WO2022213580A1 - Procédé et appareil de création de carte, dispositif électronique et support de stockage - Google Patents

Procédé et appareil de création de carte, dispositif électronique et support de stockage Download PDF

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Publication number
WO2022213580A1
WO2022213580A1 PCT/CN2021/126211 CN2021126211W WO2022213580A1 WO 2022213580 A1 WO2022213580 A1 WO 2022213580A1 CN 2021126211 W CN2021126211 W CN 2021126211W WO 2022213580 A1 WO2022213580 A1 WO 2022213580A1
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road
candidate
coordinates
trajectory
point
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PCT/CN2021/126211
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English (en)
Chinese (zh)
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张永乐
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阿波罗智联(北京)科技有限公司
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G01C21/32—Structuring or formatting of map data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases

Definitions

  • the present disclosure relates to the technical field of intelligent transportation, in particular to the technical field of map data fusion, and in particular to a method, device, electronic device and storage medium for generating a map.
  • the high-precision maps used in intelligent transportation contain less map data and cannot provide rich map element information.
  • the present disclosure provides a method, device, electronic device and storage medium for generating a map.
  • a method for generating a map comprising:
  • first map data set includes coordinates of each first track point corresponding to each lane
  • second map data set includes each first track point corresponding to each road.
  • a fused map dataset is generated.
  • an apparatus for generating a map comprising:
  • the acquisition module is used to acquire a first map data set and a second map data set, wherein the first map data set contains coordinates of each first track point corresponding to each lane, and the second map data set contains each Coordinates and road information of each second track point corresponding to the road;
  • a matching module configured to determine a road matching each of the lanes according to the degree of matching between the coordinates of each first trajectory point corresponding to each of the lanes and the coordinates of each of the second trajectory points corresponding to each of the roads;
  • the generating module is configured to generate a fused map data set based on the coordinates of each first track point corresponding to each of the lanes and the road information corresponding to the road matched with each of the lanes.
  • an electronic device comprising:
  • the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to execute the map according to the embodiment of the above aspect. Generate method.
  • a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the generation of the map according to the embodiments of the above aspect method.
  • a computer program product including a computer program, which, when executed by a processor, implements the method for generating a map according to the embodiments of the above-mentioned aspect.
  • the map generation method, device, electronic device and storage medium provided by the present disclosure, by acquiring the first map data set and the second map data set, according to the coordinates of each first track point of each lane in the first map data set and the second
  • the matching degree between the coordinates of each second track point of each road in the map data set determines the road matching each lane, and then based on the coordinates of each first track point corresponding to each lane and the corresponding road corresponding to each lane
  • the fused map data set is generated, and the fused map data set containing both the lane and the road information of the road is obtained, which enriches the map data.
  • FIG. 1 is a schematic flowchart of a method for generating a map according to an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of a method for generating a map according to another embodiment of the present disclosure
  • 3 is an example diagram of the positional relationship between each first trajectory point corresponding to a lane and a candidate road;
  • FIG. 4 is a schematic structural diagram of an apparatus for generating a map according to an embodiment of the present disclosure
  • FIG. 5 is a schematic structural diagram of an apparatus for generating a map according to another embodiment of the present disclosure.
  • FIG. 6 is a schematic structural diagram of an apparatus for generating a map according to another embodiment of the present disclosure.
  • FIG. 7 is a block diagram of an electronic device used to implement the method for generating a map according to an embodiment of the present disclosure.
  • the high-precision maps used in intelligent transportation contain less map data, and can only provide lane-level information, but cannot provide rich map element information, and need to collect a large amount of road information (such as speed limit information, road names, Points of interest, etc.) can be used to make high-precision maps, which results in a very large workload for making high-precision maps, resulting in a long online cycle for high-precision maps and low production efficiency.
  • road information such as speed limit information, road names, Points of interest, etc.
  • the high-precision map road network can be bound to the ordinary navigation map road network and data fusion can be performed.
  • the binding and fusion of map data are mainly realized in two ways.
