Method for estimating object speed using point cloud radar, point cloud radar and system

Abstract

A method for estimating the speed of an object using a point cloud radar (210, 60), a point cloud radar (210, 60) and a system (20), the method comprising: scanning an environment using the point cloud radar (210, 60) to obtain motion state parameters of multiple object points on the object corresponding to the multiple sampling points obtained by the scan (S110); clustering the multiple sampling points to divide into multiple cluster groups, wherein each sampling point in the same cluster group is obtained by scanning the same object in the environment (S120); estimating the speed of the object based on the motion state parameters of the object points corresponding to the sampling points in the same cluster group (S130). Thus, the speed of the object can be estimated using the information of the multiple sampling points measured by the point cloud radar (210, 60), reducing the measurement deviation caused by the measurement noise, improving the speed measurement accuracy, improving the radar's measurement performance for scenes requiring extremely accurate speed measurement such as lane change, and effectively ensuring driving safety.

Description

Technical Field

[0001] The present invention generally relates to the field of radar, and in particular to a method for estimating the speed of an object using a point cloud radar, a point cloud radar and a system. Background Art

[0002] In recent years, autonomous driving and assisted driving systems have developed very rapidly. Millimeter-wave radar has become one of the important sensors in this field due to its advantages of all-day, all-weather, and low cost. However, the resolution of traditional millimeter-wave radar is limited, resulting in only a few scattered point targets being observed for some larger targets such as vehicles, resulting in poorer ranging performance than lidar. However, lidar is too expensive and difficult to meet automotive-grade certification. In view of this, point cloud radars with large bandwidth and high resolution performance have developed rapidly and become a hot spot that everyone is chasing after.

[0003] The point cloud radar observes an increase in the number of scattering points of the same target. However, due to the existence of system noise, the speeds of the various scattering points of the target measured are different, and the true speed of the target cannot be accurately obtained. In particular, when the target changes lanes, it is often difficult to accurately obtain the lateral moving speed of the target, resulting in the inability to predict whether the target has the intention to change lanes, thereby affecting the decision-making of the self-vehicle; and the target speed obtained by the radar is the radial speed along the propagation direction of the radar wave, resulting in the inability to obtain the accurate speed of the target through the measurement results.

[0004] In order to solve this problem, a method for estimating the speed of an object using a point cloud radar, a point cloud radar and a system are needed. Summary of the invention

[0005] A series of simplified concepts are introduced in the Summary of the Invention, which will be further described in detail in the Detailed Description of the Invention. The Summary of the Invention does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0006] In view of the existence of the above technical problems, it is necessary to propose a method for estimating the speed of an object using a point cloud radar, a point cloud radar and a system and a computer-readable storage medium to solve the existing problem of accurately estimating the speed of an object using a point cloud radar.

[0007] According to one aspect of an embodiment of the present invention, a method for estimating the speed of an object using a point cloud radar is provided, the method comprising: scanning an environment through a point cloud radar to obtain motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by the scan; clustering the multiple sampling points to divide the multiple clustering groups, wherein the respective sampling points in the same clustering group are obtained by scanning the same object in the environment; and estimating the speed of the object according to the motion state parameters of the object points corresponding to the sampling points in the same clustering group.

[0008] According to another aspect of an embodiment of the present invention, a point cloud radar system is provided, which includes: a point cloud radar, used to scan an environment to obtain multiple sampling points; a processing device coupled to the point cloud radar, used to: obtain motion state parameters of multiple object points on the object corresponding to the multiple sampling points; cluster the multiple sampling points to divide them into multiple cluster groups, wherein each of the sampling points in the same cluster group is obtained by scanning the same object in the environment; and estimate the speed of the object based on the motion state parameters of the object points corresponding to the sampling points in the same cluster group.

[0009] According to another aspect of an embodiment of the present invention, a point cloud radar is provided, comprising: a memory for storing a computer program, and a processor coupled to the memory for executing the computer program to implement the following steps: obtaining motion state parameters of a plurality of object points on an object corresponding to a plurality of sampling points obtained by scanning an environment through the point cloud radar; clustering the plurality of sampling points to divide the plurality of cluster groups, wherein the respective sampling points in the same cluster group are obtained by scanning the same object in the environment; and estimating the speed of the object according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.

[0010] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, comprising computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method described above can be executed.

[0011] The method for estimating the speed of an object using a point cloud radar, the point cloud radar, the system, and the computer-readable medium of the embodiments of the present invention can estimate the speed of the object using information of multiple sampling points measured by the point cloud radar, reduce the measurement deviation caused by measurement noise, improve the speed measurement accuracy, and improve the radar's measurement performance in scenarios where extremely accurate speed measurement is required, such as lane changing, thereby effectively ensuring driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The following drawings of the present invention are used to understand the present invention as part of the present invention. The embodiments of the present invention are shown in the drawings and the description thereof is used to explain the principle of the present invention.

[0013] In the attached figure:

[0014] Figure 1 A flowchart of the steps of a method for estimating the speed of an object using a point cloud radar according to an embodiment of the present invention is shown;

[0015] Figure 2 A schematic structural block diagram of a point cloud radar system according to an embodiment of the present invention is shown;

[0016] Figure 3 A schematic top view of a point cloud radar performing scanning measurement according to an embodiment of the present invention is shown;

[0017] Figure 4 A schematic diagram showing a geometric relationship when deriving an object velocity without measurement noise according to an embodiment of the present invention is shown;

[0018] Figure 5 An exemplary schematic diagram of cluster groups according to an embodiment of the present invention is shown.

