Dynamic position reference method and device, electronic equipment and computer readable medium

Abstract

The invention discloses a dynamic position reference method and device, electronic equipment and a computer readable medium, and relates to the technical field of navigation. The method comprises the following steps: generating a planned driving path based on a starting place and a destination, and obtaining navigation map data corresponding to the planned driving path; for each position reference point in the navigation map data, matching candidate nodes in downloaded high-precision map data; in the advancing direction, whether at least two position reference points closest to the current position can be matched with at least one candidate node or not is judged; if not, downloading the high-precision map data of the next grid of the current driving grid from the cloud; if yes, high-precision map data corresponding to the two nearest position reference points are obtained from the downloaded high-precision map data in the advancing direction. According to the embodiment, correct operation of the sectional type dynamic position reference path can be guaranteed, and the situation that matching cannot be conducted or matching is wrong is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation, and in particular, to a dynamic position reference method, apparatus, electronic device, and computer-readable medium. Background Art

[0002] In the field of autonomous driving, there is a pilot function, that is, using a navigation map and a high-precision map to provide lane-level path planning for an autonomous driving system, and enabling a vehicle to travel from a navigation starting point to an ending point according to the planned path. During the driving process, the vehicle performs a series of actions such as automatic lane change, acceleration and deceleration, overtaking, entering and exiting ramps, merging and diverging.

[0003] During the implementation of this function, it depends on the position reference of the navigation map and the high-precision map, that is, matching the set navigation road segment (Link) with the lane group (Lanegroup) in the high-precision map, so that the required high-precision map data can be called. The position reference methods mainly include the pre-matching (Precode) method of using a map ID for position reference, and the dynamic position reference (Dynamic Location Reference) of using map element attributes for matching. For example, the open-source algorithm OpenLR developed by TomTom company belongs to a kind of dynamic position reference. The present invention is based on this algorithm for improvement.

[0004] Generally, the navigation map and the high-precision map are stored in the vehicle-mounted ECU, and the dynamic position reference will also be completed entirely on the vehicle side. However, when the storage capacity of the vehicle-mounted ECU is limited or the amount of high-precision map data is too large, the high-precision map cannot be entirely stored on the vehicle side, but is stored in the cloud. At this time, the general dynamic position reference is completed in the cloud. Cloud position reference also involves business and data security issues. If the vehicle uses different map suppliers, the map suppliers will worry about data security issues such as data leakage in the cloud. In this case, the dynamic position reference can only be performed on the vehicle side. That is, as the vehicle moves, the high-precision map of the vehicle's surrounding positions is downloaded segment by segment from the cloud, and then the data of the downloaded high-precision map is dynamically referenced with the navigation map on the vehicle side. However, this segmented dynamic position reference will result in situations of unmatched or mis-matched compared with the complete dynamic position reference. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a dynamic position reference method, apparatus, electronic device, and computer-readable medium to solve the technical problems of unmatched or mis-matched.

[0006] To achieve the above object, according to one aspect of the embodiments of the present invention, there is provided a dynamic position reference method, the method including:

[0007] Generate a planned driving route based on the origin and destination, and obtain the navigation map data corresponding to the planned driving route;

[0008] For each position reference point in the navigation map data, match candidate nodes in the downloaded high-precision map data;

[0009] Along the traveling direction, determine whether at least two position reference points closest to the current position can each match at least one candidate node;

[0010] If not, download the high-precision map data of the next grid of the currently traveled grid from the cloud;

[0011] If so, along the traveling direction, obtain the high-precision map data corresponding to the two position reference points closest to the current position from the downloaded high-precision map data.

[0012] Optionally, obtaining the high-precision map data corresponding to the two position reference points closest to the current position from the downloaded high-precision map data includes:

[0013] For each candidate node corresponding to the two position reference points closest to the current position, find the paths extending from each candidate node in the downloaded high-precision map data as candidate paths;

[0014] Calculate the degree of difference between each candidate path;

[0015] In response to the degree of difference being too small, download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0016] Optionally, calculating the degree of difference between each candidate path includes:

[0017] Calculate the score of each candidate path;

[0018] According to the scores of each candidate path, calculate the standard deviation of each candidate path.

