliness layer data, the Link2 path segment in the first lane data is a common vehicle lane, and the Link2 path segment in the timeliness layer data is a toll station. Fig. 4 and 5 are only two cases where the road properties are different, and are only used as references in the embodiment of the present application, and are not used as restrictions on the differences in the road properties. When the road attribute in the first road data is different from the road attribute in the timeliness layer data, the difference problem of the first road data is indicated.
In the embodiment of the present application, the difference problem of the first road data may be any one of the difference problems described above, or may be multiple difference problems described above.
In one possible implementation, the operation of step 103 is performed when there is a discrepancy problem with the first road data.
In one possible implementation, the operation of step 104 is performed when there is no discrepancy in the first road data.
Step 103: when the first road data has a difference problem, generating electronic horizon data according to the first road data and the timeliness layer data;
specifically, if the first road data has the difference problem, the timeliness layer data and the first road data are fused to obtain fused road data, namely expanded road network data, and then electronic horizon data is generated according to the expanded road network data.
When the first road data has the difference problem, the timeliness layer data can make up the difference between the first road data and the actual road condition, the timeliness layer data and the first road data are fused, the road data which is closer to the actual road condition can be obtained, and the accuracy of the electronic horizon data generated by the electronic horizon module by adopting the fused road data is higher.
Step 104: when the first road data does not have the difference problem, generating electronic horizon data according to the first road data.
Specifically, if the first road data does not have the difference problem, it is indicated that the first road data is close to the actual road condition, and the first road data contains time-lapse pattern data, so that the electronic horizon data can be directly generated according to the first road data, and the accuracy of the generated electronic horizon data is higher.
In the embodiment of the present application, the timeliness layer data is generated after manual auditing, so that before the high-precision map and the timeliness layer data corresponding to the current position of the vehicle are acquired, the timeliness layer data needs to be generated first, see fig. 6, and the specific implementation flow of generating the timeliness layer data is as follows:
step 601: acquiring road environment data perceived by the current position of a vehicle and high-precision map road data corresponding to the current position of the vehicle;
specifically, road environment data of the vehicle at the current position is sensed by the vehicle-mounted sensing sensor, a high-precision map of a corresponding city is loaded from the cloud through GPS positioning information, and the road data of the high-precision map corresponding to the vehicle at the current position is obtained from the high-precision map.
Step 602: matching the road environment data with the road data of the high-precision map;
specifically, the perceived road environment data and the high-precision map road data are matched, and whether the road environment data and the high-precision map road data have difference data or not is judged, for example, whether the perceived lane path is consistent with the lane path in the high-precision map or not is judged; and/or whether the perceived number of road lanes is the same as the number of road lanes in the high-precision map; and/or whether the perceived road attribute is the same as the road attribute in the high-definition map.
Step 603: when first difference data exists between the road environment data and the high-precision map data, a first difference event is generated and reported to the cloud;
in the embodiment of the present application, the first differential data may be a lane path from a to C that exists in the perceived road environment data and does not exist in the high-precision map data, such as a lane path from a to C that exists in a2 and does not exist in a1 in fig. 2; the first differential data may also be lanes that are present in the perceived road environment data, but not in the high-precision map data, such as the other two lanes present in b2 and not in b1 in fig. 3; the first differential data may be lane data with different road attributes in the perceived road data and the high-precision map data, for example, the road attribute of the lane1 in c1 in fig. 4 is a motor vehicle lane, and the road attribute of the lane1 in c2 is an emergency lane, so the first differential data is the lane1; for another example, the road attribute of the Link2 path segment in d1 of fig. 5 is a motor vehicle lane, and the road attribute of the Link2 path segment in d2 is a toll station, so the first differential data is the Link2 path segment. The above examples of the first differential data are merely references, and are not particularly limited.
When first difference data exists between the road environment data and the high-precision map data, a first difference event is generated according to the first difference data, and the first difference event is reported to the cloud end to wait for auditing.
