CN110045376B - Drivable region acquisition method, computer-readable storage medium, and terminal device - Google Patents
Drivable region acquisition method, computer-readable storage medium, and terminal device Download PDFInfo
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- CN110045376B CN110045376B CN201910349294.1A CN201910349294A CN110045376B CN 110045376 B CN110045376 B CN 110045376B CN 201910349294 A CN201910349294 A CN 201910349294A CN 110045376 B CN110045376 B CN 110045376B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
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Abstract
The invention provides a travelable region acquisition method, a computer-readable storage medium and a terminal device, wherein point cloud data are acquired frame by a millimeter wave radar installed on a vehicle; acquiring CFAR points which are relatively static with the ground in the point cloud data according to the driving speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar; obtaining the motion state of the vehicle and translating the grid probability map according to the running speed of the vehicle and the yaw angle information of the vehicle; projecting all CFAR points which are relatively static with the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map; and taking the CFAR point with the probability value higher than the first preset value as the point of the obstacle, and taking the other areas except the point of the obstacle as travelable areas. By processing the point cloud data, the information of the obstacle which is relatively static to the ground is accurately acquired, and the obstacle is displayed on a display screen to obtain a drivable area.
Description
Technical Field
The invention belongs to the technical field of vehicle driving, and particularly relates to a travelable region acquisition method, a computer-readable storage medium and a terminal device.
Background
With the rapid development of the automobile industry in recent years, traffic accidents have become a global problem, the number of fatalities of traffic accidents is estimated to exceed 50 and more than ten thousand every year around the world, and the demands for traffic improvement and driving safety are continuously increasing.
The obstacle detection is carried out in real time in the vehicle running process, and the travelable area is obtained so as to carry out operation such as obstacle avoidance and the like, and the driving safety can be effectively improved. Although the prior art has the scheme of detecting obstacles to obtain a drivable area, the detection accuracy of the prior art needs to be improved, and only a well-defined obstacle type can be detected, so that the prior art has difficulty in dealing with sudden or unknown types of obstacles.
Disclosure of Invention
In view of this, embodiments of the present invention provide a travelable region acquiring method, a computer-readable storage medium, and a terminal device, so as to solve the problem in the prior art that detection of a travelable region is inaccurate.
A first aspect of an embodiment of the present invention provides a travelable area acquisition method, including:
collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle, wherein the time intervals of any two adjacent frames are the same;
acquiring CFAR points which are static relative to the ground in the point cloud data according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar;
acquiring the motion state of the vehicle according to the running speed of the vehicle and the yaw angle information of the vehicle, and translating the grid probability map according to the motion state of the vehicle;
projecting all CFAR points which are relatively static to the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map;
and acquiring CFAR points with probability values higher than a first preset value in the grid probability map as points of the obstacles, and acquiring areas except the CFAR points with probability values higher than the first preset value in the grid probability map as travelable areas.
A second aspect of embodiments of the present invention provides a computer-readable storage medium storing computer-readable instructions, which when executed by a processor implement the steps of:
collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle, wherein the time intervals of any two adjacent frames are the same;
acquiring CFAR points which are static relative to the ground in the point cloud data according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar;
acquiring the motion state of the vehicle according to the running speed of the vehicle and the yaw angle information of the vehicle, and translating the grid probability map according to the motion state of the vehicle;
projecting all CFAR points which are relatively static to the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map;
and acquiring CFAR points with probability values higher than a first preset value in the grid probability map as points of the obstacles, and acquiring areas except the CFAR points with probability values higher than the first preset value in the grid probability map as travelable areas.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, where the processor executes the computer-readable instructions to implement the following steps:
collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle, wherein the time intervals of any two adjacent frames are the same;
acquiring CFAR points which are static relative to the ground in the point cloud data according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar;
acquiring the motion state of the vehicle according to the running speed of the vehicle and the yaw angle information of the vehicle, and translating the grid probability map according to the motion state of the vehicle;
projecting all CFAR points which are relatively static to the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map;
and acquiring CFAR points with probability values higher than a first preset value in the grid probability map as points of the obstacles, and acquiring areas except the CFAR points with probability values higher than the first preset value in the grid probability map as travelable areas.
