WO2021185104A1 - 一种车道线信息确定方法及装置 - Google Patents
一种车道线信息确定方法及装置 Download PDFInfo
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- WO2021185104A1 WO2021185104A1 PCT/CN2021/079498 CN2021079498W WO2021185104A1 WO 2021185104 A1 WO2021185104 A1 WO 2021185104A1 CN 2021079498 W CN2021079498 W CN 2021079498W WO 2021185104 A1 WO2021185104 A1 WO 2021185104A1
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- lane line
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
Definitions
- This application relates to the technical field of smart cars, in particular to a method and device for determining lane line information.
- a vehicle supporting automated driving functions came into being.
- the vehicle needs to identify and track obstacles, pedestrians and other vehicles on the road.
- the vehicle needs to determine road information.
- the road information that the vehicle needs to determine usually includes lane line information, such as the curvature of the lane line.
- vehicles usually adopt a road structured way to determine the road parameters of the lane line.
- road structuring refers to characterizing the structure of a road through a mathematical model, which is usually a curve equation including road information.
- vehicles when they obtain the curve equation of the lane line, they usually first take a picture of the lane line through a camera, obtain an image containing the lane line, and then perform image analysis on the image to obtain the coordinates of multiple locations in the lane line, and then check all the lane lines. Curve fitting is performed on the coordinates of the multiple locations, so as to obtain a curve equation of a lane line, and the curve equation includes the lane line information.
- the inventor found in the research process of this application that vehicles often run on roads in different environments.
- the accuracy of image analysis on images containing lane lines is more susceptible to the influence of the external environment.
- the accuracy of the coordinates of the determined lane line is low, resulting in low accuracy of the lane line information obtained through the prior art, and further, it will reduce the vehicle performance. Accuracy of target recognition and tracking.
- the sharpness of the image captured by the camera is low, which results in low accuracy of image analysis on the image; or, when the camera is in a distance from the lane line that needs to be captured When it is farther, in the image captured by the camera, the lane line occupies fewer pixels, which will also lead to lower accuracy of image analysis.
- an embodiment of the present application discloses a lane line information determination problem. Method and device.
- an embodiment of the present application discloses a method for determining lane line information, including:
- the lane line information is determined according to the first coordinate of the road edge and the second coordinate of the lane line. This solution is compared with the solution in the prior art that only uses the coordinates of the lane line to determine the lane line information. The accuracy of the determined lane line information is relatively high.
- the method 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 distance parameter between the road edge and the lane line 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 determination of the second road parameter can be improved.
- the accuracy of the collection further improves the accuracy of determining the lane line information.
- 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.
- the distance parameter between the edge of the road and the lane line includes the Mahalanobis distance, and further includes:
- the fourth road parameter set of the lane line is obtained by curve fitting the second coordinates, and the parameter type of at least one road parameter in the fourth road parameter set is the same as that of the first road parameter set. At least one road parameter has the same parameter type;
- the Mahalanobis distance between the road edge and the lane line is calculated.
- the determining the second road parameter set of the lane line of the road according to the first road parameter set includes:
- 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.
- the accuracy of obtaining the first road parameter set is often higher than the accuracy of obtaining the second road parameter set. Therefore, it is determined that at least one road parameter in the second road parameter set and at least one of the first road parameter set is determined.
- the road parameters are the same in one-to-one correspondence, which can improve the accuracy of determining the second road parameter set, and further, can also improve the accuracy of determining the lane line information.
- the determining the second road parameter set of the lane line of the road according to the first road parameter set includes:
- the fifth road parameter set of the lane line is obtained by curve fitting the second coordinates, and the parameter type of at least one road parameter in the fifth road parameter set is the same as that of the first road parameter set. At least one road parameter has the same parameter type;
- the fifth road parameter set and the first road parameter set are fused by a first fusion algorithm, and the fusion result is the second road parameter set.
- 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 the first fusion algorithm, and the fusion result is used as the second road
- the parameter set because the accuracy of determining the first road parameter combination is high, this method can improve the accuracy of determining the second road parameter set, and further, the accuracy of determining the lane line information can also be improved.
- the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.
- the determining the third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line includes:
- the third road parameter set is determined according to the curve equation, and 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.
- the first road parameter set includes at least one of the following road parameters: heading angle, curvature, and curvature change rate of the road edge;
- the third road parameter set includes the lateral offset of the lane line.
- an apparatus for determining lane line information including:
- a processor a first transceiver interface, and a second transceiver interface
- the first transceiver interface is used to receive first detection information of radar, where the first detection information includes information about the edge of the road;
- the second transceiver interface is used to receive second detection information of the imaging device, where the first detection information includes information related to lane lines of the road;
- the processor is configured to determine the first coordinate of the road edge according to the first detection information, and determine the second coordinate of the lane line according to the second detection information;
- the processor is further configured to determine the first road parameter set of the road edge according to the first coordinates of the road edge of the road, and determine the second road of the lane line of the road according to the first road parameter set Parameter set, determine the third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, the second road parameter set and the third road parameter set include all State the lane line information.
- the processor is further configured to: before determining the second road parameter set of the lane line of the road according to the first road parameter set, according to the road edge and the lane The distance parameter between the lines determines that the edge of the road is parallel to the lane line.
- 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.
- the distance parameter between the edge of the road and the lane line includes Mahalanobis distance
- the processor is further configured to obtain a fourth road parameter set of the lane line by performing curve fitting on the second coordinate, and the parameter type of at least one road parameter in the fourth road parameter set is related to the The parameter type of at least one road parameter in the first road parameter set is the same;
- the Mahalanobis distance between the road edge and the lane line is calculated.
- the processor is specifically configured to determine that at least one road parameter in the second road parameter set is identical to at least one road parameter in the first road parameter set in a one-to-one correspondence.
- the processor is specifically configured to obtain a fifth road parameter set of the lane line by performing curve fitting on the second coordinate, and at least one of the fifth road parameter set is The parameter type of one road parameter is the same as the parameter type of at least one road parameter in the first road parameter set;
- the fifth road parameter set and the first road parameter set are fused by a first fusion algorithm, and the fusion result is the second road parameter set.
- the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.
- the processor is specifically configured to determine the curve equation of the lane line by performing curve fitting on the second road parameter set and the second coordinate of the lane line;
- the third road parameter set is determined according to the curve equation, and 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.
- the first road parameter set includes at least one of the following road parameters: heading angle, curvature, and curvature change rate of the road edge;
- the third road parameter set includes at least the lateral offset of the lane line.
- an embodiment of the present application provides a terminal device, including:
- At least one processor and memory At least one processor and memory
- the memory is used 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 device executes the method described in the first aspect.
- an embodiment of the present application provides a computer-readable storage medium, which is characterized in that:
- Instructions are stored in the computer-readable storage medium, which when run on a computer, cause the computer to execute the method as described in the first aspect
- embodiments of the present application provide a computer program product containing instructions, which when the computer program product runs on an electronic device, cause the electronic device to execute the method described in the first aspect.
- an embodiment of the present application provides a smart car, the smart car includes the lane line information determining device described in the second aspect, or the smart car includes the terminal device described in the third aspect.
- the first road parameter set of the road edge can be determined according to the first coordinate of the road edge, and the first road parameter set of the road edge and the second coordinate of the lane line can be combined to jointly determine the lane line information. That is to say, in the solution of the embodiment of the present application, the lane line information is determined according to the first coordinates of the road edge and the second coordinate of the lane line. Compared with the scheme, the accuracy of the determined lane line information is higher.
- the solution of the present application can further improve the accuracy of vehicle target recognition and tracking.
- FIG. 1 is a schematic diagram of a vehicle driving scene disclosed in an embodiment of the application
- FIG. 2 is a schematic diagram of a work flow of a method for determining lane line information disclosed in an embodiment of the application;
- FIG. 3 is a schematic diagram of another vehicle driving scene disclosed in an embodiment of the application.
- FIG. 4 is a schematic diagram of a workflow of another method for determining lane line information disclosed in an embodiment of the application;
- FIG. 5 is a schematic diagram of a workflow of another method for determining lane line information disclosed in an embodiment of the application.
- Fig. 6 is a schematic structural diagram of a lane line information determining device disclosed in an embodiment of the application.
- FIG. 7 is a schematic structural diagram of a terminal device disclosed in an embodiment of the application.
- FIG. 8 is a schematic diagram of a connection relationship of a terminal device disclosed in an embodiment of the application.
- an embodiment of the present application discloses a lane line information determination problem. Method and device.
- the method for determining lane line information disclosed in the embodiments of the present application is generally applied to a device for determining lane line information.
- the lane line information determining device can be installed in a vehicle, and the vehicle can support an automated driving function, so that the lane line information is determined according to the solution disclosed in the embodiment of the present application during the process of automated driving of the vehicle.
- the device for determining lane line information may be set in a remote computer. In this case, after determining the lane line information according to the scheme disclosed in the embodiment of the present application, the remote computer transmits the determined lane line information to the corresponding Vehicles.
- the lane line information determination device may also be in other forms, which is not limited in the embodiment of the present application.
- the device for determining lane line information usually has a processor built in.
- the processor can obtain detection information transmitted by the sensor, and determine lane line information based on the detection information.
- the lane line information determining device may be an on-board processor of the vehicle.
- the senor may be arranged in the lane line information determining device and connected to the processor in the lane line information determining device, or the sensor may also be independent of the lane line information.
- the device of the information determining device After acquiring the detection information, the sensor may transmit the detection information to the processor, so that the processor, based on the received detection information, determines the lane line information according to the solution disclosed in the embodiment of the present application.
- the processor is provided with an interface for data interaction with the sensor, through which the processor can obtain the detection information transmitted by the sensor.
- the senor includes a radar and an imaging device.
- the imaging device may be at least one of a camera or a video camera
- the radar may be at least one of multiple types of radars such as laser radar, millimeter wave radar, or ultrasonic radar, which is not limited in the embodiment of the present application.
- roads usually include road edges and lane lines.
- lane lines are usually marked with special colors, such as yellow or white.
- the dashed line is the lane line in the road
- the solid line is the road edge in the road.
- the radar can use electromagnetic waves to detect the edge of the road. Specifically, the radar can transmit electromagnetic waves to the edge of the road and receive the echo after the electromagnetic wave touches the edge of the road. Based on this, the radar can determine the geographic location of the edge of the road. Further, after determining the geographic location of the road edge, the radar may further determine the coordinates of the road edge based on the geographic location of the road edge. In this case, the detection information of the radar may include information about the geographic location of the road edge and/or the coordinates of the road edge. After acquiring the detection information of the radar, the lane line information determining device may determine the coordinates of the road edge according to the detection information of the radar.
- the imaging device may take a picture or record a video of the area where the lane line is located to obtain an image including the lane line. Further, the imaging device may also perform image analysis on the image including the lane line to obtain the coordinates of the lane line.
- 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 determining device may determine the coordinates of the lane line according to the detection information of the imaging device.
- the method for determining lane line information disclosed in the embodiment of the present application includes the following steps:
- Step S11 Determine the first road parameter set of the road edge according to the first coordinates of the road edge of the road.
- the first road parameter set includes road parameters of at least one road edge.
- roads often include road edges and lane lines.
- the first information of the road edge may be determined according to the detection information of the radar.
- the first information of the road edge can be used to calculate the road parameters of the road edge.
- the first information of the road edge is the first coordinates of the road edge.
- an interface for data interaction with the radar is usually set, and the detection information of the radar can be obtained through the interface.
- the detection information of the radar may include the geographic location of the road edge and/or the information of the coordinates of the road edge.
- the detection information of the radar includes the geographic location of the road edge
- the first coordinate can be determined by the geographic location of the road edge; when the detection information of the radar is the coordinate of the road edge, the first coordinate can be determined from the radar's
- the first coordinate is arbitrarily selected in the detection information.
- the road parameter of the road edge is usually road information of the road edge, and the road edge often includes multiple types of road parameters.
- 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.
- the curvature and the curvature change rate are usually the same. Therefore, the first road parameter set includes at least one of the following road parameters: the heading angle, the curvature and the curvature change rate of the road edge.
- the curve equation of the road edge can be determined by means of curve fitting.
- the road parameter in the first road parameter set of the road edge is included. Therefore, by curve fitting the first coordinate of the road edge, the first road parameter set of the road edge can be determined.
- Step S12 Determine a second road parameter set of the lane line of the road according to the first road parameter set.
- the second road parameter set includes at least one road parameter of the lane line.
- the first road parameter set is used to characterize road edge information
- the second road parameter set is used to characterize lane line information
- the types of road parameters included in the first road parameter set are the same as those in the second road parameter set.
- the types of road parameters included in the road parameter set correspond to the same one-to-one.
- the road parameters in the first road parameter set include the heading angle C 1R , the curvature C 2R and the curvature change rate C 3R of the road edge
- the road parameters in the second road parameter set include all the road parameters.
- the heading angle C 1C , the curvature C 2C and the curvature change rate C 3C of the lane line of the lane line are described.
- the road parameters in the first road parameter set include the heading angle C 1R and the curvature C 2R of the road edge
- the road parameters in the second road parameter set include the lane The heading angle of the line C 1C and the curvature C 2C .
- the second road parameter set includes road parameters of at least one lane line, and the types of road parameters included in the first road parameter set are the same as those of the road parameters.
- the types of road parameters included in the second road parameter set correspond to the same one-to-one correspondence. Therefore, in this embodiment of the present application, the second road parameter set may be determined through the first road parameter set.
