CN119178442A

CN119178442A - Lane matching method, device, equipment, storage medium and product

Info

Publication number: CN119178442A
Application number: CN202411680471.1A
Authority: CN
Inventors: 陈建国
Original assignee: Zhejiang Geely Holding Group Co Ltd; Zhejiang Zeekr Intelligent Technology Co Ltd
Current assignee: Zhejiang Geely Holding Group Co Ltd; Zhejiang Zeekr Intelligent Technology Co Ltd
Priority date: 2024-11-22
Filing date: 2024-11-22
Publication date: 2024-12-24
Anticipated expiration: 2044-11-22
Also published as: CN119178442B

Abstract

The application discloses a lane matching method, a lane matching device, lane matching equipment, lane matching storage media and lane matching products, and relates to the technical field of intelligent driving, wherein the lane information of a target area in a driving map is acquired by the method, and the lane information comprises a lane connection relation and lane coordinates; the intelligent driving system comprises a lane connection relation, lane coordinates, a lane-to-lane topological relation, a vehicle-to-vehicle topological relation, a intelligent driving map data set and a map data support of the intelligent driving system, wherein the lane connection relation and the lane coordinates are used for establishing the lane-to-lane topological relation, the vehicle lane topological relation is established based on a nearest projection method, the lane-to-vehicle topological relation is determined according to the vehicle lane-to-lane topological relation, the vehicle lane topological relation and the lane-to-vehicle topological relation are determined, and the corresponding relation between the vehicles and the lanes in the intelligent driving map data set is determined by the method, so that the reliability and the efficiency of the map data support of the intelligent driving system are improved.

Description

Lane matching method, device, equipment, storage medium and product

Technical Field

The application relates to the technical field of intelligent driving, in particular to a lane matching method, a lane matching device, lane matching equipment, a lane matching storage medium and a lane matching product.

Background

In the intelligent driving field, the construction of a high-precision map data set is one of key technologies for realizing accurate navigation and decision-making of an automatic driving vehicle. With the development of intelligent driving technology, the requirements on the accuracy and the real-time performance of map data are higher and higher, and an efficient and accurate map data production link is needed to meet the requirements of an intelligent driving system.

In the prior art, the production of map data generally relies on large-scale manual labeling and complex data processing procedures, which are not only inefficient but also costly. Especially, the prior art often lacks enough flexibility and accuracy when processing the relation between the vehicle and the lane in the intelligent driving map data set, firstly, the data processing flow is not enough automatic, so that the production efficiency is low, secondly, the update of the vehicle and the lane information in the map data is not enough in real time, the quick-change traffic environment requirement cannot be met, and thirdly, the precision and the efficiency are required to be improved when the prior art processes the complex relation between the vehicle and the lane in the map. The existence of the problems limits the performance and reliability of the map data support in the intelligent driving system, and influences the wide application of the intelligent driving vehicle in the actual traffic environment.

The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.

Disclosure of Invention

The main object of the present application is to provide a lane matching method, apparatus, device, storage medium and computer program product, aiming at improving the reliability and efficiency of map data support in intelligent driving system.

In order to achieve the above object, the present application provides a lane matching method, which includes:

acquiring lane information of a target area in a driving map, wherein the lane information comprises a lane connection relationship and lane coordinates;

Constructing a topological relation among lanes based on the lane connection relation and lane coordinates;

constructing a vehicle lane topological relation based on a nearest projection method, and determining a lane vehicle topological relation according to the vehicle lane topological relation;

And determining the topological relation among the vehicles based on the topological relation among the lanes, the topological relation among the vehicle lanes and the topological relation among the lane vehicles.

In an embodiment, the target area includes a target lane and a target associated lane, the target lane includes a target lane sub-line, and the step of constructing a topological relationship between lanes based on the lane connection relationship and lane coordinates includes:

calculating the midpoint coordinates of the target lane according to the starting point coordinates and the ending point coordinates of the target lane;

Determining a first lane to be determined and a second lane to be determined based on the minimum projection distance from the point coordinates of the target lane to the target associated lane;

constructing a transformation coordinate system based on the starting point coordinates of the target lane and the sub-line orientation angles of the target lane sub-lines;

converting the lane start point coordinates of the first lane to be determined and the second lane to be determined according to the conversion coordinate system to obtain the start point conversion coordinates of the first lane to be determined and the start point conversion coordinates of the second lane to be determined;

And determining a left adjacent lane and a right adjacent lane of the target lane according to the starting point transformation coordinates of the first to-be-determined lane and the starting point transformation coordinates of the second to-be-determined lane.

In an embodiment, the step of constructing the inter-lane topology based on the lane connection relationship and the lane coordinates further includes:

calculating a first Euclidean distance from the starting point coordinate of the target lane to the ending point coordinate of the target associated lane, and a second Euclidean distance from the ending point coordinate of the target lane to the starting point coordinate of the target associated lane;

and determining a precursor lane and a subsequent lane of the target lane according to the first Euclidean distance and the second Euclidean distance.

In one embodiment, the target area includes a target vehicle and a target lane, and the step of constructing a vehicle lane topology based on the nearest projection method and determining a lane vehicle topology according to the vehicle lane topology includes:

Calculating the projection distance from the target vehicle to the target lane;

screening out the minimum projection distance in the projection distance from the target vehicle to the target lane;

if the minimum projection distance is smaller than a preset distance threshold value, lane information corresponding to the minimum projection distance value is obtained;

correlating the target vehicle with lane information corresponding to the minimum projection distance value to determine a vehicle lane topological relation;

And adding the corresponding target vehicle information in the vehicle lane topological relation into a lane vehicle dictionary of a corresponding lane to obtain the lane vehicle topological relation.