[0019] Figure 6 A schematic structural block diagram of a point cloud radar according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0021] In order to solve the above technical problems, the present invention provides a method for estimating the speed of an object using a point cloud radar, the method comprising: scanning an environment through a point cloud radar, obtaining motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by the scan; clustering the multiple sampling points to divide them into multiple cluster groups, wherein each of the sampling points in the same cluster group is obtained by scanning the same object in the environment; and estimating the speed of the object according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.

[0022] The method of estimating the speed of an object using a point cloud radar of the present invention can estimate the speed of the object using information of multiple sampling points measured by the point cloud radar, thereby reducing the measurement deviation caused by measurement noise, improving the speed measurement accuracy, and improving the radar's measurement performance in scenarios where extremely accurate speed measurement is required, such as lane changing, thereby effectively ensuring driving safety.

[0023] The method for estimating the speed of an object using a point cloud radar, a point cloud radar, a system, and a computer-readable medium according to an embodiment of the present invention are described in detail below with reference to specific embodiments.

[0024] According to one embodiment, a method for estimating the velocity of an object using a point cloud radar is provided. Figure 1 , Figure 1 A flowchart of a method 100 for estimating the velocity of an object using a point cloud radar according to an embodiment of the present invention is shown.

[0025] Exemplarily, the point cloud radar may be a point cloud millimeter wave radar, a point cloud laser radar, etc. Exemplarily, the point cloud radar may be mounted on any movable platform, such as any fully autonomous driving vehicle, semi-autonomous driving vehicle, drone, etc. with various autonomous driving levels (e.g., level 0-5). Exemplarily, the object may be any other vehicle, pedestrian, bicycle, and various other stationary or moving objects, etc., located in the environment where the point cloud radar is located, and the present invention is not limited thereto.

[0026] Among them, the point cloud radar may include any components known in the art, such as a power separator, a transmitting switch, a receiving switch, a transmitting antenna, a receiving antenna, a mixer, a control circuit, a low noise amplifier, a digital signal processor, etc. For the sake of brevity, the present invention does not introduce them in detail.

[0027] like Figure 1 As shown, method 100 may include the following steps:

[0028] Step S110: Scan the environment by using a point cloud radar to obtain motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by the scan.

[0029] Each of the multiple sampling points corresponds to an object point on the scanned object.

[0030] Exemplarily, the motion state parameters may include the measured relative distance, relative angle, relative speed, etc. between the object point and the point cloud radar. The relative angle may be the angle between the line connecting the object point and the point cloud radar and the first direction.

[0031] Step S120: clustering the plurality of sampling points to divide them into a plurality of cluster groups, wherein each sampling point in the same cluster group is obtained by scanning the same object in the environment.

[0032] Exemplarily, any clustering algorithm known in the art may be used for clustering, such as K-means clustering algorithm, mean shift clustering algorithm, DBSCAN clustering algorithm, maximum expectation clustering algorithm using GMM, or agglomerative hierarchical clustering algorithm, etc., and the present invention is not limited to this.

[0033] Step S130: Estimate the speed of the object (ie, the true relative speed) according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.

[0034] Specifically, the information of the sampling points in the same cluster group can be used to construct a noisy overdetermined equation, and then the overdetermined equation is solved using the least squares method to obtain the velocity of the object.

[0035] The following describes how to use the least squares method to estimate v x and v y The solution process.

[0036] According to the motion state parameters of the object point, the following equation can be constructed:

[0037] v e =Av r

[0038] in,

[0039] v e =[v1 v2…v I ] T

[0040]

[0041] v r =[v x v y ] T

[0042] According to the above equation, the following equation can be obtained by using the least squares method:

[0043] v r =(A T A) -1 A T v e (1)

[0044] Exemplarily, a weighted operation may be performed on the motion state parameters of the object points corresponding to the sampling points in the cluster group to obtain the speed of the object.

[0045] Wherein, the weight coefficient of the weighted operation can be set as needed. Exemplarily, method 100 may also include: obtaining the intensity information of each sampling point, and determining the weight coefficient of the weighted operation according to the intensity information. Wherein, the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object. The greater the scattering intensity at the object point, the greater the intensity of the corresponding sampling point, and the smaller the scattering intensity at the object point, the smaller the intensity of the corresponding sampling point. Object points with large scattering intensity are strong scattering points, and their corresponding radar echo signal-to-noise ratio is better, so that the sampling point information measured by the radar is more accurate. Therefore, increasing the weight influence of strong scattering points can obtain more accurate estimation results.

[0046] When the weight coefficient of the weighted operation is determined according to the intensity information P of the sampling point, the above equation (1) is correspondingly transformed into:

[0047] V r =(A T PA) -1 A T Pv e

[0048] in, is a diagonal matrix with diagonal elements p i is the intensity information of sampling point i obtained by radar measurement.

[0049] Exemplarily, the method 100 may further include: i , as the weight coefficient of the motion state parameter of the object point corresponding to the sampling point.

