一种车道线信息确定方法及装置

Lane line information determination method and device

文档序号：9585 发布日期：2021-09-17 浏览：27次中文

1. A lane line information determination method is characterized by comprising the following steps:

determining a first road parameter set of a road edge according to a first coordinate of the road edge;

determining a second road parameter set of the lane line of the road according to the first road parameter set;

and determining a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, wherein the second road parameter set and the third road parameter set comprise the lane line information.

2. The method of claim 1, prior to determining a second set of road parameters for a lane line of the road from the first set of road parameters, further comprising:

and determining that the road edge is parallel to the lane line according to the distance parameter between the road edge and the lane line.

3. The method of claim 2,

the distance parameter between the road edge and the lane line comprises at least one of the following distance parameters: euclidean distance, mahalanobis distance, and minkowski distance.

4. The method of claim 3, wherein the distance parameter between the road edge and the lane line comprises a mahalanobis distance, further comprising:

obtaining a fourth road parameter set of the lane line by performing curve fitting on the second coordinate, wherein the parameter type of at least one road parameter in the fourth road parameter set is the same as the parameter type of at least one road parameter in the first road parameter set;

and calculating the Mahalanobis distance between the road edge and the lane line according to the first road parameter set and the fourth road parameter set.

5. The method according to any one of claims 1 to 4, wherein said determining a second set of road parameters of a lane line of said road from said first set of road parameters comprises:

and determining that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence manner.

6. The method according to any one of claims 1 to 4, wherein said determining a second set of road parameters of a lane line of said road from said first set of road parameters comprises:

obtaining a fifth road parameter set of the lane line by performing curve fitting on the second coordinate, wherein the parameter type of at least one road parameter in the fifth road parameter set is the same as the parameter type of at least one road parameter in the first road parameter set;

and fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein a fusion result is the second road parameter set.

7. The method of claim 6,

the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.

8. The method according to any one of claims 1 to 4, wherein determining a third set of road parameters of the lane line from the second set of road parameters and the second coordinates of the lane line comprises:

determining a curve equation of the lane line by curve fitting the second set of road parameters and a second coordinate of the lane line;

9. The method of claim 1,

the first set of road parameters comprises at least one of the following road parameters: the course angle, curvature and curvature change rate of the road edge;

the third set of road parameters comprises a lateral offset of the lane line.

10. A lane line information determination device, characterized by comprising:

the device comprises a processor, a first transceiving interface and a second transceiving interface;

the first transceiving interface is used for receiving first detection information of a radar, and the first detection information comprises relevant information of a road edge of a road;

the second transceiving interface is used for receiving second detection information of an imaging device, and the first detection information comprises relevant information of a lane line of the road;

the processor is used for determining a first coordinate of the road edge according to the first detection information and determining a second coordinate of the lane line according to the second detection information;

the processor is further configured to determine a first road parameter set of a road edge of a road according to a first coordinate of the road edge, determine a second road parameter set of a lane line of the road according to the first road parameter set, and determine a third road parameter set of the lane line according to the second road parameter set and a second coordinate of the lane line, where the second road parameter set and the third road parameter set include the lane line information.

11. The apparatus of claim 10,

the processor is further configured to determine that the road edge is parallel to the lane line according to a distance parameter between the road edge and the lane line before determining a second road parameter set of the lane line of the road according to the first road parameter set.

12. The apparatus of claim 11,

the distance parameter between the road edge and the lane line comprises at least one of the following distance parameters: euclidean distance, mahalanobis distance, and minkowski distance.

13. The apparatus of claim 12, wherein the distance parameter between the road edge and the lane line comprises a Mahalanobis distance,

the processor is further configured to perform curve fitting on the second coordinate to obtain a fourth road parameter set of the lane line, where a parameter type of at least one road parameter in the fourth road parameter set is the same as a parameter type of at least one road parameter in the first road parameter set;

and calculating the Mahalanobis distance between the road edge and the lane line according to the first road parameter set and the fourth road parameter set.

14. The apparatus according to any one of claims 10 to 13,

the processor is specifically configured to determine that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence.

15. The apparatus according to any one of claims 10 to 13,

the processor is specifically configured to perform curve fitting on the second coordinate to obtain a fifth road parameter set of the lane line, where a parameter type of at least one road parameter in the fifth road parameter set is the same as a parameter type of at least one road parameter in the first road parameter set;

and fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein a fusion result is the second road parameter set.

16. The apparatus of claim 15,

the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.

17. The apparatus according to any one of claims 10 to 13,

the processor is specifically configured to determine a curve equation of the lane line by performing curve fitting on the second road parameter set and the second coordinate of the lane line;

18. The apparatus of claim 10,

the first set of road parameters comprises at least one of the following road parameters: the course angle, curvature and curvature change rate of the road edge;

the third set of road parameters comprises at least a lateral offset of the lane line.

19. A terminal device, comprising:

at least one processor and a memory, wherein the memory,

the memory to store program instructions;

the processor is configured to call and execute the program instructions stored in the memory to cause the terminal device to execute the lane line information determination method according to any one of claims 1 to 9.

20. A computer-readable storage medium, characterized in that,

the computer-readable storage medium has stored therein instructions that, when executed on a computer, cause the computer to execute the lane line information determination method according to any one of claims 1 to 9.

Background

In order to meet the requirement of unmanned driving, a vehicle supporting an automatic driving function is produced. During driving, the vehicle needs to perform target recognition and tracking on obstacles, pedestrians, other vehicles and the like in the road. To implement this function, the vehicle needs to determine road information.

Since the lane line is included in the road, the road information that the vehicle needs to determine generally includes lane line information, such as the curvature of the lane line. At present, vehicles usually adopt a road structuring mode to determine road parameters of a lane line. Where road structuring refers to the structure of a road being characterized by a mathematical model, typically a curve equation, which includes road information.

When a vehicle acquires a curve equation of a lane line at present, the lane line is usually photographed by a camera to acquire an image containing the lane line, the image is analyzed to acquire coordinates of a plurality of places in the lane line, and then curve fitting is performed on the coordinates of the plurality of places to acquire the curve equation of the lane line, wherein the curve equation includes information of the lane line.

However, the inventor finds in the research process of the present application that the vehicle often runs on roads in different environments, in which case the accuracy of image analysis on an image including a lane line is more susceptible to the external environment, and when the accuracy is low, in this case, the accuracy of the determined coordinates of the lane line is low, which results in low accuracy of the lane line information acquired by the prior art, and further reduces the accuracy of target recognition and tracking of the vehicle. For example, when the vehicle runs in an environment with poor lighting conditions, the image captured by the camera has low definition, which results in low accuracy of image analysis on the image; or, when the distance between the camera and the lane line to be photographed is long, in the image photographed by the camera, the lane line occupies fewer pixels, which also results in low accuracy of image analysis.

Disclosure of Invention

In order to solve the problem that in the prior art, when the lane line information is determined through an image shot by a camera, the lane line information is easily influenced by an external environment, so that the accuracy of the obtained lane line information is low, the embodiment of the application discloses a method and a device for determining the lane line information.

In a first aspect, an embodiment of the present application discloses a lane line information determining method, including:

determining a first road parameter set of a road edge according to a first coordinate of the road edge;

determining a second road parameter set of the lane line of the road according to the first road parameter set;

In the scheme of the embodiment of the application, the lane line information is determined according to the first coordinate of the road edge and the second coordinate of the lane line, and compared with a scheme in the prior art in which the lane line information is determined only through the coordinate of the lane line, the accuracy of the determined lane line information is higher.

