Method for resolving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters

Abstract

The invention relates to a method for resolving speed ambiguity based on the speed difference of a vehicle-borne millimeter-wave radar point cloud cluster, comprising: obtaining point cloud cluster information and an unambiguous speed interval length of a previous frame, as well as point cloud data, a vehicle speed, an unambiguous speed interval length and frame period data of a current frame; moving the unambiguous speed interval of the current frame to a point with the vehicle speed as a center point, and then compensating the point cloud speed to a new unambiguous speed interval; clustering the point cloud, and extracting the position and speed information of the point cloud with the highest energy in each clustered point cloud cluster as feature information of the point cloud cluster; traversing the point cloud cluster of the current frame, and using all dynamic point cloud clusters of the previous frame to perform position matching with the point cloud cluster of the current frame; for the successfully matched point cloud cluster, using the unambiguous speed interval length and the point cloud cluster speed difference between the two frames to perform speed resolution processing; synchronizing the speed ambiguity result to all point clouds in the point cloud cluster, and recording the dynamic point cloud cluster information and the unambiguous speed interval length of the current frame.

Description

Technical Field

[0001] The present invention relates to the field of radar signal processing, and in particular to a method, device, processor and computer-readable storage medium thereof for solving velocity ambiguity based on velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters. Background Art

[0002] As an important component of vehicle body sensors in autonomous driving, vehicle-mounted millimeter-wave radar mainly operates at a frequency band of around 77 GHz. Due to its high frequency and short wavelength, it can provide high resolution and accuracy, accurately identify the position and speed information of the target, and has high anti-interference characteristics, and can operate stably in complex environments. At present, vehicle-mounted millimeter-wave radars mostly use the linear frequency modulation continuous wave (FMCW) working mode, and achieve orthogonality between channels through the TDMA (Time Division Multiple Access) signal transmission method. Limited by the frequency sweep cycle, in many cases the maximum unambiguous speed is difficult to meet the actual use requirements, so it is necessary to use the speed deambiguation algorithm to further improve the speed measurement range of the millimeter-wave radar.

[0003] Commonly used speed deambiguation methods include: (1) adding a delayed transmission signal after the normal transmission signal, and deambiguating the speed through the phase difference between the delayed signal and the normal signal; (2) using the Chinese remainder theorem to transmit two sets of signals with different periods. Since the two sets of maximum unambiguous speeds are different, when there is speed ambiguity, the fuzzy speeds obtained by the two sets of signals are different. The Chinese remainder theorem can then be used to solve the target's true speed.

[0004] The prior art CN202311285621.4 discloses matching clusters and tracks, using the predicted value of the track as the gate center of the track to determine whether the cluster position falls within the gate of a certain track, and by calculating all possible speeds of the cluster to determine whether a certain speed falls within the gate of the track. The above conditions must be met to resolve speed ambiguity. This method requires the existence of a given track, and also requires calculating all possible true speeds before matching a true speed.

[0005] The prior art CN202311559928.9 discloses that the fuzzy speeds of the matching targets in two frames are superimposed by the maximum unfuzzy speed multiples, all possible true speeds of the targets in the two frames are calculated, and then the speed difference between the possible true speeds is calculated to obtain the minimum absolute value of the speed difference. If the minimum value is less than a certain threshold, the speed defuzzification is successful. When the fuzzy coefficient range is large, the number of traversals of this method increases, and more processing time is required.

[0006] Based on this, in order to reduce the amount of computation for resolving speed ambiguity and reduce the complexity of solving all possible speeds and then performing speed matching, it is necessary to propose a solution to overcome the defects of the current existing technology. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a method, device, processor and computer-readable storage medium for solving speed ambiguity based on the speed difference of vehicle-mounted millimeter-wave radar point cloud clusters. The method starts from the perspective of point cloud clusters and reduces the amount of calculation for directly processing point clouds in traditional methods; the unambiguous speed interval is moved according to the vehicle speed, ensuring that when the vehicle speed exceeds the ambiguity interval, the static target point cloud with a relative speed opposite to the vehicle speed will not be blurred into a dynamic target point cloud, further reducing the amount of calculation; at the same time, the ambiguity coefficient of the current frame and the previous frame can be solved by performing certain operations using the speed difference of the point cloud cluster and the maximum unambiguous speed range, which speeds up the calculation speed compared to the conventional method of solving all possible speeds and then matching them.