[0050] Exemplarily, the method 100 may further include: solving a first velocity component of the relative velocity in the first direction and a second velocity component in the second direction according to the measured relative velocity of the object point relative to the point cloud radar and the angle between the line connecting the object point and the point cloud radar and the first direction, wherein the second direction is perpendicular to the first direction. Exemplarily, the first direction may be the left-right direction between the movable platform where the point cloud radar is located and the scanned object, and the second direction may be the front-back direction between the movable platform where the point cloud radar is located and the scanned object.

[0051] Exemplarily, the method 100 may further include: controlling the movement of the movable platform according to the first velocity component and / or the second velocity component.

[0052] Exemplarily, method 100 may also include: determining a first motion state of an object in front of the movable platform in a first direction based on the first velocity component, and determining a second motion state of the movable platform and the object in front of the movable platform in a second direction based on the second velocity component, so as to control the movement of the movable platform according to the first motion state and the second motion state.

[0053] For example, when the right direction is set as the positive direction of the first direction, the left-right distance between the movable platform and the object can be determined to be increasing according to the positive first velocity component; the left-right distance between the movable platform and the object can be determined to be decreasing according to the negative first velocity component; and the left-right distance between the movable platform and the object can be determined to be zero according to the zero first velocity component. For another example, when the left direction is set as the positive direction of the first direction, the left-right distance between the movable platform and the object can be determined to be decreasing according to the positive first velocity component; the left-right distance between the movable platform and the object can be determined to be increasing according to the negative first velocity component; and the left-right distance between the movable platform and the object can be determined to be zero according to the zero first velocity component.

[0054] For example, when the second direction is set to be the positive direction of the second direction, the second velocity component is positive, and it can be determined that the distance between the movable platform and the object is increasing; the second velocity component is negative, and it can be determined that the distance between the movable platform and the object is decreasing; the second velocity component is zero, and it can be determined that the distance between the movable platform and the object remains unchanged. For another example, when the second direction is set to be the positive direction of the second direction, the second velocity component is positive, and it can be determined that the distance between the movable platform and the object is decreasing; the second velocity component is negative, and it can be determined that the distance between the movable platform and the object is increasing; the second velocity component is zero, and it can be determined that the distance between the movable platform and the object remains unchanged.

[0055] Exemplarily, method 100 may also include: calculating a first relative distance of the relative distance in the first direction and a second relative distance of the relative distance in the second direction based on the measured relative distance between the object point and the point cloud radar and the angle between the line connecting the object point and the point cloud radar and the first direction, so as to control the movement of the movable platform according to the first relative distance and the second relative distance.

[0056] For example, when the first relative distance is small, that is, the left and right distance between the movable platform and the object is small, the movable platform can be controlled to move an appropriate distance away from the object to avoid collision in the left and right directions; when the second relative distance is small, that is, the front and back distance between the movable platform and the object is small, the movable platform can be controlled to slow down appropriately to increase the front and back distance between the movable platform and the object, thereby avoiding collision in the front and back directions.

[0057] Exemplarily, method 100 may further include: calculating a theoretical relative speed of an object point relative to the point cloud radar based on the estimated speed of the object and the angle between a line connecting the object point and the point cloud radar and the first direction, and evaluating the performance of the point cloud radar based on the theoretical relative speed.

[0058] Exemplarily, the method 100 may further include: calculating the difference between the theoretical relative speed and the measured relative speed, and evaluating the performance of the point cloud radar based on the difference. Specifically, if the difference between the theoretical relative speed and the actual relative speed is small, the performance of the point cloud radar is good; if the difference between the theoretical relative speed and the actual relative speed is large, the performance of the point cloud radar is poor, and an alarm signal may be issued, such as an alarm signal of sound, text, or graphics, to prompt the user to debug or repair the point cloud radar.

[0059] Exemplarily, the method 100 may further include: calculating a ratio of the difference to the measured relative speed, and evaluating the performance of the point cloud radar based on the ratio. Specifically, if the ratio of the difference to the measured relative speed is small, the performance of the point cloud radar is good; if the ratio of the difference to the measured relative speed is large, the performance of the point cloud radar is poor, and an alarm signal may be issued, such as an alarm signal of sound, text, or graphics, to prompt the user to debug or repair the point cloud radar.

[0060] The method of estimating the speed of an object using a point cloud radar in this embodiment uses a point cloud radar to measure multiple sampling points, and uses the information of the multiple sampling points obtained by the measurement to estimate the speed of the object, which reduces the measurement deviation caused by measurement noise, improves the speed measurement accuracy, improves the radar's measurement performance in scenes where extremely accurate speed measurement is required, such as lane change, and effectively ensures driving safety. Further increasing the weight influence of strong sampling points makes the obtained estimation result more accurate.

[0061] According to another embodiment, a point cloud radar system is provided. Figure 2 , Figure 2 : A schematic structural block diagram of a point cloud radar system 20 according to an embodiment of the present invention is shown. In one embodiment, the point cloud radar system 20 includes at least a point cloud radar 210 and a processing device 220 coupled to the point cloud radar. Although the point cloud radar 210 and the processing device 220 are shown as independent devices, those skilled in the art should understand that the processing device 220 can also be integrated into the point cloud radar 210, and the present invention is not limited to this.

[0062] Among them, the point cloud radar 210 is used to scan the environment to obtain multiple sampling points, each of which corresponds to an object point on the scanned object. Exemplarily, the point cloud radar 210 can be a point cloud millimeter wave radar, a point cloud laser radar, etc. Exemplarily, the point cloud radar 210 can be mounted on any movable platform, such as any fully automatic driving vehicle, semi-automatic driving vehicle, drone, etc. with various automatic driving levels (e.g., level 0-5). Exemplarily, the object can be any other vehicle, pedestrian, bicycle, and various other stationary or moving objects located in the environment where the point cloud radar 210 is located, and the present invention is not limited to this.