In an embodiment, the step of associating the target vehicle with the lane information corresponding to the minimum projection distance value, and determining the vehicle lane topology further includes:

Acquiring target lane sub-line information of the target lane, wherein the target lane sub-line information comprises a sub-line starting point coordinate, a sub-line midpoint coordinate and a sub-line length;

calculating the projection proportion of the target vehicle on the target lane sub-line based on the lane sub-line starting point coordinate, the lane sub-line midpoint coordinate and the lane sub-line length;

And determining the projection distance and the projection proportion of the target vehicle on the target lane based on the projection proportion of the target vehicle on the target lane sub-line.

In an embodiment, the target area includes a target lane, and the step of determining the topological relation between vehicles based on the topological relation between lanes, the topological relation between vehicles lanes, and the topological relation between vehicles in lanes includes:

acquiring left and right adjacent and predecessor successor lane information of the target lane through the topological relation among lanes;

acquiring inter-vehicle information on the target lane through the lane vehicle topological relation;

Determining inter-vehicle information outside the target lane according to the left-right adjacent and predecessor lane information of the target lane;

and determining the topological relation among the vehicles according to the information among the vehicles on the target lane and the information among the vehicles outside the target lane.

In addition, in order to achieve the above object, the present application also proposes a lane matching apparatus including:

the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring lane information of a target area in a driving map, and the lane information comprises a lane connection relation and lane coordinates;

the construction module is used for constructing a topological relation between lanes based on the lane connection relation and lane coordinates;

the first determining module is used for constructing a vehicle lane topological relation based on a nearest projection method and determining a lane vehicle topological relation according to the vehicle lane topological relation;

And the second determining module is used for determining the topological relation among the vehicles based on the topological relation among the lanes, the topological relation among the vehicle lanes and the topological relation among the vehicles in the lanes.

In addition, in order to achieve the above object, the application also proposes a lane matching device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the lane matching method as described above.

In addition, in order to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the lane matching method as described above.

Furthermore, to achieve the above object, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the lane matching method as described above.

According to one or more technical schemes, lane information of a target area in a driving map is obtained, the lane information comprises a lane connection relation and lane coordinates, a topological relation between lanes is built based on the lane connection relation and the lane coordinates, a vehicle lane topological relation is built based on a nearest projection method, the lane vehicle topological relation is determined according to the vehicle lane topological relation, the vehicle topological relation is determined based on the inter-lane topological relation, the vehicle lane topological relation and the lane vehicle topological relation, and the corresponding relation between vehicles and between lanes in a driving map data set is determined through the method, so that reliability and efficiency of map data support of an intelligent driving system are improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it will be obvious to those skilled in the art that other drawings can be obtained according to these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a lane matching method according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a topology relationship between lanes and vehicles in the lane matching method of the present application

FIG. 3 is a schematic flow chart of a lane matching method according to a second embodiment of the present application;

FIG. 4 is a schematic diagram illustrating ascending sort of vehicles according to distance from the starting point of a lane in the lane matching method of the present application;

FIG. 5 is a schematic diagram showing calculation of a distance from a projection point of a vehicle on a lane to a start point in the lane matching method according to the present application;

FIG. 6 is a schematic diagram of the topology relationship between vehicles according to the lane matching method of the present application;

FIG. 7 is a schematic block diagram of a lane matching apparatus according to an embodiment of the present application;

fig. 8 is a schematic diagram of an apparatus structure of a hardware operating environment related to a lane matching method according to an embodiment of the present application.

The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.

For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.

The main solution of the embodiment of the application is to construct an algorithm of map topology in a high-precision map data set, aiming at improving the efficiency and precision of intelligent driving data processing. The algorithm builds map topology from two dimensions, frame level and scene level.

At the frame level, the algorithm first builds lane information, including coordinates of lanes, midpoint coordinates, start and end coordinates, length information, orientation, etc., for each frame data in the scene. Next, relationships between the lanes, such as left and right adjacent lanes and front and rear adjacent lanes, are defined, and these relationships are determined by calculating the projected distances of the midpoints of the lanes to the other lanes. In addition, a relation between the vehicle and the lane is constructed, and the lane where the vehicle is located is determined according to the principle that the projection distance between the vehicle and the lane is minimum. Then, the specific position and the position proportion of the vehicle from the starting point of the lane are further calculated through the position information of the vehicle on the lane. And finally, constructing a connection between the lanes and the vehicles, adding the vehicle information into a list of corresponding lanes, and sequencing the vehicles according to the distance from the starting point of the lanes.

At field Jing Jibie, the algorithm builds lane information containing the full scene and the association relationship between lanes to form a scene level map topology. Including details of each lane such as track point coordinates, line point coordinates in sub-line segments, start point coordinates, end point coordinates, length, orientation, etc. The scene-level topological relation can quickly inquire the needed lane information through the lane numbers, and the effective organization and management of the whole scene map data can be realized.

By the structural and systematic topology construction method, the technical scheme of the invention can effectively solve the problems of low data processing efficiency, untimely updating and inaccurate processing of the relation between the vehicle and the lane in the prior art, and provides finer and real-time map data support for intelligent driving.

Since the production of map data in the prior art generally relies on large-scale manual labeling and complex data processing procedures, this is not only inefficient but also costly. Furthermore, the prior art often lacks sufficient flexibility and accuracy in handling the relationship between the vehicle and the lane. For example, the prior art may not accurately determine the lane attribution of the vehicle in a complex traffic scene, or may not accurately update the relationship information of the vehicle and the lane in time in the case of a lane change of the vehicle, an intrusion of the vehicle into the lane, and the like.

The defects of the prior art are mainly characterized in that firstly, the data processing flow is not enough to be automated, so that the production efficiency is low, secondly, the updating of map data is not enough in real time and cannot meet the rapidly-changing traffic environment requirements, and thirdly, the precision and the efficiency of the prior art are required to be improved when the complex relation between the vehicle and the lane is processed. The existence of these problems limits the performance and reliability of the intelligent driving system, and affects the wide application of the intelligent driving vehicle in the actual traffic environment.