- the second road parameter set may be determined in a variety of ways, for example, the second road parameter set may be determined by fusing the first road parameter set.
- the second road parameter set may be determined by fusing the first road parameter set.
- several feasible implementation manners for determining the second road parameter set based on the first road parameter set are introduced.
- Step S13 Determine a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, and the second road parameter set and the third road parameter set include all State the lane line information.
- the third road parameter set includes at least one road parameter of the lane line, and the third road parameter set includes at least one type of road parameter that is different from that of the road parameter included in the second road parameter set.
- Road parameters Exemplarily, the third road parameter set includes the lateral offset of the lane line. Combining the road parameters in the second road parameter set and the road parameters in the third road parameter set, it is usually possible to determine a lane line.
- an interface for data interaction with the imaging device is usually set, through which the detection information of the imaging device can be obtained.
- the detection information of the imaging device may include the image of the lane line and/or the information of the coordinates of the lane line.
- the second coordinate of the lane line may be determined by performing image analysis on the image.
- the second coordinates can be arbitrarily selected from the detection information of the imaging device, so that the device executing the method of the embodiment of the present application can determine the lane The second coordinate of the line.
- the second coordinate of the lane line and the road parameters included in the second road parameter set can be curve-curved.
- the curve equation of the lane line is determined by the fitting method, and the curve equation of the lane line includes the road parameters included in the third road parameter set, so that the third road can be determined according to the curve equation of the lane line Parameter collection.
- the embodiment of the application discloses a method for determining lane line information.
- a first road parameter set of the road edge is determined according to the first coordinate in the road edge, and then the first road parameter set of the lane line is determined according to the first road parameter set.
- Road parameter set determining the 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 Including the lane line information.
- an image containing lane lines is acquired through a camera, and then image analysis is performed on the image to obtain the coordinates of the lane line, and then curve fitting is performed on the coordinates to obtain the curve equation of the lane line.
- the curve equation includes The road parameters of the lane line. That is, in the prior art, the coordinates of the lane line are determined only by the image containing the lane line, and then the lane line information is determined by the coordinates of the lane line.
- the accuracy of image analysis on an image containing lane lines is more susceptible to the influence of the external environment. When the accuracy is low, the accuracy of the coordinates of the determined lane line is low. Accordingly, it is determined by the prior art.
- the accuracy of the lane line information is low. And further, vehicles usually realize target recognition and tracking through road information. Therefore, when the accuracy of lane line information is low, the accuracy of target recognition and tracking by the vehicle will decrease.
- the ambient light conditions of the vehicle are poor.
- the sharpness of the image containing the lane line captured by the camera is low, so image analysis
- the accuracy of the vehicle is low, which will result in low accuracy of the determined lane line information, and further, will reduce the accuracy of vehicle target recognition and tracking.
- the camera is farther away from the lane line to be captured.
- the lane line occupies fewer pixels, which will also lead to the accuracy of the image analysis Lower, resulting in lower accuracy of the determined lane line information, and further reducing the accuracy of vehicle target recognition and tracking.
- the first road parameter set of the road edge can be determined according to the first coordinate of the road edge, and the first road parameter set of the road edge and the second coordinate of the lane line can be combined to jointly determine the lane.
- Line information That is to say, in the solution of the embodiment of the present application, the lane line information is determined according to the first coordinates of the road edge and the second coordinate of the lane line. Compared with the scheme, the accuracy of the determined lane line information is higher.
- the solution of the present application can further improve the accuracy of vehicle target recognition and tracking.
- the first coordinate of the road edge is usually determined by the detection information of the radar, and the radar determines the detection information by the electromagnetic wave emitted to the road edge, so the detection information of the radar is not easily affected by the external environment.
- the accuracy of the first coordinate of the road edge adopted in the embodiment of the present application is generally higher. Therefore, compared with the prior art, the solution of the present application can determine lane line information with higher accuracy.
- the embodiment of the present application may also determine the first coordinate of the road edge in other ways, which is not limited in the embodiment of the present application.
- the curve equation of the road edge can be determined by curve fitting the first coordinate, and the curve equation includes the first road parameter set of the road edge. Road parameters.
- curve fitting algorithms can be used to determine the curve equation of the road edge.
- the curve fitting algorithm used may include at least one of least squares, Hough transform, and RANSAC (Random Sample Consensus). Of course, other forms of curve may also be used.
- the fitting algorithm is not limited in the embodiment of this application.
- the first road parameter set includes at least one road parameter of a road edge.
- the first road parameter set includes: the heading angle, curvature, and curvature change rate of the road edge; or, the The first road parameter set includes any two road parameters among the heading angle, curvature, and curvature change rate of the road edge; or, the first road parameter set includes the heading angle, curvature, and curvature change rate of the road edge Any road parameter in.
- the curve equation of the road edge may be in various forms.
- the curve equation of the road edge can be a cubic curve equation or a quadratic curve equation, or the road edge can be divided into multiple segments, and each segment of the road edge can be represented by a different curve equation.
- the curve equation of the road edge is a cubic curve equation
- the curve equation of the road edge may be a clothoid cubic curve equation.
- curve equation of the road edge may also be another form of curve equation, which is not limited in the embodiment of the present application.
- the curve equation of the road edge is clothoid cubic curve equation
- the clothoid cubic curve equation has the following form:
- (x, y) represents the first coordinate in the edge of the road
- C 0R , C 1R , C 2R and C 3R respectively represent a road parameter of the edge of the road.
- C 0R can represent the lateral offset of the road edge
- C 1R can represent the heading angle of the road edge
- C 2R can represent the curvature of the road edge
- C 3R can represent the curvature change rate of the road edge.
- the lateral offset of an object on the edge of the road refers to the amount of lateral displacement of the object relative to the edge of the road.
- FIG. 3 is disclosed in the embodiment of the present application.
- Fig. 3 is a schematic diagram of a scene of vehicle operation, which includes the lateral offset of the edge of the road, and also includes the heading angle of the edge of the road.
- C 0R , C 1R , C 2R, and C 3R may also represent other types of road parameters of the road edge, which are not limited in the embodiment of the present application.
- the first coordinate in the road edge is determined according to the detection information of the radar, and then the curve fitting algorithm is used to calculate the a first coordinate curve fitting, to determine the C 0R C 0R formula (1), the value C 1R, C 2R and C 3R, wherein the formula (1), C 1R, C 2R and C 3R i.e. Is the road parameter of the road edge, and then the road parameter in the first road set is selected from the road parameters of the road edge, so as to determine the first road parameter set of the road edge.
- the road parameters in the first road parameter set of the road edge usually satisfy the following condition: the road parameters in the first road set are usually approximately equal to the road parameters of the same type in the lane line.
- the road parameters in the first road set are usually approximately equal to the road parameters of the same type in the lane line.
- the first threshold can be set according to the accuracy of the radar and/or imaging device, and can also be adjusted according to the user's usage requirements.
- the first road parameter set usually includes the at least one road parameter: the heading angle, the curvature, and the curvature change rate of the road edge.
- the types of road parameters included in the first road parameter set are the same as the types of road parameters included in the second road parameter set in a one-to-one correspondence.
- the types of road parameters included in the road parameter set may determine the types of road parameters included in the second road parameter set.
- the operation includes determining the third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line.
- this operation usually includes the following steps:
- the third road parameter set is determined according to the curve equation, and 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.
- the third road parameter set usually includes the lateral offset of the lane line.
- the curve fitting algorithm used may include at least one of least squares, Hough transform, and RANSAC (Random Sample Consensus). Of course, other forms of curve may also be used.
- the fitting algorithm is not limited in the embodiment of this application.
- the curve equation of the lane line may also be in various forms.
- the curve equation of the lane line can be a cubic curve equation, or a quadratic curve equation, or the lane line can be divided into multiple segments, and each segment of the lane line can be represented by a different curve equation.
- 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.
- curve equation of the lane line can also be another form of curve equation, which is not limited in the embodiment of the present application.
- the curve equation of the lane line is clothoid cubic curve equation
- the clothoid cubic curve equation has the following form:
- (x, y) represents the second coordinate of the lane line
- C 0C , C 1C , C 2C and C 3C respectively represent a road parameter of the lane line.
- C 0C may indicate the lateral offset of the lane line
- C 1C may indicate the heading angle of the lane line
- C 2C may indicate the curvature of the lane line
- C 3C may indicate the curvature change rate of the lane line.
- C 0C , C 1C , C 2C and C 3C may also be other forms of road parameters, which are not limited in the embodiment of the present application.
- the lateral offset of an object in the lane line refers to the amount of lateral displacement of the object relative to the lane line.
- the second road parameter is at least one of C 1C , C 2C and C 3C , and the first road included in the first road parameter set
- the type of the parameter is the same as the type of the second road parameter included in the second road parameter set in a one-to-one correspondence.
- the second road parameter set includes the heading of the lane line The angle C 1C , the curvature of the lane line C 2C, and the rate of change of curvature of the lane line C 3C .
- the third road parameter set includes at least one road parameter of a different type from the road parameter included in the second road parameter set.
- the third road parameter set includes the lane The horizontal offset of the line C 0C .
- the third road parameter set that is, the lateral offset C 0C of the lane line
- the road parameters included in the second road parameter set and the third road parameter set may also be other road parameters, which are not limited in the embodiment of the present application.
- the second road parameter set includes the heading angle C 1C of the lane line and the curvature C 2C of the lane line
- the third road parameter set includes the curvature change rate of the lane line C 3C and lateral offset C 0C .
- the road edge and lane line of the same road are usually parallel.
- the road parameters included in the first road parameter set are the same as those in the second road parameter set.
- the road parameters of the same type are approximately equal. Therefore, the second road parameter set of the lane line of the road can be determined according to the first road parameter set.
- the road edge is parallel to the lane line, which means that except for the position parameter, the curve parameters of the road edge and the lane line are approximately equal.
- the curve parameter is used to characterize the degree of curvature of the curve.
- the position parameter includes a lateral offset
- the curve parameter includes a heading angle, a curvature, a rate of change of curvature, and the like.
- a certain curve parameter of the road edge and the lane line is approximately equal, which means that the difference between this curve parameter of the road edge and this curve parameter of the lane line is less than a certain threshold.
- the threshold can be set according to a priori information, and can also be adjusted according to the accuracy of the imaging device and the radar.
- the road edge and lane line of the same road may not be parallel.
- the second road parameter set of the lane line is usually determined. Large error.
- Step S21 Determine the first road parameter set of the road edge according to the first coordinates of the road edge of the road.
- step S21 is the same as the specific execution process of step S11, which can be referred to each other, and will not be repeated here.
- Step S22 Determine whether the road edge and the lane line are parallel according to the distance parameter between the road edge and the lane line. If yes, perform the operation of step S23.
- Step S23 Determine a second road parameter set of the lane line of the road according to the first road parameter set
- Step S24 Determine a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, and the second road parameter set and the third road parameter set include all State the lane line information.
- step S23 to step S24 is the same as the specific execution process of step S12 to step S13, which can be referred to each other, and will not be repeated here.
- the method 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: according to the road edge and the The distance parameter between lane lines determines that the road edge is parallel to the lane line, that is, 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 , The operation of determining the second road parameter set of the lane line of the road according to the first road parameter set is performed.
- the second road parameter set of the lane line is determined according to the first road parameter set of the road edge, which can improve the determination of the The accuracy of the second road parameter set further improves the accuracy of determining the lane line information.
- step S22 if it is determined in step S22 that the road edge is not parallel to the lane line, the first road parameter set that does not currently pass the road edge is determined to determine the second road of the lane line The parameters are set, and the operation of step S21 can also be returned to.
- the first road parameter set of the road edge is determined according to the first coordinates of the road edge of the road.
- 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.
- the distance parameter may also be of other types, which is not limited in the embodiment of the present application.
- the road edge when the distance parameter between the road edge and the lane line includes Euclidean distance, if the Euclidean distance between the road edge and the lane line is less than a first distance threshold, the road edge may be determined Parallel to the lane line.
- the road edge may be determined Parallel to the lane line.
- the distance parameter between the road edge and the lane line includes the Minkowski distance
- the Minkowski distance between the road edge and the lane line is less than the third distance threshold, then It can be determined that the road edge is parallel to the lane line.
- first distance threshold, second distance threshold, and third distance threshold can be set according to prior information, and can also be adjusted according to requirements. For example, it can be adjusted according to the accuracy of the imaging device and radar.
- the distance parameter between the road edge and the lane line includes the Mahalanobis distance. In this case, the following steps are further included:
- the fourth road parameter set of the lane line is obtained by curve fitting the second coordinates, and the parameter type of at least one road parameter in the fourth road parameter set is the same as that of the first road parameter set. At least one road parameter has the same parameter type;
- the Mahalanobis distance between the road edge and the lane line is calculated.
- curve fitting may be performed on the second coordinates to obtain a curve equation including the lane line, and the curve equation includes the lane
- the fourth road parameter set can be determined through the curve equation.
- the Mahalanobis distance between the edge of the road and the lane line can be calculated by the following formula:
- C Radar represents the matrix formed by the M first road parameters
- C Camera represents the matrix formed by the M second road parameters.
- P Radar is a physical quantity representing the accuracy of C Radar
- P Camera is a physical quantity representing the accuracy of C Camera.
- P Radar is the covariance of C Radar
- P Camera represents the covariance of C Camera.
- the first road parameter set includes the heading angle C 1R of the road edge, the curvature C 2R of the road edge, and the curvature change rate of the road edge C 3R
- C Radar (C 1R , C 2R , C 3R ).