  • One is to perform data fusion manually, which is inefficient; the other is to perform data fusion by manual + machine method.
  • the present disclosure provides a map generation method, device, electronic device and storage medium.
  • the matching degree between the coordinates of the track points and the coordinates of the second track points of each road in the second map data set determines the road that matches each lane, and then based on the coordinates of the first track points corresponding to each lane and the coordinates of the first track points corresponding to each lane.
  • the road information corresponding to the roads matched with the lanes is used to generate a fused map dataset, thereby obtaining a fused map dataset that includes both lanes and road information of the road, which enriches the map data.
  • FIG. 1 is a schematic flowchart of a method for generating a map according to an embodiment of the present disclosure. As shown in FIG. 1 , the method for generating a map may include the following steps:
  • Step 101 Obtain a first map data set and a second map data set, wherein the first map data set contains coordinates of each first track point corresponding to each lane, and the second map data set contains each second map data set corresponding to each road. Track point coordinates and road information.
  • the road information may include, but is not limited to, road names, speed limit information, points of interest (POI), and the like.
  • the first map data set and the second map data set may be maps containing different map information.
  • the first map data set may be a high-precision map that provides lane level information
  • the second map data set Can be a navigation map that provides road-level information.
  • Step 102 Determine a road matching each lane according to the degree of matching between the coordinates of each first track point corresponding to each lane and the coordinates of each second track point corresponding to each road.
  • each lane in the first map data set it needs to be matched with the road in the second map data set, according to the coordinates of each first track point corresponding to each lane and the corresponding The matching degree between the coordinates of the second track point to obtain the matching road.
  • the matching degree can be represented by the distance between the first trajectory point and the corresponding second trajectory point. The closer the distance is, the higher the matching degree between the first trajectory point and the corresponding second trajectory point is. .
  • the distance between the first trajectory point and the second trajectory point can be obtained by calculating the Euclidean distance according to the coordinates of the first trajectory point and the corresponding coordinates of the second trajectory point. For any lane in the first map data set, after calculating the distance between the coordinates of each first track point corresponding to the lane and the coordinates of each second track point corresponding to each road, it can be determined according to the calculated distance. This lane matches the road.
  • a distance threshold can be preset, and among the coordinates of the second track points corresponding to each road, the distance between the coordinates of the first track points corresponding to the lane is not less than the preset distance threshold.
  • the target second track point The road that contains the most target second trajectory points is determined as the road matching the lane.
  • the matching degree can be represented by the transition probability and emission probability of each first trajectory point corresponding to the lane to each road, and determine the transition probability and emission probability of each first trajectory point corresponding to the lane to each road according to the The matching degree between the lane and each road, and then determine the road matching the lane according to the matching degree. It should be noted that this manner will be described in detail in subsequent embodiments, and will not be repeated here.
  • Step 103 Generate a fused map data set based on the coordinates of each first track point corresponding to each lane and the road information corresponding to the road matching each lane.
  • the road information of the road matching each lane can be obtained from the second map data set, and then based on the first map data set corresponding to each lane
  • the coordinates of the track points and the road information corresponding to the road matched with each lane are stored in association with the coordinates of each first track point corresponding to the lane and the road information corresponding to the road matched with each lane to generate a fused map data set. Therefore, the application based on the fused map dataset can obtain richer map information, including not only the lane-level information, but also the road name, POI, speed limit information, etc.
  • the first map data set includes coordinates of each first track point corresponding to each lane
  • the second map data set includes each The coordinates and road information of each second track point corresponding to the road, according to the degree of matching between the coordinates of each first track point corresponding to each lane and the coordinates of each second track point corresponding to each road, determine the road that matches each lane , based on the coordinates of each first track point corresponding to each lane and the road information corresponding to the road matching each lane, a fused map data set is generated, thereby obtaining a road information including both lanes and roads.
  • the map dataset is fused to enrich the map data, and the road information corresponding to the road is obtained by matching the lane with the road and the lane information is merged with the map data, without the need to manually collect the road information and manually bind the lane to the road. It not only reduces the difficulty of generating high-precision maps, but also realizes automatic matching of map road networks and automatic processing of map data fusion, which improves the efficiency of map generation.
