Auxiliary test method and device based on millimeter wave traffic radar, and storage medium

Abstract

The invention discloses an auxiliary test method and device based on a millimeter wave traffic radar, and a storage medium. The method comprises the following steps: initializing parameters; setting relevant parameter thresholds of the candidate point cloud data according to the initialization parameters; obtaining candidate point cloud data of each lane; and reading the candidate point cloud data of each lane frame by frame until all the candidate point cloud data are read, and storing and feeding back the read candidate point cloud data. According to the method, the efficiency of the millimeter wave traffic radar in the point cloud debugging optimization stage can be improved, the workload of signal processing engineers and related testers is reduced, measurement and calculation are carried out on a millimeter wave radar chip and between point cloud output and a tracking module through the technology, few resources are occupied, a large amount of data training is not needed, and additional calculation power is avoided.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to an auxiliary testing method, device and storage medium based on millimeter wave traffic radar. Background Art

[0002] The software algorithms of millimeter-wave traffic radar generally include signal processing and data processing algorithms (i.e., tracking algorithms). The signal processing algorithm is the process of converting the raw data sampled by the ADC into point cloud data through calculation and processing, while the tracking algorithm is the process of processing point cloud data, including tracking, target classification, data fusion, and higher-level application algorithms. The basis of the tracking part is high-quality point cloud data. Obtaining high-quality point cloud data is often accompanied by a large amount of tedious and repetitive testing work. During the pole test optimization stage of the traffic radar, a large amount of real-time traffic data needs to be collected for analysis, and then the signal processing module is optimized so that the millimeter-wave traffic radar can output continuous and stable point cloud data that meets the tracking requirements. Although the millimeter-wave traffic radar has a small size and high resolution, and is affected by bad weather such as wind, rain, and fog compared to other sensors, during the test and tuning process, the testers need to perform a lot of test tuning and data analysis work in severe weather such as severe cold and heat in scenes similar to the actual installation of the traffic radar, such as overpasses. This is undoubtedly a severe test. Summary of the invention

[0003] The main purpose of the present invention is to provide an auxiliary test method, device and storage medium based on millimeter wave traffic radar, aiming to solve the above technical problems.

[0004] To achieve the above object, the present invention provides an auxiliary testing method based on millimeter wave traffic radar.

[0005] The auxiliary test method based on millimeter wave traffic radar comprises the following steps:

[0006] Initialization parameters;

[0007] Set the threshold of relevant parameters of candidate point cloud data according to the initialization parameters;

[0008] Obtain candidate point cloud data for each lane;

[0009] The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the candidate point cloud data after reading is stored and fed back.

[0010] In one embodiment, the step of initializing parameters includes:

[0011] A constant speed test is performed on a traffic road with multiple lanes and divided into two test groups. Multiple groups of tests are performed on each single lane, and the original point cloud data is saved.

[0012] In one embodiment, the original point cloud data includes radial distance, azimuth angle, altitude, radial velocity, and signal strength.

[0013] In one embodiment, the step of setting the threshold of the parameter related to the candidate point cloud data according to the initialization parameter comprises:

[0014] Set the preset state of candidate point cloud data, the preset threshold of frame loss, and the preset position of longitudinal domain state;

[0015] When the state bit of the candidate point cloud data matches the preset state, no subsequent algorithm is performed;

[0016] When the number of consecutive frame loss reaches the preset frame loss threshold, it means that the area has lost frames;

[0017] Each lane is divided into several continuous areas in the longitudinal direction. When the longitudinal domain status bit matches the longitudinal domain status preset bit, it means that there is no frame loss phenomenon in the longitudinal domain.

[0018] In one embodiment, before the step of obtaining candidate point cloud data for each lane, the method further includes:

[0019] The required candidate point cloud data is screened out by feature threshold screening and spatial area screening.

[0020] In one embodiment, the step of screening out required candidate point cloud data by feature threshold screening and spatial region screening includes:

[0021] Set the upper and lower speed thresholds according to the speed of the test target vehicle with the initialization parameters to filter out the dynamic and static point clouds of non-test target vehicles;

[0022] A spatial coordinate system is established based on the installation position of the millimeter-wave traffic radar as the origin, the original point cloud data is converted from the spherical coordinate system to the Cartesian coordinate system, the scene area within the lane is identified as the data acquisition space area, and the scene area outside the lane is identified as the data screening area.