In an alternative design, before determining the second set of road parameters of the lane line of the road from the first set of road parameters, the method further includes:

and determining that the road edge is parallel to the lane line according to the distance parameter between the road edge and the lane line.

In the embodiment of the application, only when the road edge is determined to be parallel to the lane line, the second road parameter set of the lane line is determined according to the first road parameter set of the road edge, so that the accuracy of determining the second road parameter set can be improved, and the accuracy of determining the lane line information is further improved.

In an alternative design, the distance parameter between the road edge and the lane line includes at least one of the following distance parameters: euclidean distance, mahalanobis distance, and minkowski distance.

In an alternative design, the distance parameter between the road edge and the lane line includes mahalanobis distance, and further includes:

and calculating the Mahalanobis distance between the road edge and the lane line according to the first road parameter set and the fourth road parameter set.

In an alternative design, the determining a second set of road parameters for the lane line of the road from the first set of road parameters includes:

and determining that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence manner.

The accuracy of obtaining the first road parameter set is often higher than the accuracy of obtaining the second road parameter set, so that the one-to-one correspondence between at least one road parameter in the second road parameter set and at least one road parameter in the first road parameter set is the same, the accuracy of determining the second road parameter set can be improved, and further, the accuracy of determining the lane line information can be improved.

In an alternative design, the determining a second set of road parameters for the lane line of the road from the first set of road parameters includes:

and fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein a fusion result is the second road parameter set.

In the implementation manner, the road parameters included in the first road parameter set of the road edge and the road parameters included in the fifth road parameter set of the lane line are fused through the first fusion algorithm, and the fusion result is used as the second road parameter set.

In an alternative design, the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.

In an alternative design, the determining a third set of road parameters for the lane line based on the second set of road parameters and the second coordinates for the lane line includes:

determining a curve equation of the lane line by curve fitting the second set of road parameters and a second coordinate of the lane line;

In an alternative design, the first set of road parameters includes at least one of the following road parameters: the course angle, curvature and curvature change rate of the road edge;

the third set of road parameters comprises a lateral offset of the lane line.

In a second aspect, an embodiment of the present application provides a lane line information determining apparatus, including:

the device comprises a processor, a first transceiving interface and a second transceiving interface;

the first transceiving interface is used for receiving first detection information of a radar, and the first detection information comprises relevant information of a road edge of a road;

In an alternative design, the processor is further configured to determine that the road edge is parallel to the lane line based on a distance parameter between the road edge and the lane line before determining the second set of road parameters of the lane line of the road based on the first set of road parameters.

In an alternative design, the distance parameter between the road edge and the lane line comprises a mahalanobis distance,

and calculating the Mahalanobis distance between the road edge and the lane line according to the first road parameter set and the fourth road parameter set.

In an optional design, the processor is specifically configured to determine that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence.

In an optional design, the processor is specifically configured to perform curve fitting on the second coordinate to obtain a fifth road parameter set of the lane line, where a parameter type of at least one road parameter in the fifth road parameter set is the same as a parameter type of at least one road parameter in the first road parameter set;

and fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein a fusion result is the second road parameter set.

In an alternative design, the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.

In an alternative design, the processor is specifically configured to determine a curve equation of the lane line by curve fitting the second set of road parameters and the second coordinate of the lane line;

In an alternative design, the first set of road parameters includes at least one of the following road parameters: the course angle, curvature and curvature change rate of the road edge;

the third set of road parameters comprises at least a lateral offset of the lane line.

In a third aspect, an embodiment of the present application provides a terminal apparatus, including:

at least one processor and memory;

wherein the memory is to store program instructions;

the at least one processor is configured to call and execute program instructions stored in the memory, and when the processor executes the program instructions, the apparatus is caused to perform the method according to the first aspect.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein,

the computer-readable storage medium has stored therein instructions which, when run on a computer, cause the computer to perform the method according to the first aspect

In a fifth aspect, embodiments of the present application provide a computer program product comprising instructions that, when run on an electronic device, cause the electronic device to perform the method according to the first aspect.

In a sixth aspect, an embodiment of the present application provides an intelligent vehicle, where the intelligent vehicle includes the lane line information determination device in the second aspect, or the intelligent vehicle includes the terminal device in the third aspect.

In the scheme disclosed by the embodiment of the application, the first road parameter set of the road edge can be determined according to the first coordinate of the road edge, and the lane line information is determined jointly by combining the first road parameter set of the road edge and the second coordinate of the lane line. That is to say, in the solution of the embodiment of the present application, the lane line information is determined according to the first coordinate of the road edge and the second coordinate of the lane line, and compared with the prior art in which the lane line information is determined only by the coordinate of the lane line, the accuracy of the determined lane line information is higher.

Furthermore, the accuracy of the lane line information determined by the scheme is high, so that the accuracy of target identification and tracking of the vehicle can be further improved.

Drawings

In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without any creative effort.

Fig. 1 is a schematic view of a driving scene of a vehicle according to an embodiment of the present application;

fig. 2 is a schematic workflow diagram of a lane line information determination method disclosed in an embodiment of the present application;

FIG. 3 is a schematic view of a driving scene of another vehicle according to the embodiment of the present application;

fig. 4 is a schematic workflow diagram of another lane line information determination method disclosed in the embodiment of the present application;

fig. 5 is a schematic workflow diagram of another lane line information determination method disclosed in the embodiment of the present application;

fig. 6 is a schematic structural diagram of a lane line information determining apparatus disclosed in an embodiment of the present application;

fig. 7 is a schematic structural diagram of a terminal device disclosed in an embodiment of the present application;

fig. 8 is a schematic connection relationship diagram of a terminal device disclosed in an embodiment of the present application.

Detailed Description

The lane line information determining method disclosed in the embodiment of the present application is generally applied to a lane line information determining apparatus. The lane line information determining device can be installed in a vehicle which can support an automatic driving function, so that the lane line information is determined according to the scheme disclosed in the embodiment of the application in the process of automatic driving of the vehicle. Alternatively, the lane line information determination device may be provided in a remote computer, in which case the remote computer transmits the determined lane line information to the corresponding vehicle after determining the lane line information according to the scheme disclosed in the embodiment of the present application.

Of course, the lane line information determination device may be in other forms, and the embodiment of the present application is not limited thereto.

In addition, the lane line information determining apparatus generally includes a processor, and the processor may acquire the detection information transmitted from the sensor during the acquisition of the road information and determine the lane line information based on the detection information. When the lane line information determination device is installed in a vehicle, the lane line information determination device may be an onboard processor of the vehicle.

In the embodiment of the present application, the sensor may be provided in the lane line information determination device and connected to a processor in the lane line information determination device, or may be a device independent of the lane line information determination device. After acquiring the detection information, the sensor may transmit the detection information to the processor, so that the processor determines lane line information according to the scheme disclosed in the embodiment of the present application based on the received detection information. Correspondingly, an interface for data interaction with the sensor is arranged in the processor, and the processor can acquire the detection information transmitted by the sensor through the interface.

In an embodiment of the present application, the sensor includes: radar and imaging devices. The imaging device may be at least one of a camera or a video camera, and the radar may be at least one of a plurality of types of radars such as a laser radar, a millimeter wave radar, or an ultrasonic radar, which is not limited in this embodiment of the present application.