[0019] Optionally, in response to the degree of difference being too small, downloading the high-precision map data of the next grid of the currently traveled grid from the cloud includes:

[0020] Determine whether the standard deviation is greater than or equal to the standard deviation threshold;

[0021] If so, select the shortest path between the candidate paths with the highest scores as the target path, and find the high-precision map data corresponding to the target path from the downloaded high-precision map data;

[0022] If not, download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0023] Optionally, download high-precision map data of the next grid of the currently traveled grid from the cloud, including:

[0024] Determine whether high-precision map data of the next grid of the currently traveled grid exists in the cloud;

[0025] If so, download the high-precision map data of the next grid from the cloud;

[0026] If not, screen out the shortest path between the currently highest-scoring candidate paths as the target path, and find the high-precision map data corresponding to the target path from the downloaded high-precision map data.

[0027] Optionally, the method further includes:

[0028] Determine whether all position reference points in the navigation map data can match candidate nodes in the downloaded high-precision map data;

[0029] If not, after traveling a preset distance, download high-precision map data of the next grid of the currently traveled grid from the cloud;

[0030] If so, end.

[0031] In addition, according to another aspect of the embodiments of the present invention, a dynamic position reference device is provided, and the device includes:

[0032] An acquisition module, configured to generate a planned travel path based on a starting point and a destination, and acquire navigation map data corresponding to the planned travel path;

[0033] A matching module, configured to match candidate nodes in the downloaded high-precision map data for each position reference point in the navigation map data;

[0034] A processing module, configured to determine, along the traveling direction, whether at least two position reference points closest to the current position can respectively match at least one candidate node; if not, download high-precision map data of the next grid of the currently traveled grid from the cloud; if so, along the traveling direction, acquire high-precision map data corresponding to the two closest position reference points from the downloaded high-precision map data.

[0035] Optionally, the processing module is further configured to:

[0036] For each candidate node corresponding to the two position reference points closest to the current position, find paths extending from the candidate nodes from the downloaded high-precision map data as candidate paths;

[0037] Calculate the degree of difference of each candidate path;

[0038] In response to the degree of difference being too small, download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0039] Optionally, the processing module is further configured to:

[0040] Calculate the scores of each candidate path;

[0041] Calculate the standard deviation of each candidate path according to the scores of each candidate path.

[0042] Optionally, the processing module is further configured to:

[0043] Determine whether the standard deviation is greater than or equal to the standard deviation threshold;

[0044] If so, screen out the shortest path between the candidate paths with the highest scores as the target path, and find the high-precision map data corresponding to the target path from the downloaded high-precision map data;

[0045] If not, download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0046] Optionally, the processing module is further configured to:

[0047] Determine whether there is high-precision map data of the next grid of the currently traveled grid in the cloud;

[0048] If so, download the high-precision map data of the next grid from the cloud;

[0049] If not, screen out the shortest path between the currently highest-scoring candidate paths as the target path, and find the high-precision map data corresponding to the target path from the downloaded high-precision map data.

[0050] Optionally, the processing module is further configured to:

[0051] Determine whether all position reference points in the navigation map data are matched with candidate nodes in the downloaded high-precision map data;

[0052] If not, after traveling a preset distance, download the high-precision map data of the next grid of the currently traveled grid from the cloud;

[0053] If so, end.

[0054] According to another aspect of the embodiments of the present invention, a vehicle is further provided, and the vehicle may include the dynamic position reference device provided in each of the above embodiments.

[0055] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:

[0056] One or more processors;

[0057] A storage device for storing one or more programs,

[0058] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above embodiments.

[0059] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable medium, on which a computer program is stored, and when the program is executed by a processor, the method described in any of the above embodiments is implemented.

[0060] According to another aspect of the embodiments of the present invention, there is also provided a computer program product, including a computer program, and when the computer program is executed by a processor, the method described in any of the above embodiments is implemented.

[0061] One of the embodiments of the above invention has the following advantages or beneficial effects: The dynamic position reference method provided by the embodiments of the present invention adds a determination logic for determining whether at least two position reference points closest to the current position can respectively match at least one candidate node on the basis of the existing dynamic position reference method. In the case where the determination result is negative, high-precision map data of the next grid is downloaded from the cloud to ensure the correct operation of the segmented dynamic position reference path and avoid the situation of unmatched or mis-matched.