[0023] In one embodiment, the step of reading the candidate point cloud data of each lane frame by frame until all the candidate point cloud data are read, and storing and feeding back the candidate point cloud data after reading includes:

[0024] If the number of candidate point cloud data in a certain frame is zero, the previous frame of the frame is used as the starting frame for frame loss determination;

[0025] The frame number of the frame is recorded, and the next frame is read and judged until the number of candidate point cloud data is non-zero. The non-zero frame is the frame for frame loss judgment termination.

[0026] In one embodiment, the candidate point cloud data of each lane is read frame by frame until all candidate point cloud data are read, and the step of storing and feeding back the candidate point cloud data after reading includes:

[0027] If the number of candidate point cloud data in this frame is not zero, continue to read the next frame until all candidate point cloud data are read, complete all tests of multiple lanes in sequence, and store the test group, test times, longitudinal domain, longitudinal domain status bit, etc. as target data.

[0028] In addition, to achieve the above-mentioned purpose, the present invention also provides an auxiliary testing method based on millimeter-wave traffic radar, and the auxiliary testing method based on millimeter-wave traffic radar includes: a memory, a processor, and an auxiliary testing program stored in the memory and executable on the processor, and the auxiliary testing program implements the steps of the auxiliary testing method described above when executed by the processor.

[0029] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which an auxiliary test program is stored, and when the auxiliary test program is executed by a processor, the steps of the auxiliary test method based on millimeter wave traffic radar as described above are implemented.

[0030] Beneficial effects that can be achieved by the present invention: An auxiliary testing method based on millimeter wave traffic radar proposed in an embodiment of the present invention includes:

[0031] Initialization parameters;

[0032] Set the threshold of relevant parameters of candidate point cloud data according to the initialization parameters;

[0033] Obtain candidate point cloud data for each lane;

[0034] The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the candidate point cloud data after reading is stored and fed back.

[0035] This application can improve the efficiency of millimeter-wave traffic radar in the point cloud debugging and optimization stage, and reduce the workload of signal processing engineers and related testers. This technology is measured on the millimeter-wave radar chip, between the point cloud output and the tracking module, occupies few resources, does not require a large amount of data training, and avoids additional computing power. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the structure of a device in a hardware operating environment involved in an embodiment of the present invention;

[0037] Figure 2 It is a flow chart of the first embodiment of the auxiliary testing method based on millimeter wave traffic radar of the present invention.

[0038] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0039] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0040] like Figure 1 As shown, Figure 1 It is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiment of the present invention.

[0041] The terminal of the embodiment of the present invention can be a PC, or it can be a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III, dynamic image experts compression standard audio layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, dynamic image experts compression standard audio layer 4) player, a portable computer and other portable terminal devices with display function.

[0042] like Figure 1 As shown, the terminal may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0043] Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. Among them, sensors include light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display screen according to the brightness of the ambient light, and the proximity sensor may turn off the display screen and / or backlight when the mobile terminal is moved to the ear. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; of course, the mobile terminal may also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., which will not be repeated here.

[0044] Those skilled in the art will understand that Figure 1 The terminal structure shown in the figure does not constitute a limitation on the terminal, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0045] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an auxiliary test program.

[0046] exist Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the auxiliary test program stored in the memory 1005 and perform the following operations:

[0047] Initialization parameters;

[0048] Set the threshold of relevant parameters of candidate point cloud data according to the initialization parameters;

[0049] Obtain candidate point cloud data for each lane;

[0050] The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the candidate point cloud data after reading is stored and fed back.

[0051] Further, the processor 1001 may call the auxiliary test program stored in the memory 1005, and further perform the following operations:

[0052] A constant speed test is performed on a traffic road with multiple lanes and divided into two test groups. Multiple groups of tests are performed on each single lane, and the original point cloud data is saved.