The application provides a solution, and provides a new map topology construction algorithm in a high-precision map data set aiming at the defects in the prior art. The algorithm aims to improve the production efficiency of map data through an automatic data processing flow, and improve the instantaneity and accuracy of the map data by constructing a more accurate relation model between a vehicle and a lane. By the method, the problems in the prior art can be effectively solved, and more reliable and efficient map data support is provided for an intelligent driving system.

It should be noted that, the execution body of the embodiment may be a computing service device with lane matching, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, or an electronic device, a lane matching system, or the like capable of implementing the above functions. The present embodiment and the following embodiments will be described below by taking a lane matching system as an example.

Based on this, an embodiment of the present application provides a lane matching method, referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the lane matching method of the present application.

In this embodiment, the lane matching method includes steps S1000 to S4000:

step S1000, lane information of a target area in a driving map is obtained, wherein the lane information comprises a lane connection relation and lane coordinates;

in this embodiment, the relationship between the vehicle agent and the lane needs to be frequently processed in the intelligent driving data set, for example, it needs to determine which lane the vehicle is located in, the positional relationship between different vehicles, whether the vehicle changes lanes, whether the vehicle invades the own vehicle lane, and so on. If the map topology is completely constructed, the method has important significance for the whole data processing and target mining, and therefore, the application provides a map topology construction method.

The embodiment of the application constructs a map topology from two dimensions, one constructs a map topology at a frame level, one constructs a map topology at a scene level, herein referred to as a scene, one scene typically contains several frames of data (where a scene may contain several frames of data, such as a scene includes 1000 frames of data), and a certain frame contains map information and information of each vehicle.

Referring to fig. 2, fig. 2 is a schematic diagram of a topological relation structure between lanes and vehicles in the lane matching method according to the present application, a frame-level map topology is constructed, for each frame of data in a scene, lane information, vehicle information, a relation between lanes, a relation between vehicles and lanes, a relation between lanes and a relation between vehicles, and a relation between vehicles form a frame-level map topology together, and any associated road is quickly searched through a road, any associated vehicle information is quickly searched through a vehicle, and any associated road is quickly searched through a vehicle.

The lane information not only comprises the connection relation of the lanes, but also covers the coordinates of the lanes. The lane connection relationship determines how lanes are related to each other, and the lane coordinates accurately describe the specific positions of the lanes on the map, so that accurate acquisition of the information is crucial for construction of subsequent topological relationships.

Additionally, the lane information also includes lane sub-line information, which also includes some data that can characterize the position and attribute of the lane sub-line, such as the start point coordinates and the end point coordinates of the lane. Based on these coordinates, the midpoint coordinates of the lane can be further calculated, which helps describe the geometry and spatial position of the lane in more detail.

For example, in one embodiment, the coordinates of the midpoint of the lane may be calculated by measuring or obtaining the coordinates of the start and end points of the lane from existing map data, and then using a geometric algorithm. The coordinate information is stored in a corresponding data structure for subsequent processing and inquiry, and frame-level map topology lane information is stored in a dictionary lane_info, wherein a key value is the number laneid of a lane, a value is specific information of the lane, and the coordinate information is also composed of a dictionary, so that detailed information or required information of the lane can be quickly obtained through the lane number laneid. The specific information of the lane mainly comprises coordinate information point_coordinates of the lane, and the coordinate information point_coordinates is composed of coordinates of all points of the lane;

the method comprises the steps that (1) a lane sub-line segment center_ coods is composed of two adjacent coordinate points on a lane, wherein the coordinates of two end points of the sub-line segment are p1 (x 1, y 1) and p2 (x 2, y 2), and the center point coordinate of the sub-line segment is ((x 1+x2)/2 and (y1+y2)/2) to obtain the center point coordinate of each sub-line segment;

The start point coordinate information start of the lane, the coordinates of which are marked as (x_s, y_s), the end point coordinate information end of the lane, the coordinates of which are marked as (x_e, y_e), the length information lengths of the sub-line segment of the lane, the two end point coordinates of which are marked as p1 (x 1, y 1), p2 (x 2, y 2), the sub-line segment length is sqrt ((x 2-x 1) ×2-x 1) + (y 2-y 1) ×2-y 1);

the length information length of the lane, the length of the lane is obtained by accumulating the lengths of the sub-line segments;

The heading information of the lane indicates the direction of the lane under odom coordinate system, and the calculation formula is arctan2 (x_e-x_s, y_e-y_s);

If the orientation information headings of the sub-line segment of the lane indicates that the coordinates of the two end points of the sub-line segment are p1 (x 1, y 1), p2 (x 2, y 2), the orientation information of the sub-line segment is calculated as arctan2 (x 2-x1, y2-y 2), and the number of the sub-line segments of the lane is represented by n_segments.

S2000, constructing a topological relation between lanes based on the lane connection relation and lane coordinates;

it should be noted that, constructing the topological relation between lanes based on the lane connection relation and the lane coordinates is a key step for realizing lane information networking. The construction of the topological relation between the lanes involves determining the relative position and connection mode between the lanes.

Additionally, it should be noted that the specific operation of constructing the topological relation between the lanes includes calculating the coordinates of the middle points of the lanes according to the coordinates of the start points and the coordinates of the end points of the lanes, and then determining the first pending lane and the second pending lane based on the minimum projection distances of the coordinates of the middle points to the other lanes. By the method, the left-right adjacent relation and the front-back connection relation between the lanes can be clarified, namely, the needed lane numbers can be quickly inquired through the relation between the lanes, and the detailed information of the lanes can be obtained from the lane information dictionary.

For example, in one specific embodiment, the midpoint coordinates of the lanes are first calculated, and then the projected distances of these midpoint coordinates to other lanes are calculated to determine the left and right adjacent lanes. In addition, a transformation coordinate system can be constructed according to the lane starting point coordinates and the sub-line orientation angle of the lane sub-line information, and the front-back connection relationship of the lane can be further determined.