In addition, roads typically include road edges and lane lines, which in actual scenes are typically marked in a particular color, such as by yellow or white. In the scene diagram of the vehicle driving shown in fig. 1, the dotted line is a lane line in the road, and the solid line is a road edge in the road.

In this case, the radar can detect the road edge using electromagnetic waves. Specifically, the radar can emit electromagnetic waves to the road edge and receive echoes of the electromagnetic waves after contacting the road edge, and accordingly, the radar can determine the geographic position of the road edge. Further, after the radar determines the geographical position of the road edge, the radar may further determine the coordinates of the road edge according to the geographical position of the road edge. In this case, the detection information of the radar may include information of the geographical position of the road edge and/or the coordinates of the road edge. After acquiring the detection information of the radar, the lane line information determination device may determine the coordinates of the road edge from the detection information of the radar.

The imaging device can photograph or record a video in the area where the lane line is located to acquire an image including the lane line, and further, the imaging device can analyze the image including the lane line to acquire the coordinate of the lane line. In this case, the detection information of the imaging device may include the information including the image of the lane line and/or the coordinates of the lane line. After acquiring the detection information of the imaging device, the lane line information determination device may determine the coordinates of the lane line from the detection information of the imaging device.

The following describes a lane line information determination method disclosed in the embodiment of the present application with reference to specific drawings and a workflow.

Referring to a work flow diagram shown in fig. 2, the lane line information determining method disclosed in the embodiment of the present application includes the following steps:

and step S11, determining a first road parameter set of the road edge according to the first coordinate of the road edge of the road. Wherein the first set of road parameters comprises road parameters of at least one road edge.

Referring to the scene diagram shown in fig. 1, a road often includes a road edge and a lane line. In a possible implementation manner of the embodiment of the present application, the first information of the road edge may be determined according to detection information of the radar. Wherein the first information of the road edge can be used for calculating road parameters of the road edge. Illustratively, the first information of the road edge is a first coordinate of the road edge.

In the device for executing the lane line information determining method disclosed in the embodiment of the present application, an interface for performing data interaction with a radar is usually set, and detection information of the radar can be acquired through the interface. Wherein the detection information of the radar may comprise information of the geographical position of the road edge and/or the coordinates of the road edge. When the detection information of the radar comprises the geographical position of the road edge, determining the first coordinate through the geographical position of the road edge; when the coordinates of the detection information road edge of the radar are determined, the first coordinates may be arbitrarily selected from the detection information of the radar.

The road parameters of the road edge are usually the road information of the road edge, and the road edge often includes multiple types of road parameters. For example, the road parameters of the road edge may include: the lateral offset, the heading angle, the curvature and the curvature change rate of the road edge, in this case, since the heading angle, the curvature and the curvature change rate of the road edge and the lane line of the same road are generally the same, the first road parameter set includes at least one of the following road parameters: the course angle, curvature and curvature rate of change of the road edge.

In addition, in the embodiment of the present application, after the first coordinate of the road edge is determined, the curve equation of the road edge may be determined by means of curve fitting. And in the curve equation of the road edge, the road parameters in the first road parameter set of the road edge are included. Thus, by curve fitting to a first coordinate of a road edge, a first set of road parameters for the road edge may be determined.

And step S12, determining a second road parameter set of the lane line of the road according to the first road parameter set. Wherein the second set of road parameters comprises road parameters of at least one of the lane lines.

In the embodiment of the application, a first road parameter set is used for representing road edge information, a second road parameter set is used for representing lane line information, and the types of road parameters in the first road parameter set are in one-to-one correspondence with the types of road parameters in the second road parameter set.

Illustratively, the road parameters in the first road parameter set include a heading angle C of the road edge_1RCurvature C_2RAnd rate of change of curvature C_3RIf the road parameter in the second set of road parameters includes the course angle C of the lane line_1CCurvature C_2CAnd rate of change of curvature C_3C。

Or, in another example, the road parameter in the first set of road parameters comprises a heading angle C of the road edge_1RAnd curvature C_2RIf the road parameter in the second road parameter set includes the course angle C of the lane line_1CAnd curvature C_2C。

Since the first road parameter set includes at least one road parameter of a road edge, the second road parameter set includes at least one road parameter of a lane line, and the types of the road parameters included in the first road parameter set are the same as the types of the road parameters included in the second road parameter set in a one-to-one correspondence manner, in this embodiment of the present application, the second road parameter set may be determined by the first road parameter set.

In addition, the second set of road parameters may be determined in a number of ways, for example by fusing the first set of road parameters. Furthermore, in order to clearly determine the manner of the second road parameter set, in the following embodiments, several possible implementations of determining the second road parameter set according to the first road parameter set are described.

Step S13, determining a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, where the second road parameter set and the third road parameter set include the lane line information.

Wherein the third road parameter set comprises at least one road parameter of the lane line, and the third road parameter set comprises at least one road parameter of a type different from that of the road parameter comprised in the second road parameter set. Illustratively, the third set of road parameters includes a lateral offset of the lane line. In combination with the road parameters of the second set of road parameters and the road parameters of the third set of road parameters, a lane line can generally be determined.

In addition, in the apparatus for executing the lane line information determining method disclosed in the embodiment of the present application, an interface for performing data interaction with the imaging apparatus is usually further provided, and the detection information of the imaging apparatus can be acquired through the interface. The detection information of the imaging device may include an image of the lane line and/or information of coordinates of the lane line. When the detection information of the imaging device includes the image of the lane line, the second coordinate of the lane line may be determined by performing image analysis on the image. When the detection information of the imaging device includes the coordinates of the lane line, the second coordinates may be arbitrarily selected from the detection information of the imaging device, thereby causing the device that performs the method of the embodiment of the present application to determine the second coordinates of the lane line.

In addition, in the above step, after the second set of road parameters and the second coordinates of the lane lines are obtained, the curve equation of the lane lines may be determined by curve fitting the road parameters included in the second set of road parameters and the second coordinates of the lane lines, and the curve equation of the lane lines includes the road parameters included in the third set of road parameters, so that the third set of road parameters may be determined according to the curve equation of the lane lines.

The embodiment of the application discloses a method for determining lane line information, in the method, a first road parameter set of a road edge is determined according to a first coordinate in the road edge, a second road parameter set of a lane line is determined according to the first road parameter set, and a third road parameter set of the lane line is determined according to the second road parameter set and a second coordinate of the lane line, wherein the second road parameter set and the third road parameter set comprise the lane line information.

In the prior art, an image including a lane line is acquired through a camera, then image analysis is performed on the image to acquire coordinates of the lane line, curve fitting is performed on the coordinates to acquire a curve equation of the lane line, and the curve equation includes road parameters of the lane line. That is, in the related art, the coordinates of the lane line are determined only by the image including the lane line, and the lane line information is determined by the coordinates of the lane line. However, the accuracy of image analysis of an image including a lane line is more susceptible to the external environment, and when the accuracy is low, the accuracy of the determined coordinates of the lane line is low, and accordingly, the accuracy of the lane line information determined by the prior art is low. Further, the vehicle usually implements target recognition and tracking through the road information, and therefore, when the accuracy of the lane line information is low, the accuracy of the target recognition and tracking performed by the vehicle is also reduced.

For example, when the vehicle is driven in rainy weather or in a tunnel, the ambient lighting condition of the vehicle is poor, and in this case, the definition of the image including the lane line captured by the camera is low, so the accuracy of image analysis is low, which results in low accuracy of the determined lane line information, and further reduces the accuracy of target identification and tracking performed by the vehicle.