[0063] Among them, the point cloud radar 210 may include any components known in the art, such as a power separator, a transmitting switch, a receiving switch, a transmitting antenna, a receiving antenna, a mixer, a control circuit, a low noise amplifier, a digital signal processor, etc. For the sake of brevity, the present invention does not introduce them in detail.

[0064] refer to Figure 3 , Figure 3 FIG. 2 shows a top view of a point cloud radar 210 performing scanning measurement according to an embodiment. Figure 3 The object scanned by the point cloud radar 210 is shown as another vehicle, but this is merely illustrative and is not intended to be limiting.

[0065] Assume that the point cloud radar 210 observes I sampling points on the target vehicle, and the relative distance, relative speed and relative angle between the object point on the target vehicle and the point cloud radar 210 corresponding to the i-th sampling point measured by the point cloud radar 210 are (r i ,v i ,θ i ), the actual relative speed of the object point on the target vehicle relative to the point cloud radar is v r For a rigid body, such as a vehicle, the true relative velocity of each object point on it should be the same, so the true relative velocity of the target vehicle relative to the point cloud radar should also be v r .

[0066] In the absence of measurement noise, according to Figure 4 The geometric relationship diagram shown in the figure can deduce the following relationship:

[0067] Assume that the target vehicle has a true relative velocity v relative to the point cloud radar r The components along the x-axis and y-axis are v x and v y , then:

[0068]

[0069] The measured relative velocity vi Equal to the true relative velocity v r The projection along the direction of radar wave propagation is:

[0070] v i =v x cosθ i +v y sinθ i

[0071] In the absence of measurement noise, the unknown variable v can be solved by combining the relative speed and relative angle information of the two object points. x and v y , thereby solving the real relative speed v of the target vehicle r .

[0072] However, measurement noise will inevitably appear in the actual measurement process, which makes it impossible to estimate the accurate velocity information of the object through the information of two object points on the object.

[0073] The processing device 220 of the present invention can obtain more accurate measurement results by performing a series of processing on the information of the sampling points.

[0074] Specifically, the processing device 220 may obtain motion state parameters of multiple object points on the object corresponding to the multiple sampling points. Exemplarily, the motion state parameters may include the measured relative distance, relative angle, relative speed, etc. between the object point and the point cloud radar 210. The relative angle may be the angle between the line connecting the object point and the point cloud radar and the first direction.

[0075] Then, the processing device 220 may cluster the multiple sampling points to divide them into multiple cluster groups, wherein each sampling point in the same cluster group is obtained by scanning the same object in the environment. Figure 5 , Figure 5 An exemplary schematic diagram of cluster groups according to an embodiment of the present invention is shown.

[0076] Exemplarily, any clustering algorithm known in the art may be used for clustering, such as K-means clustering algorithm, mean shift clustering algorithm, DBSCAN clustering algorithm, maximum expectation clustering algorithm using GMM, or agglomerative hierarchical clustering algorithm, etc., and the present invention is not limited to this.

[0077] Then, the processing device 220 may estimate the speed of the object (ie, the true relative speed) according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.

[0078] Since the point cloud radar 210 obtains multiple sampling points, the processing device 220 can obtain information of the multiple sampling points, so it is possible to construct a noisy overdetermined equation using the information of the sampling points, and then use the least squares method to solve the overdetermined equation to obtain the speed of the object.

[0079] The following describes how to estimate v using the least squares method. x and v y The solution process.

[0080] According to the motion state parameters of the object point, the following equation can be constructed:

[0081] V e =AV r

[0082] in,

[0083] V e =[V1 v2…v I ] T

[0084]

[0085] v r =[v x v y ] T

[0086] According to the above equation, the following equation can be obtained by using the least squares method:

[0087] v r =(A T A) -1 A T v e (1)

[0088] Exemplarily, the processing device 220 may perform a weighted operation on the motion state parameters of the object points corresponding to the sampling points in the cluster group to obtain the speed of the object.

[0089] The weight coefficient of the weighted operation can be set as needed. Exemplarily, the processing device 220 can obtain the intensity information of each sampling point and determine the weight coefficient of the weighted operation according to the intensity information. The intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object. The greater the scattering intensity at the object point, the greater the intensity of the corresponding sampling point, and the smaller the scattering intensity at the object point, the smaller the intensity of the corresponding sampling point. Object points with large scattering intensity are strong scattering points, and their corresponding radar echo signal-to-noise ratio is better, so that the sampling point information measured by the radar is more accurate. Therefore, increasing the weight influence of strong scattering points can obtain more accurate estimation results.

[0090] When the weight coefficient of the weighted operation is determined according to the intensity information P of the sampling point, the above equation (1) is correspondingly transformed into:

[0091] v r =(A T PA) -1 A T pv e

[0092] in, is a diagonal matrix with diagonal elements p i is the intensity information of sampling point i obtained by radar measurement.

[0093] For example, the processing device 220 may convert the intensity information p of each sampling point into i , as the weight coefficient of the motion state parameter of the object point corresponding to the sampling point.