The invention provides a travelable area acquisition method, a computer-readable storage medium and a terminal device, comprising: collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle; according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar, obtaining CFAR points which are relatively static with the ground in the point cloud data; obtaining the motion state of the vehicle and translating the grid probability map according to the running speed of the vehicle and the yaw angle information of the vehicle; projecting all CFAR points which are relatively static with the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map; and taking the CFAR point with the probability value higher than the first preset value as the point of the obstacle, and taking the other areas except the point of the obstacle as travelable areas. The point cloud data collected by the millimeter wave radar is processed, so that the information of the obstacle which is relatively static to the ground is accurately obtained, the obstacle is displayed on a display screen, and a driving feasible area is obtained.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flowchart of a travelable area acquisition method according to an embodiment of the present invention;
fig. 2 is a block diagram of a travelable area acquiring apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a travelable area acquisition terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The embodiment of the invention provides a travelable area acquisition method. With reference to fig. 1, the method comprises:
s101, collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle, wherein the time intervals of any two adjacent frames are the same.
Alternatively, millimeter wave radars may be installed at a plurality of positions of the vehicle, such as left front, right front, left rear, and right rear directions of the vehicle. Each millimeter wave radar scans the road condition frame by frame in real time to obtain point cloud data corresponding to each frame.
It should be noted that, in the process of scanning by the millimeter wave radar, the time interval between any two adjacent frames is the same.
And S102, acquiring CFAR points which are static relative to the ground in the point cloud data according to the running speed of the vehicle and the Doppler speed of each CFAR point with constant false alarm rate in the point cloud data detected by the millimeter wave radar.
The Doppler velocity of the CFAR point can be directly acquired through the millimeter wave radar.
S103, acquiring the motion state of the vehicle according to the running speed of the vehicle and the yaw angle information of the vehicle, and translating the grid probability map according to the motion state of the vehicle.
And calculating the motion state of the current frame of the vehicle relative to the previous frame according to the running speed of the vehicle and the yaw angle information of the vehicle, and obtaining the positions of all points in the grid probability map of the previous frame in the grid probability map corresponding to the current frame.
Specifically, the translation amount of each pixel point in the grid probability map in the X-axis direction and the translation amount in the Y-axis direction in the preset coordinate system are obtained according to a first expression, where the first expression is:
wherein: v is the running speed of the vehicle, t is the time interval of any two adjacent frames, and theta is the difference of the yaw angles of the vehicle between the previous frame and the current frame;
obtaining the rotation amount of each pixel point in the grid probability map in the X-axis direction and the rotation amount of each pixel point in the Y-axis direction in a preset coordinate system according to a second expression:
x′＝x·cosθ-y·sinθ
y′＝x·sinθ+y·cosθ
wherein x and y are horizontal and vertical coordinates of a point in the grid probability map after translation, and x 'and y' are coordinates of the point in the grid probability map after rotation.
For example, in two adjacent frames, the coordinates of the target CFAR point in the previous frame are (x1, y1), and the coordinates of the target CFAR point in the next frame are (x2, y2), but the coordinates of the target CFAR point in the two frames are different, but the target CFAR point is the same target point.
And S104, projecting all CFAR points which are relatively static to the ground in each frame into a grid probability map, and calculating the probability value of each CFAR point in the grid probability map.
Optionally, according to the grid probability map after the translation, the coordinates of the CFAR points that are stationary relative to the ground in the grid probability map corresponding to each frame are calculated, so as to obtain the probability values of the CFAR points that are stationary relative to the ground.
Optionally, in a point that the millimeter wave radar passes between reaching the image boundary points, if there is a CFAR point that is stationary relative to the ground, increasing probability values of a CFAR point closest to the millimeter wave radar and a neighborhood point of the CFAR point closest to the millimeter wave radar, and reducing the probability value of the CFAR point closest to the millimeter wave radar to the point that the millimeter wave radar passes between;
and if no CFAR point which is static relative to the ground exists in the points which pass by the millimeter wave radar when reaching the image boundary points, reducing the probability values of all the points which pass by the millimeter wave radar when reaching the image boundary points.
And S105, acquiring CFAR points with probability values higher than a first preset value in the grid probability map as points of the obstacles, and acquiring areas except the CFAR points with probability values higher than the first preset value in the grid probability map as travelable areas.
If the obstacle exists in the traffic condition, the millimeter wave radar monitors for multiple times, and if the probability value of the corresponding position of the same CFAR point in the grid probability map is larger, the point can be indicated as the obstacle, so in the embodiment of the present invention, all the points with the probability values higher than the first preset value, which are acquired in step S104, are taken as the points of the obstacle.
And determining the point with the probability value larger than the first preset value as the obstacle information in the driving process, displaying the obstacle information on a display, and taking other areas as drivable areas, so that the drivable areas are accurately extracted, and a guarantee is provided for safe driving.