Or, in another scene, the distance between the camera and the lane line to be photographed is far, in this case, in the image including the lane line, the lane line occupies fewer pixels, which also results in lower accuracy of image analysis, thereby resulting in lower accuracy of determined lane line information, and further reducing accuracy of target recognition and tracking performed by the vehicle.

Furthermore, the accuracy of the lane line information determined by the scheme is high, so that the accuracy of target identification and tracking of the vehicle can be further improved.

In addition, in this application embodiment, the first coordinate of road border is usually confirmed through the detected information of radar, and the radar confirms the detected information through the electromagnetic wave of launching to road border, therefore the detected information of radar is difficult for receiving external environment's influence, and under this condition, the accuracy of the first coordinate of road border that this application embodiment adopted is usually higher, therefore, compare with prior art, the higher lane line information of accuracy can be confirmed to the scheme of this application.

Of course, the first coordinate of the road edge may also be determined in other ways in the embodiment of the present application, which is not limited in the embodiment of the present application.

In this embodiment of the application, a curve equation of the road edge may be determined by curve fitting the first coordinate, where the curve equation includes a road parameter included in the first road parameter set of the road edge.

In the embodiment of the present application, the curve equation of the road edge may be determined by various curve fitting algorithms. For example, in the embodiment of the present application, the curve fitting algorithm used may include at least one of a least square method, a hough transform, and ransac (random Sample consensus), and of course, other forms of curve fitting algorithms may also be used, which is not limited in the embodiment of the present application.

In addition, the first road parameter set includes a road parameter of at least one road edge, and exemplarily, the first road parameter set includes: the course angle, curvature and curvature change rate of the road edge; or the first road parameter set comprises any two road parameters of course angle, curvature and curvature change rate of the road edge; or the first road parameter set comprises any one road parameter of course angle, curvature and curvature change rate of the road edge.

In addition, in the embodiment of the present application, the curve equation of the road edge may be in various forms. For example, the curve equation of the road edge may be a cubic curve equation, or a quadratic curve equation, or the road edge may be divided into a plurality of sections, each of which is expressed by a different curve equation. When the curve equation of the road edge is a cubic curve equation, the curve equation of the road edge may be a clothoid (clothoid) cubic curve equation.

Of course, the curve equation of the road edge may also be a curve equation in other forms, which is not limited in the embodiment of the present application.

In one example of the application, the curve equation for the road edge is a clothoid cubic curve equation of the form:

y＝C_0R+C_1Rx+C_2Rx²+C_3Rx³equation (1).

In formula (1), (x, y) represents a first coordinate in the road edge, C_0R、C_1R、C_2RAnd C_3REach representing a road parameter of a road edge. In this example, C_0RMay represent the lateral offset of the road edge, C_1RCan represent the course angle of the road edge, C_2RCan represent the curvature of the road edge, C_3RMay represent the rate of change of curvature of the road edge.

The lateral offset of an object at the edge of a road refers to the amount of displacement of the object in the lateral direction relative to the edge of the road. In order to clarify the lateral offset of the road edge, fig. 3 is disclosed in the embodiment of the present application. FIG. 3 is a schematic view of a vehicle operating scene, including a lateral offset of the road edge, and further including a heading angle of the road edge.

Of course, when the road parameters of the road edge include other types, C_0R、C_1R、C_2RAnd C_3ROther types of road parameters of the road edge may also be represented, and the application is practicalThe examples are not intended to limit the scope thereof.

In addition, when the equation of the curve of the road edge is as shown in formula (1), in the embodiment of the present application, a first coordinate in the road edge is determined according to the detection information of the radar, and then the first coordinate is curve-fitted according to a curve-fitting algorithm, thereby determining C in formula (1)_0R、C_1R、C_2RAnd C_3RWherein C in the formula (1)_0R、C_1R、C_2RAnd C_3RThe road parameters are the road parameters of the road edge, and then the road parameters in the first road set are selected from the road parameters of the road edge, so that the first road parameter set of the road edge is determined.

Wherein the road parameters in the first set of road parameters of the road edge typically satisfy the following condition: the road parameters in the first road set are typically approximately equal to the same type of road parameters in the lane lines. And when the road parameter of a certain type of the road edge is equal to the road parameter of the same type of the lane line, or the difference value of the two road parameters is smaller than a first threshold value, determining that the two road parameters are approximately equal. The value of the first threshold value can be set according to the accuracy of the radar and/or the imaging device, and can be adjusted according to the use requirements of the user.

Since in the same road the heading angle of the road edge is generally approximately equal to the heading angle of the lane line, the curvature of the road edge is generally approximately equal to the curvature of the lane line, and the rate of change of the curvature of the road edge is generally approximately equal to the rate of change of the curvature of the lane line, the first set of road parameters generally includes the at least one road parameter: the course angle, curvature and curvature rate of change of the road edge.

In addition, in the embodiment of the present application, the types of the road parameters included in the first road parameter set are the same as the types of the road parameters included in the second road parameter set in a one-to-one correspondence, and therefore, the types of the road parameters included in the second road parameter set can be determined by the types of the road parameters included in the first road parameter set.

In an embodiment of the present application, the method comprises an operation of determining a third set of road parameters of the lane line from the second set of road parameters and the second coordinates of the lane line. Wherein the operation generally comprises the steps of:

determining a curve equation of the lane line by curve fitting the second set of road parameters and a second coordinate of the lane line;

determining the third road parameter set according to the curve equation, wherein the parameter type of at least one road parameter in the third road parameter set is different from the parameter type of any road parameter in the second road parameter set. For example, when the second set of road parameters includes a heading angle, a curvature, and a rate of change of curvature of the lane line, the third set of road parameters typically includes a lateral offset of the lane line.

In addition, in the embodiment of the present application, the curve equation of the lane line may be determined by various curve fitting algorithms. For example, in the embodiment of the present application, the curve fitting algorithm used may include at least one of a least square method, a hough transform, and ransac (random Sample consensus), and of course, other forms of curve fitting algorithms may also be used, which is not limited in the embodiment of the present application.

In addition, in the embodiment of the present application, the curve equation of the lane line may also be in various forms. For example, the curve equation of the lane line may be a cubic curve equation, or a quadratic curve equation, or the lane line may be divided into a plurality of segments, each of which is represented by a different curve equation. When the curve equation of the lane line is a cubic curve equation, the curve equation of the lane line may be a clothoid cubic curve equation.

Of course, the curve equation of the lane line may also be a curve equation in other forms, which is not limited in the embodiment of the present application.

Wherein, in one example of the present application, the curve equation of the lane line is a clothoid cubic curve equation of the form:

y＝C_0C+C_1Cx+C_2Cx²+C_3Cx³equation (2).

In the formula (2), (x, y) represents a second coordinate of the lane line, C_0C、C_1C、C_2CAnd C_3CEach representing a road parameter of the lane line. In this example, C_0CMay represent the lateral offset of the lane line, C_1CCan represent the course angle of the lane line, C_2CCan represent the curvature of the lane line, C_3CThe rate of change of curvature of the lane line can be represented. Of course, C_0C、C_1C、C_2CAnd C_3COther forms of road parameters may also be used, which are not limited in the embodiments of the present application.

The lateral offset of an object on a lane line refers to the amount of displacement of the object in the lateral direction relative to the lane line.