  • the matching degree may be represented by the transition probability and emission probability of each first trajectory point corresponding to the lane to each road, and by calculating the transition probability matrix and the emission probability matrix of each road, according to The corresponding transition probability matrix and emission probability matrix of each road are used to determine the road matching each lane. This process will be described in detail below in conjunction with FIG. 2 .
  • FIG. 2 is a schematic flowchart of a method for generating a map according to another embodiment of the present disclosure. As shown in FIG. 2 , on the basis of the embodiment shown in FIG. 1 , step 102 may include the following steps:
  • Step 201 Acquire a plurality of candidate roads corresponding to any lane and the coordinates of each second track point corresponding to each candidate road from the second map data set.
  • a plurality of candidate roads corresponding to the lane may be obtained from the second map data set, and the coordinates of each second track point corresponding to each candidate road may be obtained .
  • the candidate roads when acquiring a plurality of candidate roads corresponding to any lane, the candidate roads can be acquired in different ways, and the following examples are used for description.
  • the coordinates of each second track point of each road may be compared with the coordinates of each first track point corresponding to any lane, and the statistics of the coordinates of each second track point corresponding to each road and any lane may be calculated.
  • the number of second track points with the same coordinates of each first track point, and the roads are arranged in descending order of the number, and the first n roads are selected as candidate roads.
  • n is a positive integer, and the value of n can be preset.
  • the distance between the coordinates of each second track point of each road and the coordinates of each first track point corresponding to any lane may be calculated, and for each road, the second track point whose distance reaches a preset distance threshold is calculated.
  • the number of track points accounts for the proportion of the total number of second track points corresponding to the road, and a road whose proportion reaches a preset value is determined as a candidate road.
  • a plurality of candidate roads near any lane may be searched from the second map dataset based on the spatial index. Obtaining candidate roads through spatial index can effectively improve the search efficiency of candidate roads.
  • the coordinates of each second track point corresponding to each candidate road may be further obtained from the second map data set.
  • Step 202 Calculate the transition probability matrix and the emission probability matrix corresponding to each candidate road according to the coordinates of each first trajectory point corresponding to any lane and the coordinates of each second trajectory point corresponding to each candidate road.
  • the transition probability matrix and the emission probability corresponding to the candidate road may be calculated according to the coordinates of each first trajectory point corresponding to any lane and the coordinates of each second trajectory point corresponding to the candidate road matrix.
  • each element in the transition probability matrix represents the transition probability from each first trajectory point corresponding to the lane to the candidate road;
  • each element in the emission probability matrix represents each first trajectory point corresponding to the lane to the candidate road emission probability. That is to say, the number of elements of the transition probability matrix and the emission probability matrix is determined by the number of first trajectory points corresponding to any lane.
  • each adjacent first trajectory point when calculating the transition probability matrix corresponding to each candidate road, may be calculated according to the coordinates of each first trajectory point on any lane. Then, according to the coordinates of each second trajectory point corresponding to each candidate road, determine the coordinates of each projection point on each candidate road corresponding to each first trajectory point on any lane. The coordinates of each projection point on each candidate road, determine the length of the second trajectory between every two adjacent projection points on each candidate road, and then according to the length of each first trajectory corresponding to any lane and the corresponding length on each candidate road The ratio between the lengths of the second tracks determines the transition probability matrix corresponding to each candidate road.
  • the distance between each first trajectory point on any lane and the corresponding projection point can be determined first, and then according to the distance between each first trajectory point and the corresponding projection point on any lane Distance, determine the Gaussian distribution corresponding to each candidate road, and determine the emission probability matrix corresponding to each candidate road according to the Gaussian distribution corresponding to each candidate road.