[0053] Further, the processor 1001 may call the auxiliary test program stored in the memory 1005, and further perform the following operations:

[0054] Set the preset status bit of the candidate point cloud data, the preset threshold of the number of lost frames, and the preset status bit of the longitudinal domain;

[0055] When the state bit of the candidate point cloud data matches the preset state, no subsequent algorithm is performed;

[0056] When the number of consecutive frame loss reaches the preset frame loss threshold, it means that the area has lost frames;

[0057] Each lane is divided into several continuous areas in the longitudinal direction. When the longitudinal domain status bit and the longitudinal domain status preset match for the second time, it means that there is no frame loss phenomenon in the longitudinal domain.

[0058] Further, the processor 1001 may call the auxiliary test program stored in the memory 1005, and further perform the following operations:

[0059] The required candidate point cloud data is screened out by feature threshold screening and spatial area screening.

[0060] Further, the processor 1001 may call the auxiliary test program stored in the memory 1005, and further perform the following operations:

[0061] Set the upper and lower speed thresholds according to the speed of the test target vehicle with the initialization parameters to filter out the dynamic and static point clouds of non-test target vehicles;

[0062] A spatial coordinate system is established based on the installation position of the millimeter-wave traffic radar as the origin, the original point cloud data is converted from the spherical coordinate system to the Cartesian coordinate system, the scene area within the lane is identified as the data acquisition space area, and the scene area outside the lane is identified as the data screening area.

[0063] Further, the processor 1001 may call the auxiliary test program stored in the memory 1005, and further perform the following operations:

[0064] If the number of candidate point cloud data in a certain frame is zero, the previous frame of the frame is used as the starting frame for frame loss determination;

[0065] The frame number of the frame is recorded, and the next frame is read and judged until the number of candidate point cloud data is non-zero. The non-zero frame is the frame for frame loss judgment termination.

[0066] Further, the processor 1001 may call the auxiliary test program stored in the memory 1005, and further perform the following operations:

[0067] If the number of candidate point cloud data in this frame is not zero, continue to read the next frame until all candidate point cloud data are read, complete all tests of multiple lanes in sequence, and store the test group, test times, longitudinal domain, longitudinal domain status bit, etc. as target data.

[0068] The specific embodiments of the data storage device of the present invention are basically the same as the following embodiments of the auxiliary test method based on millimeter wave traffic radar, and will not be described in detail here.

[0069] Reference Figure 2 The first embodiment of the present invention provides an auxiliary test method based on millimeter wave traffic radar, and the auxiliary test method based on millimeter wave traffic radar includes:

[0070] Initialization parameters;

[0071] Set the threshold of relevant parameters of candidate point cloud data according to the initialization parameters;

[0072] Obtain candidate point cloud data for each lane;

[0073] The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the candidate point cloud data after reading is stored and fed back.

[0074] In this embodiment, the parameters need to be initialized first. A sedan with a relatively low signal reflection intensity is selected as a test vehicle from among the common vehicle types. A constant speed test is performed on a traffic road (taking a three-lane forward lane as an example). The test is divided into two test groups. Each single lane performs several tests and saves the original point cloud data. The test parameters need to be initialized before the test begins. The original point cloud data includes radial distance, azimuth angle, height, radial speed, signal strength and other motion parameters.

[0075] Secondly, obtain the relevant parameters of the candidate point cloud data, such as echo energy (power) threshold, signal-to-noise ratio threshold, and speed threshold; and identify the relevant parameters at the same time.

[0076] Furthermore, the step of setting the threshold of the parameter related to the candidate point cloud data according to the initialization parameter includes:

[0077] Set the preset status bit of the candidate point cloud data, the preset threshold of the number of lost frames, and the preset status bit of the longitudinal domain;

[0078] When the state bit of the candidate point cloud data matches the preset state, no subsequent algorithm is performed;

[0079] When the number of consecutive frame loss reaches the preset frame loss threshold, it means that the area has lost frames;

[0080] Each lane is divided into several continuous areas in the longitudinal direction. When the longitudinal domain status bit and the longitudinal domain status preset match for the second time, it means that there is no frame loss phenomenon in the longitudinal domain.

[0081] Specifically, the preset status bit of the candidate point cloud data can be set to 0. When the candidate point cloud data bit is 0, it means that the subsequent algorithm will not be performed.

[0082] In this application, the preset threshold for the number of lost frames is set to 5, that is, the number of candidate point cloud data for 5 consecutive frames or more is 0, indicating that frames are lost in this area.

