CN117429439A - Method, device, equipment and storage medium for estimating transverse gradient of curve - Google Patents
Method, device, equipment and storage medium for estimating transverse gradient of curve Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/076—Slope angle of the road
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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Abstract
The application provides a method, a device, equipment and a storage medium for estimating the transverse gradient of a curve, which comprise the following steps: when a vehicle turns over, a vehicle-mounted laser radar is adopted to scan a front curve in real time, and curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays are obtained; determining a normal vector of an outer boundary position point of an intermediate frame of the continuous at least three frames; determining an inner boundary optimal position point of the outer boundary position point of the intermediate frame on the horizontal plane where the normal vector is located; determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary as an optimal matching point of the inner boundary in the follow-up curve inner boundary point cloud data; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle. Compared with the prior art, the method and the device have the advantages that the accuracy of the directivity of the transverse gradient result is guaranteed, and accurate real-time data are provided for transverse control during vehicle turning.
Description
Technical Field
The application relates to the technical field of vehicle control, in particular to a method, a device, equipment and a storage medium for estimating the transverse gradient of a curve.
Background
With the development of automatic control technology for driving vehicles, the vehicles can assist drivers to drive through automatic control under special scenes, so that driving safety is improved. For example, a curve often has a certain lateral gradient, which is a gradient formed by a difference in height between the inside and outside of a road cross section, and if the lateral gradient of a curve in front of the vehicle is estimated incorrectly or not accurately in time, the lateral control of the vehicle during turning is affected.
In the existing method, slope information is calculated based on detection of a road by a sensor such as a camera, wherein the detection range of the camera is limited at first, effective detection cannot be performed in a curve scene, for example, obstacle shielding exists at a curve; secondly, information errors such as distance, gradient and the like calculated based on the image are large. The other is to calculate the current gradient in real time by adopting the state of the vehicle, and although the accuracy is improved, the measured gradient is the gradient at the current moment and the gradient information at the next moment can not be provided for the transverse control of the running of the vehicle.
Disclosure of Invention
The application aims to provide a method and device for estimating the transverse gradient of a curve, electronic equipment and a storage medium.
In a first aspect, an embodiment of the present application provides a method for estimating a lateral gradient of a curve, including:
when a vehicle turns over, a vehicle-mounted laser radar is adopted to scan a front curve in real time, and curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays are obtained;
determining normal vectors of outer boundary position points of middle frames of at least three continuous frames according to curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays;
determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point;
when the subsequent radar ray scans the position point of the outer boundary of the intermediate frame, determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary in curve inner boundary point cloud data at the moment as an optimal matching point of the inner boundary;
and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle.
In one possible implementation manner, the determining the normal vector of the middle frame outer boundary position point of the continuous at least three frames according to curve outer boundary point cloud data of the continuous at least three frames of the fixed radar ray includes:
and carrying out least square curve fitting based on curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays, and solving a normal vector of an intermediate frame outer boundary position point of the at least three continuous frames.
In one possible implementation manner, the determining, according to the curve width and a normal vector of the intermediate frame outer boundary position point, an inner boundary optimal position point of the intermediate frame outer boundary position point on a horizontal plane where the normal vector is located includes:
and on the horizontal plane where the normal vector is positioned, determining a position point which is away from the outer boundary position point of the middle frame and reaches the width of the curve by taking the outer boundary position point of the middle frame as a starting point and taking the normal vector as a direction, and taking the position point as an inner boundary optimal position point.
In one possible implementation, the fixed radar ray selects a ray preceding the radar ray currently emitted by the vehicle-mounted lidar.
In a second aspect, an embodiment of the present application provides a device for estimating a lateral gradient of a curve, including:
the scanning module is used for scanning the front curve in real time by adopting the vehicle-mounted laser radar when the vehicle turns over to obtain curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays;
the calculation module is used for determining the normal vector of the outer boundary position point of the middle frame of the continuous at least three frames according to the curve outer boundary point cloud data of the continuous at least three frames of the fixed radar rays; determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point; when the subsequent radar ray scans the position point of the outer boundary of the intermediate frame, determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary in curve inner boundary point cloud data at the moment as an optimal matching point of the inner boundary; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle.
In one possible implementation manner, the computing module is specifically configured to:
and carrying out least square curve fitting based on curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays, and solving a normal vector of an intermediate frame outer boundary position point of the at least three continuous frames.
In one possible implementation manner, the computing module is specifically configured to:
and on the horizontal plane where the normal vector is positioned, determining a position point which is away from the outer boundary position point of the middle frame and reaches the width of the curve by taking the outer boundary position point of the middle frame as a starting point and taking the normal vector as a direction, and taking the position point as an inner boundary optimal position point.