When the curve equation of the lane line is as shown in equation (2), the second road parameter is C_1C、C_2CAnd C_3CAnd the type of the first road parameter included in the first road parameter set is the same as the type of the second road parameter included in the second road parameter set in a one-to-one correspondence. In this case, when the first set of road parameters includes the heading angle C of the road edge_1RCurvature C_2RAnd rate of change of curvature C_3RThen the second set of road parameters includes the course angle C of the lane line_1CCurvature C of lane line_2CAnd rate of change of curvature C of lane line_3C。

In addition, the third road parameter set comprises at least one road parameter with a different type from the road parameter comprised in the second road parameter set. For example, when the second set of road parameters includes the heading angle C of the lane line_1CCurvature C of lane line_2CAnd rate of change of curvature C of lane line_3CIn time, the third road parameter set comprises the transverse offset C of the lane line_0C. And, in this case, willThe second coordinate and the road parameter of the lane line (i.e. the course angle C of the lane line) included in the second road parameter set_1CCurvature C of lane line_2CAnd rate of change of curvature C of lane line_3C) Substituting the above formula (2) into the above formula (2), the third road parameter set (i.e. the lateral offset C of the lane line) can be determined_0C)

Of course, the road parameters included in the second road parameter set and the third road parameter set may also be other road parameters, which is not limited in this embodiment of the present application. For example, in another example, the second set of road parameters includes a heading angle C of the lane line_1CAnd the curvature C of the lane line_2CSaid third set of road parameters comprises the rate of change of curvature C of said lane line_3CAnd a lateral offset C_0C。

In a scene of vehicle operation, the road edges of the same road are generally parallel to the lane lines, and when the road edges are parallel to the lane lines, the road parameters in the first road parameter set are approximately equal to the same type of road parameters in the second road parameter set. Thus, a second set of road parameters of the lane line of the road may be determined from the first set of road parameters.

The road edge is parallel to the lane line, which means that the curve parameters of the road edge and the lane line are approximately equal except for the position parameter. The curve parameter is used to characterize the degree of curvature of the curve. Illustratively, the position parameters include a lateral offset, and the curve parameters include: heading angle, curvature, rate of change of curvature, and the like.

In addition, the road edge is approximately equal to a curve parameter of the lane line, which means that the difference between the curve parameter of the road edge and the curve parameter of the lane line is less than a certain threshold. The threshold value can be set according to prior information, and can also be adjusted according to the precision of the imaging device and the radar.

However, in some special scenarios, the road edge of the same road may not be parallel to the lane line, in which case there is usually a large error in determining the second set of road parameters of the lane line if the first set of road parameters of the road edge is passed.

In this case, the present application discloses another embodiment. Referring to the schematic workflow diagram shown in fig. 4, in the embodiment of the present application, the method includes the following steps:

and step S21, determining a first road parameter set of the road edge according to the first coordinate of the road edge of the road.

The specific implementation procedure of step S21 is the same as that of step S11, and reference may be made to this step, which is not repeated herein.

And step S22, determining whether the road edge is parallel to the lane line according to the distance parameter between the road edge and the lane line. If so, the operation of step S23 is performed.

Step S23, determining a second road parameter set of the lane line of the road according to the first road parameter set;

step S24, determining a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, where the second road parameter set and the third road parameter set include the lane line information.

The specific implementation process of steps S23 to S24 is the same as the specific implementation process of steps S12 to S13, and reference may be made to these processes, which are not repeated herein.

As can be seen from the above steps, in the embodiment of the present application, before determining the second road parameter set of the lane line of the road according to the first road parameter set, the method further includes the following steps: and determining that the road edge is parallel to the lane line according to the distance parameter between the road edge and the lane line, namely, after determining that the road edge is parallel to the lane line according to the distance parameter between the road edge and the lane line, executing the operation of determining a second road parameter set of the lane line of the road according to the first road parameter set.

That is to say, in the embodiment of the present application, only when it is determined that the road edge is parallel to the lane line, the second road parameter set of the lane line is determined according to the first road parameter set of the road edge, so that the accuracy of determining the second road parameter set can be improved, and the accuracy of determining the lane line information is further improved.

Further, in the embodiment of the present application, if it is determined that the road edge is not parallel to the lane line through the determination of step S22, the first set of road parameters of the road edge is not currently passed, the second set of road parameters of the lane line is determined, and the operation of step S21 may also be performed back.

In addition, in the above description and the work flow diagram shown in fig. 4, after the first road parameter set of the road edge is determined, the operation of determining whether the road edge is parallel to the lane line is performed. In an actual application scenario, there is no strict time sequence between this operation and the operation of determining the first road parameter set of the road edge, so long as the determination operation is performed before determining the second road parameter set of the lane line according to the first road parameter set.

In an example of the embodiment of the present application, it may be determined whether the road edge is parallel to the lane line according to a distance parameter between the road edge and the lane line, and after determining that the road edge is parallel to the lane line, determine a first road parameter set of the road edge according to a first coordinate of the road edge of the road.

Alternatively, in another example of the embodiment of the present application, the operations of determining the first road parameter set of the road edge according to the first coordinate of the road edge of the road and determining the second road parameter set of the lane line of the road according to the first road parameter set may also be performed at the same time, which is not limited in the embodiment of the present application.

In addition, in the embodiment of the present application, it is determined whether the road edge is parallel to the lane line according to a distance parameter between the road edge and the lane line. Wherein the distance parameter between the road edge and the lane line comprises at least one of the following distance parameters: euclidean distance, mahalanobis distance, and minkowski distance.

Of course, the distance parameter may be of other types, which is not limited in the embodiments of the present application.

Wherein, when the distance parameter between the road edge and the lane line includes a euclidean distance, if the euclidean distance between the road edge and the lane line is less than a first distance threshold, it may be determined that the road edge is parallel to the lane line.

When the distance parameter between the road edge and the lane line includes a mahalanobis distance, the road edge may be determined to be parallel to the lane line if the mahalanobis distance between the road edge and the lane line is less than a second distance threshold.

When the distance parameter between the road edge and the lane line includes a Minkowski distance, the road edge may be determined to be parallel to the lane line if the Minkowski distance between the road edge and the lane line is less than a third distance threshold.

The first distance threshold, the second distance threshold and the third distance threshold can be set according to prior information and can be adjusted according to requirements. For example, adjustments may be made based on the accuracy of the imaging device and the radar.

In a possible implementation, the distance parameter between the road edge and the lane line includes mahalanobis distance, in which case the following steps are further included:

and calculating the Mahalanobis distance between the road edge and the lane line according to the first road parameter set and the fourth road parameter set.

In this embodiment of the application, when the fourth set of road parameters needs to be obtained, a curve equation including the lane line may be obtained by performing curve fitting on the second coordinate, where the curve equation includes the road parameters of the lane line, and the fourth set of road parameters may be determined by the curve equation.

Specifically, in the embodiment of the present application, the mahalanobis distance between the road edge and the lane line can be calculated by the following formula:

wherein, C_RadarA matrix representing the composition of said M first road parameters, C_CameraAnd representing a matrix formed by the M second road parameters. In addition, in the formula (1), P_RadarIs represented by C_RadarAnd P is the physical quantity of accuracy of_CameraIs represented by C_CameraThe physical quantity of accuracy of (2). In one possible implementation, P_RadarIs C_RadarAnd P is_CameraIs represented by C_CameraThe covariance of (a).