Further, in order to make the extracted travelable region more accurate and clear and reduce the calculation amount, the method further includes:
acquiring points with probability values higher than a second preset value in the grid probability map as points corresponding to the road edges; and in the grid probability map, distributing the points corresponding to the extracted road edges to different preset sectors in the grid probability map, and deleting CFAR points positioned at two sides of the road edge points in the sectors if the road edge points exist in the sectors.
The sectors are divided into sectors at intervals of a preset angle by taking the normal of the millimeter wave radar as a reference, for example, the sectors are divided into sectors at intervals of 1 degree by taking the normal of the millimeter wave radar as a reference.
By the method, the road edge information in the grid probability map can be obtained through calculation of a plurality of continuous frames, after the road edge information is obtained, optionally, before CFAR points which are static relative to the ground in point cloud data are finally obtained, point cloud data on two sides of the road edge are deleted in the point cloud data of each frame in the grid probability map, so that the calculated amount is reduced, and the obtaining efficiency of a travelable area is improved.
The invention provides a method for acquiring a travelable area, which comprises the following steps: collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle; according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar, obtaining CFAR points which are relatively static with the ground in the point cloud data; obtaining the motion state of the vehicle and translating the grid probability map according to the running speed of the vehicle and the yaw angle information of the vehicle; projecting all CFAR points which are relatively static with the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map; and taking the CFAR point with the probability value higher than the first preset value as the point of the obstacle, and taking the other areas except the point of the obstacle as travelable areas. The point cloud data collected by the millimeter wave radar is processed, so that the information of the obstacle which is relatively static to the ground is accurately obtained, the obstacle is displayed on a display screen, and a driving feasible area is obtained.
Fig. 2 is a schematic view of a travelable region acquiring apparatus according to an embodiment of the present invention, and with reference to fig. 2, the apparatus includes: the acquisition unit 21, the first acquisition unit 22, the second acquisition unit 23, the third acquisition unit 24 and the fourth acquisition unit 25;
the acquisition unit 21 is configured to acquire point cloud data frame by frame through a millimeter wave radar mounted on a vehicle, where time intervals of any two adjacent frames are the same;
the first obtaining unit 22 is configured to obtain a CFAR point that is stationary relative to the ground in the point cloud data according to the driving speed of the vehicle and the doppler speed of each CFAR point with a constant false alarm rate in the point cloud data detected by the millimeter wave radar;
the second obtaining unit 23 is configured to obtain a motion state of the vehicle according to the traveling speed of the vehicle and the yaw angle information of the vehicle, and translate the grid probability map according to the motion state of the vehicle;
the third obtaining unit 24 is configured to project all CFAR points that are relatively stationary with respect to the ground in each frame into a grid probability map, and calculate a probability value of each CFAR point in the grid probability map;
the fourth obtaining unit 25 is configured to obtain CFAR points with probability values higher than a first preset value in the grid probability map as points of the obstacle, and obtain areas except the CFAR points with probability values higher than the first preset value in the grid probability map as travelable areas.
That is, optionally, the second obtaining unit 23 is configured to:
and calculating the motion state of the current frame of the vehicle relative to the previous frame according to the running speed of the vehicle and the yaw angle information of the vehicle, and obtaining the positions of all points in the grid probability map of the previous frame in the grid probability map corresponding to the current frame.
Optionally, the second obtaining unit 23 is configured to:
obtaining the translation amount of each pixel point in the grid probability map in the X-axis direction and the translation amount of each pixel point in the Y-axis direction in a preset coordinate system according to a first expression, wherein the first expression is as follows:
wherein: v is the running speed of the vehicle, t is the time interval of any two adjacent frames, and theta is the difference of the yaw angles of the vehicle between the previous frame and the current frame;
obtaining the rotation amount of each pixel point in the grid probability map in the X-axis direction and the rotation amount of each pixel point in the Y-axis direction in a preset coordinate system according to a second expression:
x′＝x·cosθ-y·sinθ
y′＝x·sinθ+y·cosθ
wherein x and y are horizontal and vertical coordinates of a point in the grid probability map after translation, and x 'and y' are coordinates of the point in the grid probability map after rotation.
Optionally, the third obtaining unit 24 is configured to:
and calculating the coordinates of the CFAR points which are static relative to the ground in the grid probability map corresponding to each frame according to the grid probability map after translation to obtain the probability value of the CFAR points which are static relative to the ground.