In a possible implementation manner, the target area includes a target lane and a target associated lane, the target lane includes a target lane sub-line, and step S2000 may include steps S2100 to S2500:

step S2100, calculating the midpoint coordinates of the target lane according to the starting point coordinates and the ending point coordinates of the target lane;

S2200, determining a first lane to be determined and a second lane to be determined based on the minimum projection distance from the point coordinates of the target lane to the target associated lane;

Step S2300, constructing a transformation coordinate system based on the starting point coordinates of the target lane and the sub-line orientation angle of the target lane sub-line;

step 2400, converting the lane start point coordinates of the first lane to be determined and the second lane to be determined according to the conversion coordinate system to obtain the start point conversion coordinates of the first lane to be determined and the start point conversion coordinates of the second lane to be determined;

And step S2500, determining a left adjacent lane and a right adjacent lane of the target lane according to the starting point transformation coordinates of the first to-be-determined lane and the starting point transformation coordinates of the second to-be-determined lane.

It should be noted that, in this embodiment, establishing a topological relationship between lanes is critical to path planning and decision making in an intelligent driving system. The method comprises the steps of determining the relationship between the target lanes in the target area, namely the current frame, and the target associated lanes one by one, so that the overall inter-lane topological relationship in the target area is determined, when a certain lane in the target area is determined to be the target lane, other lanes in the target area are regarded as the associated lanes at the moment, and for any target lane, firstly, the geometric center of the lane can be determined by calculating the midpoint coordinates between the starting point coordinates and the ending point coordinates of the lane, so that the method is beneficial to understanding the spatial layout of the lane, and further provides a reference for the subsequent inter-lane relationship analysis.

Additionally, it should be noted that, with the minimum projection distance of the coordinates of the lane center to the other lanes, the system is able to identify the lanes adjacent to the current lane, i.e., the first pending lane and the second pending lane. The purpose of this step is to determine the relative position between the lanes, which lays a foundation for further topological relations.

Next, the system constructs a transformation coordinate system based on the lane start point coordinates and the sub-line orientation angle of the lane sub-line information. The core of this step is to convert the local coordinate information of the lanes into a unified frame of reference, so that the coordinate information of different lanes is comparable, thereby describing the relative direction and position between the lanes more accurately.

In addition, the system converts the lane start point coordinates of the first lane to be determined and the second lane to be determined according to the conversion coordinate system to obtain converted coordinates. This ensures that even if the lanes differ in direction in the original coordinate system, a fair comparison can be made in the new coordinate system.

And finally, the system determines a left adjacent lane and a right adjacent lane according to the transformed coordinates. The step is to compare the relative positions of the transformation coordinates, so as to define the left-right adjacent relation between the lanes and complete the construction of the topological relation between the lanes.

In a possible embodiment, the system first collects and stores the start and end coordinates of all lanes. The system then calculates the coordinates of the midpoints of each lane and measures the projected distances of these midpoints to the other lanes to determine the pending lane. The system then builds a transformation coordinate system using the lane start coordinates and the sub-line orientation angle and transforms the start coordinates of the pending lane into this new coordinate system. Finally, the system determines the left and right adjacent lanes of each lane according to the transformed coordinates, thereby completing the construction of the topological relation of the whole lane. The scheme not only improves the automation and the accuracy of the lane information processing, but also provides a clear and accurate lane network model for the intelligent driving system.

For example, as one embodiment, the information of the relationship between lanes in the frame-level map topology is stored in a dictionary lane_to_lane, where the key value is the number laneid of the lane, and the value is the information of the relationship between the lanes, and the information is also composed of a dictionary, specifically including:

left side adjacent lane information left of the lane and right side adjacent lane information right of the lane, wherein the left side adjacent lane and the right side adjacent lane of the lane can be obtained by solving the following method:

Firstly, calculating a midpoint position coordinate on a current lane, solving the minimum projection distance from the midpoint position coordinate to other lanes, taking two lanes corresponding to the minimum two projection distances, and marking as lane1 and lane2. The starting point coordinate of the current lane is recorded as P_c, the P_c is taken as an origin, a coordinate system is established by taking the direction of an orientation angle on a first sub-line of the current lane as an x coordinate axis (obtained by the lane information), the starting point coordinate P of Lane1 is converted into the coordinate system to obtain new coordinates (x_new and y_new), if y_new >0, lane1 is a left adjacent lane, lane2 is a right adjacent lane, otherwise, lane1 is a right adjacent lane, and Lane2 is a left adjacent lane.

In a possible implementation, the step S2000 may further include steps S2600 to S2700:

Step S2600, calculating a first Euclidean distance from the starting point coordinate of the target lane to the ending point coordinate of the target associated lane and a second Euclidean distance from the ending point coordinate of the target lane to the starting point coordinate of the target associated lane;

step S2700, determining a precursor lane and a subsequent lane of the target lane according to the first Euclidean distance and the second Euclidean distance.

It should be noted that, the start point coordinates and the end point coordinates of the lane in the present solution are key coordinate points for constructing the topological relationship of the lane, and they define the position and the extending direction of the lane in space. By means of the coordinates, geometric attributes and spatial relations of the lanes can be accurately calculated. The first Euclidean distance and the second Euclidean distance respectively represent the straight line distance from the starting point of the lane to the ending point of other lanes and the straight line distance from the ending point of the lane to the starting point of other lanes, and the calculation of the two distances is the basis for determining the front-back topological relation between the lanes.

Additionally, it should be noted that the purpose of calculating these euclidean distances is to determine the front-rear connection relationship between lanes, i.e., the precursor lane and the subsequent lane. This relationship is critical to understanding the lane sequences and possible travel paths of the vehicle. By comparing the Euclidean distance between each lane, the most likely logically connected lane pair can be identified, thereby constructing the precursor and subsequent topological relationship between lanes.