Illustratively, when the first road parameter set includes a heading angle C of a road edge_1RCurvature of road edge C_2RAnd rate of change of curvature C of road edge_3RWhen, C_Radar＝(C_1R、C_2R、C_3R). When the second road parameter is collected into the course angle C of the lane line_1CCurvature C of lane line_2CAnd rate of change of curvature C of lane line_3CWhen, C_Camera＝(C_1C、C_2C、C_3C)。

Through the steps, the Mahalanobis distance between the road edge and the lane line can be determined, so that whether the road edge is parallel to the lane line or not can be determined conveniently according to the Mahalanobis distance.

In the embodiment of the present application, an operation of determining the second road parameter set of the lane line of the road according to the first road parameter set is also disclosed, and the operation may be implemented in various ways.

In one possible implementation manner, the determining a second road parameter set of the lane line of the road according to the first road parameter set includes:

and determining that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence manner.

In a practical application scenario, the road edge of the same road and the lane line are generally parallel, in this case, the road parameter included in the first road parameter set tends to be approximately equal to the road parameter of the same type included in the second road parameter set, for example, the curvature of the road edge included in the first road parameter set tends to be approximately equal to the curvature of the lane line included in the second road parameter set.

In addition, the first road parameter set is usually determined by the detection information of the radar, and the detection information of the radar is not easily interfered by the environment, and under the condition, the accuracy of acquiring the first road parameter set is usually higher than that of acquiring the second road parameter set. Therefore, in the implementation manner, determining that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence manner can improve the accuracy of determining the second road parameter set, and further, can improve the accuracy of determining the lane line information.

In addition, in another possible implementation manner, the determining a second road parameter set of the lane line of the road according to the first road parameter set includes the following steps:

and fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein a fusion result is the second road parameter set.

In this case, if the fourth road parameter set is determined in advance, the fourth road parameter set may also be directly used as the fifth road parameter set, so that curve fitting is not required to be performed on the second coordinate.

Alternatively, the road parameters included in the fifth road parameter set may also be different from the road parameters included in the fourth road parameter set, in this case, the manner of obtaining the fifth road parameter set may refer to the manner of obtaining the fourth road parameter set, which is not described in detail in this embodiment of the present application.

For example, when the road parameter included in the fifth road parameter set is the same as the road parameter included in the fourth road parameter set, and the distance parameter between the road edge and the lane line includes a mahalanobis distance, referring to the workflow diagram shown in fig. 5, the lane line information determining method disclosed in the embodiment of the present application may include the following steps:

and step S31, determining a first road parameter set of the road edge according to the first coordinate of the road edge of the road.

The specific execution process of step S31 is the same as the specific execution process of step S11, and reference may be made to these processes, which are not repeated herein.

Step S32, determining a fourth road parameter set of the lane line by curve fitting of the second coordinate of the lane line of the road, and determining a distance parameter between the road edge and the lane line according to the first road parameter set and the fourth road parameter set, where the distance parameter includes at least one of the following distance parameters: euclidean distance, mahalanobis distance, and minkowski distance.

The types of the road parameters included in the fourth road parameter set are in one-to-one correspondence with the types of the road parameters included in the first road parameter set.

And step S33, determining whether the road edge is parallel to the lane line according to the distance parameter between the road edge and the lane line. If so, the operation of step S34 is performed. If not, the process returns to step S31.

And step S34, performing curve fitting on the second coordinate to obtain a fifth road parameter set of the lane line. Wherein the parameter type of at least one road parameter in the fifth road parameter set is the same as the parameter type of at least one road parameter in the first road parameter set.

And step S35, fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein the fusion result is the second road parameter set.

Step S36, determining a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, where the second road parameter set and the third road parameter set include the lane line information.

The specific execution process of step S36 is the same as the specific execution process of step S13, and reference may be made to these processes, which are not repeated herein.

In the implementation manner, the road parameters included in the first road parameter set of the road edge and the road parameters included in the fifth road parameter set of the lane line are fused by a first fusion algorithm, and the fusion result is used as the second road parameter set.

In this way, the fifth set of road parameters can be fused with the first set of road parameters into the second set of road parameters. Due to the fact that the accuracy of determining the combination of the first road parameters is high, the method can improve the accuracy of determining the second road parameter set, and further can improve the accuracy of determining the lane line information.

In the embodiment of the present application, the first fusion algorithm may be a fusion algorithm in various forms. Illustratively, the first fusion algorithm includes: a convex combination fusion (CC) algorithm and/or a covariance intersection fusion (CI) algorithm.

When the first fusion algorithm is a convex combination fusion algorithm, the fifth road parameter set and the first road parameter set can be fused through the following formula:

wherein x is_RA vector, x, representing the composition of road parameters comprised by said first set of road parameters_CA vector, P, representing the composition of road parameters included in said fifth set of road parameters_RRepresenting the covariance, P, of a vector formed by road parameters included in said first set of road parameters_CCovariance, x, of a vector of road parameters included in the fifth set of road parameters_CA vector P representing the result of the fusion of the first road parameter set and the fifth road parameter set_FAnd representing the covariance of a vector formed by the fusion result of the first road parameter set and the fifth road parameter set.

Through a formula (4) and a formula (5), a fusion result of the first road parameter set and the fifth road parameter set can be determined, wherein the fusion result of the first road parameter set and the fifth road parameter set is the second road parameter set.

In addition, when the first fusion algorithm is a covariance cross fusion algorithm, the fifth road parameter set and the first road parameter set may be fused by the following formula:

wherein x is_RA vector, x, representing the composition of road parameters comprised by said first set of road parameters_CA vector, P, representing the composition of road parameters included in said fifth set of road parameters_RRepresenting the covariance, P, of a vector formed by road parameters included in said first set of road parameters_CCovariance, x, of a vector of road parameters included in the fifth set of road parameters_CA vector P representing the result of the fusion of the first road parameter set and the fifth road parameter set_FAnd the covariance of a vector formed by the fusion result of the first road parameter set and the fifth road parameter set is represented, and w represents a weight parameter.

Through a formula (6) and a formula (7), a fusion result of the first road parameter set and the fifth road parameter set can be determined, wherein the fusion result of the first road parameter set and the fifth road parameter set is the second road parameter set.

In equations (6) and (7), w represents a weight parameter, which may be determined in various ways. The weight parameter w may be determined empirically by a skilled person, for example.

Alternatively, in another approach, the weight parameter w may be determined according to the following formula:

w＝N_R/(N_R+N_C) Formula (8);

wherein N is_RRepresenting the number of said first coordinates, N_CRepresenting the number of said second coordinates.

In the embodiment of the present application, the first coordinate is generally determined by the detection information of the radar, and the second coordinate is generally determined by the detection information of the imaging device. The detection information of the imaging device is more susceptible to the external environment than the detection information of the radar, and therefore, generally, in formula (8), the higher the number of the first coordinates, the higher the confidence of the weight parameter w.

In addition, the weight parameter w may also be determined in other ways. For example, the weighting parameter w may be set empirically, in which the weighting parameter w is set based on experience obtained through a plurality of experiments. Alternatively, other modes can be adopted, and the embodiment of the present application is not limited to this.

The following are embodiments of an apparatus of the present application that may be used to perform embodiments of the methods of the present application. For details which are not disclosed in the device embodiments of the present application, reference is made to the method embodiments of the present application.

As an implementation of the above embodiments, an embodiment of the present application discloses a lane line information determination device. Referring to the schematic structural diagram shown in fig. 6, the lane line information determining apparatus disclosed in the embodiment of the present application includes: a processor 110, a first transceiving interface 120, and a second transceiving interface 130.