Step 604: and responding to the passing of the first differential event audit, generating timeliness layer data corresponding to the current position of the vehicle according to the first differential data, and configuring a version number for the timeliness layer data.
Specifically, after the cloud detects the reported first differential event, auditing is performed manually, after the auditing is passed, timeliness layer data corresponding to the current position of the vehicle is generated according to the first differential data, a version number is configured for the timeliness layer data, and then the timeliness layer data is issued.
Further, when the vehicle runs to the current position again, and second difference data exists between the perceived road environment data and high-precision map data corresponding to the current position of the vehicle, the second difference data is different from the first difference data, namely, when the road environment of the current position changes again, a second difference event is generated according to the second difference data, and the second difference event is reported to the cloud end to wait for auditing. And responding to the passing of the second differential event audit, updating the timeliness layer data corresponding to the current position of the vehicle according to the second differential data to obtain new timeliness layer data, and updating the version number of the timeliness layer data.
When the timeliness layer data does not exist in the position of the vehicle, the perceived road environment data is matched with the high-precision map data, the difference data is obtained, a difference event is generated and reported to the cloud, after the cloud is checked, the timeliness layer data is generated according to the difference data, and the timeliness layer data is stored to the cloud. When the electronic horizon module generates electronic horizon data, high-precision map data corresponding to the current position of the vehicle and timeliness layer data can be obtained from the cloud, if the high-precision map data has a difference problem, the electronic horizon data can be generated after fusion processing is carried out on the basis of the high-precision map data and the timeliness layer data, and therefore the generated electronic horizon data is closer to an actual road environment and higher in accuracy.
Based on the same inventive concept, the present application further provides a device for generating electronic horizon data, which is used for solving the problems that when the high-precision map has a difference, the accuracy of the electronic horizon data generated by the electronic horizon module based on the high-precision map is low, and the problem of potential safety hazard exists, and the accuracy of the effectively generated electronic horizon data is referred to fig. 7, and the device comprises:
the data acquisition module 701 acquires high-precision map and time-efficiency map layer data corresponding to a current position of a vehicle, wherein the time-efficiency map layer data is differential map layer data of a high-precision map road and a real road;
the data judging module 702 reads first road data within a preset distance in front of the vehicle from the high-precision map when the version number of the high-precision map is consistent with the version number of the time-lapse layer data, and judges whether the first road data has a difference problem or not based on the first road data and the time-lapse layer data;
a first generation module 703, configured to generate electronic horizon data according to the first road data and the timeliness layer data when the first road data has a difference problem;
the second generating module 704 generates electronic horizon data according to the first road data when the first road data has no difference problem.
In one possible design, the device is further configured to: acquiring road environment data perceived by a vehicle at the current position and high-precision map road data corresponding to the vehicle at the current position; matching the road environment data with the high-precision map road data; when first difference data exists between the road environment data and the high-precision map road data, a first difference event is generated and reported to a cloud; and responding to the first differential event audit, generating time-consuming layer data corresponding to the current position of the vehicle according to the first differential data, and configuring a version number for the time-consuming layer data.
In one possible design, the determining module is specifically configured to: judging whether the first road data has a difference problem of not having the lane data contained in the time-effect layer data; and/or judging whether the first road data has a difference problem that the total number of lanes contained in the first road data is different from the total number of lanes contained in the timeliness layer data; and/or judging whether the first road data has a difference problem that the road attribute is different from the road attribute in the timeliness layer data.
In one possible design, the device is further configured to: when second differential data exists between the road environment data perceived by the vehicle at the current position and the high-precision map road data corresponding to the current position, generating a second differential event and reporting the second differential event to the cloud, wherein the second differential data is different from the first differential data; and responding to the second differential event audit, updating the timeliness layer data corresponding to the current position of the vehicle according to the second differential data, and updating the version number of the timeliness layer data.