In a possible embodiment, the system first collects and stores the start and end coordinates of all lanes. Then, the system calculates a first Euclidean distance from the starting point of each lane (the currently calculated lane is the target lane, and the target area or the lane outside the target lane in the current frame is the target associated lane) to the ending point of all other lanes, and a second Euclidean distance from the ending point of each lane to the starting point of all other lanes. By comparing these distances, the system is able to identify the predecessor and successor lanes of each lane. For example, for any given lane a, the system will find lane B such that the second euclidean distance of the end of lane a to the beginning of lane B is minimized, thereby determining that lane B is the successor of lane a. Similarly, the system may find lane C such that the first euclidean distance from the end of lane C to the start of lane a is minimized, thereby determining that lane C is the precursor lane of lane a. Therefore, the system can construct a complete lane network topology, and a solid data base is provided for path planning and traffic analysis in the intelligent driving system.

For example, a preceding lane (or a preceding lane) and a following lane information with a lane are included in the lane information, the preceding adjacent lane information of the lane is prev, and the following adjacent lane information of the lane is next. The successor and successor lanes of the lane can be obtained from the upstream front-back connection relationship, if the upstream does not have the front-back connection relationship, the method can be used for solving and obtaining the lane by the following method:

The starting point coordinate information start of the lane, the coordinates of which are marked as (x_s, y_s), calculates the Euclidean distance from the end point coordinates of other lanes to the starting point coordinates of the current lane, and takes the lane corresponding to the minimum value, wherein the lane is the successor lane of the current lane

And (3) calculating Euclidean distance from the starting point coordinates of other lanes to the ending point coordinates of the current lane, and taking the lane corresponding to the minimum value, wherein the lane is the subsequent lane of the current lane.

Through the relationship between the constructed lanes and the lanes, the detailed information of the adjacent lanes of the designated lanes can be quickly acquired.

The topological relation between the vehicle and the lane is constructed, so that the vehicle can be quickly inquired on which lane the vehicle is, the relation information between the vehicle and the lane in the frame-level map topology is stored in a dictionary obj_to_lane, wherein the key value is the number obj_id of the vehicle, the value is the lane number laneid where the vehicle is located, and the detailed information of the corresponding lane can be quickly inquired in the lane dictionary lane_info through laneid.

S3000, constructing a vehicle lane topological relation based on a nearest projection method, and determining a lane vehicle topological relation according to the vehicle lane topological relation;

It should be noted that, determining the topological relation of the vehicles in the lane is to integrate the information between the vehicles and the lane to form a detailed list of the vehicles in the lane. This step involves not only the specific location of the vehicle but also the relative location of the vehicle on the lane.

Additionally, the specific operation includes obtaining lane sub-line information in the lane information, such as a lane sub-line start point coordinate, a lane sub-line midpoint coordinate, and a lane sub-line length. Based on this information, the projected proportion of the vehicle on the lane sub-line can be calculated, thereby determining the projected distance of the vehicle on the lane.

For example, in a specific embodiment, the sub-line information of the lane is first acquired, and then the projection ratio of the vehicle on the lane is calculated based on the information. In this way, the relative position of the vehicle on the lane can be accurately described and integrated into the lane vehicle dictionary to form the lane vehicle topology.

And S4000, determining the topological relation among the vehicles based on the topological relation among the lanes, the topological relation among the vehicle lanes and the topological relation among the vehicles on the lanes.

It should be noted that, based on the inter-lane topological relationship, the vehicle lane topological relationship, and the lane vehicle topological relationship, determining the inter-vehicle topological relationship is a key for realizing the relationship networking between vehicles. This step involves the relative position and interrelationships between the vehicles and is critical to understanding and predicting the vehicle behavior patterns.

Additionally, specific operations include obtaining left and right adjacent and predecessor lane information of a lane via an inter-lane topology, obtaining inter-vehicle information on the lane via a lane vehicle topology, and then combining these information to determine inter-vehicle information outside the lane. In this way, the topological relation between vehicles can be comprehensively described.

For example, in one specific embodiment, the lane adjacency and connection relationship are first matched by the inter-lane topology lane, and then the vehicle information on the lane is obtained by the lane vehicle topology relationship. In combination with this information, the topological relation between vehicles can be further determined, thereby providing support for the decision making of the intelligent driving system.

The embodiment provides a lane matching method, which comprises the steps of constructing lane information, constructing a topological relation between lanes based on the lane connection relation and the lane coordinates, constructing a vehicle lane topological relation based on a nearest projection method, determining a lane vehicle topological relation according to the vehicle lane topological relation, determining a vehicle topological relation based on the vehicle lane topological relation, the vehicle lane topological relation and the lane vehicle topological relation, and constructing a corresponding relation between vehicles and lanes in a intelligent driving map data set by the method, so that the reliability and the efficiency of map data support of an intelligent driving system are improved.

In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, referring to fig. 3, step S3000 of the lane matching method further includes steps S3100 to S3500:

Step S3100, calculating the projection distance of the target vehicle to the target lane;

It should be noted that calculating the projected distance of the vehicle to the lane is an important step in determining the relationship between the vehicle position and the lane. In this process, the system first needs to determine the current coordinates of the current target vehicle and then project them onto the geometry of the lane to calculate the vehicle's position relative to the lane. Such projection is typically based on a spatial relationship between vehicle coordinates and lane coordinates, and may be implemented using geometric algorithms or mathematical formulas.

Step S3200, screening out the minimum projection distance in the projection distance from the target vehicle to the target lane;

It should be noted that, in this embodiment, the purpose of screening the minimum projection distance is to find the lane closest to the current position of the vehicle. The system will compare all calculated projection distances to find the minimum value among them. This minimum represents the closest distance between the vehicle and the lane, so that it can be deduced which lane the vehicle is most likely to be located in.

Step S3300, if the minimum projection distance is smaller than a preset distance threshold value, lane information corresponding to the minimum projection distance value is obtained;

It should be noted that, the distance threshold is set as a key criterion in this embodiment, so as to determine whether the vehicle is within the effective range of the lane. If the minimum projected distance of the vehicle is less than this threshold, the system will consider the vehicle to be on the lane and acquire detailed information for the lane. This threshold may be adjusted based on factors such as actual road conditions and vehicle size.