- C Camera (C 1C , C 2C , C 3C ).
- the Mahalanobis distance between the road edge and the lane line can be determined, so that it is convenient to determine whether the road edge and the lane line are parallel according to the Mahalanobis distance.
- the 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 this operation can be implemented in a variety of ways.
- the determining the second road parameter set of the lane line of the road according to the first road parameter set includes:
- 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.
- the road edge and lane line of the same road are usually parallel.
- the road parameters included in the first road parameter set are often approximately equal to the road parameters of the same type included in the second road parameter set.
- the curvature of the road edge included in the first road parameter set is often approximately equal to the curvature of the lane line included in the second road parameter set.
- the first road parameter set is usually determined by the detection information of the radar, and the detection information of the radar is often not easily interfered by the environment.
- the accuracy of obtaining the first road parameter set is often higher than that of obtaining the second road.
- the accuracy of the parameter set it is determined that at least one road parameter in the second road parameter set corresponds to at least one road parameter in the first road parameter set in a one-to-one correspondence, which can improve the determination of the second road parameter set.
- the accuracy of the parameter set can further improve the accuracy of determining the lane line information.
- the determining the second road parameter set of the lane line of the road according to the first road parameter set includes the following steps:
- the fifth road parameter set of the lane line is obtained by curve fitting the second coordinates, and the parameter type of at least one road parameter in the fifth road parameter set is the same as that of the first road parameter set. At least one road parameter has the same parameter type;
- the fifth road parameter set and the first road parameter set are fused by a first fusion algorithm, and the fusion result is the second road parameter set.
- the road parameters included in the fifth road parameter set may be the same as the road parameters included in the fourth road parameter set. In this case, if the fourth road parameter set is determined in advance, all road parameters may be directly The fourth road parameter set is used as the fifth road parameter set, so that it is not necessary to perform curve fitting on the second coordinate.
- the road parameters included in the fifth road parameter set may also be road parameters that are different from the road parameters included in the fourth road parameter set.
- the method of obtaining the fifth road parameter set can be referred to The manner of obtaining the fourth road parameter set will not be repeated in the embodiment of the present application.
- the method for determining lane line information disclosed in the embodiment of the present application may include the following steps:
- Step S31 Determine the first road parameter set of the road edge according to the first coordinates of the road edge of the road.
- step S31 is the same as the specific execution process of step S11, which can be referred to each other, and will not be repeated here.
- Step S32 Determine the fourth road parameter set of the lane line by curve fitting to the second coordinate of the lane line of the road, and determine the fourth road parameter set of the lane line according to the first road parameter set and the fourth road parameter set, The distance parameter between the edge of the road and the lane line is determined, and the distance parameter includes at least one of the following distance parameters: Euclidean distance, Mahalanobis distance, and Minkowski distance.
- the types of road parameters included in the fourth road parameter set are the same as the types of road parameters included in the first road parameter set in a one-to-one correspondence.
- Step S33 Determine whether the road edge and the lane line are parallel according to the distance parameter between the road edge and the lane line. If yes, perform the operation of step S34. If not, return to the operation of step S31.
- Step S34 Obtain a fifth road parameter set of the lane line by performing curve fitting on the second coordinate.
- 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.
- Step S35 The fifth road parameter set and the first road parameter set are fused by a first fusion algorithm, and the fusion result is the second road parameter set.
- Step S36 Determine a third road parameter set of the lane line according to the second road parameter set and the second coordinate of the lane line, and the second road parameter set and the third road parameter set include all State the lane line information.
- step S36 is the same as the specific execution process of step S13, which can be referred to each other, and will not be repeated here.
- 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 the first fusion algorithm, and the fusion result is used as the second road Parameter collection.
- the fifth road parameter set and the first road parameter set can be merged into the second road parameter set. Since the accuracy of determining the first road parameter combination is relatively high, this method can improve the accuracy of determining the second road parameter set, and further, can also improve the accuracy of determining the lane line information.
- the first fusion algorithm may be a fusion algorithm of various forms.
- the first fusion algorithm includes: a convex combination fusion (convex combination fusion, CC) algorithm and/or a covariance intersection fusion (covariance intersection fusion, CI) algorithm.
- the fifth road parameter set and the first road parameter set can be fused by the following formula:
- x R represents a vector composed of road parameters included in the first road parameter set
- x C represents a vector composed of road parameters included in the fifth road parameter set
- P R represents a vector composed of road parameters included in the first road parameter set.
- parameter vector covariance road configuration P C represents the fifth road covariance parameter set comprises parameter vector path constituted
- x C represents the first set of parameters of the fifth set of roads and road parameters fusion
- P F represents the covariance of the vector formed by the fusion result of the first road parameter set and the fifth road parameter set.
- the fusion result of the first road parameter set and the fifth road parameter set can be determined, wherein the first road parameter set and the fifth road parameter set The result of the fusion is the second road parameter set.
- the fifth road parameter set and the first road parameter set can be fused by the following formula:
- x R represents a vector composed of road parameters included in the first road parameter set
- x C represents a vector composed of road parameters included in the fifth road parameter set
- P R represents a vector composed of road parameters included in the first road parameter set.
- parameter vector covariance road configuration P C represents the fifth road covariance parameter set comprises parameter vector path constituted
- x C represents the first set of parameters of the fifth set of roads and road parameters fusion configuration result vector
- P F denotes the covariance of the parameter vector and the set of parameters of the fifth set of road first fusion results road configuration
- w represents the weight parameter.
- the fusion result of the first road parameter set and the fifth road parameter set can be determined, wherein the first road parameter set and the fifth road parameter set The result of the fusion is the second road parameter set.
- w represents a weight parameter
- the weight parameter w can be determined in a variety of ways.
- the weight parameter w can be determined by a technician based on experience.
- the weight parameter w may be determined according to the following formula:
- N R represents the number of the first coordinate
- N C represents the number of the second coordinates
- the first coordinate is usually determined by the detection information of the radar
- the second coordinate is usually determined by the detection information of the imaging device.
- the detection information of the imaging device is more susceptible to the influence of the external environment. Therefore, in formula (8), the greater the number of first coordinates, the higher the confidence of the weight parameter w.
- the weight parameter w may also be determined in other ways. For example, an empirical method can be used. In this method, the weight parameter w is set based on experience obtained from multiple experiments. Or, other methods may also be used, which are not limited in the embodiment of the present application.
- an embodiment of the present application discloses an apparatus for determining lane line information.
- the apparatus for determining lane line information disclosed in the embodiment of the present application includes: a processor 110, a first transceiver interface 120, and a second transceiver interface 130.
- the first transceiver interface 120 is used to receive first detection information of the radar, and the first detection information includes information about the road edge of the road.
- the related information of the road edge includes the geographic location of the road edge and/or the information of the coordinates of the road edge.
- the second transceiver interface 130 is configured to receive second detection information of the imaging device, and the first detection information includes information related to lane lines of the road.
- the relevant information of the lane line includes information about the geographic location of the lane line and/or the coordinates of the lane line.
- the processor 110 is configured to determine the first coordinate of the road edge according to the first detection information, and determine the second coordinate of the lane line according to the second detection information;
- the processor 110 is further configured to determine the first road parameter set of the road edge according to the first coordinates of the road edge of the road, and determine the second road parameter set of the lane line of the road according to the first road parameter set.
- a road parameter set 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, the second road parameter set and the third road parameter set including The lane line information.
- the first transceiving interface 120 and the second transceiving interface 130 may be two different transceiving interfaces. Receiving the first detection information and the second detection information.
- the first transceiver interface 120 and the second transceiver interface 130 may also be the same transceiver interface.
- the lane line determination device in the embodiment of the present application may receive the first detection information and the second detection information through the same transceiver interface.
- the processor 110 is further configured to, before determining the second road parameter set of the lane line of the road according to the first road parameter set, according to the distance between the road edge and the lane line The distance parameter determines that the edge of the road is parallel to the lane line.
- 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.
- the distance parameter between the edge of the road and the lane line includes Mahalanobis distance
- the processor 110 is further configured to obtain the fourth position of the lane line by curve fitting the second coordinates.
- the Mahalanobis distance between the road edge and the lane line is calculated.
- the processor 110 is specifically configured to determine that at least one road parameter in the second road parameter set is one-to-one with at least one road parameter in the first road parameter set. Corresponds to the same.
- the processor 110 is specifically configured to obtain a fifth road parameter set of the lane line by performing curve fitting on the second coordinate, and the fifth road parameter set
- the parameter type of at least one road parameter in is the same as the parameter type of at least one road parameter in the first road parameter set;
- the fifth road parameter set and the first road parameter set are fused by a first fusion algorithm, and the fusion result is the second road parameter set.
- the first fusion algorithm includes: a convex combination fusion algorithm and/or a covariance cross fusion algorithm.
- the processor 110 is specifically configured to determine the curve equation of the lane line by performing curve fitting on the second road parameter set and the second coordinate of the lane line ;
- the third road parameter set is determined according to the curve equation, and 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.
- the first road parameter set includes at least one of the following road parameters: heading angle, curvature, and curvature change rate of the road edge;
- the third road parameter set includes at least the lateral offset of the lane line.
- the lane line information determining device disclosed in the embodiment of the present application can determine the first road parameter set of the road edge according to the first coordinate of the road edge, and combine the first road parameter set of the road edge and the second coordinate of the lane line to jointly determine the lane Line information.
- the accuracy of the determined lane line information is higher.
- the solution of the present application can further improve the accuracy of vehicle target recognition and tracking.
- the lane line information determination device may include various forms.
- the device for determining lane line information is integrated in an imaging device.
- the imaging device receives the first detection information transmitted by the radar through the first transceiver interface 120, and transmits the first detection information to the processor 110 of the imaging device; and, the second transceiver interface 130 acquires the second detection information detected by the imaging device, and transmits the second detection information to the processor 110; the processor 110 determines the lane according to the first detection information and the second detection information
- the second road parameter set and the third road parameter set of the line, the second road parameter set and the third road parameter set include the lane line information.
- the imaging device may also use the first transceiver interface 120, or through the second transceiver interface 130, or through a different interface than the first transceiver interface 120 and the second transceiver interface 130.
- the other transceiver interfaces of the transmit the lane line information to the smart car, so that the smart car can realize functions such as target recognition based on the lane line information.
- the lane line information determining device is integrated in the radar.
- the radar receives the 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 radar; and, the first transceiver interface 120 Acquire the first detection information detected by the radar, and transmit the first detection information to the processor 110; the processor 110 determines the lane line according to the first detection information and the second detection information
- a second road parameter set and a third road parameter set, and the second road parameter set and the third road parameter set include the lane line information.
- the radar may also use the first transceiver interface 120, or through the second transceiver interface 130, or through a different interface from the first transceiver interface 120 and the second transceiver interface 130.
- Other transceiver interfaces transmit the lane line information to the smart car, so that the smart car can realize functions such as target recognition based on the lane line information.
- the lane line information determination device is integrated in the fusion module.
- the fusion module receives the first detection information transmitted by the radar through the first transceiver interface 120, and transmits the first detection information to the processor 110 of the fusion module; the fusion module passes the first detection information
- the second transceiver interface 130 receives the second detection information transmitted by the imaging device, and transmits the second detection information to the processor 110 of the fusion module; the processor 110 determines according to the first detection information and the second detection information
- a second road parameter set and a third road parameter set of a lane line, and the second road parameter set and the third road parameter set include the lane line information.
- the fusion module may be a remote computer or the like.
- the fusion module may also use the first transceiver interface 120, or through the second transceiver interface 130, or through a different interface than the first transceiver interface 120 and the second transceiver interface 120.
- the other transceiver interfaces of the interface 130 transmit the lane line information to the smart car, so that the smart car can realize functions such as target recognition through the lane line information.
- the fusion module may be a functional module provided in a smart car, for example, it may be a chip system or a circuit in the smart car.
- the smart car can use the lane line information to implement functions such as target recognition.
- the lane line information determination device is integrated in the fusion module and the smart car.
- the fusion module may include a first transceiver interface 120 and a second transceiver interface 130
- the smart car may include a processor 110, that is, the processor 110 is an on-board processor of the smart car.
- the fusion module receives the first detection information transmitted by the 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 passes The second transceiver interface 130 receives the second detection information transmitted by the imaging device, 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, and the second road parameter set and the third road parameter set include The lane line information. After determining the lane line information, the smart car can use the lane line information to implement functions such as target recognition.
- the lane line information determining device is integrated in a smart car.
- the first transceiving interface 120 and the second transceiving interface 130 are both transceiving interfaces installed in a smart car
- the processor 110 is an in-vehicle processor of the smart car.
- the first transceiver interface 120 receives the first detection information transmitted by the radar, and transmits the first detection information to the processor 110;
- the second transceiver interface 130 receives the second detection information transmitted by the imaging device Information, and transmit 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, and the second road parameter set and the third road parameter set include According to the lane line information, the smart car implements functions such as target recognition according to the lane line information.
- 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.
- the terminal device includes:
- At least one processor 1101 and memory are included. At least one processor 1101 and memory,
- the memory is used to store program instructions
- the processor is configured to call and execute program instructions stored in the memory, so that the terminal device executes all or part of the steps in the embodiments corresponding to FIG. 2, FIG. 4, and FIG. 5.
- the terminal device may further include a transceiver 1102 and a bus 1103, and the memory includes a random access memory 1104 and a read-only memory 1105.
- the processor is respectively coupled to the receiver/transmitter, the random access memory and the read-only memory through the bus.
