Lane-level data matching method, device, electronic device and storage medium

Abstract

The embodiment of the present invention discloses a lane-level data matching method, device, electronic device and storage medium; the embodiment of the present invention can be applied to scenarios such as smart transportation and vehicle networking, and can obtain the vehicle position of a target vehicle, determine the local lane high-precision data corresponding to at least one lane of the road where the vehicle position is located, and based on the positional relationship between shape points, thin out the shape points in the local lane high-precision data to obtain the lane-level thinned data corresponding to each lane, and predict the positioning area of ​​the target vehicle in each lane based on the thinned shape points of each lane according to the position of the thinned shape points and the vehicle position, determine the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance; the vehicle positioning accuracy can be maintained, the calculation overhead can be reduced, the vehicle positioning efficiency can be improved, and the rapid matching of the vehicle position and the lane high-precision data can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of map navigation technology, and in particular to a lane-level data matching method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of information collection technology, the data currently used in the field of map navigation is changing from traditional ordinary road data to high-precision road data with more accurate and rich information.

[0003] Since the shape points of high-precision data are dense and there are multiple lanes in the road, the shape point processing method used in traditional ordinary road data is used to process the high-precision data. This requires high computing performance of the equipment, occupies a large amount of computing resources, and has low vehicle positioning efficiency. Summary of the invention

[0004] The embodiments of the present invention provide a lane-level data matching method, device, electronic device and storage medium, which can maintain vehicle positioning accuracy, reduce computing overhead, improve vehicle positioning efficiency, and achieve rapid matching of vehicle position and lane high-precision data.

[0005] An embodiment of the present invention provides a lane-level data matching method, including:

[0006] Obtaining the vehicle position of the target vehicle;

[0007] Determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, wherein one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane;

[0008] Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain lane-level thinned data corresponding to each lane, and the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data;

[0009] According to the position of the thinned-out shape points and the position of the vehicle, predicting the positioning area of ​​the target vehicle in each lane based on the thinned-out shape points of each lane;

[0010] Determine the distance from the vehicle position to a point-line segment formed by two adjacent points in the positioning area, and determine a matching result of the target vehicle in the local lane high-precision data based on the distance.

[0011] Optionally, an embodiment of the present invention provides a lane-level data matching device, including:

[0012] A position acquisition unit, used to acquire the vehicle position of the target vehicle;

[0013] A data determination unit, used to determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, wherein one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane;

[0014] A data thinning unit, configured to thin the shape points in the local lane high-precision data based on the positional relationship between the shape points to obtain lane-level thinned data corresponding to each lane, wherein the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data;

[0015] An area prediction unit, configured to predict a positioning area of ​​the target vehicle in each lane based on the thinned-out shape points of each lane according to the positions of the thinned-out shape points and the vehicle position;

[0016] A result determination unit is used to determine the distance from the vehicle position to a shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

[0017] Optionally, the shape points include end point shape points and intermediate shape points between the end point shape points, and the data thinning unit is used to determine the thinned shape points among the intermediate shape points for retaining the lane direction based on the positional relationship between the intermediate shape points and the end point shape points at both ends;

[0018] The thinned shape point is used as the new endpoint shape point, and based on the positional relationship between the intermediate shape point between the adjacent endpoint shape points and the adjacent endpoint shape points, the thinned shape point used to retain the lane direction among the adjacent endpoint shape points is determined to obtain lane-level thinning data composed of the thinned shape points.

[0019] Optionally, the data thinning unit is used to determine the maximum distance between the middle shape point and the straight line formed by the two end shape points based on the shape point positions of the shape points;

[0020] If the maximum distance is greater than a preset distance threshold, the middle shape point corresponding to the maximum distance is used as the thinned shape point;

[0021] Use the thinned shape points as new endpoint shape points to split the curve formed by the shape points into two sub-curves;

[0022] Determine the maximum distance between the middle shape point on the sub-curve and the end shape point of the sub-curve;

[0023] If the maximum distance is greater than a preset distance threshold, the intermediate shape point corresponding to the maximum distance is used as a new thinned shape point;

[0024] Taking the new thinned shape point as the new endpoint shape point, the sub-curve where the new thinned shape point is located is divided into two new sub-curves, and the step of determining the maximum distance between the intermediate shape point on the sub-curve and the endpoint shape point of the sub-curve is performed until there is no new thinned shape point on the sub-curve, thereby obtaining the lane-level thinning data composed of the thinned shape points.

[0025] Optionally, the regional prediction unit is used to determine a first reference shape point among the thinned shape points according to a first shape point and a second shape point among the thinned shape points when the number of the thinned shape points is greater than a preset number of regional shape points;

[0026] Determine a second reference shape point among the thinned shape points according to the first reference shape point and the second shape point;

[0027] Calculate a first distance between the target vehicle and the first reference shape point, and a second distance between the target vehicle and the second reference shape point based on the vehicle position, the shape point position of the first reference shape point, and the shape point position of the second reference shape point;

[0028] If the first distance is less than the second distance, taking the second reference shape point as a new second shape point;

[0029] If the number of thinned-out shape points between the first shape point and the new second shape point is not greater than the number of regional shape points, the first shape point, the new second shape point and the thinned-out shape points between the first shape point and the new second shape point are used as the positioning area.

[0030] Optionally, the result determination unit is used to determine the position of the candidate positioning point of the target vehicle on each lane according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane;

[0031] Calculating the line segment distance between the vehicle position and the position of each candidate positioning point, and using the line segment distance as the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area;

[0032] Based on the distance, determining a target positioning point of the target vehicle from the candidate positioning points;

[0033] The two adjacent shape points corresponding to the target positioning point are used as the matching results of the target vehicle in the local lane high-precision data.

[0034] Optionally, the result determination unit is used to calculate the position relationship indication parameter between the candidate positioning point of the target vehicle on each lane and the shape point line segment formed by the two shape points according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane;

[0035] When the position relationship indication parameter is within a preset parameter range, the position of the candidate positioning point of the target vehicle on each lane is calculated according to the shape point positions of the two shape points and the position relationship indication parameter.

[0036] Optionally, the lane-level data matching device provided by the embodiment of the present invention further includes a driving weight calculation unit, which is used to obtain vehicle speed data and vehicle heading data of the target vehicle;

[0037] Calculating a driving weight parameter of the target vehicle based on the vehicle speed data and the vehicle heading data and a preset weight mapping relationship;

[0038] The result determination unit is used to determine a distance weight parameter of the target vehicle based on the distance;

[0039] Calculating the positioning parameter between the target vehicle and the candidate positioning point according to the distance weight parameter and the travel weight parameter;

[0040] Based on the positioning parameters, a target positioning point of the target vehicle is determined from the candidate positioning points.

[0041] Optionally, the weight mapping relationship includes a speed weight mapping relationship and an angle weight mapping relationship, and the driving weight calculation unit is used to calculate the speed weight parameter of the target vehicle based on the vehicle speed data and the speed weight mapping relationship;

[0042] Determining the driving angle of the target vehicle according to a preset angular coordinate system and the vehicle heading data;

[0043] Determine the line segment angle of each shape point line segment according to the angle coordinate system and the shape point line segment formed by two adjacent shape points in each positioning area;

[0044] Calculating an angle weight parameter of the target vehicle based on the driving angle, the line segment angle and the angle weight mapping relationship;

[0045] Based on the speed weight parameter and the angle weight parameter, a driving weight parameter of the target vehicle is calculated.

[0046] Optionally, the lane-level data matching device provided in the embodiment of the present invention further includes a driving planning unit, configured to determine a target lane corresponding to the target vehicle according to a matching result of the target vehicle in the local lane high-precision data;

[0047] Acquire lane observation data of each lane in the road where the vehicle is located;

[0048] A lane-level driving plan for the target vehicle is generated according to the lane observation data of the target lane and the lane observation data of other lanes.

[0049] Optionally, the lane-level data matching device provided in the embodiment of the present invention further includes a driving data acquisition unit, which is used to acquire vehicle speed data and vehicle heading data of the target vehicle;

[0050] The result determination unit is used to calculate the estimated positioning position of the target vehicle according to the vehicle position, the vehicle speed data and the vehicle heading data;

[0051] Determine the distance from the estimated positioning position to a shape point line segment formed by two adjacent shape points in the positioning area.

[0052] Optionally, the lane-level data matching device provided in the embodiment of the present invention further includes a second data thinning unit, which is used to obtain lane flow data of each lane in the road where the vehicle position is located;

[0053] Determining a new preset distance threshold corresponding to the original lane-level data according to the lane flow data of each lane;

[0054] Based on the new preset distance threshold, the step of determining the maximum distance between the middle shape point and the straight line formed by the two end shape points based on the shape point positions of the shape points is performed.