[0008] In order to achieve the above-mentioned object, the method, device, processor and computer-readable storage medium thereof for solving velocity ambiguity based on velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters of the present invention are as follows:

[0009] The method for solving velocity ambiguity based on the velocity difference of vehicle-borne millimeter-wave radar point cloud clusters is mainly characterized in that the method comprises the following steps:

[0010] (1) Obtain the point cloud cluster information and unambiguous speed interval length of the previous frame, as well as the point cloud data, vehicle speed, unambiguous speed interval length, and frame period data of the current frame;

[0011] (2) The unambiguous speed interval of the current frame is moved to the point with the vehicle speed as the center point, and then the point cloud speed is compensated to the new unambiguous speed interval;

[0012] (3) Cluster the point cloud and extract the position and velocity information of the point cloud with the highest energy in each clustered point cloud cluster as the feature information of the point cloud cluster;

[0013] (4) Traverse the point cloud clusters of this frame and use all dynamic point cloud clusters of the previous frame to match the point cloud clusters of this frame. If there is a dynamic point cloud cluster in the previous frame that falls within the threshold range of the x-direction and y-direction of the point cloud cluster position of this frame, the match is successful.

[0014] (5) For the successfully matched point cloud clusters, the speed ambiguity is resolved by using the unambiguous speed interval length between the two frames and the speed difference of the point cloud cluster;

[0015] (6) The defuzzified velocity result of the obtained point cloud cluster is synchronized to all point clouds in the point cloud cluster, and the dynamic point cloud cluster information and the length of the unfuzzy velocity interval of this frame are recorded.

[0016] Preferably, the step (2) is specifically as follows:

[0017] Set the unblurred speed interval lengths of this frame and the previous frame to , , where the original unambiguous speed measurement interval of this frame is ; Assume the vehicle speed is By subtracting the vehicle speed, the unambiguous speed measurement interval of this frame is converted into , and then add or subtract integer multiples of the unblurred speed interval of this frame , transport it to Within the scope; among them, it is necessary to ensure Greater than the change in the target vehicle's speed within one frame.

[0018] Preferably, the step (5) is specifically as follows:

[0019] Assume that the measurement speed of the point cloud cluster in this frame is , the speed of the point cloud cluster of the previous frame that is successfully matched is , if the current , then the following processing is performed:

[0020] Calculate the point cloud cluster velocity of the previous frame The measurement speed of the point cloud cluster in this frame The difference :

[0021] ;

[0022] Calculate the reciprocal of the difference between the length of the unblurred speed interval of this frame and the length of the unblurred speed interval of the previous frame :

[0023] ;

[0024] Calculate the velocity difference of point cloud clusters about The result of taking the remainder :

[0025] ;

[0026] in, It is the modulo function of floating point data;

[0027] According to and and The size relationship of The value of :

[0028] ;

[0029] Then and The result of the multiplication is rounded to get the blur coefficient of this frame. :

[0030] ;

[0031] in, is the rounding function.

[0032] Preferably, the fuzzy coefficient is calculated , solve the real speed of this frame in the following way , blur coefficient of the previous frame And the real speed of the previous frame :

[0033] ;

[0034] ;

[0035] .

[0036] Preferably, if the current , then the following processing is performed:

[0037] Calculate the point cloud cluster velocity of this frame The measurement speed of the point cloud cluster in the previous frame The difference :

[0038] ;

[0039] Calculate the reciprocal of the difference between the length of the unblurred speed interval of the previous frame and the length of the unblurred speed interval of the current frame :

[0040] ;

[0041] Calculate the velocity difference of point cloud clusters about The result of taking the remainder :

[0042] ;

[0043] And according to and and The size relationship between The value of is adjusted to obtain :

[0044] ;

[0045] Then and The result of the multiplication is rounded to get the blur coefficient of the previous frame. :

[0046] .

[0047] Preferably, the fuzzy coefficient is calculated , solve the real speed of the previous frame as follows , blur coefficient of this frame And the actual speed of this frame :

[0048] ;

[0049] ;

[0050] .