- the basic input output system solidified in the read-only memory or the bootloader guide system in the embedded system is started to guide the device into a normal operating state.
- the application program and the operating system are run 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 in 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.
- the functions and/or various steps and methods implemented by the road information determining apparatus in the embodiment corresponding to FIG. 5 are not repeated here for brevity.
- this embodiment may also be based on a network device implemented by a general physical server combined with network function virtualization (English: Network Function Virtualization, NFV) technology.
- the network device is a virtual network device (e.g., virtual host, virtual Router or virtual switch).
- the virtual network device may be a virtual machine (English: Virtual Machine, VM) running a program for sending notification messages, and the virtual machine is deployed on a hardware device (for example, a physical server).
- a virtual machine refers to a complete computer system with complete hardware system functions that is simulated by software and runs in a completely isolated environment.
- Those skilled in the art can virtualize multiple network devices with the above-mentioned functions on a general physical server by reading this application. I won't repeat them here.
- the terminal device disclosed in the embodiment of the present application can be used as a target recognition device.
- the target recognition device is used to determine road information, so as to perform target recognition on obstacles, pedestrians, vehicles, etc. in the road according to the road information, and further realize target tracking.
- the terminal device When determining the lane line information, the terminal device needs the first detection information of the radar and the second detection information of the imaging device, and determines the lane line information according to the first detection information and the second detection information.
- the terminal device can be implemented in a variety of forms. Referring to the schematic diagram of the connection relationship shown in FIG. 8, in one of the implementation forms, the radar 210 and the imaging device 220 are respectively connected to the terminal device 230 through the corresponding transceiver interface, and respectively transmit the data to the terminal device 230. The first detection information and the second detection information so that the terminal device can determine the lane line information.
- the terminal device may be a vehicle-mounted processor, or the terminal device may also be a remote processor.
- the remote processor may also transmit the lane line information to the corresponding vehicle, so that the vehicle can achieve the goal according to the lane line information Recognition.
- the terminal device disclosed in the embodiment of the present application is a radar or an imaging device.
- the radar receives the second detection information transmitted by the imaging device through the transceiver interface, and determines the lane line information according to the first detection information and the second detection information determined by itself .
- the radar may also transmit the lane line information to the corresponding vehicle, so that the vehicle can realize target recognition according to the lane line information.
- the imaging device receives the first detection information transmitted by the radar through the transceiver interface, and determines the lane line according to the second detection information and the first detection information determined by itself. information.
- the imaging device may also transmit the lane line information to the corresponding vehicle, so that the vehicle can realize target recognition according to the lane line information.
- terminal device may also be implemented in other forms, which is not limited in the embodiment of the present application.
- the terminal device disclosed in the embodiment of the present application can be applied to the field of intelligent driving, and in particular, can be applied to the advanced driving assistance system ADAS or automatic driving system.
- the terminal device may be set in a vehicle that supports advanced driving assistance functions or automatic driving functions, and the lane line information is determined according to the scheme disclosed in the embodiments of the application, so that the vehicle can implement advanced driving assistance or advanced driving assistance based on the lane line information. Autopilot function.
- the solutions of the embodiments of the present application can improve autonomous driving or ADAS capabilities, and therefore can be applied to the Internet of Vehicles, for example, can be applied to vehicle-to-everything (V2X), long-term evolution technology-vehicles Communication (long term evolution-vehicle, LTE-V) and inter-vehicle communication (vehicle-to-vehicle, V2V) and other systems.
- V2X vehicle-to-everything
- LTE-V long-term evolution-vehicle
- V2V inter-vehicle communication
- an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes instructions.
- the computer-readable medium when the computer-readable medium is set in any device and runs on a computer, it can implement all or part of the steps in the embodiments corresponding to FIG. 2, FIG. 4, and FIG. 5.
- the storage medium of the computer readable medium may be a magnetic disk, an optical disc, a read-only memory (English: read-only memory, abbreviated as: ROM) or a random access memory (English: random access memory, abbreviated as: RAM), etc. .
- FIG. 2 Another embodiment of the present application also discloses a computer program product containing instructions.
- the computer program product runs on an electronic device, the electronic device can implement the corresponding instructions including those shown in FIG. 2, FIG. 4, and FIG. 5. All or part of the steps in the embodiment.
- an embodiment of the present application also discloses a smart car, which includes the lane line information determining device or the terminal device disclosed in the foregoing embodiment of the present application.
- the lane line information determining device or terminal device is usually carried by a chip, integrated circuit, and/or processor built into the vehicle, and the at least one processor and memory may be implemented by different, integrated circuits and/or The processor is carried, or, the at least one processor and the memory may also be carried by a chip, an integrated circuit, or a processor.
- At least one sensor may be built in the smart car, and the detection information required in the process of determining the road information may be acquired through the sensor.
- the sensor may include a vehicle-mounted imaging device and/or a vehicle-mounted radar.
- the smart car may also be connected to a remote sensor in a wireless manner, and the detection information required in the process is determined through the remote sensor.
- the remote sensors include imaging devices and/or radars.
- the embodiment of the present application also discloses a system, which can determine road information through the method disclosed in the foregoing embodiment of the present application.
- the system includes a terminal device, an imaging device and a radar.
- the imaging device is used to obtain the second detection information
- the radar is used to obtain the first detection information
- the terminal device is used to obtain the first detection information and the second detection information, and through the solutions disclosed in the above embodiments, According to the first detection information and the second detection information, the lane line information is determined.