[0055] Correspondingly, an embodiment of the present invention further provides an electronic device, comprising a memory and a processor; the memory stores an application, and the processor is used to run the application in the memory to execute the steps in any lane-level data matching method provided in the embodiment of the present invention.

[0056] Correspondingly, an embodiment of the present invention further provides a computer-readable storage medium, which stores a plurality of instructions, and the instructions are suitable for a processor to load to execute the steps in any lane-level data matching method provided in an embodiment of the present invention.

[0057] In addition, an embodiment of the present invention further provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the steps in any lane-level data matching method provided in an embodiment of the present invention.

[0058] By adopting the scheme of the embodiment of the present invention, the vehicle position of the target vehicle can be obtained, and the local lane high-precision data corresponding to at least one lane of the road where the vehicle position is located can be determined. One local lane high-precision data includes at least two shape points of a lane, and the shape point is used to indicate the direction of the lane. Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain lane-level thinned data corresponding to each lane. The curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data. According to the position of the thinned shape points and the vehicle position, the positioning of the target vehicle in each lane is predicted based on the thinned shape points of each lane. area, determine the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance; since in the embodiment of the present invention, the high-precision local lane high-precision data is thinned to obtain the thinned shape points, and the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points, therefore, when performing lane-level positioning, the lane and / or shape point matching the target vehicle in the local lane high-precision data can be determined according to the thinned shape points in the positioning area, which can maintain the vehicle positioning accuracy, reduce the calculation overhead, improve the vehicle positioning efficiency, and realize the rapid matching of the vehicle position and the lane high-precision data. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 is a schematic diagram of a scenario of a lane-level data matching method provided by an embodiment of the present invention;

[0061] Figure 2 is a flow chart of a lane-level data matching method provided by an embodiment of the present invention;

[0062] Figure 3 is a schematic diagram of a thinning process provided by an embodiment of the present invention;

[0063] Figure 4 is a schematic diagram of a positioning area determination process provided by an embodiment of the present invention;

[0064] Figure 5 It is a schematic diagram of the relationship between the vehicle coordinates and the positions of the shape points and line segments provided by an embodiment of the present invention;

[0065] Figure 6is a schematic diagram of a projection calculation process provided by an embodiment of the present invention;

[0066] Figure 7 is a schematic diagram of the angle relationship of the target vehicle provided by an embodiment of the present invention;

[0067] Figure 8 is another flow chart of the lane-level data matching method provided by an embodiment of the present invention;

[0068] Fig. 9 is a schematic diagram of a lane-level data matching system provided by an embodiment of the present invention;

[0069] Fig.10 is a schematic diagram of the structure of a lane-level data matching device provided by an embodiment of the present invention;

[0070] Fig.11 is another structural schematic diagram of the lane-level data matching device provided by an embodiment of the present invention;

[0071] Fig.12 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0073] Embodiments of the present invention provide a lane-level data matching method, device, electronic device, and computer-readable storage medium. Specifically, embodiments of the present invention provide a lane-level data matching method applicable to a lane-level data matching device, which can be integrated in an electronic device.

[0074] The electronic device can be a terminal or other device, including but not limited to a mobile terminal and a fixed terminal. For example, a mobile terminal includes but is not limited to a smart phone, a smart watch, a tablet computer, a laptop computer, a vehicle-mounted terminal, a smart vehicle, etc., wherein a fixed terminal includes but is not limited to a desktop computer, a smart TV, etc.

[0075] The electronic device can also be a device such as a server. The server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms, but is not limited to these.

[0076] The lane-level data matching method of the embodiment of the present invention may be implemented by a terminal, or may be implemented by a terminal and a server together.

[0077] The following takes the lane-level data matching method implemented by a terminal and a server together as an example to illustrate the method.

[0078] like Figure 1 As shown, the lane-level data matching system provided by the embodiment of the present invention includes a terminal 10 and a server 20, etc.; the terminal 10 and the server 20 are connected via a network, for example, via a wired or wireless network connection, etc.

[0079] The terminal 10 may exist as a terminal for sending the vehicle position of the target vehicle to the server 20 .

[0080] Among them, the server 20 can be used to obtain the vehicle position of the target vehicle, determine the local lane high-precision data corresponding to at least one lane of the road where the vehicle position is located, and a local lane high-precision data includes at least two shape points of a lane, and the shape points are used to indicate the direction of the lane; based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain lane-level thinned data corresponding to each lane, and the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting point in the local lane high-precision data.

[0081] The server 20 can predict the positioning area of ​​the target vehicle in each lane based on the thinned shape points of each lane according to the positions of the thinned shape points and the vehicle positions, determine the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

[0082] It is understandable that the lane-level data matching step performed by the server 20 may also be performed by the terminal 10 , and this embodiment of the present invention does not limit this.

[0083] It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments.

[0084] The embodiment of the present invention will be described from the perspective of a lane-level data matching device, which can be specifically integrated in a server or a terminal.

[0085] like Figure 2 As shown, the specific process of the lane-level data matching method of this embodiment can be as follows:

[0086] 201. Obtain the vehicle position of the target vehicle.

[0087] The target vehicle may be an intelligent vehicle that needs to be positioned, or the target vehicle may be a vehicle where a terminal capable of positioning is located.

[0088] For example, the target vehicle may be an unmanned vehicle, or the target vehicle may be a vehicle equipped with a vehicle-mounted navigation terminal, and so on.

[0089] In the embodiment of the present invention, the vehicle position may be represented by longitude and latitude. For example, the vehicle position may be 22 degrees and 32 minutes north latitude and 114 degrees and 3 minutes east longitude.

[0090] Alternatively, each geographic area can be divided into n sub-areas, each sub-area has a landmark position (such as the location of a landmark building or the center point of the area, etc.). When indicating the vehicle position, the sub-area where the target vehicle is located and the relative coordinates between the target vehicle and the landmark position can be used.

[0091] For example, the vehicle position may be (3, 114, -22), indicating that the target vehicle is located in the third sub-area, 114 meters east of the landmark position, 22 meters north, and so on.

[0092] It is understandable that after the lane-level positioning of the target vehicle is achieved, the driving of the target vehicle can be planned according to the lane where the target vehicle is located. In some optional embodiments, the lane-level data matching method provided by the embodiment of the present invention may also include:

[0093] Determine the target lane corresponding to the target vehicle based on the matching result of the target vehicle in the local lane high-precision data;

[0094] Obtain lane observation data for each lane of the road where the vehicle is located;

[0095] A lane-level driving plan for the target vehicle is generated based on the lane observation data of the target lane and the lane observation data of other lanes.

[0096] The target lane is the lane where the determined target vehicle is located. The lane observation data may include but is not limited to the traffic flow data and / or lane width data of each lane.

[0097] For example, in an embodiment of the present invention, it is possible to plan whether the target vehicle can perform operations such as changing lanes based on the traffic flow data of the target lane and the traffic flow data of other lanes.

[0098] 202. Determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, where one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane.

[0099] The road where the vehicle is located generally includes at least one lane. For example, a road may include two bidirectional lanes, or a road may include a straight lane, a left-turn lane, and a right-turn lane.

[0100] Specifically, when determining the local lane high-precision data, it is possible to determine the lane high-precision data of the local range corresponding to the vehicle position from the pre-stored lane high-precision data according to the vehicle position as the local lane high-precision data. For example, if the vehicle position is 22 degrees 32 minutes north latitude and 114 degrees 3 minutes east longitude, the local lane high-precision data obtained may be the lane high-precision data within the range of 22 degrees 30 minutes north latitude to 22 degrees 34 minutes north latitude and 114 degrees 1 minute east longitude to 114 degrees 5 minutes east longitude.

[0101] Alternatively, high-precision data of local lanes in a local range corresponding to the vehicle position may be determined based on the road position corresponding to the vehicle position.

[0102] It should be noted that lane high-precision data can be high-definition road (High Definition, HD) data, or it can also be a type of data that has richer information than ordinary road data (Standard Definition, SD) data but does not meet the high-precision road data standards.

[0103] For example, high-precision road data may include road lane equations / shape point coordinates, lane types, lane speed limits, lane marking types, pole coordinates, signpost locations, camera / traffic light locations, etc. Lane high-precision data may be high-precision road data, or data that only retains part of the information in high-precision road data, for example, lane high-precision data may only include lane equations / shape point coordinates, lane types, lane speed limits, lane marking types, lane topology information, etc.

[0104] 203. Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain lane-level thinned data corresponding to each lane. The curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data.

[0105] Among them, thinning refers to reducing the number of data points while ensuring that the shape of the curve or the broken line remains unchanged as much as possible when processing data.