[0094] Exemplarily, the processing device 220 may also solve the first velocity component of the relative velocity in the first direction and the second velocity component in the second direction according to the measured relative velocity of the object point relative to the point cloud radar 210 and the angle between the line connecting the object point and the point cloud radar and the first direction, wherein the second direction is perpendicular to the first direction. Exemplarily, the first direction may be the left-right direction between the movable platform where the point cloud radar is located and the scanned object, and the second direction may be the front-back direction between the movable platform where the point cloud radar is located and the scanned object.

[0095] Exemplarily, the processing device 220 may also control the movement of the movable platform according to the first velocity component and / or the second velocity component.

[0096] Exemplarily, the processing device 220 can determine a first motion state of an object in front of the movable platform in a first direction based on the first velocity component, and determine a second motion state of the movable platform and the object in front of the movable platform in a second direction based on the second velocity component, so as to control the movement of the movable platform according to the first motion state and the second motion state.

[0097] For example, when the right direction is set as the positive direction of the first direction, the processing device 220 can determine that the left-right distance between the movable platform and the object is increasing based on the positive first velocity component; determine that the left-right distance between the movable platform and the object is decreasing based on the negative first velocity component; and determine that the left-right distance between the movable platform and the object remains unchanged based on the first velocity component being zero. For another example, when the left direction is set as the positive direction of the first direction, the processing device 220 can determine that the left-right distance between the movable platform and the object is decreasing based on the positive first velocity component; determine that the left-right distance between the movable platform and the object is increasing based on the negative first velocity component; and determine that the left-right distance between the movable platform and the object remains unchanged based on the zero first velocity component.

[0098] For example, when the backward direction is set as the positive direction of the second direction, the processing device 220 can determine that the front-to-back distance between the movable platform and the object is increasing according to the second velocity component being positive; determine that the front-to-back distance between the movable platform and the object is decreasing according to the second velocity component being negative; and determine that the front-to-back distance between the movable platform and the object is remaining unchanged according to the second velocity component being zero. For another example, when the forward direction is set as the positive direction of the second direction, the processing device 220 can determine that the front-to-back distance between the movable platform and the object is decreasing according to the second velocity component being positive; determine that the front-to-back distance between the movable platform and the object is increasing according to the second velocity component being negative; and determine that the front-to-back distance between the movable platform and the object is remaining unchanged according to the second velocity component being zero.

[0099] Exemplarily, the processing device 220 can also calculate the first relative distance of the relative distance in the first direction and the second relative distance of the relative distance in the second direction based on the measured relative distance between the object point and the point cloud radar 210 and the angle between the line connecting the object point and the point cloud radar 210 and the first direction, so as to control the movement of the movable platform according to the first relative distance and the second relative distance.

[0100] For example, when the first relative distance is small, that is, the left and right distance between the movable platform and the object is small, the processing device 220 can control the movable platform to move an appropriate distance away from the object to avoid a collision in the left and right directions; when the second relative distance is small, that is, the front and back distance between the movable platform and the object is small, the processing device 220 can control the movable platform to decelerate appropriately and increase the front and back distance between the movable platform and the object to avoid a collision in the front and back directions.

[0101] Exemplarily, the processing device 220 can also calculate the theoretical relative speed of a certain object point relative to the point cloud radar 210 based on the estimated speed of the object and the angle between the line connecting the object point and the point cloud radar 210 and the first direction, and evaluate the performance of the point cloud radar 210 based on the theoretical relative speed.

[0102] Exemplarily, the processing device 220 may also calculate the difference between the theoretical relative speed and the measured relative speed, and evaluate the performance of the point cloud radar 210 based on the difference. Specifically, if the difference between the theoretical relative speed and the actual relative speed is small, the performance of the point cloud radar 210 is good; if the difference between the theoretical relative speed and the actual relative speed is large, the performance of the point cloud radar 210 is poor, and the control device 120 may issue an alarm signal, such as an alarm signal of sound, text, or graphics, to prompt the user to debug or repair the point cloud radar 210.

[0103] Exemplarily, the processing device 220 may also calculate the ratio of the difference to the measured relative speed, and evaluate the performance of the point cloud radar 210 based on the ratio. Specifically, if the ratio of the difference to the measured relative speed is small, the performance of the point cloud radar 210 is good; if the ratio of the difference to the measured relative speed is large, the performance of the point cloud radar 210 is poor, and the control device 120 may issue an alarm signal, such as an alarm signal of sound, text, or graphics, to prompt the user to debug or repair the point cloud radar 210.

[0104] This embodiment uses a point cloud radar system, uses point cloud radar to measure multiple sampling points, and uses the information of multiple sampling points obtained by measurement to estimate the speed of the object, which reduces the measurement deviation caused by measurement noise, improves the speed measurement accuracy, and improves the radar's measurement performance in scenes where extremely accurate speed measurement is required, such as lane change, effectively ensuring driving safety. Further increasing the weight influence of strong sampling points makes the obtained estimation result more accurate.

[0105] According to another embodiment, a point cloud radar is provided. Figure 6 , Figure 6 : A schematic structural block diagram of a point cloud radar 60 according to an embodiment of the present invention is shown. Exemplarily, the point cloud radar 60 may be a point cloud millimeter wave radar, a point cloud laser radar, etc. Exemplarily, the point cloud radar 60 may be mounted on any movable platform, such as any fully automatic driving vehicle, semi-automatic driving vehicle, drone, etc. with various automatic driving levels (e.g., level 0-5). Exemplarily, the object may be any other vehicle, pedestrian, bicycle, and various other stationary or moving objects, etc., located in the environment where the point cloud radar 60 is located, and the present invention is not limited thereto.