  • the coordinates of each projection point on each candidate road corresponding to each first trajectory point on any lane may be determined according to the coordinates of each second trajectory point corresponding to each candidate road. For each first trajectory point on any lane, draw a vertical line from the first trajectory point to the candidate road. The intersection of the vertical line and the candidate road is the projection point corresponding to the first trajectory point on the candidate road. If The projected point overlaps with a certain second trajectory point of the candidate road, and the coordinates of the second trajectory point are the coordinates of the projected point. If the projected point falls between two second trajectory points, the The coordinates of the two second track points determine the coordinates of the projection point.
  • the mean value of the coordinates of the two second track points can be determined as the coordinates of the projection point, or the coordinates of the second track points that are close to each other can be determined as the coordinates of the projection point.
  • the coordinates are determined to be the coordinates of the projected point, and so on.
  • each element in the determined transition probability matrix corresponding to each candidate road is the ratio between the length of the first track point and the length of the corresponding second track point.
  • FIG. 3 is an example diagram of the positional relationship between each first trajectory point corresponding to the lane and the candidate road. As shown in Figure 3, point A is the projection point corresponding to the first trajectory point 01, and point B is the projection point corresponding to the first trajectory point 02, then the length of the first trajectory point between 01 and 02 is the same as the difference between A and B.
  • the ratio between the lengths of the second track points between is an element in the transition probability matrix corresponding to the candidate road R1.
  • transition probability can also be represented by curvature, angle, etc.
  • the embodiment of the present disclosure only uses the ratio between the trajectory lengths as the transition probability as an example to explain the present disclosure, rather than limiting the present disclosure.
  • the transition probability matrix corresponding to each candidate road determines the transition probability matrix corresponding to each candidate road, according to the relationship between each first trajectory point on any lane and the corresponding projection point.
  • the distance between each candidate road is determined, and the Gaussian distribution corresponding to each candidate road is determined, and then the emission probability matrix corresponding to each candidate road is determined according to the Gaussian distribution corresponding to each candidate road.
  • the similarity of uses the emission probability matrix to reflect the proximity between the lane and the candidate road, which provides conditions for determining the road matching the lane according to the transition probability matrix and the emission probability matrix.
  • Step 203 according to the transition probability matrix and the emission probability matrix corresponding to each candidate road, determine a target road matching any lane from each candidate road.
  • the transition probability matrix and emission probability matrix corresponding to each candidate road determine the target road matching any lane from each candidate road, including: from the transition probability of each candidate road In the matrix and the emission probability matrix, the first transition probability and the first emission probability corresponding to each first trajectory point are obtained; according to the product of the first transition probability and the first emission probability corresponding to each first trajectory point, each The first similarity value corresponding to the first track point; the second similarity value corresponding to each candidate road is determined according to each first similarity value corresponding to each first track point in each candidate road; the largest second similarity value The corresponding candidate road is determined as the target road.
  • the first similarity values of the first trajectory points corresponding to the same candidate road may be added to obtain the second similarity value of the candidate road;
  • the largest first similarity value is determined as the second similarity value of the candidate road, which is not limited in the present disclosure.
  • the candidate road corresponding to the largest second similarity value is determined as the target road, thereby realizing the automatic matching between the lanes in the first map data set and the roads in the second map data set, and improving the binding efficiency of the road network .
  • the transition probability matrix and emission probability matrix corresponding to each candidate road determine the target road matching any lane from each candidate road, including: according to the transition probability of each candidate road For each transition probability in the matrix, determine the first candidate road corresponding to the maximum transition probability; according to each transmission probability in the transmission probability matrix of each candidate road, determine the second candidate road corresponding to the maximum transmission probability; When the second candidate road is the same, the first candidate road is determined as the target road.
  • the transition probability indicates the similarity between the lane and the road
  • the emission probability indicates the proximity between the lane and the road.
  • the first candidate road determined according to the maximum transition probability is the road most similar to the lane
  • the second candidate determined according to the maximum emission probability The road is the road closest to the lane, then when the first candidate road and the second candidate road are the same road, the road is the target road that best matches the lane, thus realizing the automatic binding between the lane and the road , which improves the accuracy of lane-to-road matching.