[0106] Specifically, when thinning out the shape points, a step compression algorithm can be used, that is, a point is extracted along the continuous curve at a certain step length, and the remaining points are all compressed, and then a straight line or curve fitting approximation is used between adjacent extracted points. Alternatively, a line segment filtering algorithm can be used, that is, when the length of a line segment is less than a preset filtering value, the midpoint of the line segment is used to replace the segment.

[0107] Alternatively, in the embodiment of the present invention, the local lane high-precision data may be thinned according to the positional relationship between the shape points. The shape points may include end point shape points and intermediate shape points between the end point shape points. The step of "thinning the shape points in the local lane high-precision data based on the positional relationship between the shape points to obtain lane-level thinned data corresponding to each lane" may specifically include:

[0108] Based on the positional relationship between the middle shape point and the end shape points at both ends, determine the thinned shape points in the middle shape point for retaining the lane direction;

[0109] The thinned shape points are used as new end point shape points. Based on the positional relationship between the intermediate shape points between the adjacent end point shape points and the adjacent end point shape points, the thinned shape points for retaining the lane direction are determined among the intermediate shape points between the adjacent end point shape points to obtain lane-level thinning data composed of the thinned shape points.

[0110] The end point shape point refers to the shape point at the starting position and the shape point at the end position of a certain lane among the shape points used for the direction of the lane.

[0111] Among them, the intermediate shape points are the shape points in the local lane high-precision data except the end point shape points.

[0112] In some examples, when determining the thinned shape points used to retain the lane direction, the slopes of the straight lines formed by the middle shape points and the end shape points at both ends can be calculated based on the positional relationship between the middle shape points and the end shape points at both ends, and the middle shape points whose absolute values ​​of the slopes are greater than a preset threshold are selected as the thinned shape points.

[0113] In other examples, the step of “determining the thinned-out shape points for retaining the lane direction among the middle shape points based on the positional relationship between the middle shape points and the end shape points at both ends” may specifically include:

[0114] Based on the shape point positions of the shape points, determine the maximum distance between the middle shape point and the straight line formed by the two end shape points;

[0115] If the maximum distance is greater than the preset distance threshold, the middle shape point corresponding to the maximum distance is used as the thinned shape point.

[0116] Among them, the preset distance threshold can be defined according to actual needs, such as 1m, 0.8m, 0.5m, etc.

[0117] For example, Figure 3 As shown in (1), point 1 and point 5 are end-point shape points, and points 2, 3, and 4 are intermediate shape points. The maximum distance between the straight line formed by points 1 and 5 is determined from points 2, 3, and 4. For example, the distance corresponding to point 2 is 0.1, the distance corresponding to point 3 is 0.7, and the distance corresponding to point 4 is 0.5. Then the distance corresponding to point 3 is the maximum distance. If the maximum distance is greater than the preset distance threshold 0.5, point 3 is used as the thinned shape point.

[0118] In order to improve the matching degree between the curve formed by the shape points in the lane-level thinning data and the curve direction formed by the starting points in the local lane high-precision data, the step of "using the thinned shape points as new end-point shape points, and determining the thinned shape points for retaining the lane direction among the adjacent end-point shape points based on the positional relationship between the intermediate shape points between the adjacent end-point shape points and the adjacent end-point shape points, and obtaining the lane-level thinning data formed by the thinned shape points" may specifically include:

[0119] Use the thinned shape points as new endpoint shape points to split the curve formed by the shape points into two sub-curves;

[0120] Determine the maximum distance between the middle shape point on the sub-curve and the end shape point of the sub-curve;

[0121] If the maximum distance is greater than the preset distance threshold, the middle shape point corresponding to the maximum distance is used as the new thinned shape point;

[0122] Take the new thinned shape point as the new endpoint shape point, divide the sub-curve where the new thinned shape point is located into two new sub-curves, and execute the step of determining the maximum distance between the intermediate shape point on the sub-curve and the endpoint shape point of the sub-curve until there is no new thinned shape point on the sub-curve, and obtain the lane-level thinning data composed of the thinned shape points.

[0123] At this time, if Figure 3 As shown in (2), point 3 is used as the new endpoint shape point, and the curve is divided into a sub-curve consisting of points 1, 2, and 3, and a sub-curve consisting of points 3, 4, and 5. Point 5 is used as the intermediate shape point between the endpoint shape point and the new endpoint shape point (point 3) as point 4. The maximum distance between point 4 and the straight line formed by points 3 and 5 is calculated to be 0.6, which is greater than the preset distance threshold. In this case, point 4 can also be used as a thinned shape point.

[0124] Also, point 1 is the endpoint shape point and the intermediate shape point of the new endpoint shape point (point 3) is point 2. The maximum distance between point 2 and the straight line formed by points 1 and 3 is calculated to be 0.2, which is less than the preset distance threshold. In this case, point 2 will not be used as the thinned shape point.

[0125] It is understandable that, since the local lane high-precision data may contain data corresponding to several lanes, the lane-level data corresponding to each lane may be processed separately, either synchronously or asynchronously, and this is not limited in the embodiment of the present invention.

[0126] In actual application, the preset distance threshold in the thinning process can be adjusted according to factors such as vehicle density. In some optional embodiments, the lane-level data matching method provided by the embodiment of the present invention may also include:

[0127] Obtain lane flow data of each lane on the road where the vehicle is located;

[0128] Determine a new preset distance threshold corresponding to the original lane-level data according to the lane flow data of each lane;

[0129] Based on the new preset distance threshold, a step of determining the maximum distance between the middle shape point and the straight line formed by the two end shape points based on the shape point positions of the shape points is performed.

[0130] The lane flow data is data describing the traffic volume in the lane. For example, the lane flow data may be 30 vehicles per minute, or 2 vehicles per second, etc. Specifically, the lane flow data may be obtained by a traffic observation device installed on or beside the road, or the lane flow data may be obtained based on the positioning information of each vehicle, etc.

[0131] Specifically, when determining a new preset distance threshold, it can be determined based on a mapping relationship between preset lane flow data and the preset distance threshold, and lane flow data of each lane.

[0132] For example, before determining a new preset distance threshold, three different candidate distance thresholds may be preset, including a first candidate distance threshold, a second candidate distance threshold, and a third candidate distance threshold, wherein the first candidate distance threshold>the second candidate distance threshold>the third candidate distance threshold. In practical applications, the preset distance threshold may be set to the second candidate distance threshold by default. When the lane flow is greater than the first traffic threshold, the preset distance threshold may be modified to the first candidate distance threshold, and when the lane flow is less than the second traffic threshold, the preset distance threshold may be modified to the third candidate distance threshold.

[0133] For another example, a continuous function relationship between lane flow data and a preset distance threshold can be established through a data model, and a new preset distance threshold corresponding to the original lane-level data of each lane can be determined based on the lane flow data of each lane and the continuous function relationship.

[0134] 204. According to the positions of the thinned-out shape points and the position of the vehicle, a positioning area of ​​the target vehicle in each lane is predicted based on the thinned-out shape points of each lane.

[0135] Among them, the position of the shape point after thinning can be expressed by longitude and latitude, or each geographical area can be divided into n sub-areas, each sub-area has a landmark position, and the relative coordinates between the sub-area where the shape point is located after thinning and the shape point after thinning and the landmark position are used to express it, and so on.

[0136] Specifically, the description method of the position of the shape point after thinning can be the same as the description method of the vehicle position, or it can be different from the description method of the vehicle position. For example, both the shape point and the vehicle position can be expressed in longitude and latitude after thinning, or the shape point can be expressed in longitude and latitude after thinning, and the vehicle position can be expressed based on the relative coordinates of the landmark position in the sub-area, etc. If the two description methods are different, they can be converted, for example, the vehicle position expressed based on the relative coordinates of the landmark position in the sub-area can be converted to longitude and latitude, etc.

[0137] In the embodiment of the present invention, the amount of calculation can be further reduced by performing region screening on the thinned shape points.

[0138] Specifically, when determining the positioning area, the thinned shape points can be evenly or unevenly divided into N sub-areas, and the distance between the shape points in the middle order and the vehicle position in each sub-area is calculated, and the sub-area corresponding to the smallest distance and one or two adjacent sub-areas are used as the positioning area.