[0051] Preferably, the step (5) further comprises determining whether the following conditions are met:

[0052] a. Determine whether the currently calculated fuzzy coefficient and the actual speed are within the preset threshold range;

[0053] b. Calculate the distance difference between the point cloud cluster of this frame and the matching point cloud cluster of the previous frame , and then calculate the movement distance by multiplying the average speed of the two frames by the frame period ,judge and Whether the modulus value of the difference is within a preset threshold range;

[0054] c. Calculate the acceleration between the real speeds of the two frames and determine whether it is within the preset threshold range;

[0055] If conditions a, b and c are met at the same time, the velocity fuzzy result of the point cloud cluster solution is directly used to overwrite the original point cloud cluster velocity; otherwise, the solution process is terminated directly.

[0056] The device for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters has the following main features: the device comprises:

[0057] a processor configured to execute computer executable instructions;

[0058] A memory stores one or more computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters are implemented.

[0059] The processor for solving speed ambiguity based on the speed difference of vehicle-borne millimeter-wave radar point cloud clusters has the main feature that the processor is configured to execute computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for solving speed ambiguity based on the speed difference of vehicle-borne millimeter-wave radar point cloud clusters are implemented.

[0060] The computer-readable storage medium has the main feature of storing a computer program thereon, and the computer program can be executed by a processor to implement the steps of the above-mentioned method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters.

[0061] The method, device, processor and computer-readable storage medium thereof for solving speed ambiguity based on the speed difference of vehicle-mounted millimeter-wave radar point cloud clusters of the present invention are adopted. By using the unambiguous speed interval of transportation, it is ensured that the static target point cloud will not be blurred into the dynamic target point cloud. From the perspective of point cloud clustering, the amount of calculation is reduced compared with the traditional point cloud-based method. At the same time, the real speed of the target can be solved by using the speed difference of the point cloud cluster and the length of the unambiguous speed interval. Compared with the conventional method of solving all possible speeds and then matching them, the calculation complexity is reduced. At the same time, a subsequent verification module is added to further improve the accuracy of solving the speed ambiguity. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flow chart of the method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters.

[0063] Figure 2 It is a flow chart of the velocity fuzzy resolution algorithm of the present invention. DETAILED DESCRIPTION

[0064] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0065] Before describing in detail embodiments according to the present invention, it should be noted that, hereinafter, the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, whereby a process, method, article or apparatus comprising a series of elements includes not only these elements, but also other elements not explicitly listed or inherent to such process, method, article or apparatus.

[0066] See also Figure 1 As shown, the method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters, wherein the method comprises the following steps:

[0067] (1) Obtain the point cloud cluster information and unambiguous speed interval length of the previous frame, as well as the point cloud data, vehicle speed, unambiguous speed interval length, and frame period data of the current frame;

[0068] (2) The unambiguous speed interval of the current frame is moved to the point with the vehicle speed as the center point, and then the point cloud speed is compensated to the new unambiguous speed interval;

[0069] (3) Cluster the point cloud and extract the position and velocity information of the point cloud with the highest energy in each clustered point cloud cluster as the feature information of the point cloud cluster;

[0070] (4) Traverse the point cloud clusters of this frame and use all dynamic point cloud clusters of the previous frame to match the point cloud clusters of this frame. If there is a dynamic point cloud cluster in the previous frame that falls within the threshold range of the x-direction and y-direction of the point cloud cluster position of this frame, the match is successful.

[0071] (5) For the successfully matched point cloud clusters, the speed ambiguity is resolved by using the unambiguous speed interval length between the two frames and the speed difference of the point cloud cluster;

[0072] (6) The defuzzified velocity result of the obtained point cloud cluster is synchronized to all point clouds in the point cloud cluster, and the dynamic point cloud cluster information and the length of the unfuzzy velocity interval of this frame are recorded.

[0073] As a preferred embodiment of the present invention, the step (2) is specifically as follows:

[0074] Set the unblurred speed interval lengths of this frame and the previous frame to , , where the original unambiguous speed measurement interval of this frame is ; Assume the vehicle speed is By subtracting the vehicle speed, the unambiguous speed measurement interval of this frame is converted into , and then add or subtract integer multiples of the unblurred speed interval of this frame , transport it to Within the scope; among them, it is necessary to ensure Greater than the change in the target vehicle's speed within one frame.