  • a third similarity value corresponding to each first track point is determined, and the third similarity value is The product of the transition probability and the emission probability corresponding to each first trajectory point obtained from the transition probability matrix and the emission probability matrix of the first candidate road; determine the fourth similarity value corresponding to each first trajectory point, the fourth similarity value is the product of the transition probability and the emission probability corresponding to each first trajectory point obtained from the transition probability matrix and the emission probability matrix of the second candidate road; it is determined from each third similarity value and each fourth similarity value.
  • Maximum similarity value is determined as the target road.
  • first candidate road and the second candidate road are not the same road, calculate the product of the transition probability and the emission probability corresponding to each first trajectory point of the first candidate road to obtain a plurality of third similarity values, and calculate the second
  • the product of the transition probability and the emission probability corresponding to each first trajectory point of the candidate road is obtained to obtain a plurality of fourth similarity values, and then each third similarity value and each fourth similarity value are compared, and the largest similarity value is selected from them.
  • the candidate road corresponding to the value is determined as the target road. Therefore, it is only necessary to calculate the similarity value of the two candidate roads, which reduces the amount of calculation and is beneficial to improve the speed and efficiency of lane-to-road matching.
  • a plurality of candidate roads corresponding to any lane and the coordinates of each second track point corresponding to each candidate road are obtained from the second map data set, according to the first map data set corresponding to any lane.
  • the trajectory point coordinates and the coordinates of each second trajectory point corresponding to each candidate road are calculated, and the transition probability matrix and emission probability matrix corresponding to each candidate road are calculated, and then according to the transition probability matrix and emission probability matrix corresponding to each candidate road, from each candidate road.
  • the target road that matches any lane is determined from the candidate roads, thereby realizing the automatic matching of lanes and roads without manual processing, improving the efficiency of road network matching, and helping to shorten the online cycle of the map.
  • the product lines and coordinate systems used by different map datasets are usually inconsistent, in order to facilitate road network binding and improve the accuracy of road network binding, in a possible implementation manner of the embodiment of the present disclosure, according to The degree of matching between the coordinates of each first track point corresponding to each lane and the coordinates of each second track point corresponding to each road, before determining the road matching each lane, the first map data set and the second map The datasets are migrated to the same coordinate system.
  • FIG. 4 is a schematic structural diagram of an apparatus for generating a map according to an embodiment of the present disclosure.
  • the apparatus 40 for generating a map includes an acquiring module 410 , a matching module 420 and a generating module 430 .
  • the obtaining module 410 is configured to obtain a first map data set and a second map data set, wherein the first map data set includes coordinates of each first track point corresponding to each lane, and the second map data set It includes the coordinates of each second track point corresponding to each road and road information.
  • the first map data set is a high-precision map
  • the second map data set is a navigation map
  • the matching module 420 is configured to determine a road matching each of the lanes according to the degree of matching between the coordinates of each of the first trajectory points corresponding to each of the lanes and the coordinates of each of the second trajectory points corresponding to each of the roads.
  • the generating module 430 is configured to generate a fused map data set based on the coordinates of each first track point corresponding to each of the lanes and the road information corresponding to the road matching each of the lanes.
  • the matching module 420 includes:
  • the obtaining unit 421 is configured to obtain, from the second map data set, a plurality of candidate roads corresponding to any one of the lanes and the coordinates of each second track point corresponding to each of the candidate roads.
  • the calculation unit 422 is configured to calculate the transition probability matrix corresponding to each candidate road and the emission probability matrix.
  • the calculating unit 422 is specifically configured to: calculate the first trajectory between every two adjacent first trajectory points according to the coordinates of each first trajectory point on any of the lanes length; according to the coordinates of each second trajectory point corresponding to each of the candidate roads, determine the coordinates of each projection point on each candidate road corresponding to each first trajectory point on any of the lanes; according to each The coordinates of each projection point on the candidate road, determine the second trajectory length between every two adjacent projection points on each candidate road; according to the length of each first trajectory corresponding to any lane and each determine the transition probability matrix corresponding to each candidate road; determine the distance between each first trajectory point and the corresponding projection point on the any lane; According to the distance between each first trajectory point and the corresponding projection point on any lane, the Gaussian distribution corresponding to each candidate road is determined; according to the Gaussian distribution corresponding to each candidate road, the corresponding The emission probability matrix of .