Optionally, the third obtaining unit 24 is configured to:
in the points which pass through the millimeter wave radar between the image boundary points, if CFAR points which are static relative to the ground exist, increasing the probability values of the CFAR point closest to the millimeter wave radar and the neighborhood point of the CFAR point closest to the millimeter wave radar, and reducing the probability value of the CFAR point closest to the millimeter wave radar to the points which pass through the millimeter wave radar;
and if no CFAR point which is static relative to the ground exists in the points which pass by the millimeter wave radar when reaching the image boundary points, reducing the probability values of all the points which pass by the millimeter wave radar when reaching the image boundary points.
Optionally, the fourth obtaining unit 25 is further configured to:
acquiring points with probability values higher than a second preset value in the grid probability map as points corresponding to the road edges;
and in the grid probability map, distributing the points corresponding to the extracted road edges to different preset sectors in the grid probability map, and deleting CFAR points positioned at two sides of the road edge points in the sectors if the road edge points exist in the sectors.
The sector is divided into sectors at intervals of a preset angle by taking a normal of the millimeter wave radar as a reference.
The invention provides a travelable area acquisition device, which acquires point cloud data frame by frame through a millimeter wave radar installed on a vehicle; according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar, obtaining CFAR points which are relatively static with the ground in the point cloud data; obtaining the motion state of the vehicle and translating the grid probability map according to the running speed of the vehicle and the yaw angle information of the vehicle; projecting all CFAR points which are relatively static with the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map; and taking the CFAR point with the probability value higher than the first preset value as the point of the obstacle, and taking the other areas except the point of the obstacle as travelable areas. The point cloud data collected by the millimeter wave radar is processed, so that the information of the obstacle which is relatively static to the ground is accurately obtained, the obstacle is displayed on a display screen, and a driving feasible area is obtained.
Fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 3, the terminal device 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32, such as a travelable area acquisition program, stored in the memory 31 and executable on the processor 30. The processor 30 executes the computer program 32 to implement the steps in the above-described embodiments of the travelable region acquisition method, such as the steps 101 to 105 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 21 to 25 shown in fig. 2.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3.
The terminal device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 30, a memory 31. It will be understood by those skilled in the art that fig. 3 is only an example of the terminal device 3, and does not constitute a limitation to the terminal device 3, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device may also include an input-output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used for storing the computer program and other programs and data required by the terminal device. The memory 31 may also be used to temporarily store data that has been output or is to be output.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of the method for acquiring a travelable area according to any of the embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (7)
1. A travelable region acquisition method, characterized in that the method comprises:
collecting point cloud data frame by frame through a millimeter wave radar installed on a vehicle, wherein the time intervals of any two adjacent frames are the same;
acquiring CFAR points which are static relative to the ground in the point cloud data according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar;
acquiring the motion state of the vehicle according to the running speed of the vehicle and the yaw angle information of the vehicle, and translating the grid probability map according to the motion state of the vehicle;
projecting all CFAR points which are relatively static to the ground in each frame to a grid probability map, and calculating the probability value of each CFAR point in the grid probability map;
the method comprises the steps of obtaining CFAR points with probability values higher than a first preset value in a grid probability map as points of obstacles, and obtaining areas except the CFAR points with the probability values higher than the first preset value in the grid probability map as travelable areas;
the translating the grid probability map according to the motion state of the vehicle comprises:
calculating the motion state of the current frame of the vehicle relative to the previous frame according to the running speed of the vehicle and the yaw angle information of the vehicle, and obtaining the positions of all points in the grid probability map of the previous frame in the grid probability map corresponding to the current frame;
the calculating a probability value of each CFAR point in the grid probability map comprises:
and calculating the coordinates of the CFAR points which are static relative to the ground in the grid probability map corresponding to each frame according to the grid probability map after translation to obtain the probability value of the CFAR points which are static relative to the ground.
2. The drivable region acquiring method as set forth in claim 1, wherein the calculating of the motion state of the current frame of the vehicle relative to the previous frame on the basis of the driving speed of the vehicle and the yaw angle information of the vehicle, and the obtaining of the positions of all points in the grid probability map of the previous frame in the grid probability map corresponding to the current frame comprises:
obtaining the translation amount of each pixel point in the grid probability map in the X-axis direction and the translation amount of each pixel point in the Y-axis direction in a preset coordinate system according to a first expression, wherein the first expression is as follows:
wherein: v is the running speed of the vehicle, t is the time interval of any two adjacent frames, and theta is the difference of the yaw angles of the vehicle between the previous frame and the current frame;
obtaining the rotation amount of each pixel point in the grid probability map in the X-axis direction and the rotation amount of each pixel point in the Y-axis direction in a preset coordinate system according to a second expression:
x′＝x·cosθ-y·sinθ
y′＝x·sinθ+y·cosθ
wherein x and y are horizontal and vertical coordinates of a point in the grid probability map after translation, x 'and y' are coordinates of the point in the grid probability map after rotation, and the horizontal and vertical coordinates of the point in the grid probability map before translation are both 0.