[0083] Longitudinal domain: The longitudinal distance of a single lane is divided into several continuous areas. A status bit of 0 indicates that there is no frame loss in this section.

[0084] Test related parameters: single test times threshold for each group (preferably set to 10 times in this application), group discrimination mode bit (determines whether to perform group data comparison), group similarity threshold (indicates the degree of similarity of the vertical domain of lost frames).

[0085] Furthermore, before the step of obtaining candidate point cloud data for each lane, the method further includes:

[0086] The required candidate point cloud data is screened out by feature threshold screening and spatial area screening.

[0087] Specifically:

[0088] (1) Feature threshold screening: Set upper and lower thresholds according to the speed of the test target vehicle in the test scenario to filter out the dynamic and static point clouds of non-test target vehicles; set the echo energy (power) threshold and signal-to-noise ratio threshold of the point cloud to filter out the noise points that do not meet the target characteristics.

[0089] (2) Based on spatial area screening (data screening): Establish a spatial area of ​​interest. The installation position and angle of the millimeter-wave traffic radar are fixed in actual application, so the road scene covered by its FOV is unique. A spatial coordinate system is established with the installation position of the millimeter-wave traffic radar as the origin, and the original point cloud data is converted from the spherical coordinate system to the Cartesian coordinate system. Scene areas that are not related to the main traffic road (such as green belts, sidewalks, etc.) are marked as non-interested areas.

[0090] Taking the single lane width of 3.75m as a benchmark, the installation position of the millimeter-wave traffic radar and the lateral distance of the original point cloud in Cartesian coordinates are compared to simply determine the lane where the candidate point cloud data is located (the lane determination function can be more accurately determined as a post-processing function in the tracking layer).

[0091] (3) The speed fluctuation range is set based on the test vehicle speed to filter out dynamic candidate point cloud data.

[0092] (4) The original point cloud data that has not passed the spatial area screening, feature threshold, and dynamic screening is set to state 0 to obtain candidate point cloud data.

[0093] In one embodiment, the step of reading the candidate point cloud data of each lane frame by frame until all the candidate point cloud data are read, and storing and feeding back the candidate point cloud data after reading includes:

[0094] If the number of candidate point cloud data in a certain frame is zero, the previous frame of the frame is used as the starting frame for frame loss determination;

[0095] The frame number of the frame is recorded, and the next frame is read and judged until the number of candidate point cloud data is non-zero. The non-zero frame is the frame for frame loss judgment termination.

[0096] If the number of candidate point cloud data in this frame is not zero, continue to read the next frame until all candidate point cloud data are read, complete all tests of multiple lanes in sequence, and store the test group, test times, longitudinal domain, longitudinal domain status bit, etc. as target data.

[0097] Obtain each group of single test candidate point cloud data frame by frame and back up the original storage.

[0098] Determine whether the status bit of the candidate point cloud data is 0. If it is 0, do not enter the subsequent algorithm link to save computing power, and determine the number of candidate point cloud data for this frame.

[0099] (1) If the number of candidate point cloud data in this frame is zero: enter the frame loss judgment, the previous frame of this frame is used as the frame loss judgment start frame, the frame number of this frame is recorded, and the number of points in the next frame is read and judged until the number of candidate point cloud data is not zero, and this frame is recorded as the frame loss judgment end frame. If the number of candidate point cloud data frames that are consecutively 0 is greater than the frame loss frame number threshold, the distances corresponding to the frame loss judgment start frame and the end frame are matched with the longitudinal domain, and the longitudinal domain status bits of the covered track are all set to 1 (indicating that frame loss occurs in this longitudinal domain).

[0100] (2) If the number of candidate point cloud data in this frame is not zero, continue to read the next frame until all candidate point cloud data are read. Complete all tests of the three lanes in this group in sequence and store the test group, test times, longitudinal domain, longitudinal domain status bit, etc. as target data. Traverse the target data of this group of tests and obtain the frame loss probability of each longitudinal domain of each lane based on the longitudinal domain frame loss status and test times. If the group discrimination mode bit is 1, perform another group of tests and compare the frame loss longitudinal domains. If the frame loss longitudinal domain similarity exceeds the group similarity threshold, take the average of the frame loss probabilities of the same frame loss longitudinal domains and mark the different frame loss longitudinal domains.