[0139] Alternatively, when determining the positioning area, step 203 may specifically include:

[0140] When the number of the thinned-out shape points is greater than the preset number of regional shape points, determining a first reference shape point among the thinned-out shape points according to the first shape point and the second shape point among the thinned-out shape points;

[0141] Determine the second reference shape point among the thinned shape points according to the first reference shape point and the second shape point;

[0142] Calculate a first distance between the target vehicle and the first reference shape point, and a second distance between the target vehicle and the second reference shape point based on the vehicle position, the shape point position of the first reference shape point, and the shape point position of the second reference shape point;

[0143] If the first distance is less than the second distance, the second reference shape point is used as the new second shape point;

[0144] If the number of thinned-out shape points between the first shape point and the new second shape point is not greater than the number of regional shape points, the first shape point, the new second shape point, and the thinned-out shape points between the first shape point and the new second shape point are used as the positioning area.

[0145] The number of area points can be a custom value, such as 80 points, 100 points, or 200 points.

[0146] Specifically, when determining the first reference shape point, the middle position of the first shape point and the second shape point may be calculated based on their positions, and the shape point closest to the middle position may be used as the first reference shape point.

[0147] Alternatively, the shape points may be numbered according to their positional relationship in the positioning area; the first shape point may be the shape point corresponding to the first number, the second shape point may be the shape point corresponding to the last number, and the shape point corresponding to the middle value of the number may be taken as the first reference shape point.

[0148] For example, Figure 4 As shown, assuming that the shape points after the current lane is thinned are P1, P2, ..., PN, if N <= the number of regional shape points, no calculation is required, and the search interval is directly returned to [1, N]. If N > the number of regional shape points, P1 is used as the first shape point and PN as the second shape point.

[0149] Then, the first reference point may be PM, M=(L+N) / 2, and the second reference point may be PS, S=(M+N) / 2. The distances between the vehicle position and PM and PN are calculated to obtain the first distance and the second distance respectively.

[0150] If the first distance is smaller than the second distance, and NS<=the number of regional points, PS-PN is used as the positioning area.

[0151] In some examples, when the first distance is less than the second distance, if the number of thinned-out shape points between the first shape point and the new second shape point is greater than the number of regional shape points, the first shape point, the new second shape point, and the shape point between the first shape point and the new second shape point are taken as new thinned-out shape points, and the step of determining the first reference shape point among the thinned-out shape points is executed based on the first shape point and the second shape point among the thinned-out shape points.

[0152] For example, if the distance between the vehicle position and PM and PN is calculated, the first distance and the second distance are obtained respectively; the first distance is smaller than the second distance, and NS>the number of regional shape points, then PS-PN is used as the new thinned shape point, and the first reference shape point and the second reference shape point are re-determined from PS-PN.

[0153] In other examples, when the first distance is not less than the second distance, the first reference shape point is used as a new first shape point;

[0154] If the number of thinned-out shape points between the new first shape point and the second shape point is not greater than the number of regional shape points, the new first shape point, the second shape point and the thinned-out shape points between the new first shape point and the second shape point are used as the positioning area.

[0155] For example, if the distances between the vehicle position and PM and PN are calculated, the first distance and the second distance are obtained respectively; if the first distance is not less than the second distance, and MS<=the number of regional points, PM-PN is used as the positioning area.

[0156] In other examples, when the first distance is not less than the second distance, if the number of thinned-out shape points between the new first shape point and the second shape point is greater than the number of regional shape points, the new first shape point, the second shape point, and the thinned-out shape points between the new first shape point and the second shape point are used as new thinned-out shape points, and the step of determining the first reference shape point among the thinned-out shape points is executed based on the first shape point and the second shape point among the thinned-out shape points.

[0157] For example, if the distances between the vehicle position and PM and PN are calculated, the first distance and the second distance are obtained respectively; the first distance is not less than the second distance, and MS>the number of regional shape points, then PM-PN is used as the new thinned shape point, and the first reference shape point and the second reference shape point are re-determined from PM-PN.

[0158] 205. Determine the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

[0159] It can be understood that there are at least two shape points in the positioning area. When calculating the distance, the distance from the vehicle position to each shape point line segment can be calculated in sequence, or the distance from the vehicle position to each shape point line segment can be calculated simultaneously.

[0160] Specifically, when calculating the distance, the projection point corresponding to the vehicle position can be determined on the shape point line segment of each lane, and then the distance between the vehicle position and the projection point of each lane can be calculated to obtain the distance from the vehicle position to the shape point line segment, and then the lane positioning point can be determined. That is, step 204 can include:

[0161] According to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane, the position of the candidate positioning point of the target vehicle on each lane is determined;

[0162] Calculate the line segment distance between the vehicle position and the position of each candidate positioning point, and use the line segment distance as the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area;

[0163] Based on the distance, determine the target positioning point of the target vehicle from the candidate positioning points;

[0164] The two adjacent shape points corresponding to the target positioning point are used as the matching results of the target vehicle in the local lane high-precision data.

[0165] Among them, the candidate positioning points are the projection points of the vehicle on each lane. Specifically, with the coordinates P of the current position of the vehicle and the positioning area of ​​lane i, P can be projected onto the line segments formed by two adjacent points in the positioning area, and it is determined whether the projection point of P falls within the line segment formed by the two points, and the position of the optimal positive projection is found, that is, the candidate positioning point.

[0166] Among them, the target positioning point can be considered as the actual position of the determined target vehicle, or it can be considered as the corresponding position in the lane determined by the local lane high-precision data.

[0167] In other examples, the lane high-precision data of the lane corresponding to the target positioning point can also be used as the matching result of the target vehicle in the local lane high-precision data. The lane corresponding to the target positioning point in the local lane high-precision data can also be used as the matching result of the target vehicle in the local lane high-precision data.

[0168] In some optional embodiments, the step of “determining the position of the candidate positioning point of the target vehicle on each lane according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane” may specifically include:

[0169] According to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane, the position relationship indication parameter between the candidate positioning point of the target vehicle on each lane and the shape point line segment formed by the two shape points is calculated;

[0170] When the position relationship indication parameter is within a preset parameter range, the position of the candidate positioning point of the target vehicle on each lane is calculated according to the shape point positions and the position relationship indication parameter of the two shape points.

[0171] The position relationship indication parameter is used to indicate the position relationship between the candidate positioning point and the shape point line segment formed by the two shape points.

[0172] Specifically, Figure 5 As shown in FIG. 1 , there can be three types of positional relationships between the candidate positioning point and the shape point line segment formed by two shape points. The positional relationship indication parameter can be calculated by the following formula:

[0173]

[0174] Where P represents the vehicle positioning coordinates, A and B represent any two continuous points on the lane centerline, and M represents the projection point of P onto line segment AB (candidate positioning point). When M is between A and B, 0≤r≤1.

[0175] After obtaining the position relationship indication parameter, only the case where the position relationship indication parameter is within the preset parameter range (i.e., 0≤r≤1) can be processed subsequently, otherwise AB is updated to the next set of two adjacent shape points and the calculation is continued. When the position relationship indication parameter is within the preset parameter range, the coordinates of M can be calculated by the following formula:

[0176] M.lon=A.lon+r*(B.lon–A.lon),

[0177] M.lat=A.lat+r*(B.lat–A.lat).

[0178] Where M.lon is the longitude of M, and M.lat is the latitude of M. A.lon is the longitude of A, and A.lat is the latitude of A. B.lon is the longitude of B, and B.lon is the latitude of B.

[0179] It can be understood that after determining the line segment where the candidate positioning point is located, the distance from the vehicle position to each point line segment can be calculated without the position of the candidate positioning point, such as Figure 6 As shown, APB forms a triangle, where A and B are two adjacent points and P is the vehicle position.

[0180] In some examples, the distance S from the vehicle position to each point line segment can be calculated by the following formula:

[0181]

[0182] Among them, the vector representation can convert the longitude and latitude representation of the point into the representation of a two-dimensional plane coordinate system. The Mercator projection can be used, or the difference in longitude and latitude can be directly converted into the difference in absolute distance meters.

[0183] Alternatively, the distance S from the vehicle position to each point line segment can be calculated using the following formula:

[0184]

[0185] Where a, b, and c represent the lengths of line segments AP, AB, and PB respectively, and their lengths can be easily calculated by inputting the longitude and latitude coordinates of two points.

[0186] In actual application, in order to improve the accuracy of vehicle positioning, factors such as the vehicle's driving speed and driving direction may be comprehensively considered when determining the lane positioning point. That is, in some optional embodiments, before the step of "determining the lane positioning point of the target vehicle from the candidate positioning points based on the distance", the lane-level data matching method provided by the embodiment of the present invention may also include:

[0187] Obtain vehicle speed data and vehicle heading data of the target vehicle;

[0188] Based on the vehicle speed data and vehicle heading data, as well as a preset weight mapping relationship, the driving weight parameters of the target vehicle are calculated.

[0189] In the embodiment of the present invention, the driving weight parameter can be combined with the current vehicle speed and direction of the vehicle to further improve the accuracy of positioning.