In one possible implementation, the fixed radar ray selects a ray preceding the radar ray currently emitted by the vehicle-mounted lidar.
In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method according to the first aspect of the present application when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon computer readable instructions executable by a processor to implement the method of the first aspect of the present application.
According to the method, the device, the equipment and the storage medium for estimating the transverse gradient of the curve, when a vehicle turns, a vehicle-mounted laser radar is adopted to scan a front curve in real time, and curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays are obtained; determining normal vectors of outer boundary position points of middle frames of at least three continuous frames according to curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays; determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point; determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary as an optimal matching point of the inner boundary in the follow-up curve inner boundary point cloud data; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle. Compared with the prior art, the method and the device can not only effectively and accurately detect the transverse gradient information, but also acquire the accurate distance information of the calculated gradient position in front of the vehicle, ensure the accuracy of the directivity of the transverse gradient result, and provide accurate real-time data for transverse control during vehicle turning.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a flow chart of a method for estimating a lateral gradient of a curve provided by the present application;
FIG. 2A shows a plan view of an actual lidar scan for a vehicle over-curved;
FIG. 2B shows curve fitting results of curve outer boundary point cloud data for successive frames;
FIG. 3 is a flow chart illustrating a specific method for estimating a lateral gradient of a curve provided herein;
fig. 4 shows a schematic view of a curve lateral gradient estimation device provided by the application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
In addition, the terms "first" and "second" etc. are used to distinguish different objects and are not used to describe a particular order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Embodiments of the present application provide a method and apparatus for estimating a lateral gradient of a curve, an electronic device, and a computer-readable storage medium, and the following description is made with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of a method for estimating a lateral gradient of a curve provided in the present application is shown, and as shown in fig. 1, the method for estimating a lateral gradient of a curve may include the following steps:
and S101, adopting a vehicle-mounted laser radar to scan a front curve in real time when the vehicle turns over, and obtaining curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays.
The execution body of the embodiment may be an electronic control unit ECU of the vehicle, and the ECU may control the vehicle-mounted lidar.
As shown in fig. 2A and 2B, fig. 2A is a scanning plan view of an actual laser radar for vehicle over-bending, fig. 2B is a curve fitting result of curve outer boundary point cloud data of continuous frames, a vehicle-mounted laser radar can comprehensively detect road curve scenes, and the laser radar can well and comprehensively scan curve information, so that a field of view blind area is reduced, and effective information is increased. The curve sensing mode of the laser radar is adopted, so that the detection range is wider, and the boundary data is more accurate.
The vehicle-mounted laser radar can measure the road width before the vehicle turns over, and in general, the road width is consistent with the curve width, and because there may be an error in measuring the curve width when the vehicle turns over, the curve width obtained in step S101 is actually the road width measured by the vehicle-mounted laser radar before the vehicle turns over.
The fixed radar ray at the proper position is determined, but the fixed radar ray cannot be the last radar ray emitted by the laser radar, otherwise, the subsequent matching time is too long, the fixed radar ray can be the ray before the radar ray emitted by the vehicle-mounted laser radar recently, and particularly the ray before the radar ray emitted by the vehicle-mounted laser radar currently can be selected.
As shown in fig. 2A and 2B, the curve outer boundary point cloud data of at least three consecutive frames of the fixed radar ray may be a fixed radar ray consecutive frame T 0 、T 1 And T 2 Curve outer boundary point cloud data of moment.
S102, determining normal vectors of outer boundary position points of middle frames of at least three continuous frames according to curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays.
Specifically, based on curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays, least square method curve fitting can be performed, and normal vectors of middle frame outer boundary position points of the at least three continuous frames can be solved.
As shown in fig. 2B, successive frames T are based on a particular suitable fixed radar ray 0 、T 1 And T 2 The cloud data of the outer boundary point at the moment is subjected to least square curve fitting,solving for T 1 Outer boundary position point P of time out Corresponding normal vector D.
S103, determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is located according to the curve width and the normal vector of the middle frame outer boundary position point.
Specifically, on a horizontal plane where a normal vector of an intermediate frame outer boundary position point is located, a position point which is located away from the intermediate frame outer boundary position point and reaches the curve width is determined as an inner boundary optimal position point by taking the intermediate frame outer boundary position point as a starting point and taking the normal vector as a direction.