Based on the same inventive concept, the embodiment of the present application further provides an electronic device, where the electronic device may implement the function of the foregoing apparatus for generating electronic horizon data, and referring to fig. 8, the electronic device includes:
at least one processor 801, and a memory 802 connected to the at least one processor 801, a specific connection medium between the processor 801 and the memory 802 is not limited in the embodiment of the present application, and in fig. 8, the processor 801 and the memory 802 are connected by a bus 800 as an example. The connection between the other components of bus 800 is shown in fig. 8 by a bold line, which is merely illustrative and not limiting. Bus 800 may be divided into an address bus, a data bus, a control bus, etc., and is represented by only one thick line in fig. 8 for ease of illustration, but does not represent only one bus or one type of bus. Alternatively, the processor 801 may be referred to as a controller, and the names are not limited.
In the embodiment of the present application, the memory 802 stores instructions executable by the at least one processor 801, and the at least one processor 801 may perform the image generating method described above by executing the instructions stored in the memory 802. The processor 801 may implement the functions of the various modules in the apparatus shown in fig. 7.
The processor 801 is a control center of the apparatus, and may be connected to various parts of the entire control device by various interfaces and lines, and by executing or executing instructions stored in the memory 802 and invoking data stored in the memory 802, various functions of the apparatus and processing data, thereby performing overall monitoring of the apparatus.
In one possible design, processor 801 may include one or more processing units, and processor 801 may integrate an application processor that primarily processes operating systems, user interfaces, application programs, and the like, with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 801. In some embodiments, processor 801 and memory 802 may be implemented on the same chip, or they may be implemented separately on separate chips in some embodiments.
The processor 801 may be a general purpose processor such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, and may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. The general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method for generating electronic horizon data disclosed in connection with the embodiments of the present application may be embodied directly in hardware processor execution or in a combination of hardware and software modules in a processor.
Memory 802, as a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The Memory 802 may include at least one type of storage medium, which may include, for example, flash Memory, hard disk, multimedia card, card Memory, random access Memory (Random Access Memory, RAM), static random access Memory (Static Random Access Memory, SRAM), programmable Read-Only Memory (Programmable Read Only Memory, PROM), read-Only Memory (ROM), charged erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory), magnetic Memory, magnetic disk, optical disk, and the like. Memory 802 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 802 in the embodiments of the present application may also be circuitry or any other device capable of implementing a memory function for storing program instructions and/or data.
By programming the processor 801, the code corresponding to the method for generating electronic horizon data described in the foregoing embodiments may be solidified into a chip, so that the chip may perform the steps of the method for generating electronic horizon data of the embodiment shown in fig. 1 when running. How to design and program the processor 801 is a technology well known to those skilled in the art, and will not be described in detail herein.
Based on the same inventive concept, embodiments of the present application also provide a storage medium storing computer instructions that, when run on a computer, cause the computer to perform the method of generating electronic horizon data as discussed above.
In some possible embodiments, aspects of the method of generating electronic horizon data provided herein may also be implemented in the form of a program product comprising program code for causing a control apparatus to carry out the steps of the method of generating electronic horizon data according to various exemplary embodiments of the present application as described herein above when the program product is run on a device.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. A method of generating electronic horizon data, the method comprising:
acquiring high-precision map and timeliness map layer data corresponding to a current position of a vehicle, wherein the timeliness map layer data is differential map layer data of a high-precision map road and a real road;
when the version number of the high-precision map is consistent with the version number of the time-lapse layer data, first road data in a preset distance in front of the vehicle is read from the high-precision map, and whether the first road data has a difference problem or not is judged based on the first road data and the time-lapse layer data;
when the first road data has a difference problem, generating electronic horizon data according to the first road data and the timeliness layer data;
and when the first road data does not have the difference problem, generating electronic horizon data according to the first road data.