[0190] Accordingly, the step of “determining the lane positioning point of the target vehicle from the candidate positioning points based on the distance” may specifically include:

[0191] Based on the distance, determining a distance weight parameter of the target vehicle;

[0192] Calculate the positioning parameters between the target vehicle and the candidate positioning points according to the distance weight parameter and the driving weight parameter;

[0193] Based on the positioning parameters, the lane positioning point of the target vehicle is determined from the candidate positioning points.

[0194] Specifically, the distance weight parameter can be calculated by the following formula:

[0195]

[0196] Wherein, η is a custom constant that can be set according to actual conditions. d(i) is the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area.

[0197] It should be noted that the calculation formula of the distance weight parameter here is only an example. dis There is no restriction on the definition of , as long as (1) it is a symmetric function, (2) its range is in [0,1], and (3) the smaller d(i) is, the greater the value of w is. dis The larger the condition, the better.

[0198] Specifically, in some optional examples, the weight mapping relationship may include a speed weight mapping relationship and an angle weight mapping relationship, and the step of "calculating the driving weight parameter of the target vehicle based on the vehicle speed data and the vehicle heading data, and the preset weight mapping relationship" may specifically include:

[0199] Calculate the speed weight parameter of the target vehicle based on the vehicle speed data and the speed weight mapping relationship;

[0200] Determine the driving angle of the target vehicle based on the preset angle coordinate system and vehicle heading data;

[0201] Determine the line segment angle of each shape point line segment according to the angle coordinate system and the shape point line segment formed by two adjacent shape points in each positioning area;

[0202] Calculate the angle weight parameter of the target vehicle based on the mapping relationship among the driving angle, the line segment angle and the angle weight;

[0203] Based on the speed weight parameter and the angle weight parameter, a driving weight parameter of the target vehicle is calculated.

[0204] Among them, the speed weight parameter Where spd is the vehicle speed data.

[0205] Among them, the angle weight parameter cos(azidiff), specifically, azidiff = |α-β|, such as Figure 7 As shown, α is the driving angle of the target vehicle, and β is the segment angle of the point-line segment.

[0206] The driving weight parameter can be calculated by the following formula:

[0207]

[0208] Among them, γ, λ, φ are all constants and can be set freely.

[0209] It should be noted that the calculation formula of the driving weight parameter here is only an example. spd, There is no restriction on the definition of , as long as it satisfies the following conditions: (1) it is a symmetric function, (2) its range is in the range [0,1], and (3) when the angle is the same, the greater the speed, the smaller the result; when the speed is the same, the greater the angle, the smaller the result.

[0210] In some optional embodiments, before the step of “determining the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area”, the lane-level data matching method provided by the embodiment of the present invention may further include:

[0211] Obtain vehicle speed data and vehicle heading data of the target vehicle;

[0212] Determine the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area, including:

[0213] Calculate the estimated positioning position of the target vehicle based on the vehicle position, vehicle speed data and vehicle heading data;

[0214] Determine the distance from the estimated positioning position to the shape point line segment formed by two adjacent shape points in the positioning area.

[0215] Therefore, the actual vehicle position at the time of the positioning result can be calculated by considering the vehicle speed and heading, that is, the estimated positioning position, and then the vehicle positioning can be performed. This can improve the real-time performance of vehicle positioning.

[0216] As can be seen from the above, the embodiment of the present invention can obtain the vehicle position of the target vehicle, determine the local lane high-precision data corresponding to at least one lane of the road where the vehicle position is located, a local lane high-precision data includes at least two shape points of a lane, the shape points are used to indicate the direction of the lane, based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned out to obtain lane-level thinned data corresponding to each lane, the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data, according to the position of the thinned shape points and the vehicle position, the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points of each lane. Determine the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance; since in the embodiment of the present invention, the high-precision local lane high-precision data is thinned to obtain the thinned shape points, and the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points, therefore, when performing lane-level positioning, the lane and / or shape point matching the target vehicle in the local lane high-precision data can be determined according to the thinned shape points in the positioning area, which can maintain the vehicle positioning accuracy, reduce the calculation overhead, improve the vehicle positioning efficiency, and realize the rapid matching of the vehicle position and the lane high-precision data.

[0217] The method described in the above embodiments is further described in detail below with examples.

[0218] In this embodiment, the Figure 1 system is described below.

[0219] like Figure 8 As shown, the lane-level data matching method of this embodiment may have the following specific process:

[0220] 801. The terminal determines a vehicle position of a target vehicle, and determines local lane high-precision data corresponding to at least one lane of a road where the vehicle position is located.

[0221] like Fig. 9 As shown, the terminal can determine the vehicle position of the target vehicle through the vehicle positioning module in the lane-level data matching system. The local lane high-precision data of each lane can be determined through the map data module.

[0222] Among them, the vehicle positioning module can collect the historical status information of the vehicle during the historical positioning period, wherein the historical status information includes but is not limited to global positioning system GPS information (which can be based on ordinary GNSS positioning, PPP positioning, RTK positioning), vehicle control information, vehicle visual perception information, and inertial measurement unit IMU information. Finally, the module outputs a positioning point information P (vehicle position coordinates and longitude and latitude coordinates) at the current moment through certain algorithms and rules. The positioning point information is used to obtain local map data from the map data module and for lane matching. It is an important input for lane-level positioning judgment and a reference point for lane-level adsorption / matching results.

[0223] Among them, the map data module matches the corresponding road position according to the positioning information of the vehicle positioning module, and then obtains the local map information of the current position. Here we obtain the local lane-level road information near the vehicle positioning point. The data contains the lane-level information of the local area of ​​the vehicle positioning point, including the total number of lanes and the coordinates of the lane center line points of each lane.

[0224] Assume that the map data returns a lane group data, recorded as laneGroup, the total number of lanes in each laneGroup is consistent, and the lane line type and color are also consistent. Different laneGroup divisions may be due to different lane line types / colors, or different numbers of lanes. Lane group data can include lane topology. The map data module will return the laneGroup data information where the vehicle positioning point is located. If it is empty, it means that the area does not have the required lane-level data.

[0225] 802. The terminal thins out the shape points in the local lane high-precision data based on the positional relationship between the shape points in the local lane high-precision data to obtain lane-level thinned data corresponding to each lane. The curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data.

[0226] Specifically, when thinning out the shape points, a step size compression algorithm, a line segment filtering algorithm, etc. may be used, which is not limited in the embodiment of the present invention.

[0227] Through step 801, we can roughly know that the vehicle is in a laneGroup surface, but we cannot know which lane it is in the surface, or its specific position in the lane. A laneGroup may have hundreds, thousands, or even tens of thousands of shape points, and each lane has dense shape points. In order to reduce the amount of matching calculations, it is necessary to thin the density of the shape points and reduce the density of the points while ensuring that there is no significant loss of accuracy.

[0228] like Fig. 9As shown, the terminal can thin out the shape points in the high-precision data of the local lane through the data thinning module in the lane-level data matching system.

[0229] 803. The terminal predicts the positioning area of ​​the target vehicle in each lane based on the thinned-out shape points of each lane according to the positions of the thinned-out shape points and the position of the vehicle.

[0230] For example, it can be assumed that the shape points after the current lane is thinned are P1, P2, ..., PN. If N <= the number of regional shape points, no calculation is required and the search interval is directly returned to [1, N]. If N > the number of regional shape points, P1 is used as the first shape point and PN as the second shape point.

[0231] Then, the first reference point may be PM, M=(L+N) / 2, and the second reference point may be PS, S=(M+N) / 2. The distances between the vehicle position and PM and PN are calculated to obtain the first distance and the second distance respectively.

[0232] If the first distance is smaller than the second distance, and NS<=the number of regional points, PS-PN is used as the positioning area.

[0233] like Fig. 9 As shown, the terminal can determine the positioning area from the thinned shape points through the area determination module in the lane-level data matching system.

[0234] 804. The terminal calculates the position relationship indication parameter between the candidate positioning point of the target vehicle on each lane and the shape point line segment formed by the two shape points according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane.

[0235] Specifically, Figure 6 As shown, the position relationship indicates the parameters Where P represents the vehicle positioning coordinates, A and B represent any two continuous points on the lane centerline, and M represents the projection point of P onto line segment AB (candidate positioning point). When M is between A and B, 0≤r≤1.

[0236] 805. When the position relationship indication parameter is within a preset parameter range, the position of the candidate positioning point of the target vehicle on each lane is calculated according to the shape point positions and the position relationship indication parameter of the two shape points.

[0237] Specifically, when 0≤r≤1, the coordinates of M can be calculated by the following formula:

[0238] M.lon=A.lon+r*(B.lon–A.lon),

[0239] M.lat=A.lat+r*(B.lat–A.lat), where lon and lat may represent longitude and latitude, respectively.