- the various illustrative logic units and circuits described in the embodiments of this application can be implemented by general-purpose processors, digital information processors, application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, Discrete gates or transistor logic, discrete hardware components, or any combination of the above are designed to implement or operate the described functions.
- the general-purpose processor may be a microprocessor.
- the general-purpose processor may also be any traditional processor, controller, microcontroller, or state machine.
- the processor can also be implemented by a combination of computing devices, such as a digital information processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital information processor core, or any other similar configuration. accomplish.
- the steps of the method or algorithm described in the embodiments of the present application can be directly embedded in hardware, a software unit executed by a processor, or a combination of the two.
- the software unit can be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM or any other storage medium in the art.
- the storage medium may be connected to the processor, so that the processor can read information from the storage medium, and can store and write information to the storage medium.
- the storage medium may also be integrated into the processor.
- the processor and the storage medium may be set in the ASIC, and the ASIC may be set in the UE.
- the processor and the storage medium may also be provided in different components in the UE.
- the size of the sequence number of each process does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not correspond to the difference in the embodiments of the present application.
- the implementation process constitutes any limitation.
- the computer may be implemented in whole or in part by software, hardware, firmware, or any combination thereof.
- software it can be implemented in the form of a computer program product in whole or in part.
- the computer program product includes one or more computer instructions.
- the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
- the computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.
- the computer instructions may be transmitted from a website, computer, server, or data center.
- the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media.
- the usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).
- the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform.
- the technical solutions in the embodiments of the present invention can be embodied in the form of software products, which can be stored in a storage medium, such as ROM/RAM. , Magnetic disks, optical disks, etc., including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments of the present invention.
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Abstract
提供一种车道线信息确定方法及装置,根据道路边沿的第一坐标,确定道路边沿的第一道路参数集合(S11),再根据第一道路参数集合确定车道线的第二道路参数集合(S12),根据第二道路参数集合和车道线的第二坐标,确定车道线的第三道路参数集合(S13),其中,第二道路参数集合和第三道路参数集合包括车道线信息。与现有技术中仅通过车道线的坐标确定车道线信息的方案相比,结合道路边沿的第一道路参数集合和车道线的第二坐标,共同确定车道线信息,提高了确定车道线信息的准确度以及目标识别与跟踪的准确度。
Description
本申请涉及智能汽车技术领域,具体涉及一种车道线信息确定方法及装置。
为了满足无人驾驶的需求,一种支持自动化驾驶功能的车辆应运而生。在行驶过程中,该车辆需要对道路中的障碍物、行人和其他车辆等进行目标识别与跟踪。为了实现这一功能,该车辆需要确定道路信息。
由于道路中包括车道线,因此车辆需要确定的道路信息通常包括车道线信息,例如车道线的曲率等。目前,车辆通常采用道路结构化的方式确定车道线的道路参数。其中,道路结构化指的是通过数学模型表征道路的结构,该数学模型通常为包括道路信息的曲线方程。
目前车辆在获取车道线的曲线方程时,通常首先通过摄像头对车道线进行拍照,获取包含车道线的图像,然后对该图像进行图像分析,获取位于车道线中多个地点的坐标,再对所述多个地点的坐标进行曲线拟合,以便获取车道线的曲线方程,该曲线方程中包括所述车道线信息。
但是,发明人在本申请的研究过程中发现,车辆往往会运行在不同环境的道路中,这种情况下,对包含车道线的图像进行图像分析的准确度较易受到外界环境的影响,当该准确度较低时,这种情况下,所确定的车道线的坐标准确性较低,从而导致通过现有技术获取到的车道线信息的准确性较低,进一步的,还会降低车辆进行目标识别与跟踪的准确度。例如,当车辆运行在光照条件较差的环境时,摄像头拍摄的图像的清晰度较低,从而导致对该图像进行图像分析的准确度较低;或者,当摄像头与需要拍摄的车道线的距离较远时,在摄像头拍摄的图像中,所述车道线所占据的像素较少,也会导致图像分析的准确度较低。
发明内容
为了解决现有技术中,通过摄像头拍摄的图像确定车道线信息时,容易受到外界环境的影响,导致获取到的车道线信息准确性较低的问题,本申请实施例公开一种车道线信息确定方法及装置。
第一方面,本申请实施例公开一种车道线信息确定方法,包括:
根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合;
根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合;
根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
本申请实施例的方案中,根据道路边沿的第一坐标和车道线的第二坐标确定车道线信息,这一方案与现有技术中仅通过车道线的坐标确定车道线信息的方案相比,所确定的车道线信息准确性较高。
一种可选的设计中,在根据所述第一道路参数集合,确定所述道路的车道线的第二道 路参数集合之前,还包括:
根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行。
在本申请实施例中,只有确定道路边沿与车道线平行的情况下,才根据道路边沿的第一道路参数集合,确定车道线的第二道路参数集合,从而能够提高确定所述第二道路参数集合的准确度,进一步提高确定车道线信息的准确度。
一种可选的设计中,所述道路边沿与所述车道线之间的距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
一种可选的设计中,所述道路边沿与所述车道线之间的距离参数包括马氏距离,还包括:
通过对所述第二坐标进行曲线拟合,获取所述车道线的第四道路参数集合,所述第四道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
根据所述第一道路参数集合以及所述第四道路参数集合,计算所述道路边沿与所述车道线之间的马氏距离。
一种可选的设计中,所述根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,包括:
确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同。
获取第一道路参数集合的准确性往往高于获取第二道路参数集合的准确性,因此,确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同,能够提高确定所述第二道路参数集合的准确性,进一步的,还能够提高确定车道线信息的准确性。
一种可选的设计中,所述根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,包括:
通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
在上述实现方式中,通过第一融合算法,对道路边沿的第一道路参数集合包括的道路参数和车道线的第五道路参数集合包括的道路参数进行融合,将融合结果作为所述第二道路参数集合,由于确定第一道路参数结合的准确性较高,因此,该方式能够提高确定所述第二道路参数集合的准确性,进一步的,还能够提高确定车道线信息的准确性。
一种可选的设计中,所述第一融合算法包括:凸组合融合算法和/或协方差交叉融合算法。
一种可选的设计中,所述根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,包括:
通过对所述第二道路参数集合和所述车道线的第二坐标进行曲线拟合,确定所述车道 线的曲线方程;
根据所述曲线方程确定所述第三道路参数集合,所述第三道路参数集合中的至少一个道路参数的参数类型不同于所述第二道路参数集合中的任一道路参数的参数类型。
一种可选的设计中,所述第一道路参数集合包括以下至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率;
所述第三道路参数集合包括所述车道线的横向偏移量。
第二方面,本申请实施例提供一种车道线信息确定装置,包括:
处理器、第一收发接口和第二收发接口;
所述第一收发接口用于接收雷达的第一探测信息,所述第一探测信息包括道路的道路边沿的相关信息;
所述第二收发接口用于接收成像装置的第二探测信息,所述第一探测信息包括所述道路的车道线的相关信息;
所述处理器用于,根据所述第一探测信息,确定所述道路边沿的第一坐标,并根据所述第二探测信息,确定所述车道线的第二坐标;
所述处理器还用于,根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合,根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
一种可选的设计中,所述处理器还用于,在根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合之前,根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行。
一种可选的设计中,所述道路边沿与所述车道线之间的距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
一种可选的设计中,所述道路边沿与所述车道线之间的距离参数包括马氏距离,
所述处理器还用于,通过对所述第二坐标进行曲线拟合,获取所述车道线的第四道路参数集合,所述第四道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
根据所述第一道路参数集合以及所述第四道路参数集合,计算所述道路边沿与所述车道线之间的马氏距离。
一种可选的设计中,所述处理器具体用于,确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同。
一种可选的设计中,所述处理器具体用于,通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
一种可选的设计中,所述第一融合算法包括:凸组合融合算法和/或协方差交叉融合算法。
一种可选的设计中,所述处理器具体用于,通过对所述第二道路参数集合和所述车道 线的第二坐标进行曲线拟合,确定所述车道线的曲线方程;
根据所述曲线方程确定所述第三道路参数集合,所述第三道路参数集合中的至少一个道路参数的参数类型不同于所述第二道路参数集合中的任一道路参数的参数类型。
一种可选的设计中,所述第一道路参数集合包括以下至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率;
所述第三道路参数集合至少包括所述车道线的横向偏移量。
第三方面,本申请实施例提供一种终端装置,包括:
至少一个处理器和存储器;
其中,所述存储器,用于存储程序指令;
所述至少一个处理器,用于调用并执行所述存储器中存储的程序指令,所述处理器执行所述程序指令时,使得所述装置执行如第一方面所述的方法。
第四方面,本申请实施例提供一种计算机可读存储介质,其特征在于,
所述计算机可读存储介质中存储有指令,当其在计算机上运行时,使得所述计算机执行如第一方面所述的方法
第五方面,本申请实施例提供一种包含指令的计算机程序产品,当所述计算机程序产品在电子设备上运行时,使得所述电子设备执行如第一方面所述的方法。
第六方面,本申请实施例提供一种智能车,所述智能车包括上述第二方面所述的车道线信息确定装置,或者,所述智能车包括上述第三方面所述的终端装置。
在本申请实施例公开的方案中,能够根据道路边沿的第一坐标确定道路边沿的第一道路参数集合,并结合道路边沿的第一道路参数集合和车道线的第二坐标,共同确定车道线信息。也就是说,本申请实施例的方案中,根据道路边沿的第一坐标和车道线的第二坐标确定车道线信息,这一方案与现有技术中仅通过车道线的坐标确定车道线信息的方案相比,所确定的车道线信息准确性较高。
进一步的,由于通过本申请的方案所确定的车道线信息的准确度较高,因此,本申请的方案还能够进一步提高车辆进行目标识别与跟踪的准确度。
为了更清楚地说明本申请的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,显而易见地,对于本领域普通技术人员而言,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。
图1为本申请实施例公开的一种车辆行驶的场景示意图;
图2为本申请实施例公开的一种车道线信息确定方法的工作流程示意图;
图3为本申请实施例公开的又一种车辆行驶的场景示意图;
图4为本申请实施例公开的又一种车道线信息确定方法的工作流程示意图;
图5为本申请实施例公开的又一种车道线信息确定方法的工作流程示意图;
图6为本申请实施例公开的一种车道线信息确定装置的结构示意图;
图7为本申请实施例公开的一种终端装置的结构示意图;
图8为本申请实施例公开的一种终端装置的连接关系示意图。
为了解决现有技术中,通过摄像头拍摄的图像确定车道线信息时,容易受到外界环境的影响,导致获取到的车道线信息准确性较低的问题,本申请实施例公开一种车道线信息确定方法及装置。
其中,本申请实施例所公开的车道线信息确定方法,通常应用于车道线信息确定装置。该车道线信息确定装置可安装在车辆中,该车辆可支持自动化驾驶功能,从而在车辆进行自动化驾驶的过程中,依据本申请实施例所公开的方案确定车道线信息。或者,所述车道线信息确定装置可设置于远程计算机中,这种情况下,所述远程计算机在依据本申请实施例所公开的方案确定车道线信息之后,将确定的车道线信息传输至相应的车辆。
当然,所述车道线信息确定装置还可以为其他的形式,本申请实施例对此不作限定。
另外,该车道线信息确定装置中通常内置有处理器,在道路信息的获取过程中,该处理器可获取传感器传输的探测信息,并根据所述探测信息确定车道线信息。当该车道线信息确定装置安装在车辆中时,该车道线信息确定装置可为该车辆的车载处理器。