  • the matching unit 423 is configured to determine a target road that matches any of the lanes from the candidate roads according to the transition probability matrix and the emission probability matrix corresponding to each candidate road.
  • the matching unit 423 is specifically configured to: obtain the first transition corresponding to each first trajectory point from the transition probability matrix and the emission probability matrix of each candidate road probability and first emission probability; determine the first similarity value corresponding to each first trajectory point according to the product of the first transition probability corresponding to each first trajectory point and the first emission probability; each first similarity value corresponding to each first trajectory point in the candidate road, to determine the second similarity value corresponding to each candidate road; the candidate road corresponding to the largest second similarity value is determined as the target road .
  • the matching unit 423 is specifically configured to: determine the first candidate road corresponding to the maximum transition probability according to each transition probability in the transition probability matrix of each candidate road; For each emission probability in the emission probability matrix of each candidate road, determine the second candidate road corresponding to the maximum emission probability; if the first candidate road is the same as the second candidate road, the A candidate road is determined as the target road.
  • the matching unit 423 is specifically further configured to: determine each first trajectory when the first candidate road is different from the second candidate road the third similarity value corresponding to the point, where the third similarity value is the product of the transition probability and the emission probability corresponding to each first trajectory point obtained from the transition probability matrix and the emission probability matrix of the first candidate road; determine a fourth similarity value corresponding to each first trajectory point, where the fourth similarity value is a transition probability and emission probability corresponding to each first trajectory point obtained from the transition probability matrix and emission probability matrix of the second candidate road.
  • the product of probabilities; the maximum similarity value is determined from each third similarity value and each fourth similarity value; the candidate road corresponding to the maximum similarity value is determined as the target road.
  • the map generating apparatus 40 further includes:
  • the preprocessing module 400 is configured to migrate the first map data set and the second map data set to the same coordinate system.
  • the apparatus for generating a map obtains a first map data set and a second map data set, the first map data set includes coordinates of each first track point corresponding to each lane, and the second map data set includes each The coordinates and road information of each second track point corresponding to the road, according to the degree of matching between the coordinates of each first track point corresponding to each lane and the coordinates of each second track point corresponding to each road, determine the road that matches each lane , based on the coordinates of each first track point corresponding to each lane and the road information corresponding to the road matching each lane, a fused map data set is generated, thereby obtaining a road information including both lanes and roads.
  • the map dataset is fused to enrich the map data, and the road information corresponding to the road is obtained by matching the lane with the road and the lane information is merged with the map data, without the need to manually collect the road information and manually bind the lane to the road. It not only reduces the difficulty of generating high-precision maps, but also realizes automatic matching of map road networks and automatic processing of map data fusion, which improves the efficiency of map generation.
  • the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.
  • FIG. 7 shows a schematic block diagram of an example electronic device 700 that may be used to implement 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, mainframe computers, and other suitable computers.
  • Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices.
  • the components shown herein, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.
  • the electronic device 700 includes a computing unit 701, which can be loaded into a random access memory (Random Access Memory) according to a computer program stored in a read-only memory (Read-Only Memory, ROM) 702 or from a storage unit 708, A computer program in RAM) 703 to perform various appropriate actions and processes.
  • ROM Read-Only Memory
  • RAM random access memory
  • various programs and data required for the operation of the electronic device 700 can also be stored.
  • the computing unit 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704.
  • An Input/Output (I/O) interface 705 is also connected to the bus 704 .
  • Various components in the electronic device 700 are connected to the I/O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc. etc.; and a communication unit 709, such as a network card, modem, wireless communication transceiver, and the like.
  • the communication unit 709 allows the electronic device 700 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.
  • Computing unit 701 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphic Processing Units, GPU), various dedicated artificial intelligence (Artificial Intelligence, AI) computing chips, various operating A computational unit, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc., for the algorithm of the machine learning model.