[0062] The further effects of the above non-conventional optional manner will be described in combination with the specific embodiments below. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0064] Figure 1 is a flowchart of the dynamic position reference method according to the first embodiment of the present invention;

[0065] Figure 2 is a flowchart of the dynamic position reference method according to the second embodiment of the present invention;

[0066] Figure 3 is a flowchart of the dynamic position reference method according to the third embodiment of the present invention;

[0067] Figure 4 is a schematic diagram of a dynamic position reference device according to an embodiment of the present invention;

[0068] Figure 5 is an exemplary vehicle system architecture diagram to which the embodiment of the present invention can be applied;

[0069] Figure 6 is a schematic structural diagram of a computer system of a terminal device or a server suitable for implementing the embodiment of the present invention. Detailed implementation manners

[0070] The following describes exemplary embodiments of the present invention with reference to the accompanying drawings. Various details of the embodiments of the present invention are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0071] It should be noted that in the technical solution of the present invention, in terms of the collection, analysis, use, transmission, storage, etc. of user personal information, it complies with the provisions of relevant laws and regulations, is used for legal and reasonable purposes, is not shared, leaked or sold outside these legal uses, and is subject to the supervision and management of regulatory authorities. Necessary measures should be taken for user personal information to prevent illegal access to such personal information data, ensure that personnel with the right to access personal information data comply with the provisions of relevant laws and regulations, and ensure the security of user personal information. Once these user personal information data are no longer needed, the risk should be minimized by restricting or even prohibiting data collection and / or deleting the data.

[0072] The embodiment of the present invention relates to the path dynamic position reference of navigation maps and high-precision maps in the field of autonomous driving. The embodiment of the present invention provides a method for dynamic position reference using segmented map data. This method has low requirements for hardware capacity, can be used for map position reference between different map manufacturers, thereby reducing costs and having strong generalization.

[0073] Figure 1 is a flowchart of a dynamic position reference method according to the first embodiment of the present invention. As an embodiment of the present invention, as Figure 1 shown, the dynamic position reference method may include:

[0074] Step S101, generate a planned driving path based on the origin and the destination, and obtain the navigation map data corresponding to the planned driving path.

[0075] First, import the navigation map data encoded by the encoder into the decoder. The user can input or select the starting point and the destination on the in-vehicle display screen, or input the starting point and the destination by voice. In response to the starting point and the destination submitted by the user, the decoder generates a planned driving route based on the starting point and the destination, and then obtains the navigation map data corresponding to the planned driving route.

[0076] Among them, the navigation map data consists of multiple routes. For example, if the starting point is A and the destination is C, then the navigation map data includes two routes: Link a (location reference point A → location reference point B) → Link c (location reference point B → location reference point C).

[0077] Step S102: For each location reference point in the navigation map data, match candidate nodes in the downloaded high-precision map data.

[0078] After obtaining the navigation map data corresponding to the planned driving route, the decoder starts to download the high-precision map data from the starting point to the destination from the cloud and store it in the vehicle terminal. At the same time, match each location reference point in the navigation map data with the high-precision map data that has been downloaded to the vehicle terminal, and obtain the candidate nodes corresponding to the location reference points from the high-precision map data.

[0079] It should be noted that the high-precision map data is divided into multiple tiles and downloaded in sequence. Therefore, the high-precision map data from the starting point to the destination consists of high-precision map data of multiple tiles. The high-precision map data of each tile is downloaded to the vehicle terminal in sequence. For example, the high-precision map data of each tile is downloaded to the vehicle terminal in the order of tile1, tile2, tile3, tile4, tile5... The high-precision map is also called an advanced driver assistance map, and the advanced driver assistance map encompasses all types of high-precision map products.

[0080] Each tile of high-precision map data contains at least one node. If a certain node can match the location reference point, then this node is used as the candidate node for this location reference point, that is, position reference is achieved.

[0081] Step S103: Along the traveling direction, determine whether at least two location reference points closest to the current position can respectively match at least one candidate node; if not, execute step S104; if so, execute step S105.

[0082] In step S103, according to the matching result of step S102, along the direction of vehicle travel, it is determined whether at least two position reference points closest to the current position can each match at least one candidate node. If not, it indicates that a situation of unmatched or mis-matched may occur, so step S104 is executed; if so, step S105 is executed.