[0075] As a preferred embodiment of the present invention, the step (5) is specifically as follows:

[0076] Assume that the measurement speed of the point cloud cluster in this frame is , the speed of the point cloud cluster of the previous frame that is successfully matched is , if the current , then the following processing is performed:

[0077] Calculate the point cloud cluster velocity of the previous frame The measurement speed of the point cloud cluster in this frame The difference :

[0078] ;

[0079] Calculate the reciprocal of the difference between the length of the unblurred speed interval of this frame and the length of the unblurred speed interval of the previous frame :

[0080] ;

[0081] Calculate the velocity difference of point cloud clusters about The result of taking the remainder :

[0082] ;

[0083] in, It is the modulo function of floating point data;

[0084] According to and and The size relationship of The value of :

[0085] ;

[0086] Then and The result of the multiplication is rounded to get the blur coefficient of this frame. :

[0087] ;

[0088] in, is the rounding function.

[0089] As a preferred embodiment of the present invention, the fuzzy coefficient is calculated , solve the real speed of this frame in the following way , blur coefficient of the previous frame And the real speed of the previous frame :

[0090] ;

[0091] ;

[0092] .

[0093] As a preferred embodiment of the present invention, if the current , then the following processing is performed:

[0094] Calculate the point cloud cluster velocity of this frame The measurement speed of the point cloud cluster in the previous frame The difference :

[0095] ;

[0096] Calculate the reciprocal of the difference between the length of the unblurred speed interval of the previous frame and the length of the unblurred speed interval of the current frame :

[0097] ;

[0098] Calculate the velocity difference of point cloud clusters about The result of taking the remainder :

[0099] ;

[0100] And according to and and The size relationship between The value of is adjusted to obtain :

[0101] ;

[0102] Then and The result of the multiplication is rounded to get the blur coefficient of the previous frame. :

[0103] .

[0104] As a preferred embodiment of the present invention, the fuzzy coefficient is calculated , solve the real speed of the previous frame as follows , blur coefficient of this frame And the actual speed of this frame :

[0105] ;

[0106] ;

[0107] ;

[0108] As a preferred embodiment of the present invention, the step (5) further includes determining whether the following conditions are met:

[0109] a. Determine whether the currently calculated fuzzy coefficient and the actual speed are within the preset threshold range;

[0110] b. Calculate the distance difference between the point cloud cluster of this frame and the matching point cloud cluster of the previous frame , and then calculate the movement distance by multiplying the average speed of the two frames by the frame period ,judge and Whether the modulus value of the difference is within a preset threshold range;

[0111] c. Calculate the acceleration between the real speeds of the two frames and determine whether it is within the preset threshold range;

[0112] If conditions a, b and c are met at the same time, the velocity fuzzy result of the point cloud cluster solution is directly used to overwrite the original point cloud cluster velocity; otherwise, the solution process is terminated directly.

[0113] Each step is described in further detail below:

[0114] In step S1, it is necessary to obtain the point cloud cluster information of the previous frame, the length of the unambiguous speed interval, and the point cloud data of the current frame, the vehicle speed, the length of the unambiguous speed interval, and the frame period stored in the storage medium, and use this information as the input of the algorithm. After the acquisition is completed, enter step S2.

[0115] In step S2, the lengths of the unambiguous speed intervals of the current frame and the previous frame are , , since the length of the unambiguous speed interval is determined by the frame period, , The selection needs to ensure is greater than the change in the target vehicle speed within one frame. The original unambiguous speed measurement interval of this frame is , assuming the vehicle speed is By subtracting the vehicle speed, the unambiguous speed measurement interval is converted into , and then add or subtract integer multiples of the point cloud speed Transport to The benefit of this method is that when calculating the relative velocity of the point cloud, the relative velocity can be ensured to be - The stationary target point cloud will not become a dynamic target point cloud due to speed ambiguity. After the point cloud is clustered to form a point cloud cluster, the subsequent speed ambiguity solution can be directly skipped for the static target point cloud cluster according to certain rules and restrictions, thereby reducing the subsequent calculation amount. After compensating all point clouds, enter step S3.