3. The travelable region acquisition method according to any one of claims 1 to 2, characterized in that the method further comprises:
acquiring points with probability values higher than a second preset value in the grid probability map as points corresponding to the road edges;
and in the grid probability map, distributing the points corresponding to the extracted road edges to different preset sectors in the grid probability map, and deleting CFAR points positioned at two sides of the road edge points in the sectors if the road edge points exist in the sectors.
4. The travelable region acquisition method according to claim 3, wherein the sector is divided into one sector every a predetermined angle with reference to a normal line of the millimeter wave radar.
5. A travelable area acquisition device is characterized by comprising an acquisition unit, a first acquisition unit, a second acquisition unit, a third acquisition unit and a fourth acquisition unit;
the acquisition unit is used for acquiring point cloud data frame by frame through a millimeter wave radar installed on a vehicle, wherein the time intervals of any two adjacent frames are the same;
the first acquisition unit is used for acquiring CFAR points which are static relative to the ground in the point cloud data according to the running speed of the vehicle and the Doppler speed of each CFAR point in the point cloud data detected by the millimeter wave radar;
the second acquisition unit is used for acquiring the motion state of the vehicle according to the running speed of the vehicle and the yaw angle information of the vehicle, and translating the grid probability map according to the motion state of the vehicle;
the third acquisition unit is used for projecting all CFAR points which are relatively static to the ground in each frame into a grid probability map, and calculating the probability value of each CFAR point in the grid probability map;
the fourth obtaining unit is configured to obtain, as a point of an obstacle, a CFAR point in the grid probability map whose probability value is higher than a first preset value, and obtain, as a travelable region, a region in the grid probability map other than the CFAR point whose probability value is higher than the first preset value;
the second obtaining unit is further configured to calculate a motion state of the current frame of the vehicle relative to a previous frame according to the running speed of the vehicle and the yaw angle information of the vehicle, and obtain positions of all points in the grid probability map of the previous frame in the grid probability map corresponding to the current frame;
the third obtaining unit is further configured to calculate, according to the grid probability map after the translation, coordinates of the CFAR points that are stationary relative to the ground in the grid probability map corresponding to each frame, and obtain probability values of the CFAR points that are stationary relative to the ground.
6. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
7. A terminal device, characterized in that the terminal device comprises a memory, a processor, a computer program being stored on the memory and being executable on the processor, the processor implementing the steps of the method according to any of claims 1 to 4 when executing the computer program.
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| CN201910349294.1A CN110045376B (en) | 2019-04-28 | 2019-04-28 | Drivable region acquisition method, computer-readable storage medium, and terminal device |
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| CN201910349294.1A CN110045376B (en) | 2019-04-28 | 2019-04-28 | Drivable region acquisition method, computer-readable storage medium, and terminal device |
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| CN110045376A CN110045376A (en) | 2019-07-23 |
| CN110045376B true CN110045376B (en) | 2021-06-01 |
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| CN110550029B (en) * | 2019-08-12 | 2021-02-09 | 华为技术有限公司 | Obstacle avoiding method and device |
| CN110716209B (en) * | 2019-09-19 | 2021-12-14 | 浙江大华技术股份有限公司 | Map construction method, map construction equipment and storage device |
| CN112578404B (en) * | 2019-09-27 | 2022-10-04 | 北京地平线机器人技术研发有限公司 | Method and device for determining driving path |
| CN111427032B (en) * | 2020-04-24 | 2022-02-01 | 森思泰克河北科技有限公司 | Room wall contour recognition method based on millimeter wave radar and terminal equipment |
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| CN112639821B (en) * | 2020-05-11 | 2021-12-28 | 华为技术有限公司 | Method and system for detecting vehicle travelable area and automatic driving vehicle adopting system |
| CN113859228B (en) * | 2020-06-30 | 2023-07-25 | 上海商汤智能科技有限公司 | Vehicle control method and device, electronic equipment and storage medium |
| CN113970725A (en) * | 2020-07-24 | 2022-01-25 | 北京万集科技股份有限公司 | Calibration method, device and equipment for radar detection area and storage medium |
| CN111942374A (en) * | 2020-08-14 | 2020-11-17 | 中国第一汽车股份有限公司 | Obstacle map generation method and device, vehicle and storage medium |
| CN114521836B (en) * | 2020-08-26 | 2023-11-28 | 北京石头创新科技有限公司 | Automatic cleaning equipment |
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