[0101] Finally, after the test is completed, the target data is saved and output for precise point cloud quality tuning. If the test phase has been completed, the target data can be output together with the point cloud to participate in the tracking layer data processing and function algorithm to simply judge the working status of the radar and define the role of the algorithm problem.

[0102] This application can improve the efficiency of millimeter-wave traffic radar in the point cloud debugging and optimization stage, and reduce the workload of signal processing engineers and related testers. This technology is measured on the millimeter-wave radar chip, between the point cloud output and the tracking module, occupies few resources, does not require a large amount of data training, and avoids additional computing power.

[0103] Compared with the prior art, this application has the following advantages:

[0104] Strong scalability: The functions can be expanded according to the repetitive debugging work of special scenarios that occur during the actual debugging of the millimeter-wave traffic radar.

[0105] Less resources and computing power: This technology performs calculations between the point cloud output and the tracking module on the millimeter-wave radar chip, which takes up fewer resources and does not require a large amount of data training to avoid additional computing power.

[0106] Strong reusability: What can be done during the test and tuning phase of the millimeter-wave traffic radar? During the tracking phase, the measurement results can also be used as part of the tracking target output structure. This data can be used to reflect the quality of target tracking and detect the health status of the millimeter-wave traffic radar.

[0107] In summary, the millimeter-wave traffic radar pilot test module described in this application has the advantages of high scalability, reusability, low cost, etc., and has good promotion and application value in the field of smart transportation.

[0108] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which an auxiliary test program is stored. When the auxiliary test program is executed by a processor, the following operations are implemented:

[0109] Initialization parameters;

[0110] Set the threshold of relevant parameters of candidate point cloud data according to the initialization parameters;

[0111] Obtain candidate point cloud data for each lane;

[0112] The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the candidate point cloud data after reading is stored and fed back.

[0113] Furthermore, when the auxiliary test program is executed by the processor, the following operations are also implemented:

[0114] A constant speed test is performed on a traffic road with multiple lanes and divided into two test groups. Multiple groups of tests are performed on each single lane, and the original point cloud data is saved.

[0115] Furthermore, when the auxiliary test program is executed by the processor, the following operations are also implemented:

[0116] Set the preset status bit of the candidate point cloud data, the preset threshold of the number of lost frames, and the preset status bit of the longitudinal domain;

[0117] When the state bit of the candidate point cloud data matches the preset state, no subsequent algorithm is performed;

[0118] When the number of consecutive frame loss reaches the preset frame loss threshold, it means that the area has lost frames;

[0119] Each lane is divided into several continuous areas in the longitudinal direction. When the longitudinal domain status bit and the longitudinal domain status preset match for the second time, it means that there is no frame loss phenomenon in the longitudinal domain.

[0120] Furthermore, when the auxiliary test program is executed by the processor, the following operations are also implemented:

[0121] The required candidate point cloud data is screened out by feature threshold screening and spatial area screening.

[0122] Furthermore, when the auxiliary test program is executed by the processor, the following operations are also implemented:

[0123] Set the upper and lower speed thresholds according to the speed of the test target vehicle with the initialization parameters to filter out the dynamic and static point clouds of non-test target vehicles;

[0124] A spatial coordinate system is established based on the installation position of the millimeter-wave traffic radar as the origin, the original point cloud data is converted from the spherical coordinate system to the Cartesian coordinate system, the scene area within the lane is identified as the data acquisition space area, and the scene area outside the lane is identified as the data screening area.

[0125] Furthermore, when the auxiliary test program is executed by the processor, the following operations are also implemented:

[0126] If the number of candidate point cloud data in a certain frame is zero, the previous frame of the frame is used as the starting frame for frame loss determination;

[0127] The frame number of the frame is recorded, and the next frame is read and judged until the number of candidate point cloud data is non-zero. The non-zero frame is the frame for frame loss judgment termination.

[0128] Furthermore, when the auxiliary test program is executed by the processor, the following operations are also implemented:

[0129] If the number of candidate point cloud data in this frame is not zero, continue to read the next frame until all candidate point cloud data are read, complete all tests of multiple lanes in sequence, and store the test group, test times, longitudinal domain, longitudinal domain status bit, etc. as target data.