[0116] In step S3, the point cloud is clustered, and the position and velocity information of the point cloud with the highest energy in each clustered point cloud cluster is extracted as the feature information of the point cloud cluster. After all the point cloud clusters are extracted, step S4 is entered.

[0117] In step S4, the point cloud clusters of this frame are traversed, and all dynamic point cloud clusters of the previous frame are used to match the positions of the point cloud clusters of this frame. The matching thresholds of the x and y directions of the point cloud cluster positions of this frame are set. If there is a dynamic point cloud cluster in the previous frame that falls within the threshold range, the match is successful. This method can exclude most of the static point cloud clusters and reduce the amount of code calculation. The successfully matched point cloud clusters enter step S5.

[0118] In step S5, for the successfully matched point cloud cluster, the speed ambiguity is resolved by using the unambiguous speed interval length of the two frames and the speed difference of the point cloud cluster. The speed ambiguity resolution framework is as follows: Figure 2 As shown, the specific process is as follows:

[0119] Assume that the measurement speed of the point cloud cluster in this frame is , the speed of the successfully matched point cloud cluster on the previous frame is ,First, compare the length of the unambiguous speed interval of the two frames , , which can be divided into the following two cases:

[0120] (1) Condition:

[0121] Calculate the velocity of the point cloud cluster in the previous frame The measurement speed of the point cloud cluster in this frame The difference :

[0122] ;

[0123] Then calculate the inverse of the difference between the length of the unblurred speed interval of this frame and the length of the unblurred speed interval of the previous frame :

[0124] ;

[0125] Then calculate the velocity difference of the point cloud cluster about The result of taking the remainder :

[0126] ;

[0127] in, It is the remainder function of floating point data.

[0128] And according to about and The size relationship of The value of :

[0129] ;

[0130] Then and The result of the multiplication is rounded to get the blur coefficient of this frame. :

[0131] ;

[0132] in, is the rounding function.

[0133] The real speed of this frame is solved by the fuzzy coefficient of this frame , blur coefficient of the previous frame , Actual frame rate :

[0134] ;

[0135] ;

[0136] ;

[0137] (2) Condition:

[0138] Calculate the point cloud cluster velocity of this frame The measurement speed of the point cloud cluster on the previous frame The difference :

[0139] ;

[0140] Then calculate the inverse of the difference between the length of the unblurred speed interval of the previous frame and the length of the unblurred speed interval of the current frame :

[0141] ;

[0142] Then calculate the velocity difference of the point cloud cluster about The result of taking the remainder :

[0143] ;

[0144] And according to about and The size relationship of The value of :

[0145] ;

[0146] Then and The result of the multiplication is rounded to get the blur coefficient of the previous frame. :

[0147] ;

[0148] The real speed of the previous frame is solved by the fuzzy coefficient of the previous frame , blur coefficient of this frame , the actual speed of this frame :

[0149] ;

[0150] ;

[0151] ;

[0152] After solving the fuzzy coefficient and true speed of the current frame and the previous frame, determine whether the fuzzy coefficient and true speed of the two frames are within the preset threshold range; secondly, calculate the distance difference between the point cloud cluster of the current frame and the matching point cloud cluster of the previous frame. , and then calculate the movement distance by multiplying the average speed of the two frames by the frame period ,judge and Whether the modulus value of the difference is within the preset threshold range, and finally calculate the acceleration between the real speeds of the two frames and determine whether it is within the preset threshold. If all the above conditions are met, the point cloud cluster velocity fuzzy solution result (that is, the solved velocity result) is used to overwrite the original point cloud cluster velocity.

[0153] In step S6, the velocity defuzzification result of the point cloud cluster is synchronized to all point clouds in the point cloud cluster, and the dynamic point cloud cluster information and the unfuzzy velocity interval length of the current frame are recorded.