在本申请实施例中,所述传感器可设置在车道线信息确定装置中,并与所述车道线信息确定装置中的处理器相连接,或者,所述传感器还可以为独立于所述车道线信息确定装置的器件。在获取探测信息之后,所述传感器可向所述处理器传输探测信息,以便所述处理器基于接收到的探测信息,依据本申请实施例公开的方案确定车道线信息。相应的,所述处理器中设置有与传感器进行数据交互的接口,通过该接口,所述处理器可以获取到所述传感器传输的探测信息。
在本申请实施例中,所述传感器包括:雷达和成像装置。所述成像装置可以为摄像头或摄像机等中的至少一个,所述雷达可为激光雷达或毫米波雷达或超声波雷达等多种类型的雷达中的至少一个,本申请实施例对此不做限定。
另外,道路通常包括道路边沿和车道线,在实际场景中,车道线通常以特殊颜色标出,例如通过黄色或白色标出。在图1所示的车辆行驶的场景示意图中,其中的虚线即为道路中的车道线,实线即为道路中的道路边沿。
这种情况下,雷达能够利用电磁波对道路边沿进行探测。具体的,雷达可向道路边沿发射电磁波,并接收该电磁波接触到道路边沿后的回波,据此,雷达可确定道路边沿的地理位置。进一步的,雷达在确定道路边沿的地理位置之后,还可以进一步根据所述道路边沿的地理位置,确定道路边沿的坐标。这种情况下,所述雷达的探测信息可包括道路边沿的地理位置和/或道路边沿的坐标的信息。在获取所述雷达的探测信息之后,车道线信息确定装置可根据所述雷达的探测信息确定道路边沿的坐标。
所述成像装置可对车道线所在的区域进行拍照或录制视频,以获取包括车道线的图像,进一步的,成像装置还可以对所述包括车道线的图像进行图像分析,获取车道线的坐标。这种情况下,所述成像装置的探测信息可包括所述包括车道线的图像和/或车道线的坐标的信息。在获取所述成像装置的探测信息之后,车道线信息确定装置可根据所述成像装置的探测信息确定车道线的坐标。
以下结合具体的附图和工作流程,对本申请实施例公开的车道线信息确定方法进行介绍。
参见图2所示的工作流程示意图,本申请实施例公开的车道线信息确定方法包括以下 步骤:
步骤S11、根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合。其中,所述第一道路参数集合包括至少一个道路边沿的道路参数。
参见图1所示的场景示意图,道路往往包括道路边沿和车道线。在本申请实施例的一种可行的实现方式中,可根据雷达的探测信息确定道路边沿的第一信息。其中,所述道路边沿的第一信息能够用于计算所述道路边沿的道路参数。示例性的,所述道路边沿的第一信息为所述道路边沿的第一坐标。
执行本申请实施例公开的车道线信息确定方法的装置中,通常设定有与雷达进行数据交互的接口,通过该接口,能够获取雷达的探测信息。其中,所述雷达的探测信息可包括道路边沿的地理位置和/或道路边沿的坐标的信息。当所述雷达的探测信息包括道路边沿的地理位置时,可通过所述道路边沿的地理位置确定所述第一坐标;当所述雷达的探测信息道路边沿的坐标时,可从所述雷达的探测信息中任意选择第一坐标。
其中,所述道路边沿的道路参数通常为所述道路边沿的道路信息,所述道路边沿往往包括多种类型的道路参数。示例性的,所述道路边沿的道路参数可包括:所述道路边沿的横向偏移量、航向角、曲率和曲率变化率,这种情况下,由于同一道路的道路边沿和车道线的航向角、曲率和曲率变化率通常相同,因此,所述第一道路参数集合包括以下至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率。
另外,在本申请实施例中,在确定所述道路边沿的第一坐标之后,可通过曲线拟合的方式,确定所述道路边沿的曲线方程。在所述道路边沿的曲线方程中,包括所述道路边沿的第一道路参数集合中的道路参数。因此,通过对道路边沿的第一坐标的曲线拟合,可确定所述道路边沿的第一道路参数集合。
步骤S12、根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合。其中,所述第二道路参数集合包括至少一个所述车道线的道路参数。
在本申请实施例中,第一道路参数集合用于表征道路边沿信息,第二道路参数集合用于表征车道线信息,所述第一道路参数集合中包括的道路参数的类型与所述第二道路参数集合中包括的道路参数的类型一一对应相同。
示例性的,所述第一道路参数集合中的道路参数包括所述道路边沿的航向角C
1R、曲率C
2R和曲率变化率C
3R,则所述第二道路参数集合中的道路参数包括所述车道线的车道线的航向角C
1C、曲率C
2C和曲率变化率C
3C。
或者,在另一示例中,所述第一道路参数集合中的道路参数包括所述道路边沿的航向角C
1R和曲率C
2R,则所述第二道路参数集合中的道路参数包括所述车道线的航向角C
1C和曲率C
2C。
由于所述第一道路参数集合包括至少一个道路边沿的道路参数,第二道路参数集合包括至少一个车道线的道路参数,并且,所述第一道路参数集合中包括的道路参数的类型与所述第二道路参数集合中包括的道路参数的类型一一对应相同,因此,在本申请实施例中,可通过所述第一道路参数集合,确定所述第二道路参数集合。
另外,可通过多种方式确定所述第二道路参数集合,例如,可通过对所述第一道路参数集合进行融合的方式,确定所述第二道路参数集合。并且,为了明确确定所述第二道路参数集合的方式,在后续的实施例中,介绍了根据所述第一道路参数集合,确定所述第二 道路参数集合的几种可行的实现方式。
步骤S13、根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
其中,所述第三道路参数集合包括至少一个所述车道线的道路参数,并且,所述第三道路参数集合中,包括至少一个与所述第二道路参数集合包括的道路参数的类型不同的道路参数。示例性的,所述第三道路参数集合包括所述车道线的横向偏移量。结合所述第二道路参数集合中的道路参数和所述第三道路参数集合中的道路参数,通常能够确定一条车道线。
另外,执行本申请实施例公开的车道线信息确定方法的装置中,通常还设定有与成像装置进行数据交互的接口,通过该接口,能够获取所述成像装置的探测信息。所述成像装置的探测信息可包括所述车道线的图像和/或车道线的坐标的信息。当所述成像装置的探测信息包括所述车道线的图像时,可通过对所述图像进行图像分析,确定所述车道线的第二坐标。当所述成像装置的探测信息包括所述车道线的坐标时,可从所述成像装置的探测信息中任意选择所述第二坐标,从而使执行本申请实施例的方法的装置确定所述车道线的第二坐标。
另外,在上述步骤中,在获取所述第二道路参数集合和车道线的第二坐标之后,可通过对所述第二道路参数集合包括的道路参数和所述车道线的第二坐标进行曲线拟合的方式,确定车道线的曲线方程,所述车道线的曲线方程中包括所述第三道路参数集合包括的道路参数,从而根据所述车道线的曲线方程,能够确定所述第三道路参数集合。
本申请实施例公开一种车道线信息确定方法,在该方法中,根据道路边沿中的第一坐标,确定道路边沿的第一道路参数集合,再根据所第一道路参数集合确定车道线的第二道路参数集合,根据所述第二道路参数集合和所述车道线的第二坐标,确定车道线的第三道路参数集合,其中,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
在现有技术中,通过摄像头获取包含车道线的图像,然后对该图像进行图像分析,获取车道线的坐标,再对该坐标进行曲线拟合,获取车道线的曲线方程,该曲线方程中包括车道线的道路参数。也就是说,在现有技术中,仅通过包含车道线的图像确定车道线的坐标,再通过车道线的坐标确定车道线信息。但是,对包含车道线的图像进行图像分析的准确度较易受到外界环境的影响,当该准确度较低时,则确定的车道线的坐标准确性较低,相应的,通过现有技术确定的车道线信息的准确性较低。并且进一步的,车辆通常通过道路信息实现目标识别与跟踪,因此,当车道线信息准确性较低时,还会导致车辆进行目标识别与跟踪的准确度降低。
例如,当车辆在阴雨天气下行驶,或者车辆在隧道中行驶时,车辆所处的环境光照条件较差,这种情况下,摄像头拍摄的包含车道线的图像的清晰度较低,因此图像分析的准确度较低,这将导致确定的车道线信息的准确性较低,进一步的,还会降低车辆进行目标识别与跟踪的准确度。
或者,在另一场景中,摄像头与需要拍摄的车道线的距离较远,这种情况下,包含车道线的图像中,所述车道线占据的像素较少,也会导致图像分析的准确度较低,从而导致确定的车道线信息的准确性较低,并进一步降低车辆进行目标识别与跟踪的准确度。
而在本申请实施例公开的方案中,能够根据道路边沿的第一坐标确定道路边沿的第一 道路参数集合,并结合道路边沿的第一道路参数集合和车道线的第二坐标,共同确定车道线信息。也就是说,本申请实施例的方案中,根据道路边沿的第一坐标和车道线的第二坐标确定车道线信息,这一方案与现有技术中仅通过车道线的坐标确定车道线信息的方案相比,所确定的车道线信息准确性较高。
进一步的,由于通过本申请的方案所确定的车道线信息的准确度较高,因此,本申请的方案还能够进一步提高车辆进行目标识别与跟踪的准确度。
另外,在本申请实施例中,道路边沿的第一坐标通常通过雷达的探测信息确定,而雷达通过向道路边沿发射的电磁波确定所述探测信息,因此雷达的探测信息不易受到外界环境的影响,这种情况下,本申请实施例采用的道路边沿的第一坐标的准确性通常较高,因此,与现有技术相比,本申请的方案能够确定准确性较高的车道线信息。
当然,本申请实施例还可以通过其他方式,确定所述道路边沿的第一坐标,本申请实施例对此不作限定。
在本申请实施例中,通过可通过对所述第一坐标进行曲线拟合的方式,确定所述道路边沿的曲线方程,该曲线方程中包括所述道路边沿的第一道路参数集合中所包括的道路参数。
在本申请实施例中,可通过多种曲线拟合算法确定所述道路边沿的曲线方程。示例性的,在本申请实施例中,所采用的曲线拟合算法可包括最小二乘法、霍夫变换和RANSAC(Random Sample Consensus)中的至少一种算法,当然,还可以采用其他形式的曲线拟合算法,本申请实施例对此不做限定。
另外,所述第一道路参数集合中包括至少一个道路边沿的道路参数,示例性的,所述第一道路参数集合包括:所述道路边沿的航向角、曲率和曲率变化率;或者,所述第一道路参数集合包括所述道路边沿的航向角、曲率和曲率变化率中的任意两个道路参数;或者,所述第一道路参数集合包括所述道路边沿的航向角、曲率和曲率变化率中的任意一个道路参数。
另外,在本申请实施例中,道路边沿的曲线方程可以为多种形式。例如,道路边沿的曲线方程可以为三次曲线方程,或者为二次曲线方程,或者可将道路边沿分为多段,每段道路边沿通过不同的曲线方程表示。当所述道路边沿的曲线方程为三次曲线方程时,所述道路边沿的曲线方程可以为回旋曲线(clothoid)三次曲线方程。
当然,所述道路边沿的曲线方程还可以为其他形式的曲线方程,本申请实施例对此不作限定。
在本申请的一个示例中,所述道路边沿的曲线方程为clothoid三次曲线方程,该clothoid三次曲线方程为以下形式:
y=C
0R+C
1Rx+C
2Rx
2+C
3Rx
3 公式(1)。
在公式(1)中,(x,y)表示道路边沿中的第一坐标,C
0R、C
1R、C
2R和C
3R分别表示道路边沿的一个道路参数。在该示例中,C
0R可表示道路边沿的横向偏移量,C
1R可表示道路边沿的航向角,C
2R可表示道路边沿的曲率,C
3R可表示道路边沿的曲率变化率。
其中,某一物体在道路边沿的横向偏移量,指的是该物体相对于所述道路边沿在横向的位移量。为了明确道路边沿的横向偏移量,本申请实施例公开了图3。图3为车辆 运行的场景示意图,其中包括所述道路边沿的横向偏移量,并且,还包括所述道路边沿的航向角。
当然,当所述道路边沿的道路参数包括其他类型时,C
0R、C
1R、C
2R和C
3R还可以表示所述道路边沿的其他类型的道路参数,本申请实施例对此不作限定。
另外,当道路边沿的曲线方程如公式(1)所示时,在本申请实施例中,根据所述雷达的探测信息,确定道路边沿中的第一坐标,然后根据曲线拟合算法对所述第一坐标进行曲线拟合,从而确定公式(1)中的C
0R、C
1R、C
2R和C
3R的数值,其中,公式(1)中的C
0R、C
1R、C
2R和C
3R即为所述道路边沿的道路参数,然后,再从所述道路边沿的道路参数中选择第一道路集合中的道路参数,从而确定所述道路边沿的第一道路参数集合。
其中,所述道路边沿的第一道路参数集合中的道路参数通常满足以下条件:所述第一道路集合中的道路参数通常与车道线中相同类型的道路参数近似相等。当所述道路边沿的某一类型的道路参数与所述车道线的同一类型的道路参数相等,或者二者的差值小于第一阈值时,则确定这两种道路参数近似相等。所述第一阈值的数值可根据雷达和/或成像装置的精度设置,并且,还可以根据用户的使用需求进行调整。
由于同一条道路中,道路边沿的航向角通常与车道线的航向角近似相等,并且道路边沿的曲率通常与车道线的曲率近似相等,以及道路边沿的曲率变化率通常与车道线的曲率变化率近似相等,因此,所述第一道路参数集合通常包括所述至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率。
另外,在本申请实施例中,所述第一道路参数集合中包括的道路参数的类型与所述第二道路参数集合中包括的道路参数的类型一一对应相同,因此,通过所述第一道路参数集合包括的道路参数的类型,可确定第二道路参数集合中包括的道路参数的类型。
在本申请实施例中,包括根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合的操作。其中,该操作通常包括以下步骤:
通过对所述第二道路参数集合和所述车道线的第二坐标进行曲线拟合,确定所述车道线的曲线方程;
根据所述曲线方程确定所述第三道路参数集合,所述第三道路参数集合中的至少一个道路参数的参数类型不同于所述第二道路参数集合中的任一道路参数的参数类型。例如,当所述第二道路参数集合包括所述车道线的航向角、曲率和曲率变化率时,所述第三道路参数集合通常包括所述车道线的横向偏移量。
另外,在本申请实施例中,可通过多种曲线拟合算法确定所述车道线的曲线方程。示例性的,在本申请实施例中,所采用的曲线拟合算法可包括最小二乘法、霍夫变换和RANSAC(Random Sample Consensus)中的至少一种算法,当然,还可以采用其他形式的曲线拟合算法,本申请实施例对此不做限定。
另外,在本申请实施例中,车道线的曲线方程也可以为多种形式。例如,车道线的曲线方程可以为三次曲线方程,或者为二次曲线方程,或者可将车道线分为多段,每段车道线通过不同的曲线方程表示。当所述车道线的曲线方程为三次曲线方程时,所述车道线的曲线方程可以为clothoid三次曲线方程。
当然,所述车道线的曲线方程还可以为其他形式的曲线方程,本申请实施例对此不作 限定。
其中,在本申请的一个示例中,所述车道线的曲线方程为clothoid三次曲线方程,该clothoid三次曲线方程为以下形式:
y=C
0C+C
1Cx+C
2Cx
2+C
3Cx
3 公式(2)。
在公式(2)中,(x,y)表示车道线的第二坐标,C
0C、C
1C、C
2C和C
3C分别表示车道线的一个道路参数。在该示例中,C
0C可表示车道线的横向偏移量,C
1C可表示车道线的航向角,C
2C可表示车道线的曲率,C
3C可表示车道线的曲率变化率。当然,C
0C、C
1C、C
2C和C
3C还可以为其他形式的道路参数,本申请实施例对此不作限定。
其中,某一物体在车道线的横向偏移量,指的是该物体相对于所述车道线在横向的位移量。
当车道线的曲线方程如公式(2)所示时,第二道路参数为C
1C、C
2C和C
3C中的至少一个道路参数,并且,所述第一道路参数集合中包括的第一道路参数的类型与所述第二道路参数集合中包括的第二道路参数的类型一一对应相同。这种情况下,当所述第一道路参数集合包括所述道路边沿的航向角C
1R、曲率C
2R和曲率变化率C
3R时,则所述第二道路参数集合包括所述车道线的航向角C
1C、车道线的曲率C
2C和车道线的曲率变化率C
3C。
另外,所述第三道路参数集合中,包括至少一个与所述第二道路参数集合包括的道路参数的类型不同的道路参数。例如,当所述第二道路参数集合包括所述车道线的航向角C
1C、车道线的曲率C
2C和车道线的曲率变化率C
3C时,所述第三道路参数集合中包括所述车道线的横向偏移量C
0C。并且,这种情况下,将所述第二坐标与所述第二道路参数集合中包括的车道线的道路参数(即车道线的航向角C
1C、车道线的曲率C
2C和车道线的曲率变化率C
3C)代入公式(2)中,即可确定所述第三道路参数集合(即所述车道线的横向偏移量C
0C)
当然,所述第二道路参数集合和所述第三道路参数集合中包括的道路参数还可以为其他的道路参数,本申请实施例对此不作限定。例如,在另一示例中,所述第二道路参数集合包括所述车道线的航向角C
1C和车道线的曲率C
2C,则所述第三道路参数集合包括所述车道线的曲率变化率C
3C和横向偏移量C
0C。
在车辆运行的场景中,同一道路的道路边沿与车道线之间通常是平行的,当道路边沿与车道线平行时,第一道路参数集合中包括的道路参数与所述第二道路参数集合中相同类型的道路参数近似相等。因此,可根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合。
其中,所述道路边沿与车道线平行,指的是除了位置参数以外,所述道路边沿与车道线的曲线参数都近似相等。所述曲线参数用于表征曲线的弯曲程度。示例性的,所述位置参数包括横向偏移量,所述曲线参数包括:航向角、曲率和曲率变化率等。
另外,所述道路边沿与车道线的某一曲线参数近似相等,指的是所述道路边沿的这一曲线参数与所述车道线的这一曲线参数之间的差值小于一定阈值。该阈值可根据先验信息设置,并且,还可以根据成像装置与雷达的精度进行调整。
但是,在某些特殊场景下,同一道路的道路边沿与车道线可能并不平行,这种情况下,如果通过道路边沿的第一道路参数集合,确定车道线的第二道路参数集合,通常存在较大 误差。
这种情况下,本申请公开另一实施例。参见图4所示的工作流程示意图,在本申请实施例中,包括以下步骤:
步骤S21、根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合。
步骤S21的具体执行过程与步骤S11的具体执行过程相同,可相互参照,此处不再赘述。
步骤S22、根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线是否平行。若是,执行步骤S23的操作。
步骤S23、根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合;
步骤S24、根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
其中,步骤S23至步骤S24的具体执行过程与步骤S12至步骤S13的具体执行过程相同,可相互参照,此处不再赘述。