[0240] 806. The terminal calculates the line segment distance between the vehicle position and the position of each candidate positioning point, and determines the lane positioning point of the target vehicle from the candidate positioning points based on the line segment distance.

[0241] Specifically, the terminal may use the candidate positioning point corresponding to the minimum line segment distance as the lane positioning point of the target vehicle.

[0242] In some optional embodiments, the lane-level data matching system provided by the embodiments of the present invention may also include a motion data module, which provides real-time vehicle speed information, IMU information (such as vehicle heading angle information), steering wheel angle information, etc.; an image processing module, which provides processing results of visual road information in front of the vehicle, and the camera may be a monocular camera installed on the vehicle (installed on the windshield, or on the roof, etc.) to obtain the road image in front of the vehicle, and then analyze and process the collected image, and finally output the type and color information of the identified lane line around the vehicle (left and right) (which can be obtained by machine learning methods), as well as its confidence quality. The lane line colors include but are not limited to yellow, white, blue, green, gray, black, and others; the lane line types include but are not limited to single solid line, single dashed line, double solid line, double dashed line, left dashed and right solid, left solid and right dashed, guardrail, curb, curb, road edge, and others.

[0243] The lane-level data matching system provided by the embodiment of the present invention may also include a laser radar module: providing 3D point cloud results of radar scanning for matching with high-precision maps. Ultrasonic radar: Like the laser radar module, it provides radar measurement results and obtains the location information of some landmarks for matching with high-precision maps. Other modules: such as surround view cameras (the results of each camera can be used as an observation value), vehicle four-wheel speed, etc.

[0244] As can be seen from the above, the embodiment of the present invention can obtain the vehicle position of the target vehicle, determine the local lane high-precision data corresponding to at least one lane of the road where the vehicle position is located, a local lane high-precision data includes at least two shape points of a lane, the shape points are used to indicate the direction of the lane, based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned out to obtain lane-level thinned data corresponding to each lane, the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data, according to the position of the thinned shape points and the vehicle position, the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points of each lane. Determine the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance; since in the embodiment of the present invention, the high-precision local lane high-precision data is thinned to obtain the thinned shape points, and the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points, therefore, when performing lane-level positioning, the lane and / or shape point matching the target vehicle in the local lane high-precision data can be determined according to the thinned shape points in the positioning area, which can maintain the vehicle positioning accuracy, reduce the calculation overhead, improve the vehicle positioning efficiency, and realize the rapid matching of the vehicle position and the lane high-precision data.

[0245] In order to better implement the above method, accordingly, an embodiment of the present invention also provides a lane-level data matching device.

[0246] refer to Fig.10 , the device comprises:

[0247] The position acquisition unit 1001 may be used to acquire the vehicle position of the target vehicle;

[0248] The data determination unit 1002 may be used to determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, where one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane;

[0249] A data thinning unit 1003 is used to thin the shape points in the local lane high-precision data based on the positional relationship between the shape points to obtain lane-level thinned data corresponding to each lane, and the curve formed by the shape points in the lane-level thinned data matches the curve formed by the starting points in the local lane high-precision data;

[0250] The area prediction unit 1004 may be used to predict the positioning area of ​​the target vehicle in each lane based on the thinned-out shape points of each lane according to the positions of the thinned-out shape points and the vehicle position;

[0251] The result determination unit 1005 can be used to determine the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

[0252] In some optional embodiments, the shape points include end shape points and middle shape points between the end shape points, and the data thinning unit 1003 is used to determine the thinned shape points for retaining the lane direction among the middle shape points based on the positional relationship between the middle shape points and the end shape points at both ends;

[0253] The thinned shape points are used as new endpoint shape points. Based on the positional relationship between the intermediate shape points between the adjacent endpoint shape points and the adjacent endpoint shape points, the thinned shape points used to retain the lane direction among the adjacent endpoint shape points are determined to obtain lane-level thinning data composed of the thinned shape points.

[0254] In some optional embodiments, the data thinning unit 1003 is used to determine the maximum distance between the middle shape point and the straight line formed by the two end shape points based on the shape point positions of the shape points;

[0255] If the maximum distance is greater than the preset distance threshold, the middle shape point corresponding to the maximum distance is used as the thinned shape point;

[0256] Use the thinned shape points as new endpoint shape points to split the curve formed by the shape points into two sub-curves;

[0257] Determine the maximum distance between the middle shape point on the sub-curve and the end shape point of the sub-curve;

[0258] If the maximum distance is greater than the preset distance threshold, the middle shape point corresponding to the maximum distance is used as the new thinned shape point;

[0259] Take the new thinned shape point as the new endpoint shape point, divide the sub-curve where the new thinned shape point is located into two new sub-curves, and execute the step of determining the maximum distance between the intermediate shape point on the sub-curve and the endpoint shape point of the sub-curve until there is no new thinned shape point on the sub-curve, and obtain the lane-level thinning data composed of the thinned shape points.

[0260] In some optional embodiments, the regional prediction unit 1004 may be configured to determine a first reference shape point among the thinned shape points according to a first shape point and a second shape point among the thinned shape points when the number of the thinned shape points is greater than a preset number of regional shape points;

[0261] Determine the second reference shape point among the thinned shape points according to the first reference shape point and the second shape point;

[0262] Calculate a first distance between the target vehicle and the first reference shape point, and a second distance between the target vehicle and the second reference shape point based on the vehicle position, the shape point position of the first reference shape point, and the shape point position of the second reference shape point;

[0263] If the first distance is less than the second distance, the second reference shape point is used as the new second shape point;

[0264] If the number of thinned-out shape points between the first shape point and the new second shape point is not greater than the number of regional shape points, the first shape point, the new second shape point, and the thinned-out shape points between the first shape point and the new second shape point are used as the positioning area.

[0265] In some optional embodiments, the result determination unit 1005 may be used to determine the position of the candidate positioning point of the target vehicle on each lane according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane;

[0266] Calculate the line segment distance between the vehicle position and the position of each candidate positioning point, and use the line segment distance as the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area;

[0267] Based on the distance, determine the target positioning point of the target vehicle from the candidate positioning points;

[0268] The two adjacent shape points corresponding to the target positioning point are taken as the matching results of the target vehicle in the local lane high-precision data.

[0269] In some optional embodiments, the result determination unit 1005 may be used to calculate a position relationship indication parameter between a candidate positioning point of the target vehicle on each lane and a shape point line segment formed by the two shape points based on the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane;

[0270] When the position relationship indication parameter is within a preset parameter range, the position of the candidate positioning point of the target vehicle on each lane is calculated according to the shape point positions and the position relationship indication parameter of the two shape points.

[0271] In some optional embodiments, such as Fig.11 As shown, the lane-level data matching device provided in the embodiment of the present invention may further include a driving weight calculation unit 1006, which may be used to obtain vehicle speed data and vehicle heading data of the target vehicle;

[0272] Calculate the driving weight parameter of the target vehicle based on the vehicle speed data and the vehicle heading data and the preset weight mapping relationship;

[0273] A result determination unit may be used to determine a distance weight parameter of the target vehicle based on the distance;

[0274] Calculate the positioning parameters between the target vehicle and the candidate positioning points according to the distance weight parameter and the driving weight parameter;

[0275] Based on the positioning parameters, a target positioning point of the target vehicle is determined from the candidate positioning points.

[0276] In some optional embodiments, the weight mapping relationship may include a speed weight mapping relationship and an angle weight mapping relationship, and the driving weight calculation unit 1006 may be used to calculate the speed weight parameter of the target vehicle based on the vehicle speed data and the speed weight mapping relationship;

[0277] Determine the driving angle of the target vehicle based on the preset angle coordinate system and vehicle heading data;

[0278] Determine the line segment angle of each shape point line segment according to the angle coordinate system and the shape point line segment formed by two adjacent shape points in each positioning area;

[0279] Calculate the angle weight parameter of the target vehicle based on the mapping relationship among the driving angle, the line segment angle and the angle weight;

[0280] Based on the speed weight parameter and the angle weight parameter, a driving weight parameter of the target vehicle is calculated.

[0281] In some optional embodiments, the lane-level data matching device provided by the embodiment of the present invention may further include a driving planning unit 1007, which may be used to determine a target lane corresponding to the target vehicle according to a matching result of the target vehicle in the local lane high-precision data;

[0282] Obtain lane observation data for each lane of the road where the vehicle is located;

[0283] A lane-level driving plan for the target vehicle is generated based on the lane observation data of the target lane and the lane observation data of other lanes.