[0154] In a specific embodiment of the present invention, the present technical solution is described in detail below through a specific embodiment:

[0155] Assuming the vehicle speed in this frame is 2m / s, the length of the unambiguous speed interval in this frame is is 22. After the transport, the real unambiguous speed range of this frame is [-13m / s, 9m / s). The relative speed of the target relative to the vehicle in this frame is 12m / s. The relative speed measured by the radar is ambiguous. is -10m / s; the vehicle speed in the previous frame is still 2m / s, and the previous frame is the first frame without a matching point cloud cluster and no speed deblurring is performed. The unblurred speed interval length of the previous frame is is 23, the real unambiguous speed range of the upper frame is [-13.5m / s,9.5m / s), the relative speed of the target in the upper frame relative to the vehicle is 12.3m / s, and the relative speed of the upper frame measured by the radar is The blur is -10.7m / s. The frame period is 0.1s. After matching the two frames, the point cloud clusters are successfully matched.

[0156] Further, compare size, ;

[0157] Calculate the point cloud cluster velocity of the previous frame The measurement speed of the point cloud cluster in this frame The difference:

[0158] ;

[0159] have to = 0.7;

[0160] Then calculate the inverse of the difference between the length of the unblurred speed interval of the previous frame and the length of the unblurred speed interval of the current frame :

[0161] ;

[0162] have to = 1;

[0163] Then calculate the velocity difference of the point cloud cluster about The result of taking the remainder :

[0164] ;

[0165] have to = 0.7;

[0166] Then according to about and The size relationship of The value of :

[0167] ;

[0168] So modhalf = 0.7;

[0169] Then and The result of the multiplication is rounded to get the blur coefficient of the previous frame. :

[0170] ;

[0171] have to = 1;

[0172] The real relative speed of the previous frame is solved by the fuzzy coefficient of the previous frame , blur coefficient of this frame , the real relative speed of this frame :

[0173] ;

[0174] ;

[0175] ;

[0176] have to = 12.3m / s, = 1, = 12m / s.

[0177] Step S6:

[0178] The defuzzified velocity result of the point cloud cluster is synchronized to all point clouds in the point cloud cluster, and the dynamic point cloud cluster information and the length of the unfuzzy velocity interval of this frame are recorded.

[0179] The device for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters, wherein the device comprises:

[0180] a processor configured to execute computer-executable instructions;

[0181] A memory stores one or more computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters are implemented.

[0182] The processor for solving speed ambiguity based on the speed difference of vehicle-mounted millimeter-wave radar point cloud clusters, wherein the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the method for solving speed ambiguity based on the speed difference of vehicle-mounted millimeter-wave radar point cloud clusters are implemented.

[0183] The computer-readable storage medium stores a computer program thereon, and the computer program can be executed by a processor to implement the steps of the method for solving velocity ambiguity based on the velocity difference of a vehicle-mounted millimeter-wave radar point cloud cluster.

[0184] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0185] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device.

[0186] A person of ordinary skill in the art may understand that all or part of the steps of the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0187] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0188] In the description of this specification, the description with reference to the terms "an embodiment", "some embodiments", "example", "specific example", or "embodiment" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0189] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0190] The method, device, processor and computer-readable storage medium thereof for solving speed ambiguity based on the speed difference of vehicle-mounted millimeter-wave radar point cloud clusters of the present invention are adopted. By using the unambiguous speed interval of transportation, it is ensured that the static target point cloud will not be blurred into the dynamic target point cloud. From the perspective of point cloud clustering, the amount of calculation is reduced compared with the traditional point cloud-based method. At the same time, the real speed of the target can be solved by using the speed difference of the point cloud cluster and the length of the unambiguous speed interval. Compared with the conventional method of solving all possible speeds and then matching them, the calculation complexity is reduced. At the same time, a subsequent verification module is added to further improve the accuracy of solving the speed ambiguity.