[0106] In one embodiment, the point cloud radar 60 includes at least a memory 610 and a processor 620 coupled to the memory. It should be understood that the point cloud radar 60 may also include any elements known in the art, such as a power splitter, a transmitting switch, a receiving switch, a transmitting antenna, a receiving antenna, a mixer, a control circuit, a low noise amplifier, a digital signal processor, etc. For the sake of brevity, the present invention does not introduce them in detail.

[0107] The memory 610 stores a computer program, which can be executed by the processor 620 to implement the following steps:

[0108] Step S1: obtaining motion state parameters of multiple object points on an object corresponding to multiple sampling points obtained by scanning an environment with a point cloud radar.

[0109] Each of the multiple sampling points corresponds to an object point on the scanned object.

[0110] Exemplarily, the motion state parameters may include the measured relative distance, relative angle, relative speed, etc. between the object point and the point cloud radar 60. The relative angle may be the angle between the line connecting the object point and the point cloud radar and the first direction.

[0111] Step S2: clustering the multiple sampling points to divide them into multiple cluster groups, wherein each sampling point in the same cluster group is obtained by scanning the same object in the environment.

[0112] Exemplarily, any clustering algorithm known in the art may be used for clustering, such as K-means clustering algorithm, mean shift clustering algorithm, DBSCAN clustering algorithm, maximum expectation clustering algorithm using GMM, or agglomerative hierarchical clustering algorithm, etc., and the present invention is not limited to this.

[0113] Step S3: Estimate the speed of the object (ie, the true relative speed) according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group.

[0114] Specifically, the information of the sampling points in the same cluster group can be used to construct a noisy overdetermined equation, and then the overdetermined equation is solved using the least squares method to obtain the velocity of the object.

[0115] The following describes how to estimate v using the least squares method. x and v y The solution process.

[0116] According to the motion state parameters of the object point, the following equation can be constructed:

[0117] V e =AV r

[0118] in,

[0119] V e =[V1 v2…v I ] T

[0120]

[0121] v r =[v x v y ] T

[0122] According to the above equation, the following equation can be obtained by using the least squares method:

[0123] v r =(A T A) -1 A T v e (1)

[0124] Exemplarily, a weighted operation may be performed on the motion state parameters of the object points corresponding to the sampling points in the cluster group to obtain the speed of the object.

[0125] The weight coefficient of the weighted operation can be set as needed. Exemplarily, the method may further include the steps of obtaining the intensity information of each sampling point, and determining the weight coefficient of the weighted operation according to the intensity information. The intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object. The greater the scattering intensity at the object point, the greater the intensity of the corresponding sampling point. The smaller the scattering intensity at the object point, the smaller the intensity of the corresponding sampling point. Object points with large scattering intensity are strong scattering points, and their corresponding radar echo signal-to-noise ratio is better, so that the sampling point information measured by the radar is more accurate. Therefore, increasing the weight influence of strong scattering points can obtain more accurate estimation results.

[0126] When the weight coefficient of the weighted operation is determined according to the intensity information P of the sampling point, the above equation (1) is correspondingly transformed into:

[0127] v r =(A T PA) -1 A T Pv e

[0128] in, is a diagonal matrix with diagonal elements p i is the intensity information of sampling point i obtained by radar measurement.

[0129] For example, the intensity information p of each sampling point can be i , as the weight coefficient of the motion state parameter of the object point corresponding to the sampling point.

[0130] For example, the first velocity component of the relative velocity in the first direction and the second velocity component in the second direction can also be solved according to the measured relative velocity of the object point relative to the point cloud radar 60 and the angle between the line connecting the object point and the point cloud radar and the first direction, wherein the second direction is perpendicular to the first direction. For example, the first direction can be the left-right direction between the movable platform where the point cloud radar is located and the scanned object, and the second direction can be the front-back direction between the movable platform where the point cloud radar is located and the scanned object.

[0131] Exemplarily, the movement of the movable platform may also be controlled according to the first velocity component and / or the second velocity component.

[0132] Exemplarily, a first motion state of an object in front of the movable platform in a first direction can be determined based on the first velocity component, and a second motion state of the movable platform and the object in front of the movable platform in a second direction can be determined based on the second velocity component, so as to control the movement of the movable platform according to the first motion state and the second motion state.

[0133] For example, when the right direction is set as the positive direction of the first direction, the left-right distance between the movable platform and the object can be determined to be increasing according to the positive first velocity component; the left-right distance between the movable platform and the object can be determined to be decreasing according to the negative first velocity component; and the left-right distance between the movable platform and the object can be determined to be zero according to the zero first velocity component. For another example, when the left direction is set as the positive direction of the first direction, the left-right distance between the movable platform and the object can be determined to be decreasing according to the positive first velocity component; the left-right distance between the movable platform and the object can be determined to be increasing according to the negative first velocity component; and the left-right distance between the movable platform and the object can be determined to be zero according to the zero first velocity component.