The first transceiving interface 120 is configured to receive first probe information of a radar, where the first probe information includes information related to a road edge of a road.

In an embodiment of the present application, the information related to the road edge includes information of a geographical position of the road edge and/or coordinates of the road edge.

The second transceiving interface 130 is configured to receive second detection information of an imaging device, where the first detection information includes information related to a lane line of the road.

In an embodiment of the present application, the information related to the lane line includes information of a geographic position of the lane line and/or coordinates of the lane line.

The processor 110 is configured to determine a first coordinate of the road edge according to the first detection information, and determine a second coordinate of the lane line according to the second detection information;

the processor 110 is further configured to determine a first road parameter set of a road edge of a road according to a first coordinate of the road edge, determine a second road parameter set of a lane line of the road according to the first road parameter set, and determine a third road parameter set of the lane line according to the second road parameter set and a second coordinate of the lane line, where the second road parameter set and the third road parameter set include the lane line information.

In this embodiment, the first transceiver interface 120 and the second transceiver interface 130 may be two different transceiver interfaces, that is, in the lane line determining device in this embodiment, the first probe information and the second probe information are respectively received through the two different transceiver interfaces. In addition, the first transceiving interface 120 and the second transceiving interface 130 may be the same transceiving interface. In this case, the lane line determining apparatus according to the embodiment of the present application may receive the first probe information and the second probe information through the same transceiver interface.

Further, the processor 110 is further configured to determine that the road edge is parallel to the lane line according to a distance parameter between the road edge and the lane line before determining a second road parameter set of the lane line of the road according to the first road parameter set.

Wherein the distance parameter between the road edge and the lane line comprises at least one of the following distance parameters: euclidean distance, mahalanobis distance, and minkowski distance.

In addition, the distance parameter between the road edge and the lane line includes mahalanobis distance, and the processor 110 is further configured to obtain a fourth road parameter set of the lane line by performing curve fitting on the second coordinate, where a parameter type of at least one road parameter in the fourth road parameter set is the same as a parameter type of at least one road parameter in the first road parameter set;

and calculating the Mahalanobis distance between the road edge and the lane line according to the first road parameter set and the fourth road parameter set.

Further, in this embodiment of the application, the processor 110 is specifically configured to determine that at least one road parameter in the second road parameter set is the same as at least one road parameter in the first road parameter set in a one-to-one correspondence.

Further, in this embodiment of the application, the processor 110 is specifically configured to perform curve fitting on the second coordinate to obtain a fifth road parameter set of the lane line, where a parameter type of at least one road parameter in the fifth road parameter set is the same as a parameter type of at least one road parameter in the first road parameter set;

and fusing the fifth road parameter set and the first road parameter set through a first fusion algorithm, wherein a fusion result is the second road parameter set.

Wherein the first fusion algorithm comprises: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.

Further, in this embodiment of the application, the processor 110 is specifically configured to determine a curve equation of the lane line by performing curve fitting on the second road parameter set and the second coordinate of the lane line;

Wherein the first set of road parameters comprises at least one of the following road parameters: the course angle, curvature and curvature change rate of the road edge;

the third set of road parameters comprises at least a lateral offset of the lane line.

The lane line information determining device disclosed by the embodiment of the application can determine the first road parameter set of the road edge according to the first coordinate of the road edge, and determines lane line information jointly by combining the first road parameter set of the road edge and the second coordinate of the lane line.

Furthermore, the accuracy of the lane line information determined by the scheme is high, so that the accuracy of target identification and tracking of the vehicle can be further improved.

In the embodiment of the present application, the lane line information determination means may include various forms. In one possible form, the lane line information determination device is integrated in an image forming device. In this form, the imaging device receives the first detection information transmitted by the radar through the first transceiving interface 120, and transmits the first detection information to the processor 110 of the imaging device; the second transceiving interface 130 acquires second detection information detected by the imaging device and transmits the second detection information to the processor 110; the processor 110 determines a second road parameter set and a third road parameter set of the lane line according to the first detection information and the second detection information, where the second road parameter set and the third road parameter set include the lane line information.

Further, after determining the lane line information, the imaging device may further transmit the lane line information to the intelligent vehicle through the first transceiving interface 120, or through the second transceiving interface 130, or through another transceiving interface different from the first transceiving interface 120 and the second transceiving interface 130, so that the intelligent vehicle can implement functions such as target identification according to the lane line information.

Alternatively, in another possible form, the lane line information determination means is integrated into a radar. In this form, the radar receives the second detection information transmitted by the imaging device through the second transceiving interface 130, and transmits the second detection information to the processor 110 of the radar; the first transceiving interface 120 acquires first detection information detected by the radar, and transmits the first detection information to the processor 110; the processor 110 determines a second road parameter set and a third road parameter set of the lane line according to the first detection information and the second detection information, where the second road parameter set and the third road parameter set include the lane line information.

Further, after the lane line information is determined, the radar may further transmit the lane line information to the intelligent vehicle through the first transceiving interface 120, or through the second transceiving interface 130, or through another transceiving interface different from the first transceiving interface 120 and the second transceiving interface 130, so that the intelligent vehicle can implement functions such as target identification according to the lane line information.

In another possible form, the lane line information determination device is integrated into a fusion module. In this form, the fusion module receives the first detection information transmitted by the radar through the first transceiving interface 120, and transmits the first detection information to the processor 110 of the fusion module; the fusion module receives the second detection information transmitted by the imaging device through the second transceiving interface 130, and transmits the second detection information to the processor 110 of the fusion module; the processor 110 determines a second road parameter set and a third road parameter set of the lane line according to the first detection information and the second detection information, where the second road parameter set and the third road parameter set include the lane line information.

Wherein, the fusion module can be a remote computer and the like. In this case, after determining the lane line information, the fusion module may further transmit the lane line information to the intelligent vehicle through the first transceiving interface 120, or through the second transceiving interface 130, or through another transceiving interface different from the first transceiving interface 120 and the second transceiving interface 130, so that the intelligent vehicle realizes functions such as target identification through the lane line information.

Alternatively, the fusion module may be a functional module disposed in the smart car, for example, a chip system or a circuit in the smart car. In this case, after the fusion module determines the lane line information, the smart car may implement functions such as object recognition using the lane line information.

Alternatively, in another possible implementation manner, the lane line information determination device is integrated into the fusion module and the intelligent vehicle. In this case, the fusion module may include a first transceiver interface 120 and a second transceiver interface 130, and the smart car may include the processor 110, that is, the processor 110 is an on-board processor of the smart car.

In this form, the fusion module receives first detection information transmitted by a radar through the first transceiver interface 120, and transmits the first detection information to the processor 110 of the smart car; and, the fusion module receives second detection information transmitted by the imaging device through the second transceiver interface 130, and transmits the second detection information to the processor 110 of the smart car. The processor 110 determines a second road parameter set and a third road parameter set of the lane line according to the first detection information and the second detection information, where the second road parameter set and the third road parameter set include the lane line information. After the lane line information is determined, the intelligent vehicle can utilize the lane line information to realize functions such as target identification and the like.

Alternatively, in another possible form, the lane line information determination device is integrated into an intelligent vehicle. In this case, the first transceiving interface 120 and the second transceiving interface 130 are both transceiving interfaces installed in a smart car, and the processor 110 is an on-board processor of the smart car.