[0083] Step S104: Download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0084] Since along the direction of vehicle travel, only one position reference point closest to the current position can match at least one candidate node or no position reference point can match at least one candidate node, in order to avoid the situation of unmatched or mis-matched, the decoder downloads the high-precision map data of the next grid of the grid where the vehicle is currently traveling from the cloud.

[0085] For example, if the vehicle terminal has downloaded the high-precision map data of tile1 from the cloud and the vehicle is currently traveling to tile1, then in step S104, download the high-precision map data of tile2 from the cloud. Another example, if the vehicle terminal has downloaded the high-precision map data of tile1, tile2, and tile3 from the cloud and the vehicle is currently traveling to tile3, then in step S104, download the high-precision map data of tile4 from the cloud.

[0086] Step S105: Along the travel direction, obtain the high-precision map data corresponding to the two position reference points closest to the current position from the downloaded high-precision map data.

[0087] If along the direction of vehicle travel, at least two position reference points closest to the current position can each match at least one candidate node, then along the direction of vehicle travel, obtain the high-precision map data corresponding to the two closest position reference points from the high-precision map data downloaded to the vehicle terminal.

[0088] Optionally, obtaining the high-precision map data corresponding to the two position reference points closest to the current position from the downloaded high-precision map data includes: for each candidate node corresponding to the two position reference points closest to the current position, finding out the paths extending from each candidate node in the downloaded high-precision map data as candidate paths; calculating the difference degree of each candidate path; in response to the difference degree being too small, downloading the high-precision map data of the next grid of the currently traveled grid from the cloud. In this step, for each candidate node, finding out the path extending from the candidate node in the downloaded high-precision map data and taking it as the candidate path of the candidate node, so each candidate node may have one or more candidate paths, then calculating the difference degree of each candidate path, and finally judging whether the difference degree is too small. If so, downloading the high-precision map data of the next grid of the currently traveled grid from the cloud, otherwise, screening out the target path from each candidate path, so as to find out the high-precision map data corresponding to the target path from the downloaded high-precision map data, or. This can accurately screen out the target path, so as to accurately find out the high-precision map data corresponding to the target path from the downloaded high-precision map data.

[0089] The dynamic position reference method provided by the embodiment of the present invention adds a determination logic for determining whether at least two position reference points closest to the current position can respectively match at least one candidate node on the basis of the existing dynamic position reference method. In the case where the determination result is negative, downloading the high-precision map data of the next grid of the currently traveled grid from the cloud to ensure the correct operation of the segmented dynamic position reference path and avoid the situation of unmatched or mis-matched.

[0090] Figure 2 It is a flowchart of the dynamic position reference method according to the second embodiment of the present invention. As another embodiment of the present invention, as Figure 2 shown, the dynamic position reference method may include:

[0091] Step S201, generating a planned travel path based on the origin and destination, and obtaining the navigation map data corresponding to the planned travel path.

[0092] Step S202, for each position reference point (location referencepoint) in the navigation map data, matching candidate nodes (candidate node) in the downloaded high-precision map data.

[0093] Step S203: Along the traveling direction, determine whether at least two position reference points closest to the current position can each match at least one candidate node; if not, execute Step S204; if so, execute Step S205.

[0094] Step S204: Download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0095] Step S205: For each candidate node corresponding to the two position reference points closest to the current position, find the paths extending from each candidate node from the downloaded high-precision map data as candidate paths (candidate lines).

[0096] For each candidate node, find the path extending from this candidate node from the downloaded high-precision map data and use it as the candidate path extending from this candidate node. Therefore, each candidate node may have one or more candidate paths.

[0097] Step S206: Calculate the scores of each candidate path; according to the scores of each candidate path, calculate the standard deviation of each candidate path.

[0098] It should be noted that the calculation method of the score of the candidate path is common knowledge to those skilled in the art. Those skilled in the art can select a suitable calculation method according to actual needs, and the embodiments of the present invention do not limit this.

[0099] In Step S206, for each of the two position reference points closest to the current position, calculate the standard deviation of each candidate path corresponding to each candidate node, so as to screen out the target candidate path in the subsequent steps.

[0100] After calculating the scores of each candidate path corresponding to each candidate node, calculate the standard deviation of all candidate paths according to the scores of each candidate path, and determine the difference degree of these candidate paths by the size of the standard deviation. Optionally, the standard deviation of all candidate paths is calculated using the following formula:

[0101]

[0102] where σ represents the standard deviation of the candidate path, score n represents the score of the candidate path, and n represents the total number of candidate paths.