[0284] In some optional embodiments, the lane-level data matching device provided by the embodiment of the present invention may further include a driving data acquisition unit 1008, which may be used to acquire vehicle speed data and vehicle heading data of the target vehicle;

[0285] The result determination unit 1005 may be used to calculate the estimated positioning position of the target vehicle based on the vehicle position, vehicle speed data and vehicle heading data;

[0286] Determine the distance from the estimated positioning position to the shape point line segment formed by two adjacent shape points in the positioning area.

[0287] In some optional embodiments, the lane-level data matching device provided by the embodiment of the present invention may further include a data updating and thinning unit 1009, which may be used to obtain lane flow data of each lane in the road where the vehicle is located;

[0288] Determine a new preset distance threshold corresponding to the original lane-level data according to the lane flow data of each lane;

[0289] Based on the new preset distance threshold, a step is performed to determine the maximum distance between the intermediate shape point and the straight line formed by the original starting shape point and the original ending shape point based on the shape point position of the shape point.

[0290] As can be seen from the above, through the lane-level data matching device, the vehicle position of the target vehicle can be obtained, and the local lane high-precision data corresponding to at least one lane of the road where the vehicle position is located can be determined. A local lane high-precision data includes at least two shape points of a lane, and the shape points are used to indicate the direction of the lane. Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain the lane-level thinned data corresponding to each lane. The curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data. According to the position of the thinned shape points and the vehicle position, the positioning of the target vehicle in each lane is predicted based on the thinned shape points of each lane. area, determine the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance; because in the embodiment of the present invention, the high-precision local lane high-precision data is thinned to obtain the thinned shape points, and the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points, therefore, when performing lane-level positioning, the lane and / or shape point matching the target vehicle in the local lane high-precision data can be determined according to the thinned shape points in the positioning area, which can maintain the vehicle positioning accuracy, reduce the calculation overhead, improve the vehicle positioning efficiency, and realize the rapid matching of the vehicle position and the lane high-precision data.

[0291] In addition, an embodiment of the present invention further provides an electronic device, which may be a terminal or a server, etc. Fig.12 As shown, it shows a schematic diagram of the structure of an electronic device involved in an embodiment of the present invention, specifically:

[0292] The electronic device may include a radio frequency (RF) circuit 1201, a memory 1202 including one or more computer-readable storage media, an input unit 1203, a display unit 1204, a sensor 1205, an audio circuit 1206, a wireless fidelity (WiFi) module 1207, a processor 1208 including one or more processing cores, and a power supply 1209. Those skilled in the art will appreciate that Fig.12 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0293] The RF circuit 1201 can be used for receiving and sending signals during the process of sending and receiving information or making calls. In particular, after receiving the downlink information of the base station, it is handed over to one or more processors 1208 for processing; in addition, the data related to the uplink is sent to the base station. Usually, the RF circuit 1201 includes but is not limited to an antenna, at least one amplifier, a tuner, one or more oscillators, a subscriber identity module (SIM) card, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF circuit 1201 can also communicate with the network and other devices through wireless communication. Wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0294] The memory 1202 can be used to store software programs and modules. The processor 1208 executes various functional applications and data processing by running the software programs and modules stored in the memory 1202. The memory 1202 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, a phone book, etc.), etc. In addition, the memory 1202 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 1202 may also include a memory controller to provide the processor 1208 and the input unit 1203 with access to the memory 1202.

[0295] The input unit 1203 can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, in a specific embodiment, the input unit 1203 may include a touch-sensitive surface and other input devices. The touch-sensitive surface, also known as a touch display screen or a touch pad, can collect the user's touch operations on or near it (such as the user's operation on or near the touch-sensitive surface using any suitable object or accessory such as a finger, a stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch-sensitive surface may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into the touch point coordinates, and then sends it to the processor 1208, and can receive and execute the command sent by the processor 1208. In addition, the touch-sensitive surface can be implemented using multiple types such as resistive, capacitive, infrared and surface acoustic waves. In addition to the touch-sensitive surface, the input unit 1203 may also include other input devices. Specifically, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, and the like.

[0296] The display unit 1204 can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit 1204 may include a display panel. Optionally, the display panel may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch-sensitive surface may cover the display panel. When the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor 1208 to determine the type of touch event. The processor 1208 then provides a corresponding visual output on the display panel according to the type of touch event. Although in Fig.12 In the embodiment, the touch-sensitive surface and the display panel are used as two independent components to realize input and output functions, but in some embodiments, the touch-sensitive surface and the display panel can be integrated to realize input and output functions.

[0297] The electronic device may also include at least one sensor 1205, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel according to the brightness of the ambient light, and the proximity sensor may turn off the display panel and / or backlight when the electronic device is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that can be configured in the electronic device, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0298] The audio circuit 1206, the speaker, and the microphone can provide an audio interface between the user and the electronic device. The audio circuit 1206 can transmit the electrical signal converted from the received audio data to the speaker, which is converted into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1206 and converted into audio data, and then the audio data is output to the processor 1208 for processing, and then sent to another electronic device through the RF circuit 1201, or the audio data is output to the memory 1202 for further processing. The audio circuit 1206 may also include an earplug jack to provide communication between an external headset and the electronic device.

[0299] WiFi is a short-range wireless transmission technology. Electronic devices can help users send and receive emails, browse web pages, and access streaming media through WiFi module 1207. It provides users with wireless broadband Internet access. Fig.12 A WiFi module 1207 is shown, but it is understandable that it is not an essential component of the electronic device and can be omitted as needed without changing the essence of the invention.

[0300] The processor 1208 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire mobile phone. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 1202 and calling data stored in the memory 1202. Optionally, the processor 1208 may include one or more processing cores; preferably, the processor 1208 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 1208.

[0301] The electronic device also includes a power supply 1209 (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the processor 1208 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 1209 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0302] Although not shown, the electronic device may also include a camera, a Bluetooth module, etc., which will not be described in detail here. Specifically in this embodiment, the processor 1208 in the electronic device will load the executable files corresponding to the processes of one or more applications into the memory 1202 according to the following instructions, and the processor 1208 will run the application stored in the memory 1202 to achieve various functions, as follows:

[0303] Obtaining the vehicle position of the target vehicle;

[0304] Determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, where one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane;

[0305] Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain the lane-level thinned data corresponding to each lane. The curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data.

[0306] According to the position of the thinned shape points and the position of the vehicle, the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points of each lane;

[0307] Determine the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

[0308] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0309] To this end, an embodiment of the present invention provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any lane-level data matching method provided in the embodiment of the present invention. For example, the instructions can execute the following steps:

[0310] Obtaining the vehicle position of the target vehicle;

[0311] Determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, where one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane;

[0312] Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain the lane-level thinned data corresponding to each lane. The curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data.

[0313] According to the position of the thinned shape points and the position of the vehicle, the positioning area of ​​the target vehicle in each lane is predicted based on the thinned shape points of each lane;

[0314] Determine the distance from the vehicle position to the point-line segment formed by two adjacent points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

[0315] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0316] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0317] Since the instructions stored in the computer-readable storage medium can execute the steps in any lane-level data matching method provided in the embodiments of the present invention, the beneficial effects that can be achieved by any lane-level data matching method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0318] According to one aspect of the present application, a computer program product or a computer program is also provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the methods provided in various optional implementations of the above embodiments.

[0319] The above is a detailed introduction to a lane-level data matching method, device, electronic device and storage medium provided in an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A lane-level data matching method, characterized in that: include: Obtaining the vehicle position of the target vehicle; Determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, wherein one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane; Based on the positional relationship between the shape points, the shape points in the local lane high-precision data are thinned to obtain lane-level thinned data corresponding to each lane, and the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data; According to the positions of the thinned-out shape points and the vehicle position, the positioning area of ​​the target vehicle in each lane is predicted based on the thinned-out shape points of each lane, including: when the number of the thinned-out shape points is greater than the number of preset regional shape points, a first reference shape point among the thinned-out shape points is determined according to the first shape point and the second shape point among the thinned-out shape points; a second reference shape point among the thinned-out shape points is determined according to the first reference shape point and the second shape point; based on the vehicle position, the shape point position of the first reference shape point and the shape point position of the second reference shape point, a first distance between the target vehicle and the first reference shape point and a second distance between the target vehicle and the second reference shape point are calculated; if the first distance is less than the second distance, the second reference shape point is used as a new second shape point; if the number of the thinned-out shape points between the first shape point and the new second shape point is not greater than the number of regional shape points, the first shape point, the new second shape point and the thinned-out shape points between the first shape point and the new second shape point are used as the positioning area; Determine the distance from the vehicle position to a point-line segment formed by two adjacent points in the positioning area, and determine a matching result of the target vehicle in the local lane high-precision data based on the distance.