As shown in FIGS. 2A and 2B, the optimal position point P of the inner boundary can be determined according to the width of the curve and the normal vector D best 。
And S104, determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary in curve inner boundary point cloud data at the moment as an optimal matching point of the inner boundary when the subsequent radar ray scans through the position point of the outer boundary of the intermediate frame.
Specifically, the intermediate frame outer boundary position point P is passed according to the subsequent radar ray scan out Acquiring the scanned curve inner boundary point cloud data, and determining an inner boundary optimal position point P based on the shortest Euclidean distance in the curve inner boundary point cloud data best Corresponding inner boundary optimal matching point P in 。
And S105, calculating the transverse gradient of the curve in front of the vehicle according to the optimal matching point of the outer boundary position point of the intermediate frame and the inner boundary.
Specifically, based on the intermediate frame outer boundary position point P out Optimal matching point P of inner boundary in And solving the two-point position information to obtain the transverse gradient of the position in front of the vehicle, and providing accurate transverse gradient information for transverse control of the vehicle for driving the curve next.
For ease of understanding, a flowchart of a specific curve lateral gradient estimation method provided in the present application is shown in fig. 3.
The method and the device have the advantages that the outer boundary is predetermined, the optimal matching point is determined based on the direction of the transverse gradient, the optimal matching is completed based on the Euclidean distance according to the point cloud data of the subsequent frames, the accuracy of the direction of the transverse gradient result is ensured, and the method and the device are in accordance with practice.
According to the method for estimating the transverse gradient of the curve, when a vehicle turns over, a vehicle-mounted laser radar is adopted to scan a front curve in real time, and curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays are obtained; determining normal vectors of outer boundary position points of middle frames of at least three continuous frames according to curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays; determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point; determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary as an optimal matching point of the inner boundary in the follow-up curve inner boundary point cloud data; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle. Compared with the prior art, the method and the device can not only effectively and accurately detect the transverse gradient information, but also acquire the accurate distance information of the calculated gradient position in front of the vehicle, ensure the accuracy of the directivity of the transverse gradient result, and provide accurate real-time data for transverse control during vehicle turning.
In the above embodiment, a method for estimating a lateral gradient of a curve is provided, and correspondingly, the application also provides a device for estimating a lateral gradient of a curve, where the device for estimating a lateral gradient of a curve can be implemented by software, hardware or a combination of software and hardware. For example, the curve lateral gradient estimation device may include integrated or separate functional modules or units to perform the corresponding steps in the methods described above. Referring to fig. 4, a schematic diagram of a curve lateral gradient estimation device according to some embodiments of the present application is shown. Since the apparatus embodiments are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 4, the curve lateral gradient estimation device 10 may include:
the scanning module 101 is used for scanning a front curve in real time by adopting a vehicle-mounted laser radar when a vehicle turns over to obtain curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays;
a calculation module 102, configured to determine a normal vector of an intermediate frame outer boundary position point of the continuous at least three frames according to curve outer boundary point cloud data of the continuous at least three frames of the fixed radar ray; determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point; when the subsequent radar ray scans the position point of the outer boundary of the intermediate frame, determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary in curve inner boundary point cloud data at the moment as an optimal matching point of the inner boundary; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle.
In one possible implementation, the computing module 102 is specifically configured to:
and carrying out least square curve fitting based on curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays, and solving a normal vector of an intermediate frame outer boundary position point of the at least three continuous frames.
In one possible implementation, the computing module 102 is specifically configured to:
and on the horizontal plane where the normal vector is positioned, determining a position point which is away from the outer boundary position point of the middle frame and reaches the width of the curve by taking the outer boundary position point of the middle frame as a starting point and taking the normal vector as a direction, and taking the position point as an inner boundary optimal position point.
In one possible implementation, the fixed radar ray selects a ray preceding the radar ray currently emitted by the vehicle-mounted lidar.
According to the curve transverse gradient estimation device, when a vehicle turns over, a vehicle-mounted laser radar is adopted to scan a front curve in real time, and curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays are obtained; determining normal vectors of outer boundary position points of middle frames of at least three continuous frames according to curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays; determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point; determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary as an optimal matching point of the inner boundary in the follow-up curve inner boundary point cloud data; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle. Compared with the prior art, the method and the device can not only effectively and accurately detect the transverse gradient information, but also acquire the accurate distance information of the calculated gradient position in front of the vehicle, ensure the accuracy of the directivity of the transverse gradient result, and provide accurate real-time data for transverse control during vehicle turning.
The embodiment of the application also provides an electronic device corresponding to the method for estimating the transverse gradient of the curve provided by the previous embodiment, wherein the electronic device can be a vehicle ECU, a mobile phone, a notebook computer, a tablet computer, a desktop computer and the like so as to execute the method for estimating the transverse gradient of the curve.