[0103] Step S207: Determine whether the standard deviation is greater than or equal to the standard deviation threshold; if so, execute Step S208; if not, execute Step S204.

[0104] The standard deviation threshold θ can be preset. If σ≥θ, step S208 is executed; if σ<θ, step S204 is executed, that is, the high-precision map data of the next grid of the currently traveled grid is downloaded from the cloud.

[0105] Step S208: Screen out the shortest path between the candidate paths with the highest scores as the target path, and find the high-precision map data corresponding to the target path from the downloaded high-precision map data.

[0106] If σ≥θ, it indicates that the differences between the candidate paths are large. Therefore, the shortest path between the candidate paths with the highest scores can be screened out as the target path, and then the high-precision map data corresponding to the target path is found from the downloaded high-precision map data. Therefore, the embodiments of the present invention can accurately achieve dynamic position reference and avoid the situation of matching errors.

[0107] Figure 3 It is a flowchart of the dynamic position reference method according to the third embodiment of the present invention. As another embodiment of the present invention, as Figure 3 shown, the dynamic position reference method may include:

[0108] Step S301: Generate a planned driving path based on the origin and the destination, and obtain the navigation map data corresponding to the planned driving path.

[0109] Step S302: For each position reference point in the navigation map data, match candidate nodes in the downloaded high-precision map data.

[0110] Step S303: Along the traveling direction, determine whether at least two position reference points closest to the current position can respectively match at least one candidate node; if not, execute step S304; if so, execute step S305.

[0111] Step S304: Download the high-precision map data of the next grid of the currently traveled grid from the cloud.

[0112] Step S305: For each candidate node corresponding to the two position reference points closest to the current position, find the paths extending from the candidate nodes from the downloaded high-precision map data as candidate paths.

[0113] Step S306: Calculate the scores of the candidate paths, and calculate the standard deviation of the candidate paths according to the scores of the candidate paths.

[0114] Step S307: Determine whether the standard deviation is greater than or equal to the standard deviation threshold; if so, execute step S308; if not, execute step S309.

[0115] Step S308, screen out the shortest path among the candidate paths with the highest scores as the target path, and find out the high-precision map data corresponding to the target path from the downloaded high-precision map data.

[0116] Step S309, determine whether there is high-precision map data of the next grid of the currently traveled grid in the cloud; if so, execute Step S304; if not, then execute Step S308.

[0117] If σ≥θ, screen out the shortest path among the candidate paths with the highest scores as the target path, and find out the high-precision map data corresponding to the target path from the downloaded high-precision map data; if σ<θ, determine whether there is high-precision map data of the next grid of the currently traveled grid in the cloud. If there is, download the high-precision map data of the next grid of the currently traveled grid from the cloud; if not, screen out the shortest path among the candidate paths with the highest scores as the target path, and find out the high-precision map data corresponding to the target path from the downloaded high-precision map data.

[0118] Step S310, determine whether all the position reference points in the navigation map data are matched with candidate nodes in the downloaded high-precision map data; if not, after traveling a preset distance, execute Step S304; if so, end.

[0119] Next, after traveling a certain distance (such as 1000 meters or 1500 meters) according to the preset distance, download the high-precision map data of the next grid of the currently traveled grid from the cloud, and then start a new round of dynamic position reference until the dynamic position reference of all the position reference points in the navigation map data is completed.

[0120] Figure 4 It is a schematic diagram of a dynamic position reference device according to an embodiment of the present invention. As Figure 4 shown, the dynamic position reference device 400 includes an acquisition module 401, a matching module 402, and a processing module 403; wherein, the acquisition module 401 is used to generate a planned travel path based on the origin and the destination, and acquire the navigation map data corresponding to the planned travel path; the matching module 402 is used to match candidate nodes for each position reference point in the navigation map data in the downloaded high-precision map data; the processing module 403 is used to determine, along the traveling direction, whether at least two position reference points closest to the current position can be respectively matched with at least one candidate node; if not, download the high-precision map data of the next grid of the currently traveled grid from the cloud; if so, along the traveling direction, acquire the high-precision map data corresponding to the two nearest position reference points from the downloaded high-precision map data.