根据上述步骤可知,在本申请实施例中,在根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合之前,还包括以下步骤:根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行,即在根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行之后,才执行根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合的操作。
也就是说,在本申请实施例中,只有确定道路边沿与车道线平行的情况下,才根据道路边沿的第一道路参数集合,确定车道线的第二道路参数集合,从而能够提高确定所述第二道路参数集合的准确度,进一步提高确定车道线信息的准确度。
进一步的,在本申请实施例中,如果经过步骤S22的判断,确定所述道路边沿与所述车道线不平行,则当前不通过道路边沿的第一道路参数集合,确定车道线的第二道路参数集合,并且还可以返回执行步骤S21的操作。
另外,在上述描述以及图4所示的工作流程示意图中,在确定所述道路边沿的第一道路参数集合之后,再执行判断所述道路边沿与所述车道线是否平行的操作。在实际的应用场景中,这一操作与确定所述道路边沿的第一道路参数集合的操作之间并无严格的时间先后顺序,只要在根据所述第一道路参数集合,确定所述车道线的第二道路参数集合之前,执行该判断操作即可。
在本申请实施例的一个示例中,可首先根据所述道路边沿与所述车道线之间的距离参数,判断所述道路边沿与所述车道线是否平行,并在确定所述道路边沿与所述车道线平行之后,再根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合。
或者,在本申请实施例的另一个示例中,还可同时执行根据所述道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合,以及根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合的操作,本申请实施例对此不做限定。
另外,在本申请实施例中,根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线是否平行。其中,所述道路边沿与所述车道线之间的距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
当然,所述距离参数还可以为其他类型,本申请实施例对此不作限定。
其中,当所述道路边沿与所述车道线之间的距离参数包括欧式距离时,如果所述道路边沿与所述车道线之间的欧式距离小于第一距离阈值,则可确定所述道路边沿与所述车道线平行。
当所述道路边沿与所述车道线之间的距离参数包括马氏距离时,如果所述道路边沿与所述车道线之间的马氏距离小于第二距离阈值,则可确定所述道路边沿与所述车道线平行。
当所述道路边沿与所述车道线之间的距离参数包括明可夫斯基距离时,如果所述道路边沿与所述车道线之间的明可夫斯基距离小于第三距离阈值,则可确定所述道路边沿与所述车道线平行。
其中,上述的第一距离阈值、第二距离阈值和第三距离阈值,可根据先验信息设置,并且,还可以根据需求进行调整。例如,可根据成像装置和雷达的精度进行调整。
在一种可行的实现方式中,所述道路边沿与所述车道线之间的距离参数包括马氏距离,这种情况下,还包括以下步骤:
通过对所述第二坐标进行曲线拟合,获取所述车道线的第四道路参数集合,所述第四道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
根据所述第一道路参数集合以及所述第四道路参数集合,计算所述道路边沿与所述车道线之间的马氏距离。
在本申请实施例中,当需要获取所述第四道路参数集合时,可通过对所述第二坐标进行曲线拟合,获取包括所述车道线的曲线方程,该曲线方程中包括所述车道线的道路参数,通过所述曲线方程,可确定所述第四道路参数集合。
具体的,在本申请实施例中,可通过以下公式计算道路边沿与车道线之间的马氏距离:
其中,C
Radar表示所述M个第一道路参数构成的矩阵,C
Camera表示所述M个第二道路参数构成的矩阵。另外,在公式(1)中,P
Radar为表示C
Radar的精确度的物理量,并且P
Camera为表示C
Camera的精确度的物理量。在一种可行的实现方式中,P
Radar为C
Radar的协方差,并且P
Camera表示C
Camera的协方差。
示例性的,当所述第一道路参数集合包括道路边沿的航向角C
1R、道路边沿的曲率C
2R和道路边沿的曲率变化率C
3R时,C
Radar=(C
1R、C
2R、C
3R)。当所述第二道路参数集合车道线的航向角C
1C、车道线的曲率C
2C和车道线的曲率变化率C
3C时,C
Camera=(C
1C、C
2C、C
3C)。
通过上述步骤,能够确定道路边沿与车道线之间的马氏距离,从而便于根据所述马氏距离,确定所述道路边沿与车道线是否平行。
在本申请实施例中,还公开了根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合的操作,该操作可通过多种方式实现。
在其中一个可行的实现方式中,所述根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,包括:
确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同。
在实际应用场景中,同一道路的道路边沿与车道线通常是平行的,这种情况下,第一道路参数集合包括的道路参数往往与第二道路参数集合包括的同一类型的道路参数近似相等,例如,第一道路参数集合中包括的道路边沿的曲率往往与第二道路参数集合中包括的车道线的曲率近似相等。
并且,所述第一道路参数集合通常通过雷达的探测信息确定,而雷达的探测信息往往不易受到环境的干扰,这种情况下,获取第一道路参数集合的准确性往往高于获取第二道路参数集合的准确性。因此,在上述实现方式中,确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同,能够提高确定所述第二道路参数集合的准确性,进一步的,还能够提高确定车道线信息的准确性。
另外,在另一可行的实现方式中,所述根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,包括以下步骤:
通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
其中,所述第五道路参数集合包括的道路参数可与所述第四道路参数集合包括的道路参数相同,这种情况下,如果预先确定了所述第四道路参数集合,还可以直接将所述第四道路参数集合作为所述第五道路参数集合,从而无需再对所述第二坐标进行曲线拟合。
或者,所述第五道路参数集合包括的道路参数也可为与所述第四道路参数集合包括的道路参数不同的道路参数,这种情况下,获取所述第五道路参数集合的方式可参见获取所述第四道路参数集合的方式,本申请实施例对此不再赘述。
示例性的,当所述第五道路参数集合包括的道路参数与所述第四道路参数集合包括的道路参数相同,并且,通过所述道路边沿与所述车道线之间的距离参数包括马氏距离时,参见图5所示的工作流程示意图,本申请实施例公开的车道线信息确定方法可包括以下步骤:
步骤S31、根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合。
其中,步骤S31的具体执行过程与步骤S11的具体执行过程相同,可相互参照,此处不再赘述。
步骤S32、通过对所述道路的车道线的第二坐标的曲线拟合,确定所述车道线的第四道路参数集合,并根据所述第一道路参数集合与所述第四道路参数集合,确定所述道路边沿与所述车道线之间的距离参数,该距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
其中,所述第四道路参数集合包括的道路参数的类型,与所述第一道路参数集合包括的道路参数的类型一一对应相同。
步骤S33、根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线是否平行。若是,执行步骤S34的操作。若否,返回执行步骤S31的操作。
步骤S34、通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合。其中,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合 中的至少一个道路参数的参数类型相同。
步骤S35、通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
步骤S36、根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
其中,步骤S36的具体执行过程与步骤S13的具体执行过程相同,可相互参照,此处不再赘述。
在上述实现方式中,通过第一融合算法,对道路边沿的第一道路参数集合包括的道路参数和车道线的第五道路参数集合包括的道路参数进行融合,将融合结果作为所述第二道路参数集合。
通过这一方式,能够将第五道路参数集合与第一道路参数集合融合为所述第二道路参数集合。由于确定第一道路参数结合的准确性较高,因此,该方式能够提高确定所述第二道路参数集合的准确性,进一步的,还能够提高确定车道线信息的准确性。
在本申请实施例中,所述第一融合算法可为多种形式的融合算法。示例性的,所述第一融合算法包括:凸组合融合(convex combination fusion,CC)算法和/或协方差交叉融合(covariance intersection fusion,CI)算法。
其中,当所述第一融合算法为凸组合融合算法时,可通过以下公式对所述第五道路参数集合和所述第一道路参数集合进行融合:
其中,x
R表示所述第一道路参数集合包括的道路参数构成的向量,x
C表示所述第五道路参数集合包括的道路参数构成的向量,P
R表示所述第一道路参数集合包括的道路参数构成的向量的协方差,P
C表示所述第五道路参数集合包括的道路参数构成的向量的协方差,x
C表示所述第一道路参数集合和所述第五道路参数集合的融合结果构成的向量,P
F表示所述第一道路参数集合和所述第五道路参数集合的融合结果构成的向量的协方差。
通过公式(4)和公式(5),即可确定所述第一道路参数集合和所述第五道路参数集合的融合结果,其中,所述第一道路参数集合和所述第五道路参数集合的融合结果即为所述第二道路参数集合。
另外,当所述第一融合算法为协方差交叉融合算法时,可通过以下公式对所述第五道路参数集合和所述第一道路参数集合进行融合:
其中,x
R表示所述第一道路参数集合包括的道路参数构成的向量,x
C表示所述第五道路参数集合包括的道路参数构成的向量,P
R表示所述第一道路参数集合包括的道路参数构成的向量的协方差,P
C表示所述第五道路参数集合包括的道路参数构成的向量的协方差,x
C表示所述第一道路参数集合和所述第五道路参数集合的融合结果构成的向量,P
F表示所述第一道路参数集合和所述第五道路参数集合的融合结果构成的向 量的协方差,w表示权重参数。
通过公式(6)和公式(7),即可确定所述第一道路参数集合和所述第五道路参数集合的融合结果,其中,所述第一道路参数集合和所述第五道路参数集合的融合结果即为所述第二道路参数集合。
在公式(6)和公式(7)中,w表示权重参数,该权重参数w可通过多种方式确定。例如,可由技术人员根据经验确定所述权重参数w。
或者,在另一种方式中,可根据以下公式确定所述权重参数w:
w=N
R/(N
R+N
C) 公式(8);
其中,N
R表示所述第一坐标的数量,N
C表示所述第二坐标的数量。
在本申请实施例中,第一坐标通常由雷达的探测信息确定,第二坐标通常由成像装置的探测信息确定。与雷达的探测信息相比,成像装置的探测信息较易受到外界环境的影响,因此,通常在公式(8)中,第一坐标的数量越多,所述权重参数w的置信度越高。
另外,还可通过其他方式确定所述权重参数w。例如,可通过经验法,这一方法中,根据多次试验所获取的经验设定所述权重参数w。或者,还可采用其他方式,本申请实施例对此不做限定。
下述为本申请的装置实施例,可以用于执行本申请的方法实施例。对于本申请装置实施例中未披露的细节,请参照本申请的方法实施例。
作为对上述各实施例的实现,本申请实施例公开一种车道线信息确定装置。参见图6所示的结构示意图,本申请实施例公开的车道线信息确定装置包括:处理器110、第一收发接口120和第二收发接口130。
其中,所述第一收发接口120用于接收雷达的第一探测信息,所述第一探测信息包括道路的道路边沿的相关信息。
在本申请实施例中,所述道路边沿的相关信息包括所述道路边沿的地理位置和/或道路边沿的坐标的信息。
所述第二收发接口130用于接收成像装置的第二探测信息,所述第一探测信息包括所述道路的车道线的相关信息。
在本申请实施例中,所述车道线的相关信息包括所述车道线的地理位置和/或车道线的坐标的信息。
所述处理器110用于,根据所述第一探测信息,确定所述道路边沿的第一坐标,并根据所述第二探测信息,确定所述车道线的第二坐标;
所述处理器110还用于,根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合,根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
在本申请实施例中,所述第一收发接口120和第二收发接口130可为两个不同的收发接口,即本申请实施例中的车道线确定装置中,通过两个不同的收发接口分别接收所述第一探测信息和所述第二探测信息。另外,所述第一收发接口120和第二收发接口130也可 为同一收发接口。这种情况下,本申请实施例中的车道线确定装置可通过同一收发接口接收所述第一探测信息和所述第二探测信息。
进一步的,所述处理器110还用于,在根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合之前,根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行。
其中,所述道路边沿与所述车道线之间的距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
另外,所述道路边沿与所述车道线之间的距离参数包括马氏距离,所述处理器110还用于,通过对所述第二坐标进行曲线拟合,获取所述车道线的第四道路参数集合,所述第四道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
根据所述第一道路参数集合以及所述第四道路参数集合,计算所述道路边沿与所述车道线之间的马氏距离。
进一步的,在本申请实施例中,所述处理器110具体用于,确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同。
进一步的,在本申请实施例中,所述处理器110具体用于,通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;
通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
其中,所述第一融合算法包括:凸组合融合算法和/或协方差交叉融合算法。
进一步的,在本申请实施例中,所述处理器110具体用于,通过对所述第二道路参数集合和所述车道线的第二坐标进行曲线拟合,确定所述车道线的曲线方程;
根据所述曲线方程确定所述第三道路参数集合,所述第三道路参数集合中的至少一个道路参数的参数类型不同于所述第二道路参数集合中的任一道路参数的参数类型。
其中,所述第一道路参数集合包括以下至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率;
所述第三道路参数集合至少包括所述车道线的横向偏移量。
本申请实施例公开的车道线信息确定装置能够根据道路边沿的第一坐标确定道路边沿的第一道路参数集合,并结合道路边沿的第一道路参数集合和车道线的第二坐标,共同确定车道线信息,这一方案与现有技术中仅通过车道线的坐标确定车道线信息的方案相比,所确定的车道线信息准确性较高。
进一步的,由于通过本申请的方案所确定的车道线信息的准确度较高,因此,本申请的方案还能够进一步提高车辆进行目标识别与跟踪的准确度。
在本申请实施例中,所述车道线信息确定装置可包括多种形式。在其中一种可行的形式中,所述车道线信息确定装置集成于成像装置中。该形式中,所述成像装置通过所述第一收发接口120接收雷达传输的第一探测信息,并将所述第一探测信息传输至成像装置的处理器110;并且,所述第二收发接口130获取所述成像装置探测到的第二探测信息,并将所述第二探测信息传输至所述处理器110;所述处理器110根据所述第一探测信息和第 二探测信息,确定车道线的第二道路参数集合和第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
进一步的,在确定所述车道线信息之后,所述成像装置还可通过第一收发接口120,或者通过第二收发接口130,或者通过不同于所述第一收发接口120和第二收发接口130的其他收发接口,将所述车道线信息传输至智能车,以便所述智能车根据所述车道线信息实现目标识别等功能。
或者,在另一种可行的形式中,所述车道线信息确定装置集成于雷达中。该形式中,所述雷达通过所述第二收发接口130接收成像装置传输的第二探测信息,并将所述第二探测信息传输至雷达的处理器110;并且,所述第一收发接口120获取所述雷达探测到的第一探测信息,并将所述第一探测信息传输至所述处理器110;所述处理器110根据所述第一探测信息和第二探测信息,确定车道线的第二道路参数集合和第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
进一步的,在确定所述车道线信息之后,所述雷达还可通过第一收发接口120,或者通过第二收发接口130,或者通过不同于所述第一收发接口120和第二收发接口130的其他收发接口,将所述车道线信息传输至智能车,以便所述智能车根据所述车道线信息实现目标识别等功能。
在另外一种可行的形式中,所述车道线信息确定装置集成于融合模块中。该形式中,所述融合模块通过所述第一收发接口120接收雷达传输的第一探测信息,并将所述第一探测信息传输至融合模块的处理器110;所述融合模块通过所述第二收发接口130接收成像装置传输的第二探测信息,并将所述第二探测信息传输至融合模块的处理器110;所述处理器110根据所述第一探测信息和第二探测信息,确定车道线的第二道路参数集合和第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