[0191] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters, characterized in that: The method comprises the following steps: (1) Obtain the point cloud cluster information and unambiguous speed interval length of the previous frame, as well as the point cloud data, vehicle speed, unambiguous speed interval length, and frame period data of the current frame; (2) The unambiguous speed interval of the current frame is moved to the point with the vehicle speed as the center point, and then the point cloud speed is compensated to the new unambiguous speed interval; (3) Cluster the point cloud and extract the position and velocity information of the point cloud with the highest energy in each clustered point cloud cluster as the feature information of the point cloud cluster; (4) Traverse the point cloud clusters of this frame and use all dynamic point cloud clusters of the previous frame to match the point cloud clusters of this frame. If there is a dynamic point cloud cluster in the previous frame that falls within the threshold range of the x-direction and y-direction of the point cloud cluster position of this frame, the match is successful. (5) For the successfully matched point cloud clusters, the speed ambiguity is resolved by using the unambiguous speed interval length between the two frames and the speed difference of the point cloud cluster; (6) The defuzzified velocity result of the obtained point cloud cluster is synchronized to all point clouds in the point cloud cluster, and the dynamic point cloud cluster information and the length of the unfuzzy velocity interval of this frame are recorded.

2. The method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters according to claim 1 is characterized in that: The step (2) is specifically as follows: Set the unblurred speed interval lengths of this frame and the previous frame to , , where the original unambiguous speed measurement interval of this frame is ; Assume the vehicle speed is By subtracting the vehicle speed, the unambiguous speed measurement interval of this frame is converted into , and then add or subtract integer multiples of the unblurred speed interval of this frame , transport it to Within the scope; among them, it is necessary to ensure Greater than the change in the target vehicle's speed within one frame.

3. The method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters according to claim 2 is characterized in that: The step (5) is specifically as follows: Assume that the measurement speed of the point cloud cluster in this frame is , the speed of the point cloud cluster of the previous frame that is successfully matched is , if the current , then the following processing is performed: Calculate the point cloud cluster velocity of the previous frame The measurement speed of the point cloud cluster in this frame The difference : ； Calculate the reciprocal of the difference between the length of the unblurred speed interval of this frame and the length of the unblurred speed interval of the previous frame : ； Calculate the velocity difference of point cloud clusters about The result of taking the remainder : ； in, It is the modulo function of floating point data; According to and and The size relationship of The value of : ； Then and The result of the multiplication is rounded to get the blur coefficient of this frame. : ； in, is the rounding function.

4. The method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters according to claim 3 is characterized in that: The fuzzy coefficient calculated , solve the real speed of this frame in the following way , blur coefficient of the previous frame And the real speed of the previous frame : ； ； 。 5. The method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters according to claim 3 is characterized in that: If the current , then the following processing is performed: Calculate the point cloud cluster velocity of this frame The measurement speed of the point cloud cluster in the previous frame The difference : ； Calculate the reciprocal of the difference between the length of the unblurred speed interval of the previous frame and the length of the unblurred speed interval of the current frame : ； Calculate the velocity difference of point cloud clusters about The result of taking the remainder : ； And according to and and The size relationship between The value of is adjusted to obtain : ； Then and The result of the multiplication is rounded to get the blur coefficient of the previous frame. : 。 6. The method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters according to claim 5 is characterized in that: The fuzzy coefficient calculated , solve the real speed of the previous frame as follows , blur coefficient of this frame And the actual speed of this frame : ； ； 。 7. The method for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters according to claim 4 or 6, characterized in that: The step (5) further includes determining whether the following conditions are met: a. Determine whether the currently calculated fuzzy coefficient and the actual speed are within the preset threshold range; b. Calculate the distance difference between the point cloud cluster of this frame and the matching point cloud cluster of the previous frame , and then calculate the movement distance by multiplying the average speed of the two frames by the frame period ,judge and Whether the modulus value of the difference is within a preset threshold range; c. Calculate the acceleration between the real speeds of the two frames and determine whether it is within the preset threshold range; If conditions a, b and c are met at the same time, the velocity fuzzy result of the point cloud cluster solution is directly used to overwrite the original point cloud cluster velocity; otherwise, the solution process is terminated directly.

8. A device for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters, characterized in that: The device comprises: a processor configured to execute computer executable instructions; A memory stores one or more computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters according to any one of claims 1 to 7 are implemented.

9. A processor for solving velocity ambiguity based on velocity difference of vehicle-borne millimeter-wave radar point cloud clusters, characterized in that: The processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the method for solving velocity ambiguity based on the velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program can be executed by a processor to implement the steps of the method for resolving velocity ambiguity based on velocity difference of vehicle-mounted millimeter-wave radar point cloud clusters as described in any one of claims 1 to 7.

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