Step S3400, associating the target vehicle with the lane information corresponding to the minimum projection distance value to determine the vehicle lane topological relation;

It should be noted that, once the lane in which the vehicle is located is determined, the system establishes an association relationship between the vehicle and the lane. The association relationship forms the basis of the vehicle lane topological relationship, and is important for understanding the position and behavior mode of the vehicle in the traffic network. The system will record the vehicle's associated information with the lane for further analysis and decision making.

In a specific embodiment, the system first obtains the current coordinates of the vehicle by means of sensors or map data, etc. The system then calculates the projected distance of the vehicle coordinates on each lane and screens out the minimum of these distances. If this minimum value is less than the preset threshold value, the system will acquire detailed information of the corresponding lane, such as lane number, position, length, etc. Finally, the system associates the vehicle with the lane, establishes a vehicle lane topological relation and provides support for decision making of the intelligent driving system.

Step S3500, adding the corresponding target vehicle information in the vehicle lane topological relation to a lane vehicle dictionary of the corresponding lane to obtain the lane vehicle topological relation.

It should be noted that, in this embodiment, a mapping relationship between a lane and a vehicle is established, which is helpful for the system to manage and track the vehicle on the lane. The lane vehicle dictionary is a data structure for storing information of all vehicles on each lane, wherein the key is an identifier of the lane and the value is a list containing detailed information of all vehicles on the lane.

In a specific embodiment, the system maintains a lane vehicle dictionary for each lane. When the system determines the topological relation between the vehicle and the lane, for example, the vehicle is located on the lane B, the system adds the detailed information of the vehicle as an entry into the dictionary entry corresponding to the lane B. The detailed information of the vehicle may include a unique identifier of the vehicle, location, speed, acceleration, etc.

If the information of the vehicle is not contained in the lane vehicle dictionary of lane B, the system will create a new entry and add the vehicle information as a list element. If vehicle information already exists in the dictionary, the system will simply append the information of the new vehicle to the end of the existing list. Thus, the lane vehicle dictionary of the lane B reflects the set of all vehicles on the lane at the current moment, and data support is provided for analysis of traffic flow and prediction of vehicle behaviors. In this way, the system is able to update and maintain an accurate view of the vehicle topology of the lane in real time.

For example, referring to fig. 4, fig. 4 is a schematic diagram of ascending order of vehicles according to a distance from a lane start point in the lane matching method of the present application, the construction of a topological relation between lanes and vehicles may enable quick inquiry of vehicle information on a lane, the relation information between lanes and vehicles in a frame-level map topology is stored in a dictionary lane_to_obj, wherein a key value is a number laneid of a lane, a value is a list formed by all vehicle numbers obj_id on the lane, and if the lane has no vehicles in a frame, the value corresponding to the lane number is an empty list. The detailed information of the vehicle can be quickly acquired in the vehicle information dictionary by the vehicle number obj_id.

The method for establishing the topological relation between the lanes and the vehicles is as follows, firstly, each lane is provided with a list for storing the vehicles on the lane. And secondly, obtaining the binding relation between the vehicle and the lane according to the relation between the vehicle and the lane, and if the vehicle is successfully bound, adding the number obj_id of the vehicle into a list corresponding to the binding lane.

The lane to vehicle relationship lane to obj holds only the relevant vehicles on lane if there are multiple vehicles on that lane and the vehicles are not ordered. In this case, it is necessary to sort all vehicles in ascending order according to the distance from the lane start point, wherein the distance from the vehicle to the lane start point has been calculated from the time of the vehicle information construction, so that the relationship between the vehicles can be easily established. And updating the corresponding information with the corresponding key value laneid in the lane_to_obj by using the ordered list of the vehicle obj_ids. As shown in fig. 3, in the relationship lane_to_obj between the lanes and the vehicles, for the lane, the corresponding attribute is a list of vehicle numbers ordered in ascending order of the vehicle-to-lane start point distance s.

In a possible implementation, step S3400 may further include steps S3410 to S3430:

step S3410, obtaining target lane sub-line information of the target lane, wherein the target lane sub-line information comprises a sub-line starting point coordinate, a sub-line midpoint coordinate and a sub-line length;

Step S3420, calculating the projection proportion of the target vehicle on the target lane sub-line based on the lane sub-line starting point coordinate, the lane sub-line midpoint coordinate and the lane sub-line length;

And step S3430, determining the projection distance and the projection proportion of the target vehicle on the target lane based on the projection proportion of the target vehicle on the target lane sub-line.

It should be noted that, the scheme aims at realizing the fine management of the vehicle position and the high-efficiency analysis of the lane utilization by accurately acquiring the lane sub-line information and calculating the projection proportion of the vehicle on the lane sub-line information. Lane sub-line information, including a start point coordinate, a midpoint coordinate, and a length, is an essential element describing geometric characteristics of a lane, and collectively defines a spatial shape and a size of the lane.

Additionally, it should be noted that calculating the projection ratio of the vehicle on the lane sub-line is a key step, which involves mapping the position of the vehicle into the local coordinate system of the lane. By comparing the relative positions of the vehicle and the lane sub-line, the specific position of the vehicle on the lane sub-line, and the proportion it occupies, can be determined.

Furthermore, based on the projection ratio of the vehicle on the lane sub-line, the projection distance of the vehicle on the lane and the projection ratio of the vehicle on the lane can be further determined. This step not only helps to understand the specific location of the vehicle in the lane, but also provides important data for lane capacity analysis, traffic flow prediction, and vehicle behavior prediction.

In a possible embodiment, the system first obtains the sub-line information of the lanes, including the start point coordinates, the middle point coordinates and the length of each lane, through a high-precision map or real-time sensor data. The system then uses this information and the current coordinates of the vehicle to determine the position of the vehicle relative to the lane sub-line by geometric calculation. The system calculates the vertical distance from the vehicle to the starting point of the lane sub-line, and calculates the projection proportion of the vehicle according to the length of the lane sub-line. If the projected proportion of the vehicle is 0.6, this means that the vehicle is located at 60% of the length of the lane. And finally, integrating the data by the system, updating the topological relation between the vehicle and the lane, and providing support for intelligent driving decision. By the mode, the system can monitor and analyze traffic conditions in real time, optimize traffic flow and improve road use efficiency.