[0134] For example, when the second direction is set to be the positive direction of the second direction, the second velocity component is positive, and it can be determined that the distance between the movable platform and the object is increasing; the second velocity component is negative, and it can be determined that the distance between the movable platform and the object is decreasing; the second velocity component is zero, and it can be determined that the distance between the movable platform and the object remains unchanged. For another example, when the second direction is set to be the positive direction of the second direction, the second velocity component is positive, and it can be determined that the distance between the movable platform and the object is decreasing; the second velocity component is negative, and it can be determined that the distance between the movable platform and the object is increasing; the second velocity component is zero, and it can be determined that the distance between the movable platform and the object remains unchanged.

[0135] Exemplarily, a first relative distance of the relative distance in the first direction and a second relative distance of the relative distance in the second direction can also be calculated based on the measured relative distance between the object point and the point cloud radar 60 and the angle between the line connecting the object point, the point cloud radar 60 and the first direction, so as to control the movement of the movable platform according to the first relative distance and the second relative distance.

[0136] For example, when the first relative distance is small, that is, the left and right distance between the movable platform and the object is small, the movable platform can be controlled to move an appropriate distance away from the object to avoid collision in the left and right directions; when the second relative distance is small, that is, the front and back distance between the movable platform and the object is small, the movable platform can be controlled to slow down appropriately to increase the front and back distance between the movable platform and the object, thereby avoiding collision in the front and back directions.

[0137] Exemplarily, the theoretical relative speed of a certain object point relative to the point cloud radar 60 can also be calculated based on the estimated speed of the object and the angle between the line connecting the object point and the point cloud radar 60 and the first direction, and the performance of the point cloud radar 60 can be evaluated based on the theoretical relative speed.

[0138] Exemplarily, the difference between the theoretical relative speed and the measured relative speed can also be calculated, and the performance of the point cloud radar 60 can be evaluated based on the difference. Specifically, if the difference between the theoretical relative speed and the actual relative speed is small, the performance of the point cloud radar 60 is good; if the difference between the theoretical relative speed and the actual relative speed is large, the performance of the point cloud radar 60 is poor, and an alarm signal, such as a sound, text, graphic, etc., can be issued to prompt the user to debug and repair the point cloud radar 60.

[0139] Exemplarily, the ratio of the difference to the measured relative speed can also be calculated, and the performance of the point cloud radar 60 can be evaluated based on the ratio. Specifically, if the ratio of the difference to the measured relative speed is small, the performance of the point cloud radar 60 is good; if the ratio of the difference to the measured relative speed is large, the performance of the point cloud radar 60 is poor, and an alarm signal, such as an alarm signal of sound, text, or graphics, can be issued to prompt the user to debug or repair the point cloud radar 60.

[0140] The point cloud radar of this embodiment measures multiple sampling points and estimates the speed of the object using the information of the multiple sampling points obtained by the measurement, which reduces the measurement deviation caused by the measurement noise, improves the speed measurement accuracy, improves the radar's measurement performance in scenes where the speed measurement requires extremely accurate lane changes, and effectively ensures driving safety. Further increasing the weight influence of strong sampling points makes the estimation result more accurate.

[0141] This embodiment provides a computer-readable medium, on which a computer program is stored. When the computer program is run, the method for estimating the speed of an object using a point cloud radar as described above is executed.

[0142] According to the computer readable medium of the present invention, when the computer program thereon is executed, the speed of the object can be estimated using the information of multiple sampling points obtained by point cloud radar measurement, thereby reducing the measurement deviation caused by measurement noise, improving the speed measurement accuracy, and improving the radar's measurement performance for scenes requiring extremely accurate speed measurement such as lane change, thereby effectively ensuring driving safety. Further increasing the weight influence of strong sampling points makes the obtained estimation result more accurate.

[0143] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as required by the appended claims.

[0144] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0146] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0147] Similarly, it should be understood that in order to streamline the present invention and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present invention should not be interpreted as reflecting the following intention: the claimed invention requires more features than the features explicitly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0148] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0149] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0150] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some modules in the article analysis device according to an embodiment of the present invention. The present invention may also be implemented as a device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0151] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0152] The above is only a specific embodiment of the present invention or an explanation of a specific embodiment. The protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for estimating object speed using point cloud radar, characterized in that: The method comprises: Scanning the environment through a point cloud radar to obtain motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by the scan; Clustering the plurality of sampling points to divide them into a plurality of cluster groups, wherein the sampling points in the same cluster group are obtained by scanning the same object in the environment; Performing a weighted operation on the motion state parameters of the object point corresponding to each of the sampling points in the cluster group to obtain the speed of the object; The method further comprises: Acquire the intensity information of each sampling point; wherein the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object, and the scattering intensity is used to characterize the radar echo signal-to-noise ratio; Determining a weight coefficient of the weighted operation according to the strength information; The method further comprises: Calculating a theoretical relative speed of the object point relative to the point cloud radar according to the estimated speed of the object and an angle between a line connecting the object point and the point cloud radar and the first direction; The difference between the theoretical relative speed and the relative speed is calculated, and the performance of the point cloud radar is evaluated based on the difference, and an alarm signal is issued when the performance of the point cloud radar is poor.

2. The method according to claim 1, characterized in that The intensity information of each sampling point is used as a weight coefficient of the motion state parameter of the object point corresponding to the sampling point.

3. The method according to any one of claims 1 to 2, characterized in that: The motion state parameters of the object point include a relative speed of the object point relative to the point cloud radar and an angle between a line connecting the object point and the point cloud radar and a first direction.