2. The method of claim 1, further comprising, prior to the acquiring the high-precision map and the time-lapse map layer data corresponding to the current location of the vehicle:
acquiring road environment data perceived by a vehicle at the current position and high-precision map road data corresponding to the vehicle at the current position;
matching the road environment data with the high-precision map road data;
when first difference data exists between the road environment data and the high-precision map road data, a first difference event is generated and reported to a cloud;
and responding to the first differential event audit, generating time-consuming layer data corresponding to the current position of the vehicle according to the first differential data, and configuring a version number for the time-consuming layer data.
3. The method of claim 1, wherein the determining whether the first road data has a disparity problem comprises:
judging whether the first road data has a difference problem of not having the lane data contained in the time-effect layer data; and/or
Judging whether the first road data has a difference problem that the total number of lanes contained in the first road data is different from the total number of lanes contained in the timeliness layer data; and/or
And judging whether the first road data has the difference problem that the road attribute is different from the road attribute in the time-effect layer data.
4. The method of claim 2, wherein after the generating the time-lapse layer data corresponding to the current location of the vehicle and configuring a version number for the time-lapse layer data, further comprising:
when second differential data exists between the road environment data perceived by the vehicle at the current position and the high-precision map road data corresponding to the current position, generating a second differential event and reporting the second differential event to the cloud, wherein the second differential data is different from the first differential data;
and responding to the second differential event audit, updating the timeliness layer data corresponding to the current position of the vehicle according to the second differential data, and updating the version number of the timeliness layer data.
5. An apparatus for generating electronic horizon data, the apparatus comprising:
the system comprises a data acquisition module, a display module and a display module, wherein the data acquisition module acquires high-precision map and time-efficiency map layer data corresponding to a current position of a vehicle, and the time-efficiency map layer data is differential map layer data of a high-precision map road and a real road;
the data judging module is used for reading first road data in a preset distance in front of the vehicle from the high-precision map when the version number of the high-precision map is consistent with the version number of the time-lapse layer data, and judging whether the first road data has a difference problem or not based on the first road data and the time-lapse layer data;
the first generation module is used for generating electronic horizon data according to the first road data and the timeliness layer data when the first road data has a difference problem;
and the second generation module is used for generating electronic horizon data according to the first road data when the first road data has no difference problem.
6. The apparatus of claim 5, wherein the apparatus is further configured to:
acquiring road environment data perceived by a vehicle at the current position and high-precision map road data corresponding to the vehicle at the current position;
matching the road environment data with the high-precision map road data;
when first difference data exists between the road environment data and the high-precision map road data, a first difference event is generated and reported to a cloud;
and responding to the first differential event audit, generating time-consuming layer data corresponding to the current position of the vehicle according to the first differential data, and configuring a version number for the time-consuming layer data.
7. The apparatus of claim 5, wherein the judging module is specifically configured to:
judging whether the first road data has a difference problem of not having the lane data contained in the time-effect layer data; and/or
Judging whether the first road data has a difference problem that the total number of lanes contained in the first road data is different from the total number of lanes contained in the timeliness layer data; and/or
And judging whether the first road data has the difference problem that the road attribute is different from the road attribute in the time-effect layer data.
8. The apparatus of claim 5, wherein the apparatus is further configured to:
when second differential data exists between the road environment data perceived by the vehicle at the current position and the high-precision map road data corresponding to the current position, generating a second differential event and reporting the second differential event to the cloud, wherein the second differential data is different from the first differential data;
and responding to the second differential event audit, updating the timeliness layer data corresponding to the current position of the vehicle according to the second differential data, and updating the version number of the timeliness layer data.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for carrying out the method steps of any one of claims 1-4 when executing a computer program stored on said memory.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program which, when executed by a processor, implements the method steps of any of claims 1-4.
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| CN202311592239.8A CN117456047A (en) | 2023-11-27 | 2023-11-27 | Method and device for generating electronic horizon data and electronic equipment |
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| CN202311592239.8A CN117456047A (en) | 2023-11-27 | 2023-11-27 | Method and device for generating electronic horizon data and electronic equipment |
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