In this form, the first transceiver interface 120 receives first probe information transmitted by radar and transmits the first probe information to the processor 110; the second transceiving interface 130 receives second detection information transmitted by the imaging device and transmits the second detection information to the processor 110. The processor 110 determines a second road parameter set and a third road parameter set of the lane line according to the first detection information and the second detection information, where the second road parameter set and the third road parameter set include the lane line information, and the intelligent vehicle realizes functions such as target identification according to the lane line information.

Of course, the lane line information determining device disclosed in the embodiment of the present application may also be in other forms, which is not limited in the embodiment of the present application.

Correspondingly, the embodiment of the application also discloses a terminal device corresponding to the lane line determining method. Referring to the schematic structural diagram shown in fig. 7, the terminal apparatus includes:

at least one processor 1101 and a memory,

wherein the memory is to store program instructions;

the processor is configured to call and execute the program instructions stored in the memory, so as to cause the terminal device to perform all or part of the steps in the embodiments corresponding to fig. 2, fig. 4, and fig. 5.

Further, the terminal device may further include: a transceiver 1102 and a bus 1103 that includes a random access memory 1104 and a read only memory 1105.

The processor is coupled to the transceiver, the random access memory and the read only memory through the bus respectively. When the mobile terminal control device needs to be operated, the device is guided to enter a normal operation state by starting a basic input and output system solidified in a read only memory or a bootloader guiding system in an embedded system. After the device enters a normal operation state, an application program and an operating system are operated in the random access memory, so that the mobile terminal control device executes all or part of the steps in the embodiments corresponding to fig. 2, fig. 4 and fig. 5.

The device according to the embodiment of the present invention may correspond to the road information determining device in the embodiment corresponding to fig. 2, fig. 4, and fig. 5, and the processor in the road information determining device may implement the functions of the road information determining device and/or various steps and methods implemented in the embodiment corresponding to fig. 2, fig. 4, and fig. 5, and for brevity, no further description is provided here.

It should be noted that, this embodiment may also be implemented based on a Network device implemented by combining a general physical server with a Network Function Virtualization (NFV) technology, where the Network device is a virtual Network device (e.g., a virtual host, a virtual router, or a virtual switch). The Virtual network device may be a Virtual Machine (VM) running a program for sending an advertisement message, and the VM is deployed on a hardware device (e.g., a physical server). A virtual machine refers to a complete computer system with complete hardware system functionality, which is emulated by software, running in a completely isolated environment. Through reading the application, a person skilled in the art can virtually simulate a plurality of network devices with the above functions on a general physical server. And will not be described in detail herein.

Further, the terminal device disclosed in the embodiment of the present application may be used as a target identification device. The target recognition device is used for determining road information, so that the target recognition is carried out on obstacles, pedestrians, vehicles and the like in the road according to the road information, and the target tracking can be further realized.

When determining the lane line information, the terminal device needs first detection information of the radar and second detection information of the imaging device, and determines the lane line information according to the first detection information and the second detection information.

Wherein the terminal device may be implemented in various forms. Referring to the schematic connection relationship diagram shown in fig. 8, in one implementation form, a radar 210 and an imaging device 220 are respectively connected to the terminal device 230 through corresponding transceiving interfaces, and respectively transmit the first detection information and the second detection information to the terminal device 230, so that the terminal device determines lane line information.

In this implementation, the terminal device may be an onboard processor, or the terminal device may also be a remote processor. When the terminal device is a remote processor, the remote processor can also transmit the lane line information to the corresponding vehicle after determining the lane line information, so that the vehicle can realize target identification according to the lane line information.

In another implementation manner, the terminal device disclosed in the embodiment of the present application is a radar or an imaging device. When the terminal device is a radar, the radar receives second detection information transmitted by the imaging device through a transceiving interface, and determines lane line information according to the first detection information and the second detection information determined by the radar.

Further, after the lane line information is determined, the radar may also transmit the lane line information to a corresponding vehicle, so that the vehicle can recognize a target according to the lane line information.

In addition, when the terminal device is an imaging device, the imaging device receives first detection information transmitted by the radar through a transceiving interface, and determines lane line information according to second detection information determined by the imaging device and the first detection information.

Further, after determining the lane line information, the imaging device may further transmit the lane line information to a corresponding vehicle, so that the vehicle can recognize a target according to the lane line information.

Of course, the terminal device may also be implemented in other forms, which is not limited in this application.

Furthermore, the terminal device disclosed in the embodiment of the application can be applied to the field of intelligent driving, and particularly can be applied to an Advanced Driver Assistance System (ADAS) or an automatic driving system. For example, the terminal device may be disposed in a vehicle supporting an advanced assistant driving function or an automatic driving function, and determine lane line information according to a scheme disclosed in an embodiment of the present application, so that the vehicle implements the advanced assistant driving or the automatic driving function according to the lane line information.

In this case, the solution of the embodiment of the present application can improve the automatic driving or ADAS capability, and thus can be applied to the car networking, for example, in systems such as vehicle-to-vehicle communication technology (V2X), long term evolution-vehicle communication (LTE-V), and vehicle-to-vehicle communication (V2V).

In particular implementations, embodiments of the present application also provide a computer-readable storage medium, which includes instructions. Wherein a computer readable medium disposed in any apparatus, which when executed on a computer, may perform all or a portion of the steps of the embodiments corresponding to fig. 2, 4, and 5. The storage medium of the computer readable medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).

In addition, another embodiment of the present application also discloses a computer program product containing instructions, which when run on an electronic device, enables the electronic device to implement all or part of the steps in the embodiments corresponding to fig. 2, fig. 4 and fig. 5.

Further, the embodiment of the application also discloses an intelligent vehicle, and the intelligent vehicle comprises the lane line information determining device or the terminal device disclosed by the previous embodiment of the application. In this case, the lane line information determination device or the terminal device is typically carried by a chip, an integrated circuit, and/or a processor or the like built in the vehicle, and the at least one processor and the memory may be carried by different integrated circuits and/or processors, or may be carried by one chip or one integrated circuit or one processor.

In addition, at least one sensor can be arranged in the intelligent vehicle, the sensor can acquire detection information required in the road information determination process, and the sensor can comprise a vehicle-mounted imaging device and/or a vehicle-mounted radar. Or, the intelligent vehicle can also be connected with a remote sensor in a wireless mode, and detection information required in the process is determined through the remote sensor. The remote sensor includes an imaging device and/or a radar.

The embodiment of the application also discloses a system, and the system can determine the road information by the method disclosed by the previous embodiment of the application. The system comprises a terminal device, an imaging device and a radar. The imaging device is used for acquiring second detection information, the radar is used for acquiring first detection information, the terminal device is used for acquiring the first detection information and the second detection information, and lane line information is determined according to the first detection information and the second detection information through the scheme disclosed by each embodiment.

The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital information processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital information processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital information processor core, or any other similar configuration.

The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be located in a UE. In the alternative, the processor and the storage medium may reside in different components in the UE.

It should be understood that, in the various embodiments of the present application, the size of the serial number of each process does not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

The same and similar parts among the various embodiments of the present specification may be referred to, and each embodiment is described with emphasis on differences from the other embodiments. In particular, as to the apparatus and system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple and reference may be made to the description of the method embodiments in relevant places.

Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.

The same and similar parts in the various embodiments in this specification may be referred to each other. In particular, for the embodiments of the road constraint determining apparatus disclosed in the present application, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the description in the method embodiments.

The above-described embodiments of the present invention should not be construed as limiting the scope of the present invention.

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Lane line information determination method and device

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