4. The method according to claim 3, characterized in that The method further comprises: A first velocity component of the relative velocity in the first direction and a second velocity component in the second direction are calculated according to the relative velocity and the included angle, wherein the second direction is perpendicular to the first direction.

5. The method according to claim 4, characterized in that The point cloud radar is mounted on a movable platform, and the method further comprises: The movement of the movable platform is controlled based on the first velocity component and / or the second velocity component.

6. The method according to claim 5, characterized in that Wherein controlling the movement of the movable platform according to the first velocity component and / or the second velocity component comprises: A first motion state of an object in front of the movable platform in the first direction is determined according to the first velocity component, and a second motion state of the movable platform and the object in front of the movable platform in the second direction is determined according to the second velocity component, so as to control the movement of the movable platform according to the first motion state and the second motion state.

7. The method according to claim 5, characterized in that The motion state parameter of the object point also includes the relative distance between the object point and the point cloud radar, wherein the method further includes: A first relative distance of the relative distance in the first direction and a second relative distance of the relative distance in the second direction are calculated according to the relative distance and the included angle, so as to control the movement of the movable platform according to the first relative distance and the second relative distance.

8. A point cloud radar system, characterized in that: The point cloud radar system comprises: Point cloud radar, used to scan the environment to obtain multiple sampling points, A processing device coupled to the point cloud radar, for: Acquire motion state parameters of a plurality of object points on the object corresponding to the plurality of sampling points; Clustering the plurality of sampling points to divide them into a plurality of cluster groups, wherein the sampling points in the same cluster group are obtained by scanning the same object in the environment; estimating the speed of the object according to the motion state parameters of the object points corresponding to the sampling points in the same cluster group; The processing device is also used for: Performing a weighted operation on the motion state parameters of the object point corresponding to each of the sampling points in the cluster group to obtain the speed of the object; The processing device is also used for: Acquire the intensity information of each sampling point; wherein the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object, and the scattering intensity is used to characterize the radar echo signal-to-noise ratio; Determining a weight coefficient of the weighted operation according to the strength information; The processing device is also used for: Calculating a theoretical relative speed of the object point relative to the point cloud radar according to the estimated speed of the object and an angle between a line connecting the object point and the point cloud radar and a first direction; and evaluating the performance of the point cloud radar based on the theoretical relative speed; The processing device is specifically used for: The difference between the theoretical relative speed and the relative speed is calculated, and the performance of the point cloud radar is evaluated based on the difference, and an alarm signal is issued when the performance of the point cloud radar is poor.

9. The point cloud radar system according to claim 8, characterized in that: The processing device is also used for: The intensity information of each sampling point is used as a weight coefficient of the motion state parameter of the object point corresponding to the sampling point.

10. The point cloud radar system according to any one of claims 8 to 9, characterized in that: The motion state parameters of the object point include a relative speed of the object point relative to the point cloud radar and an angle between a line connecting the object point and the point cloud radar and a first direction.

11. The point cloud radar system according to claim 10, characterized in that: The processing device is also used for: A first velocity component of the relative velocity in the first direction and a second velocity component in the second direction are calculated according to the relative velocity and the included angle, wherein the second direction is perpendicular to the first direction.

12. The point cloud radar system according to claim 11, characterized in that: The point cloud radar is mounted on a movable platform, and the processing device is also used for: The movement of the movable platform is controlled based on the first velocity component and / or the second velocity component.

13. The point cloud radar system according to claim 12, characterized in that: The processing device is also used for: A first motion state of an object in front of the movable platform in the first direction is determined according to the first velocity component, and a second motion state of the movable platform and the object in front of the movable platform in the second direction is determined according to the second velocity component, so as to control the movement of the movable platform according to the first motion state and the second motion state.

14. The point cloud radar system according to claim 12, characterized in that: The motion state parameter of the object point also includes the relative distance between the object point and the point cloud radar, and the processing device is further used for: A first relative distance of the relative distance in the first direction and a second relative distance of the relative distance in the second direction are calculated according to the relative distance and the included angle, so as to control the movement of the movable platform according to the first relative distance and the second relative distance.

15. A point cloud radar, characterized in that: include: memory for storing computer programs, and A processor coupled to the memory is configured to execute the computer program to implement the following steps: Obtain motion state parameters of multiple object points on the object corresponding to multiple sampling points obtained by scanning the environment through the point cloud radar; Clustering the plurality of sampling points to divide them into a plurality of cluster groups, wherein the sampling points in the same cluster group are obtained by scanning the same object in the environment; Performing a weighted operation on the motion state parameters of the object point corresponding to each of the sampling points in the cluster group to obtain the speed of the object; Acquire the intensity information of each sampling point; wherein the intensity information of the sampling point corresponds to the scattering intensity of the radar wave at the corresponding object point on the object, and the scattering intensity is used to characterize the radar echo signal-to-noise ratio; Determining a weight coefficient of the weighted operation according to the strength information; Calculating a theoretical relative speed of the object point relative to the point cloud radar according to the estimated speed of the object and an angle between a line connecting the object point and the point cloud radar and the first direction; The difference between the theoretical relative speed and the relative speed is calculated, and the performance of the point cloud radar is evaluated based on the difference, and an alarm signal is issued when the performance of the point cloud radar is poor.

16. A computer-readable storage medium, characterized in that: The method comprises computer executable instructions, which, when executed by a processor, can perform the method according to any one of claims 1 to 7.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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