[0121] Optionally, the processing module 403 is further configured to:

[0122] For each candidate node corresponding to the two position reference points closest to the current position, find a path extending from each candidate node in the downloaded high-precision map data as a candidate path;

[0123] Calculate the degree of difference of each candidate path;

[0124] In response to the degree of difference being too small, download high-precision map data of the next grid of the currently traveled grid from the cloud.

[0125] Optionally, the processing module 403 is further configured to:

[0126] Calculate the score of each candidate path;

[0127] Calculate the standard deviation of each candidate path according to the scores of each candidate path.

[0128] Optionally, the processing module 403 is further configured to:

[0129] Determine whether the standard deviation is greater than or equal to a standard deviation threshold;

[0130] If so, select the shortest path between the candidate paths with the highest scores as the target path, and find the high-precision map data corresponding to the target path in the downloaded high-precision map data;

[0131] If not, download high-precision map data of the next grid of the currently traveled grid from the cloud.

[0132] Optionally, the processing module 403 is further configured to:

[0133] Determine whether high-precision map data of the next grid of the currently traveled grid exists in the cloud;

[0134] If so, download the high-precision map data of the next grid from the cloud;

[0135] If not, select the shortest path between the candidate paths with the highest scores as the target path, and find the high-precision map data corresponding to the target path in the downloaded high-precision map data.

[0136] Optionally, the processing module 403 is further configured to:

[0137] Determine whether all position reference points in the navigation map data are matched to candidate nodes in the downloaded high-precision map data;

[0138] If not, after traveling a preset distance, download the high-precision map data of the next grid of the currently traveled grid from the cloud;

[0139] If so, end.

[0140] An embodiment of the present invention further provides a vehicle, which may include the dynamic position reference device provided in each of the above embodiments.

[0141] Figure 5 An exemplary vehicle system architecture 500 to which the dynamic position reference method or the dynamic position reference device of the embodiments of the present invention can be applied is shown.

[0142] As Figure 5 shown, the vehicle system architecture 500 may include various systems, such as an autonomous driving system 501, a power system 502, a sensor system 503, a control system 504, one or more peripheral devices 505, a power supply 506, a computer system 507, and a user interface 508. Optionally, the vehicle system architecture 500 may include more or fewer systems, and each system may include multiple elements. In addition, each system and element of the vehicle system architecture 500 may be interconnected by wire or wirelessly.

[0143] Among them, the vehicle system architecture 500 includes an autonomous driving system 501, and the autonomous driving system 501 may be in a fully or partially autonomous driving mode. For example, the autonomous driving system 501 can automatically control the vehicle to travel without interacting with people; the autonomous driving system 501 can also adjust the autonomous driving behavior of the autonomous driving system 501 by interacting with people while controlling the vehicle to drive autonomously in the autonomous driving mode. Specifically, the autonomous driving system 501 can generate a planned driving path based on the origin and the destination, and obtain the navigation map data corresponding to the planned driving path; for each position reference point in the navigation map data, match candidate nodes in the downloaded high-precision map data; along the traveling direction, determine whether at least two position reference points closest to the current position can respectively match at least one candidate node; if not, download the high-precision map data of the next grid from the cloud; if so, along the traveling direction, obtain the high-precision map data corresponding to the two position reference points closest to the current position from the downloaded high-precision map data.

[0144] The power system 502 may include components that provide powered movement for the vehicle. For example, the power system 502 may include an engine, an energy source, a transmission, wheels, tires, etc. Among them, the engine can be an internal combustion engine, an electric motor, an air compression engine, or a combination of other types of engines, such as a hybrid engine composed of a gasoline engine and an electric motor, or a hybrid engine composed of an internal combustion engine and an air compression engine. The engine converts the energy source into mechanical energy and provides it to the transmission. Examples of the energy source may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other power sources. The energy source can also provide energy for other systems of the vehicle. In addition, the transmission may include a gearbox, a differential, a drive shaft, a clutch, etc.

[0145] The sensor system 503 may include sensors that sense the surrounding environment of the vehicle. For example, a positioning system (which can be a global positioning system (GPS) system, or it can also be a Beidou system or other positioning systems), radar, a lidar, an inertial measurement unit (IMU), and a camera. The positioning system can be used to locate the geographical position of the vehicle. The IMU is used to sense the changes in the position and orientation of the vehicle based on inertial acceleration. In one embodiment, the IMU can be a combination of an accelerometer and a gyroscope. Radar can use radio signals to sense objects within the surrounding environment of the vehicle. In some embodiments, in addition to sensing objects, radar can also be used to sense the speed and / or forward direction of the objects, etc.