2. The lane-level data matching method according to claim 1, characterized in that: The shape points include end point shape points and intermediate shape points between the end point shape points; The thinning of the shape points in the local lane high-precision data based on the positional relationship between the shape points to obtain lane-level thinned data corresponding to each lane includes: Based on the positional relationship between the middle shape point and the end shape points at both ends, determine the thinned shape points in the middle shape point for retaining the lane direction; Taking the thinned shape point as the new endpoint shape point, based on the positional relationship between the intermediate shape point between the adjacent endpoint shape points and the adjacent endpoint shape point, the thinned shape point for retaining the lane direction among the intermediate shape points between the adjacent endpoint shape points is determined to obtain the lane-level thinned data composed of the thinned shape points.

3. The lane-level data matching method according to claim 2, characterized in that: The step of determining the thinned-out shape points for retaining the lane direction among the middle shape points based on the positional relationship between the middle shape points and the end shape points at both ends includes: Based on the shape point positions of the shape points, determining the maximum distance between the middle shape point and the straight line formed by the two end shape points; If the maximum distance is greater than a preset distance threshold, the middle shape point corresponding to the maximum distance is used as the thinned shape point; The thinned shape point is used as a new end point shape point, and based on the positional relationship between the middle shape point between the adjacent end point shape points and the adjacent end point shape points, a thinned shape point between the adjacent end point shape points for retaining the lane direction is determined to obtain lane-level thinned data composed of the thinned shape points, including: Use the thinned shape points as new endpoint shape points to split the curve formed by the shape points into two sub-curves; Determine the maximum distance between the middle shape point on the sub-curve and the end shape point of the sub-curve; If the maximum distance is greater than a preset distance threshold, the intermediate shape point corresponding to the maximum distance is used as a new thinned shape point; Taking the new thinned shape point as the new endpoint shape point, the sub-curve where the new thinned shape point is located is divided into two new sub-curves, and the step of determining the maximum distance between the intermediate shape point on the sub-curve and the endpoint shape point of the sub-curve is performed until there is no new thinned shape point on the sub-curve, thereby obtaining the lane-level thinning data composed of the thinned shape points.

4. The lane-level data matching method according to claim 1, characterized in that: The method of predicting the positioning area of ​​the target vehicle in each lane based on the thinned-out shape points of each lane according to the positions of the thinned-out shape points and the vehicle position, further comprises: The thinned shape points are evenly or unevenly divided into N sub-areas, and the distance between the shape points in the middle order and the vehicle position in each sub-area is calculated. The sub-area corresponding to the smallest distance and one or two adjacent sub-areas are used as positioning areas.

5. The lane-level data matching method according to claim 1, characterized in that: The step of determining a distance from the vehicle position to a point-line segment formed by two adjacent points in the positioning area, and determining a matching result of the target vehicle in the local lane high-precision data based on the distance, comprises: Determine the position of the candidate positioning point of the target vehicle on each lane according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane; Calculating the line segment distance between the vehicle position and the position of each candidate positioning point, and using the line segment distance as the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area; Based on the distance, determining a target positioning point of the target vehicle from the candidate positioning points; The two adjacent shape points corresponding to the target positioning point are used as the matching results of the target vehicle in the local lane high-precision data.

6. The lane-level data matching method according to claim 5, characterized in that: Determining the position of the candidate positioning point of the target vehicle on each lane according to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane includes: According to the vehicle position and the shape point positions of any two adjacent shape points in the positioning area of ​​each lane, calculating the position relationship indication parameter between the candidate positioning point of the target vehicle on each lane and the shape point line segment formed by the two shape points; When the position relationship indication parameter is within a preset parameter range, the position of the candidate positioning point of the target vehicle on each lane is calculated according to the shape point positions of the two shape points and the position relationship indication parameter.

7. The lane-level data matching method according to claim 5, characterized in that: Before determining the target positioning point of the target vehicle from the candidate positioning points based on the distance, the method further includes: Acquiring vehicle speed data and vehicle heading data of the target vehicle; Calculating a driving weight parameter of the target vehicle based on the vehicle speed data and the vehicle heading data and a preset weight mapping relationship; The step of determining the target positioning point of the target vehicle from the candidate positioning points based on the distance includes: Based on the distance, determining a distance weight parameter of the target vehicle; Calculating the positioning parameter between the target vehicle and the candidate positioning point according to the distance weight parameter and the travel weight parameter; Based on the positioning parameters, a target positioning point of the target vehicle is determined from the candidate positioning points.

8. The lane level data matching method according to claim 7, characterized in that: The weight mapping relationship includes a speed weight mapping relationship and an angle weight mapping relationship. The driving weight parameter of the target vehicle is calculated based on the vehicle speed data and the vehicle heading data, and a preset weight mapping relationship, including: Calculating a speed weight parameter of the target vehicle based on the vehicle speed data and the speed weight mapping relationship; Determining the driving angle of the target vehicle according to a preset angular coordinate system and the vehicle heading data; Determine the line segment angle of each shape point line segment according to the angle coordinate system and the shape point line segment formed by two adjacent shape points in each positioning area; Calculating an angle weight parameter of the target vehicle based on the driving angle, the line segment angle and the angle weight mapping relationship; Based on the speed weight parameter and the angle weight parameter, a driving weight parameter of the target vehicle is calculated.

9. The lane level data matching method according to claim 1, characterized in that: The method further comprises: Determining a target lane corresponding to the target vehicle according to a matching result of the target vehicle in the local lane high-precision data; Acquire lane observation data of each lane in the road where the vehicle is located; A lane-level driving plan for the target vehicle is generated according to the lane observation data of the target lane and the lane observation data of other lanes.

10. The lane-level data matching method according to claim 1, characterized in that: Before determining the distance from the vehicle position to the shape point line segment formed by two adjacent shape points in the positioning area, the method further includes: Acquiring vehicle speed data and vehicle heading data of the target vehicle; Determining the distance from the vehicle position to a point-line segment formed by two adjacent points in the positioning area includes: Calculating the estimated positioning position of the target vehicle according to the vehicle position, the vehicle speed data and the vehicle heading data; Determine the distance from the estimated positioning position to a shape point line segment formed by two adjacent shape points in the positioning area.

11. The lane level data matching method according to claim 3, characterized in that: The method further comprises: Obtaining lane flow data of each lane in the road where the vehicle is located; Determine a new preset distance threshold corresponding to the original lane-level data according to the lane flow data of each lane; Based on the new preset distance threshold, the step of determining the maximum distance between the middle shape point and the straight line formed by the two end shape points based on the shape point positions of the shape points is performed.

12. A lane-level data matching device, characterized in that: include: A position acquisition unit, used to acquire the vehicle position of the target vehicle; A data determination unit, used to determine local lane high-precision data corresponding to at least one lane of the road where the vehicle is located, wherein one local lane high-precision data includes at least two shape points of one lane, and the shape points are used to indicate the direction of the lane; A data thinning unit, configured to thin the shape points in the local lane high-precision data based on the positional relationship between the shape points to obtain lane-level thinned data corresponding to each lane, wherein the curve formed by the shape points in the lane-level thinned data matches the direction of the curve formed by the starting points in the local lane high-precision data; A regional prediction unit, for predicting the positioning area of ​​the target vehicle in each lane based on the thinned-out shape points of each lane according to the positions of the thinned-out shape points and the vehicle position, including: when the number of the thinned-out shape points is greater than the preset number of regional shape points, determining the first reference shape point among the thinned-out shape points according to the first shape point and the second shape point among the thinned-out shape points; determining the second reference shape point among the thinned-out shape points according to the first reference shape point and the second shape point; calculating the first distance between the target vehicle and the first reference shape point, and the second distance between the target vehicle and the second reference shape point based on the vehicle position, the shape point position of the first reference shape point, and the shape point position of the second reference shape point; if the first distance is less than the second distance, taking the second reference shape point as a new second shape point; if the number of the thinned-out shape points between the first shape point and the new second shape point is not greater than the number of regional shape points, taking the first shape point, the new second shape point, and the thinned-out shape points between the first shape point and the new second shape point as the positioning area; A result determination unit is used to determine the distance from the vehicle position to a shape point line segment formed by two adjacent shape points in the positioning area, and determine the matching result of the target vehicle in the local lane high-precision data based on the distance.

13. An electronic device, characterized in that: It comprises a memory and a processor; the memory stores an application program, and the processor is used to run the application program in the memory to execute the steps in the lane-level data matching method described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the lane-level data matching method according to any one of claims 1 to 11.

15. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the lane-level data matching method according to any one of claims 1 to 11 are implemented.

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