  • the computing unit 701 executes the various methods and processes described above, such as a map generation method. For example, in some embodiments, the method of generating the map may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 708 .
  • part or all of the computer program may be loaded and/or installed on electronic device 700 via ROM 702 and/or communication unit 709 .
  • the computer program When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the map generation method described above may be performed.
  • the computing unit 701 may be configured to perform the method of generating the map by any other suitable means (eg, by means of firmware).
  • FPGAs Field Programmable Gate Arrays
  • ASICs Application-Specific Integrated Circuits
  • ASSP Application Specific Standard Product
  • SOC System On Chip
  • CPLD Load Programmable Logic Device
  • These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that
  • the processor which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an output device.
  • Program code for implementing the method of generating a map 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 code, when executed by the processor or controller, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented.
  • the program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.
  • a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device.
  • the machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium.
  • Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing.
  • machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (Electrically Programmable Read-Only-Memory, EPROM) or flash memory, optical fiber, portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), optical storage device, magnetic storage device, or a combination of the above any suitable combination.
  • RAM random access memory
  • ROM read only memory
  • EPROM erasable programmable read only memory
  • flash memory electrical fiber
  • portable compact disk read-only memory Compact Disc Read-Only Memory
  • CD-ROM Compact Disc Read-Only Memory
  • magnetic storage device or a combination of the above any suitable combination.
  • the systems and techniques described herein may be implemented on a computer having a display device (eg, a cathode ray tube (CRT) or a liquid crystal display) for displaying information to the user (Liquid Crystal Display, LCD monitor); and a keyboard and pointing device (eg, mouse or trackball) through which a user can provide input to the computer.
  • a display device eg, a cathode ray tube (CRT) or a liquid crystal display
  • LCD monitor Liquid Crystal Display
  • keyboard and pointing device eg, mouse or trackball
  • Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.
  • the systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system.
  • the components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN), the Internet, and blockchain networks.
  • a computer system can include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other.
  • the server can be a cloud server, also known as a cloud computing server or a cloud host. It is a host product in the cloud computing service system to solve the problems existing in traditional physical hosts and VPS services (Virtual Private Server, virtual private server). The management is difficult and the business expansion is weak.
  • the server can also be a server of a distributed system, or a server combined with a blockchain.
  • the present disclosure also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for generating a map according to the foregoing embodiments.

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  • Engineering & Computer Science (AREA)
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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
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Abstract

La présente invention concerne un procédé et un appareil de création de carte, ainsi qu'un dispositif électronique et un support de stockage, qui se rapportent au domaine technique du trafic intelligent, et concernent en particulier le domaine technique de la fusion des données cartographiques. Le procédé comprend les étapes consistant à : acquérir un premier ensemble de données cartographiques et un second ensemble de données cartographiques, le premier ensemble de données cartographiques contenant toutes les premières coordonnées de point de trajectoire correspondant à chaque voie de circulation, et le second ensemble de données cartographiques contenant toutes les secondes coordonnées de point de trajectoire correspondant à chaque route, et des informations de route (101) ; en fonction du degré de correspondance entre toutes les premières coordonnées de point de trajectoire correspondant à chaque voie de circulation et toutes les secondes coordonnées de point de trajectoire correspondant à chaque route, déterminer une route qui correspond à chaque voie de circulation (102) ; et générer un ensemble de données cartographiques fusionnées sur la base de toutes les premières coordonnées de point de trajectoire correspondant à chaque voie de circulation et des informations de route correspondant à la route qui correspond à chaque voie de circulation (103). Un ensemble de données cartographiques fusionnées contenant à la fois une voie de circulation et des informations de route d'une route peut être obtenu, ce qui permet d'enrichir les données cartographiques.
PCT/CN2021/126211 2021-04-09 2021-10-25 Procédé et appareil de création de carte, dispositif électronique et support de stockage WO2022213580A1 (fr)

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CN114187412B (zh) * 2021-11-11 2024-03-22 北京百度网讯科技有限公司 高精地图生成方法、装置、电子设备及存储介质
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