[0130] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as the embodiments of the auxiliary test method based on the millimeter-wave traffic radar described above, and will not be described in detail here.

[0131] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0132] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0133] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0134] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An auxiliary test method based on millimeter wave traffic radar, characterized in that: The auxiliary test method based on millimeter wave traffic radar comprises the following steps: Initialization parameters; Set the threshold of relevant parameters of candidate point cloud data according to the initialization parameters; Obtain candidate point cloud data for each lane; The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the candidate point cloud data after reading is stored and fed back.

2. The auxiliary test method based on millimeter wave traffic radar as claimed in claim 1, characterized in that: The step of initializing parameters comprises: A constant speed test is performed on a traffic road with multiple lanes and divided into two test groups. Multiple groups of tests are performed on each single lane, and the original point cloud data is saved.

3. The auxiliary test method based on millimeter wave traffic radar according to claim 2 is characterized in that: The original point cloud data includes radial distance, azimuth angle, altitude, radial velocity, and signal strength.

4. The auxiliary test method based on millimeter wave traffic radar according to claim 1 is characterized in that: The step of setting the threshold of the relevant parameters of the candidate point cloud data according to the initialization parameters comprises: Set the preset status bit of the candidate point cloud data, the preset threshold of the number of lost frames, and the preset status bit of the longitudinal domain; When the state bit of the candidate point cloud data matches the preset state, no subsequent algorithm is performed; When the number of consecutive frame loss reaches the preset frame loss threshold, it means that the area has lost frames; Each lane is divided into several continuous areas in the longitudinal direction. When the longitudinal domain status bit matches the longitudinal domain status preset bit, it means that there is no frame loss phenomenon in the longitudinal domain.

5. The auxiliary test method based on millimeter wave traffic radar according to claim 1 is characterized in that: Before the step of obtaining candidate point cloud data for each lane, the method further includes: The required candidate point cloud data is screened out by feature threshold screening and spatial area screening.

6. The auxiliary test method based on millimeter wave traffic radar according to claim 5 is characterized in that: The steps of filtering out the required candidate point cloud data by feature threshold screening and spatial area screening include: Set the upper and lower speed thresholds according to the speed of the test target vehicle with the initialization parameters to filter out the dynamic and static point clouds of non-test target vehicles; A spatial coordinate system is established based on the installation position of the millimeter-wave traffic radar as the origin, the original point cloud data is converted from the spherical coordinate system to the Cartesian coordinate system, the scene area within the lane is identified as the data acquisition space area, and the scene area outside the lane is identified as the data screening area.

7. The auxiliary test method based on millimeter wave traffic radar as claimed in claim 1, characterized in that: The step of reading the candidate point cloud data of each lane frame by frame until all the candidate point cloud data are read, and storing and feeding back the candidate point cloud data after reading includes: If the number of candidate point cloud data in a certain frame is zero, the previous frame of the frame is used as the starting frame for frame loss determination; The frame number of the frame is recorded, and the next frame is read and judged until the number of candidate point cloud data is non-zero. The non-zero frame is the frame for frame loss judgment termination.

8. The auxiliary test method based on millimeter wave traffic radar according to claim 7 is characterized in that: The candidate point cloud data of each lane is read frame by frame until all the candidate point cloud data are read, and the steps of storing and feeding back the candidate point cloud data after reading include: If the number of candidate point cloud data in this frame is not zero, continue to read the next frame until all candidate point cloud data are read, complete all tests of multiple lanes in sequence, and store the test group, test times, longitudinal domain, longitudinal domain status bit, etc. as target data.

9. An auxiliary test device based on millimeter wave traffic radar, characterized in that: The auxiliary test equipment based on millimeter-wave traffic radar includes: a memory, a processor, and an auxiliary test program stored in the memory and executable on the processor. When the auxiliary test program is executed by the processor, the steps of the auxiliary test method based on millimeter-wave traffic radar as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an auxiliary testing program, and when the auxiliary testing program is executed by a processor, the steps of the auxiliary testing method according to any one of claims 1 to 8 are implemented.

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