For example, referring to fig. 5, fig. 5 is a schematic diagram of calculating a distance between a projection point of a vehicle and a starting point of a lane, binding the vehicle and the lane, wherein the principle of binding is that the projection distance of the vehicle to the lane is minimum, and the lane laneid with the minimum projection is regarded as the lane where the vehicle is located. If the minimum projection distance calculated by the vehicle is greater than a certain threshold value, the vehicle is considered not to be on any lane, and the lane bound by the vehicle can be marked as-1 at the moment so as to indicate that the lane bound by the vehicle is unsuccessful.

The method comprises the steps of constructing vehicle information, storing frame-level map topology lane information in a dictionary obj_info, wherein key values are the numbers obj_id of vehicles, value values are specific information of the vehicles and comprise a dictionary, the specific information comprises coordinates (obj_x, obj_y) of the vehicles, the distance s between the vehicles and the starting point of the lane on the corresponding lane, namely specific position information of the vehicles on the lane, the specific position of the vehicles on the lane occupies a proportion s_port of the whole lane length, s and s_port are obtained through vector calculation, and the route length indicated by green in lane is marked as s value as shown in fig. 4.

The s and s_port calculation method includes the following steps that firstly, according to the obj_id of a vehicle, the lane number laneid of the vehicle is obtained from the obj_to_lane, further, the detailed information of the lane of the vehicle is obtained according to the lane_info, mainly the number n_segments of the sub-line segments of the vehicle are used, the coordinate information point_records of the lane, the length information lengths of the sub-line segments of the lane are used, then, the parameter initialization s=0.0, the following operations are sequentially carried out on each sub-line segment from the starting point of the lane, firstly, the index of the sub-line segment is recorded as the lane_seg_idx, and then, the starting point coordinates of the sub-line segments are solved:

lane_seg_start=lane_info[obj_lane_id]['point_coords'][lane_seg_idx]

solving the endpoint coordinates of the sub-line segments:

lane_seg_end=lane_info[obj_lane_id]['point_coords'][lane_seg_idx+1]

sub-line segment length:

lane_seg_length=self.lane_info[obj_lane_id]['lengths'][lane_seg_idx]

Record obj_records (obj_x, obj_y) as vehicle coordinates

And then calculating:

s_seg_portion=sum((obj_coords-lane_seg_start)*(lane_seg_end-lane_seg_start))/lane_seg_length^2

If s_seg_port >1.0, s is reassigned to s+Lane_seg_Length, and Lane_seg_idx+1 is assigned, if a sub-segment corresponding to Lane_seg_idx+1 exists, the calculation is performed from 3.1. If s_seg_portion < =1.0, the vehicle projection point falls in the sub-line segment, s+lane_seg_length is the distance from the vehicle to the lane head point, and s is reassigned to s+lane_seg_length is s_seg_portion.

And finally, obtaining the total length of the lane through the lane_info [ obj_lane_id ] [ length '], wherein s/lane_info [ obj_lane_id ] [ length' ] is s_port, and the position of the vehicle occupies the ratio of the length of the whole lane.

In a possible implementation manner, the step S4000 may further include steps S4100 to S4400:

S4100, obtaining left and right adjacent and predecessor successor lane information of the target lane through the topological relation among the lanes;

step S4200, obtaining the inter-vehicle information on the target lane through the lane vehicle topology relationship;

step S4300, determining inter-vehicle information outside the target lane according to the left-right adjacent and preceding subsequent lane information of the target lane;

And step S4400, determining the topological relation among vehicles according to the information among vehicles on the target lane and the information among vehicles outside the target lane.

It should be noted that, by comprehensively utilizing the topological relation between lanes and the topological relation between the vehicles of the lanes, the scheme aims to establish a comprehensive traffic network model which can accurately describe the interrelationship between the lanes and between the vehicles. The inter-lane topology includes left and right adjacent lanes of the lane and predecessor lane information that is critical to understanding the network structure of the lane. The lane vehicle topology provides specific information of the vehicles on the lanes, including the positions, speeds and the like of the vehicles.

Additionally, the system can expand the analysis range to determine the inter-vehicle information outside the lane by the left-right adjacent of the lane and the preceding subsequent lane information. This includes those vehicles on adjacent lanes that may have an impact on the current lane traffic flow, for example, by analyzing the vehicle driving status of adjacent lanes, the likelihood of lane change behavior or traffic congestion may be predicted.

In addition, the system can determine the topology relationship between vehicles in combination with the information between vehicles on the lane and the information between vehicles outside the lane. This step is achieved by comprehensively considering the position, speed and relative distance between the vehicles on the lanes. This helps to build a dynamic view of the traffic network, enabling the intelligent driving system to more accurately predict traffic conditions and make decisions.

In one possible implementation, the system first obtains the topological relationship between lanes by analyzing high-precision map data and real-time traffic monitoring data. The system then uses the vehicle positioning data and the lane information to construct a lane vehicle topology, recording the position and status of the vehicle on each lane. The system then identifies adjacent lanes outside the lane by topology between the lanes and analyzes the vehicle information on these lanes to predict vehicle behavior that may have an impact on the current lane traffic flow. And finally, the system integrates the vehicle information on the lane and the vehicle information outside the lane, establishes the topological relation among vehicles and provides data support for traffic management and intelligent driving decision. By the scheme, the running efficiency and the safety of the traffic system can be effectively improved.