[0146] Among them, in order to detect environmental information, objects, etc. located in front of, behind, or on the side of the vehicle, radar, cameras, etc. can be configured at appropriate positions outside the vehicle. For example, in order to obtain an image of the front of the vehicle, the camera can be configured close to the front windshield inside the vehicle. Or, the camera can be configured around the front bumper or radiator grille. For example, in order to obtain an image of the rear of the vehicle, the camera can be configured close to the rear window glass inside the vehicle. Or, the camera can be configured around the rear bumper, trunk, or tailgate. In order to obtain an image of the side of the vehicle, the camera can be configured close to at least one of the side windows inside the vehicle. Or, the camera can be configured around the side mirror, fender, or door, etc.

[0147] The lidar can use laser to sense objects in the environment where the vehicle is located.

[0148] The camera can be used to capture multiple images of the surrounding environment of the vehicle. The camera can be a static or video camera.

[0149] The control system 504 may include a software system for implementing autonomous driving, such as a route planning system, an obstacle avoidance system, a vision system for image analysis, etc. The control system 504 may also include hardware systems such as a throttle and a steering wheel system. Additionally, the control system 504 may alternatively include components other than those shown and described. Or some of the components shown above may also be reduced.

[0150] The control system 504 interacts with external sensors, other autonomous driving devices, other computer systems, or users through the peripheral device 505. The peripheral device 505 may include a wireless communication system, an in-vehicle computer, a microphone, and / or a speaker.

[0151] In some embodiments, the peripheral device 505 provides a means for the user of the control system 504 to interact with the user interface. For example, the in-vehicle computer may provide information to the user of the vehicle. The user interface may also operate the in-vehicle computer to receive user input. The in-vehicle computer may be operated through a touch screen. In other cases, the peripheral device may provide a means for communicating with other devices located inside the vehicle. For example, the microphone may receive audio (e.g., voice commands or other audio inputs) from the user of the control system 504. Similarly, the speaker may output audio to the user of the control system 504.

[0152] The wireless communication system may communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system may use networks such as cellular networks, WiFi, and wireless local area network (WLAN) to communicate, or may also communicate directly with devices using infrared links, Bluetooth, or ZigBee. Other wireless protocols, such as various communication systems for autonomous driving, etc.

[0153] The power source 506 may supply power to various components of the vehicle. The power source 506 may be a rechargeable lithium-ion or lead-acid battery.

[0154] Part or all of the functions for implementing autonomous driving are controlled by the computer system 507. The computer system 507 may include at least one processor that executes instructions stored in a non-transitory computer-readable medium such as a memory. The computer system 507 provides the execution code for implementing autonomous driving to the above-mentioned autonomous driving system.

[0155] The processor can be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, the processor can be a special-purpose device such as an application specific integrated circuit (ASIC) or other hardware-based processor. Those of ordinary skill in the art should understand that the processor, computer, or memory can actually include multiple processors, computers, or memories that may or may not be stored within the same physical housing. For example, the memory can be a hard disk drive or other storage medium located within a housing different from the computer. Thus, a reference to a processor or computer will be understood to include a reference to a collection of processors or computers or memories that may or may not operate in parallel. Instead of using a single processor to perform the steps described herein, some components such as the steering component and the deceleration component can each have their own processor that only performs calculations related to the component-specific functions.

[0156] A user interface 508 for providing information to or receiving information from a user of the vehicle. Optionally, the user interface 508 can include one or more input / output devices within the set of peripheral devices 505, such as a wireless communication system, an in-vehicle computer, a microphone, and a speaker.

[0157] It should be understood that the above components are only examples, and in actual applications, the components in the above-mentioned modules or systems may be added or deleted according to actual needs. Figure 5 It should not be construed as a limitation on the embodiments of the present application.

[0158] As Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601 that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage section 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0159] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as required. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 610 as required so that a computer program read therefrom is installed into the storage section 608 as required.

[0160] Specifically, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by a central processing unit (CPU) 601, the above-described functions defined in the system of the present invention are executed.