其中,所述融合模块可为远程计算机等。这种情况下,在确定所述车道线信息之后,所述融合模块还可通过第一收发接口120,或者通过第二收发接口130,或者通过不同于所述第一收发接口120和第二收发接口130的其他收发接口,将所述车道线信息传输至智能车,以便所述智能车通过所述车道线信息实现目标识别等功能。
或者,所述融合模块可为设置在智能车中的一种功能模块,例如可以为所述智能车中的芯片系统或电路。这种情况下,在所述融合模块确定所述车道线信息之后,所述智能车可利用所述车道线信息实现目标识别等功能。
或者,在另外一种可行的实现方式中,所述车道线信息确定装置集成于融合模块和智能车中。这种情况下,所述融合模块可包括第一收发接口120和第二收发接口130,所述智能车可包括处理器110,即所述处理器110为所述智能车的车载处理器。
在该形式中,所述融合模块通过所述第一收发接口120接收雷达传输的第一探测信息,并将所述第一探测信息传输至智能车的处理器110;并且,所述融合模块通过所述第二收发接口130接收成像装置传输的第二探测信息,并将所述第二探测信息传输至智能车的处理器110。所述处理器110根据所述第一探测信息和第二探测信息,确定车道线的第二道路参数集合和第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。在确定所述车道线信息之后,所述智能车可利用所述车道线信息实现目标识别等功能。
或者,在另外一种可行的形式中,所述车道线信息确定装置集成于智能车中。这种情况下,所述第一收发接口120和第二收发接口130均为安装于智能车中的收发接口,所述处理器110为所述智能车的车载处理器。
在该形式中,所述第一收发接口120接收雷达传输的第一探测信息,并将所述第一探测信息传输至处理器110;所述第二收发接口130接收成像装置传输的第二探测信息,并将所述第二探测信息传输至处理器110。所述处理器110根据所述第一探测信息和第二探测信息,确定车道线的第二道路参数集合和第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息,所述智能车根据所述车道线信息实现目标识别等功能。
当然,本申请实施例公开的所述车道线信息确定装置还可以为其他形式,本申请实施例对此不作限定。
相应的,与上述的车道线确定方法相对应的,本申请实施例还公开一种终端装置。参见图7所示的结构示意图,所述终端装置包括:
至少一个处理器1101和存储器,
其中,所述存储器,用于存储程序指令;
所述处理器,用于调用并执行所述存储器中存储的程序指令,以使所述终端装置执行图2、图4和图5对应的实施例中的全部或部分步骤。
进一步的,该终端装置还可以包括:收发器1102和总线1103,所述存储器包括随机存取存储器1104和只读存储器1105。
其中,处理器通过总线分别耦接收发器、随机存取存储器以及只读存储器。其中,当需要运行该移动终端操控装置时,通过固化在只读存储器中的基本输入输出系统或者嵌入式系统中的bootloader引导系统进行启动,引导该装置进入正常运行状态。在该装置进入正常运行状态后,在随机存取存储器中运行应用程序和操作系统,从而使所述移动终端操控装置执行图2、图4和图5对应的实施例中的全部或部分步骤。
本发明实施例的装置可对应于上述图2、图4和图5所对应的实施例中的道路信息确定装置,并且,该道路信息确定装置中的处理器等可以实现图2、图4和图5所对应的实施例中的道路信息确定装置所具有的功能和/或所实施的各种步骤和方法,为了简洁,在此不再赘述。
需要说明的是,本实施例也可以基于通用的物理服务器结合网络功能虚拟化(英文:Network Function Virtualization,NFV)技术实现的网络设备,所述网络设备为虚拟网络设备(如,虚拟主机、虚拟路由器或虚拟交换机)。所述虚拟网络设备可以是运行有用于发送通告报文功能的程序的虚拟机(英文:Virtual Machine,VM),所述虚拟机部署在硬件设备上(例如,物理服务器)。虚拟机指通过软件模拟的具有完整硬件系统功能的、运行在一个完全隔离环境中的完整计算机系统。本领域技术人员通过阅读本申请即可在通用物理服务器上虚拟出具有上述功能的多个网络设备。此处不再赘述。
进一步的,本申请实施例公开的终端装置,可用作目标识别设备。该目标识别设备用于确定道路信息,以便根据该道路信息,对道路中的障碍物、行人和车辆等进行目标识别,并可进一步实现目标跟踪。
所述终端装置在确定车道线信息时,需要雷达的第一探测信息以及成像装置的第二探测信息,并根据所述第一探测信息和第二探测信息,确定车道线信息。
其中,所述终端装置可通过多种形式实现。参见图8所示的连接关系示意图,在其中一种实现形式中,雷达210和成像装置220分别通过相应的收发接口,与所述终端装置230相连接,并分别向所述终端装置230传输所述第一探测信息和第二探测信息,以便所述终端装置确定车道线信息。
这一实现实行中,所述终端装置可为车载处理器,或者,所述终端装置还可以为远程处理器。当所述终端装置为远程处理器时,所述远程处理器在确定所述车道线信息之后,还可向相应的车辆传输所述车道线信息,以便所述车辆根据所述车道线信息实现目标识别。
在另一种实现方式中,本申请实施例公开的终端装置为雷达或成像装置。其中,当所述终端装置为雷达时,所述雷达通过收发接口接收所述成像装置传输的第二探测信息,并根据自身确定的第一探测信息和所述第二探测信息,确定车道线信息。
进一步的,在确定所述车道线信息之后,所述雷达还可向相应的车辆传输所述车道线信息,以便所述车辆根据所述车道线信息实现目标识别。
另外,当所述终端装置为成像装置时,所述成像装置通过收发接口接收所述雷达传输的第一探测信息,并根据自身确定的第二探测信息和所述第一探测信息,确定车道线信息。
进一步的,在确定所述车道线信息之后,所述成像装置还可向相应的车辆传输所述车道线信息,以便所述车辆根据所述车道线信息实现目标识别。
当然,所述终端装置还可通过其他形式实现,本申请实施例对此不做限定。
进一步的,本申请实施例公开的终端装置可应用于智能驾驶领域,特别是可应用于高级辅助驾驶系统ADAS或者自动驾驶系统。例如,所述终端装置可设置在支持高级辅助驾驶功能或自动驾驶功能的车辆中,并依据本申请实施例公开的方案确定车道线信息,以便该车辆根据所述车道线信息实现高级辅助驾驶或自动驾驶功能。
这种情况下,本申请实施例的方案能够提升自动驾驶或ADAS能力,因此可应用于车联网中,例如,可应用于车载通信技术(vehicle-to-everything,V2X)、长期演进技术-车辆通信(long term evolution-vehicle,LTE-V)和车辆间通信(vehicle-to-vehicle,V2V)等系统中。
具体实现中,本申请实施例还提供一种计算机可读存储介质,该计算机可读存储介质包括指令。其中,设置在任意设备中计算机可读介质其在计算机上运行时,可实施包括图2、图4和图5对应的实施例中的全部或部分步骤。所述计算机可读介质的存储介质可为磁碟、光盘、只读存储记忆体(英文:read-only memory,简称:ROM)或随机存储记忆体(英文:random access memory,简称:RAM)等。
另外,本申请另一实施例还公开一种包含指令的计算机程序产品,当所述计算机程序产品在电子设备上运行时,使得所述电子设备可实施包括图2、图4和图5对应的实施例中的全部或部分步骤。
进一步的,本申请实施例还公开一种智能车,所述智能车包括本申请的前述实施例公 开的车道线信息确定装置或终端装置。这种情况下,通常由车辆内置的芯片、集成电路和/或处理器等承载所述车道线信息确定装置或终端装置,所述至少一个处理器和存储器可通过不同的、集成电路和/或处理器承载,或者,也可通过一个芯片或者一个集成电路或者一个处理器承载所述至少一个处理器和存储器。
另外,所述智能车中还可内置至少一个传感器,通过传感器获取道路信息确定过程中所需的探测信息,所述传感器可包括车载的成像装置和/或车载的雷达。或者,所述智能车还可以通过无线方式与远程的传感器相连接,通过所述远程的传感器确定过程中所需的探测信息。该远程的传感器包括成像装置和/或雷达。
本申请实施例还公开一种系统,该系统能够通过本申请前述实施例公开的方法确定道路信息。其中,所述系统包括终端装置、成像装置和雷达。所述成像装置用于获取第二探测信息,所述雷达用于获取第一探测信息,所述终端装置获取所述第一探测信息和第二探测信息,并通过上述各个实施例公开的方案,根据所述第一探测信息和第二探测信息,确定车道线信息。
本申请实施例中所描述的各种说明性的逻辑单元和电路可以通过通用处理器,数字信息处理器,专用集成电路(ASIC),现场可编程门阵列(FPGA)或其它可编程逻辑装置,离散门或晶体管逻辑,离散硬件部件,或上述任何组合的设计来实现或操作所描述的功能。通用处理器可以为微处理器,可选地,该通用处理器也可以为任何传统的处理器、控制器、微控制器或状态机。处理器也可以通过计算装置的组合来实现,例如数字信息处理器和微处理器,多个微处理器,一个或多个微处理器联合一个数字信息处理器核,或任何其它类似的配置来实现。
本申请实施例中所描述的方法或算法的步骤可以直接嵌入硬件、处理器执行的软件单元、或者这两者的结合。软件单元可以存储于RAM存储器、闪存、ROM存储器、EPROM存储器、EEPROM存储器、寄存器、硬盘、可移动磁盘、CD-ROM或本领域中其它任意形式的存储媒介中。示例性地,存储媒介可以与处理器连接,以使得处理器可以从存储媒介中读取信息,并可以向存储媒介存写信息。可选地,存储媒介还可以集成到处理器中。处理器和存储媒介可以设置于ASIC中,ASIC可以设置于UE中。可选地,处理器和存储媒介也可以设置于UE中的不同的部件中。
应理解,在本申请的各种实施例中,各过程的序号的大小并不意味着执行顺序的先后,各过程的执行顺序应以其功能和内在逻辑确定,而不应对本申请实施例的实施过程构成任何限定。
在上述实施例中,可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时,可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时,全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中,或者从一个计算机可读存储介质向另一个计算机可读存储介质传输,例如,所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、 数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质,(例如,软盘、硬盘、磁带)、光介质(例如,DVD)、或者半导体介质(例如固态硬盘Solid State Disk(SSD))等。
本说明书的各个实施例之间相同相似的部分互相参见即可,每个实施例重点介绍的都是与其他实施例不同之处。尤其,对于装置和系统实施例而言,由于其基本相似于方法实施例,所以描述的比较简单,相关之处参见方法实施例部分的说明即可。
本领域的技术人员可以清楚地了解到本发明实施例中的技术可借助软件加必需的通用硬件平台的方式来实现。基于这样的理解,本发明实施例中的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来,该计算机软件产品可以存储在存储介质中,如ROM/RAM、磁碟、光盘等,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本发明各个实施例或者实施例的某些部分所述的方法。
本说明书中各个实施例之间相同相似的部分互相参见即可。尤其,对于本申请公开的道路约束确定装置的实施例而言,由于其基本相似于方法实施例,所以描述的比较简单,相关之处参见方法实施例中的说明即可。
以上所述的本发明实施方式并不构成对本发明保护范围的限定。
Claims (20)
- 一种车道线信息确定方法,其特征在于,包括:根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合;根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合;根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
- 根据权利要求1所述的方法,其特征在于,在根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合之前,还包括:根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行。
- 根据权利要求2所述的方法,其特征在于,所述道路边沿与所述车道线之间的距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
- 根据权利要求3所述的方法,其特征在于,所述道路边沿与所述车道线之间的距离参数包括马氏距离,还包括:通过对所述第二坐标进行曲线拟合,获取所述车道线的第四道路参数集合,所述第四道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;根据所述第一道路参数集合以及所述第四道路参数集合,计算所述道路边沿与所述车道线之间的马氏距离。
- 根据权利要求1至4任一项所述的方法,其特征在于,所述根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,包括:确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同。
- 根据权利要求1至4任一项所述的方法,其特征在于,所述根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,包括:通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
- 根据权利要求6所述的方法,其特征在于,所述第一融合算法包括:凸组合融合算法和/或协方差交叉融合算法。
- 根据权利要求1至4任一项所述的方法,其特征在于,所述根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,包括:通过对所述第二道路参数集合和所述车道线的第二坐标进行曲线拟合,确定所述车道线的曲线方程;根据所述曲线方程确定所述第三道路参数集合,所述第三道路参数集合中的至少一个道路参数的参数类型不同于所述第二道路参数集合中的任一道路参数的参数类型。
- 根据权利要求1所述的方法,其特征在于,所述第一道路参数集合包括以下至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率;所述第三道路参数集合包括所述车道线的横向偏移量。
- 一种车道线信息确定装置,其特征在于,包括:处理器、第一收发接口和第二收发接口;所述第一收发接口用于接收雷达的第一探测信息,所述第一探测信息包括道路的道路边沿的相关信息;所述第二收发接口用于接收成像装置的第二探测信息,所述第一探测信息包括所述道路的车道线的相关信息;所述处理器用于,根据所述第一探测信息,确定所述道路边沿的第一坐标,并根据所述第二探测信息,确定所述车道线的第二坐标;所述处理器还用于,根据道路的道路边沿的第一坐标,确定所述道路边沿的第一道路参数集合,根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合,根据所述第二道路参数集合和所述车道线的第二坐标,确定所述车道线的第三道路参数集合,所述第二道路参数集合和所述第三道路参数集合包括所述车道线信息。
- 根据权利要求10所述的装置,其特征在于,所述处理器还用于,在根据所述第一道路参数集合,确定所述道路的车道线的第二道路参数集合之前,根据所述道路边沿与所述车道线之间的距离参数,确定所述道路边沿与所述车道线平行。
- 根据权利要求11所述的装置,其特征在于,所述道路边沿与所述车道线之间的距离参数包括以下至少一种距离参数:欧氏距离、马氏距离和明可夫斯基距离。
- 根据权利要求12所述的装置,其特征在于,所述道路边沿与所述车道线之间的距离参数包括马氏距离,所述处理器还用于,通过对所述第二坐标进行曲线拟合,获取所述车道线的第四道路参数集合,所述第四道路参数集合中的至少一个道路参数的参数类型与所述第一 道路参数集合中的至少一个道路参数的参数类型相同;根据所述第一道路参数集合以及所述第四道路参数集合,计算所述道路边沿与所述车道线之间的马氏距离。
- 根据权利要求10至13任一项所述的装置,其特征在于,所述处理器具体用于,确定所述第二道路参数集合中的至少一个道路参数与所述第一道路参数集合中的至少一个道路参数一一对应相同。
- 根据权利要求10至13任一项所述的装置,其特征在于,所述处理器具体用于,通过对所述第二坐标进行曲线拟合,获取所述车道线的第五道路参数集合,所述第五道路参数集合中的至少一个道路参数的参数类型与所述第一道路参数集合中的至少一个道路参数的参数类型相同;通过第一融合算法对所述第五道路参数集合和所述第一道路参数集合进行融合,融合结果为所述第二道路参数集合。
- 根据权利要求15所述的装置,其特征在于,所述第一融合算法包括:凸组合融合算法和/或协方差交叉融合算法。
- 根据权利要求10至13任一项所述的装置,其特征在于,所述处理器具体用于,通过对所述第二道路参数集合和所述车道线的第二坐标进行曲线拟合,确定所述车道线的曲线方程;根据所述曲线方程确定所述第三道路参数集合,所述第三道路参数集合中的至少一个道路参数的参数类型不同于所述第二道路参数集合中的任一道路参数的参数类型。
- 根据权利要求10所述的装置,其特征在于,所述第一道路参数集合包括以下至少一个道路参数:所述道路边沿的航向角、曲率和曲率变化率;所述第三道路参数集合至少包括所述车道线的横向偏移量。
- 一种终端装置,其特征在于,包括:至少一个处理器和存储器,所述存储器,用于存储程序指令;所述处理器,用于调用并执行所述存储器中存储的程序指令,以使所述终端装置执行权利要求1-9任一项所述的车道线信息确定方法。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质中存储有指令,当其在计算机上运行时,使得所述计算机执行如权利要求1-9任一项所述的车道线信息确定方法。
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