For example, referring to fig. 6, fig. 6 is a schematic diagram of a topological relation between vehicles in the lane matching method according to the present application, in which the relation information between vehicles in the frame-level map topology is stored in a dictionary obj_to_obj, wherein the key value is the number obj_id of the vehicle, and the value is the surrounding vehicle information of the vehicle, and the method is also composed of a dictionary, and specifically includes a vehicle number list left on the adjacent lane on the left side of the lane where the vehicle is located, a vehicle number list right on the adjacent lane on the right side of the lane where the vehicle is located, a vehicle number list front in front of the same lane sequence where the vehicle is located, and a vehicle number list back behind the same lane sequence where the vehicle is located.

The topology relation construction method between vehicles is as follows, the front-back left-right relation between lanes can be obtained through lane_to_lane, the lane laneid where the vehicle is located can be obtained through obj_to_lane, the vehicle information on the lane can be obtained through lane_to_obj, the front vehicle number list front of the current vehicle can be obtained through a method of recursively searching on the lane, the rear vehicle number list back, the vehicle number list left on the left adjacent lane and the vehicle number list right on the right adjacent lane can be obtained.

As shown in fig. 4, the adjacent lane on the left side of lane1 is lane2, and two subsequent lanes of lane1 are lane3 and lane4. The left adjacent vehicle number list left of the vehicle 0 is [ vehicle 1, vehicle 2, vehicle 3, vehicle 4];

Since the lane in which the vehicle 0 is located does not have a right-side adjacent lane, the right-side adjacent vehicle number list right of the vehicle 0 is empty, the front vehicle number list front of the vehicle 0 is [ vehicle 11, vehicle 12], and the rear vehicle number list back of the vehicle 0 is [ vehicle 10].

It should be noted that the foregoing examples are only for understanding the present application, and are not meant to limit the lane matching method of the present application, and more forms of simple transformation based on the technical concept are all within the scope of the present application.

Referring to fig. 7, the present application further provides a lane matching apparatus 70, which includes:

An obtaining module 71, configured to obtain lane information of a target area in a driving map, where the lane information includes a lane connection relationship and lane coordinates;

a construction module 72 for constructing a topology relationship between lanes based on the lane connection relationship and lane coordinates;

A first determining module 73, configured to construct a vehicle lane topology based on a nearest projection method, and determine a lane vehicle topology according to the vehicle lane topology;

A second determination module 74 for determining an inter-vehicle topology based on the inter-lane topology, the vehicle lane topology, and the lane vehicle topology.

The lane matching device provided by the application can solve the technical problem of lane matching by adopting the lane matching method in the embodiment. Compared with the prior art, the lane matching device has the same beneficial effects as the lane matching method provided by the embodiment, and other technical features in the lane matching device are the same as the features disclosed by the method of the embodiment, and are not repeated herein.

The application provides a lane matching device which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the lane matching method in the first embodiment.

Referring now to fig. 8, a schematic diagram of a lane matching apparatus suitable for use in implementing an embodiment of the present application is shown. The lane matching apparatus in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (Portable Application Description: tablet computers), PMPs (Portable MEDIA PLAYER: portable multimedia players), vehicle-mounted terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The lane matching apparatus shown in fig. 8 is only one example, and should not bring any limitation to the function and the range of use of the embodiment of the present application.

As shown in fig. 8, the lane matching apparatus may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the lane matching apparatus are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communication means 1009 may allow the lane matching apparatus to communicate with other apparatuses wirelessly or by wire to exchange data. While lane matching devices with various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.

Compared with the prior art, the lane matching device provided by the application has the advantages that the lane matching device provided by the application has the same advantages as the lane matching method provided by the embodiment, and other technical characteristics in the lane matching device are the same as the characteristics disclosed by the method of the embodiment, and are not repeated herein.

It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon for performing the lane matching method in the above-described embodiments.

The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.

The computer-readable storage medium may be included in the lane matching apparatus or may exist alone without being incorporated in the lane matching apparatus.

The computer readable storage medium carries one or more programs which when executed by the lane matching device, cause the lane matching device to acquire lane information of a target area in a driving map, wherein the lane information comprises a lane connection relation and lane coordinates, construct a topological relation between lanes based on the lane connection relation and the lane coordinates, construct a vehicle lane topological relation based on a nearest projection method, determine a lane vehicle topological relation according to the vehicle lane topological relation, and determine the topological relation between vehicles based on the topological relation between lanes, the vehicle lane topological relation and the lane vehicle topological relation.

Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer programs) for executing the lane matching method, so that the technical problem of lane matching can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the lane matching method provided by the above embodiment, and are not described herein.

The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a lane matching method as described above.

The computer program product provided by the application can solve the technical problem of lane matching. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as those of the lane matching method provided by the above embodiment, and are not described herein.

The foregoing description is only a partial embodiment of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and the accompanying drawings under the technical concept of the present application, or the direct/indirect application in other related technical fields are included in the scope of the present application.

Claims

1. A lane matching method, the method comprising:

2. The method of claim 1, wherein the target area includes a target lane and a target-associated lane, the target lane includes a target lane sub-line, and the constructing the inter-lane topology based on the lane connection and lane coordinates includes:

3. The method of claim 2, wherein the constructing the inter-lane topology based on the lane connection and lane coordinates further comprises:

4. The method of claim 1, wherein the target area includes a target vehicle and a target lane, wherein the constructing a vehicle lane topology based on the nearest projection method and determining a lane vehicle topology based on the vehicle lane topology comprises:

Calculating the projection distance from the target vehicle to the target lane;

5. The method of claim 4, wherein the step of associating the target vehicle with lane information corresponding to the minimum projected distance further comprises, after the step of determining a vehicle lane topology:

6. The method of claim 1, wherein the target area includes a target lane therein, and wherein the step of determining the inter-vehicle topology based on the inter-lane topology, the vehicle lane topology, and the lane vehicle topology comprises:

7. A lane matching apparatus, the apparatus comprising:

8. A lane matching apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the lane matching method as claimed in any one of claims 1 to 6.

9. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the lane matching method according to any one of claims 1 to 6.

10. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the steps of the lane matching method according to any one of claims 1 to 6.