[0161] It should be noted that the computer-readable medium shown in the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer programs according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0163] The modules involved in the embodiments of the present invention can be implemented in software or in hardware. The described modules can also be provided in a processor. For example, it can be described as: a processor includes an acquisition module, a matching module, and a processing module. In some cases, the names of these modules do not constitute a limitation to the module itself.

[0164] As another aspect, the present invention also provides a computer-readable medium, which can be included in the device described in the above embodiments; or can exist alone without being assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by a device, the device implements the following method: generating a planned driving route based on a starting point and a destination, and obtaining navigation map data corresponding to the planned driving route; for each position reference point in the navigation map data, matching candidate nodes in the downloaded high-precision map data; along the traveling direction, determining whether at least two position reference points closest to the current position can respectively match at least one candidate node; if not, downloading the high-precision map data of the next grid from the cloud; if so, along the traveling direction, obtaining the high-precision map data corresponding to the two closest position reference points from the downloaded high-precision map data.

[0165] As another aspect, embodiments of the present invention also provide a computer program product, including a computer program, where the computer program implements the method described in any of the above embodiments when executed by a processor.

[0166] The dynamic position reference method provided by the embodiments of the present invention adds a determination logic for determining whether at least two position reference points closest to the current position can respectively match at least one candidate node on the basis of the existing dynamic position reference method. In the case where the determination result is negative, the high-precision map data of the next grid is downloaded from the cloud to ensure the correct operation of the segmented dynamic position reference path and avoid the situation of unable to match or incorrect matching.

[0167] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dynamic position reference method, characterized in that: include: Generate a planned driving route based on the origin and the destination, and obtain navigation map data corresponding to the planned driving route; For each location reference point in the navigation map data, matching a candidate node in the downloaded high-precision map data; Along the traveling direction, determine whether at least two position reference points closest to the current position can be matched to at least one candidate node respectively; If not, then download the high-precision map data of the next grid of the current driving grid from the cloud; If so, high-precision map data corresponding to the two location reference points closest to the current location are obtained from the downloaded high-precision map data along the traveling direction.

2. The method according to claim 1, characterized in that Acquiring high-precision map data corresponding to two location reference points closest to the current location from the downloaded high-precision map data, including: For each candidate node corresponding to the two position reference points closest to the current position, searching the downloaded high-precision map data for a path extending from each candidate node as a candidate path; Calculate the degree of difference of each candidate path; In response to the degree of difference being too small, high-precision map data of a next grid of the currently traveling grid is downloaded from the cloud.

3. The method according to claim 2, characterized in that Calculate the difference between each candidate path, including: Calculate the score of each candidate path; According to the scores of the candidate paths, the standard deviations of the candidate paths are calculated.

4. The method according to claim 3, characterized in that In response to the difference being too small, downloading high-precision map data of the next grid of the currently traveling grid from the cloud, including: Determining whether the standard deviation is greater than or equal to a standard deviation threshold; If yes, the shortest path between the candidate paths with the highest score is selected as the target path, and the high-precision map data corresponding to the target path is searched from the downloaded high-precision map data; If not, high-precision map data of the next grid of the current driving grid is downloaded from the cloud.

5. The method according to claim 4, characterized in that Download high-precision map data of the next grid of the current grid from the cloud, including: Determine whether there is high-precision map data of the next grid of the current driving grid in the cloud; If yes, download the high-precision map data of the next grid from the cloud; If not, the shortest path between the candidate paths with the highest current scores is selected as the target path, and the high-precision map data corresponding to the target path is searched from the downloaded high-precision map data.

6. The method according to claim 1, characterized in that The method further comprises: Determine whether all the location reference points in the navigation map data are matched to candidate nodes in the downloaded high-precision map data; If not, after traveling a preset distance, high-precision map data of the next grid of the currently traveling grid is downloaded from the cloud; If yes, then end.

7. A dynamic position reference device, characterized in that: include: An acquisition module, used to generate a planned driving route based on a departure point and a destination, and acquire navigation map data corresponding to the planned driving route; A matching module, for matching a candidate node in the downloaded high-precision map data for each position reference point in the navigation map data; A processing module is used to determine whether at least two position reference points closest to the current position can be matched to at least one candidate node respectively along the travel direction; if not, download high-precision map data of the next grid of the current travel grid from the cloud; If so, high-precision map data corresponding to the two nearest location reference points are obtained from the downloaded high-precision map data along the traveling direction.

8. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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