The electronic device provided by the embodiment of the application and the curve transverse gradient estimation method provided by the embodiment of the application are the same in conception and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
The present application further provides a computer readable storage medium corresponding to the method for estimating a lateral gradient of a curve provided in the foregoing embodiments, on which a computer program (i.e. a program product) is stored, which when executed by a processor, performs the method for estimating a lateral gradient of a curve provided in any of the foregoing embodiments.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above embodiment of the present application has the same advantageous effects as the method adopted, operated or implemented by the application program stored therein, because of the same inventive concept as the method for estimating the lateral gradient of the curve provided by the embodiment of the present application.
It is noted that the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the embodiments, and are intended to be included within the scope of the claims and description.
Claims (10)
1. A method of estimating a lateral gradient of a curve, comprising:
when a vehicle turns over, a vehicle-mounted laser radar is adopted to scan a front curve in real time, and curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays are obtained;
determining normal vectors of outer boundary position points of middle frames of at least three continuous frames according to curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays;
determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point;
when the subsequent radar ray scans the position point of the outer boundary of the intermediate frame, determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary in curve inner boundary point cloud data at the moment as an optimal matching point of the inner boundary;
and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle.
2. The method of claim 1, wherein determining a normal vector of an intermediate frame outer boundary position point of the continuous at least three frames from curve outer boundary point cloud data of the continuous at least three frames of the fixed radar ray comprises:
and carrying out least square curve fitting based on curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays, and solving a normal vector of an intermediate frame outer boundary position point of the at least three continuous frames.
3. The method of claim 1, wherein determining an inner boundary optimal position point of the intermediate frame outer boundary position point on a horizontal plane in which the normal vector is located based on the curve width and a normal vector of the intermediate frame outer boundary position point comprises:
and on the horizontal plane where the normal vector is positioned, determining a position point which is away from the outer boundary position point of the middle frame and reaches the width of the curve by taking the outer boundary position point of the middle frame as a starting point and taking the normal vector as a direction, and taking the position point as an inner boundary optimal position point.
4. The method of claim 1, wherein the fixed radar ray is a ray preceding a radar ray currently emitted by the vehicle lidar.
5. A curve lateral gradient estimation device, characterized by comprising:
the scanning module is used for scanning the front curve in real time by adopting the vehicle-mounted laser radar when the vehicle turns over to obtain curve width and curve outer boundary point cloud data of at least three continuous frames of fixed radar rays;
the calculation module is used for determining the normal vector of the outer boundary position point of the middle frame of the continuous at least three frames according to the curve outer boundary point cloud data of the continuous at least three frames of the fixed radar rays; determining an inner boundary optimal position point of the middle frame outer boundary position point on a horizontal plane where the normal vector is positioned according to the curve width and the normal vector of the middle frame outer boundary position point; when the subsequent radar ray scans the position point of the outer boundary of the intermediate frame, determining a position point with the shortest Euclidean distance between the position point and the optimal position point of the inner boundary in curve inner boundary point cloud data at the moment as an optimal matching point of the inner boundary; and calculating according to the position point of the outer boundary of the intermediate frame and the optimal matching point of the inner boundary to obtain the transverse gradient of the curve in front of the vehicle.
6. The apparatus according to claim 5, wherein the computing module is specifically configured to:
and carrying out least square curve fitting based on curve outer boundary point cloud data of at least three continuous frames of the fixed radar rays, and solving a normal vector of an intermediate frame outer boundary position point of the at least three continuous frames.
7. The apparatus according to claim 5, wherein the computing module is specifically configured to:
and on the horizontal plane where the normal vector is positioned, determining a position point which is away from the outer boundary position point of the middle frame and reaches the width of the curve by taking the outer boundary position point of the middle frame as a starting point and taking the normal vector as a direction, and taking the position point as an inner boundary optimal position point.
8. The apparatus of claim 5, wherein the fixed radar line is a line preceding a radar line currently emitted by the vehicle lidar.
9. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 4 when the computer program is run by the processor.
10. A computer readable storage medium having stored thereon computer readable instructions executable by a processor to implement the method of any one of claims 1 to 4.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311381096.6A CN117429439A (en) | 2023-10-23 | 2023-10-23 | Method, device, equipment and storage medium for estimating transverse gradient of curve |
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| CN202311381096.6A CN117429439A (en) | 2023-10-23 | 2023-10-23 | Method, device, equipment and storage medium for estimating transverse gradient of curve |
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