Method, device, storage medium and electronic device for determining point trace set

Abstract

The present application discloses a method, device, storage medium and electronic device for determining a point trace set. The method includes: obtaining the driving state information of the self-vehicle object and the first point trace set collected by the self-vehicle object in the current frame; when the driving state information of the self-vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a first screening operation on the first point trace set to obtain a candidate point trace set; performing a key screening operation on the candidate point trace set to obtain a second point trace set; when the driving state information of the self-vehicle object does not meet the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a second screening operation on the first point trace set to obtain a third point trace set. The present application solves the technical problem in the prior art that a point trace set cannot be accurately and efficiently determined.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving, and more specifically, to a method, device, storage medium and electronic device for determining a set of point traces. Background Art

[0002] The challenges faced by automotive millimeter-wave radars are particularly prominent in complex road environments. Such environments have diverse characteristics, including but not limited to dense vehicles, complex infrastructure, and changeable weather conditions, which together lead to the existence of a large number of real targets and multipath reflection points. In radar design, the number of output traces is usually strictly limited to balance the use of hardware resources and the real-time performance of signal processing. However, this limitation is highlighted in complex scenarios, specifically manifested in that close-range uninteresting targets (such as static obstacles, road markings, or non-threatening vehicles) occupy too much of the limited trace output capacity, thereby squeezing the space for medium and long-range targets of interest. This not only leads to missed detection of key targets, but may also trigger the radar's region of interest (ROI) function abnormality alarm, affecting the reliability and performance of the driver assistance system.

[0003] Traditional response strategies often relax the limit on the total number of output points, aiming to capture more medium and long-range target points by increasing processing capacity. But in the long run, first of all, increasing the limit on point output will significantly increase the computational complexity and memory requirements of signal processing, which may lead to longer frame processing time and affect the real-time response capability of the radar. Secondly, under certain extreme conditions, such as when a vehicle passes through a high-reflectivity environment (such as under a bridge or at a tunnel exit), in order to ensure the detection of medium and long-range targets, the system may even need to double the limit on the number of points, which undoubtedly poses a severe challenge to hardware resources, especially in the context of limited memory and strict frame duration restrictions. In other words, there is a technical problem in the existing technology that it is impossible to accurately and efficiently determine the point set. Summary of the invention

[0004] The embodiments of the present application provide a method, device, storage medium and electronic device for determining a point trace set, so as to at least solve the technical problem in the prior art that a point trace set cannot be accurately and efficiently determined.

[0005] According to one aspect of an embodiment of the present application, a method for determining a point trace set is provided, comprising: obtaining driving state information of a vehicle object and a first point trace set collected by the vehicle object in a current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm; when the driving state information of the vehicle object satisfies a target measurement point condition and the number of first points of the first point trace object is greater than a first quantity threshold, performing a first screening operation on the first point trace set to obtain a candidate point trace set, wherein the number of candidate points of the candidate point trace objects included in the candidate point trace set is less than or equal to a second quantity threshold, and the second quantity threshold is greater than the first quantity threshold; performing a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first quantity threshold, and the second point trace set is used for object recognition operation; when the driving state information of the vehicle object does not meet the target measuring point condition, and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a second screening operation on the first point trace set to obtain a third point trace set, wherein the number of third point traces of the third point trace object included in the third point trace set is less than or equal to the first quantity threshold, and the third point trace set is used for object recognition operation.

[0006] According to another aspect of an embodiment of the present application, a device for determining a point trace set is also provided, including: an acquisition unit, which acquires driving state information of a vehicle object and a first point trace set collected by the vehicle object in a current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm; a first determination unit, which, when the driving state information of the vehicle object meets the target measurement point condition and the number of first points of the first point trace object is greater than a first quantity threshold, performs a first screening operation on the first point trace set to obtain a candidate point trace set, wherein the number of candidate points of the candidate point trace objects included in the candidate point trace set is less than or equal to a second quantity threshold, and the second quantity threshold greater than the first quantity threshold; a second determining unit, performing a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first quantity threshold, and the second point trace set is used for object recognition operation; a third determining unit, when the driving state information of the vehicle object does not meet the target measuring point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a second screening operation on the first point trace set to obtain a third point trace set, wherein the number of third point traces of the third point trace object included in the third point trace set is less than or equal to the first quantity threshold, and the third point trace set is used for object recognition operation.

[0007] As an optional solution, the above-mentioned acquisition unit is also used to obtain the road shape information of the vehicle object; obtain a point trace processing identifier that matches the historical point trace set collected in the previous frame, wherein the point trace processing identifier is used to indicate the processing method for the first point trace set in the current frame; obtain the number of historical point traces of the historical point trace object included in the historical point trace set.

[0008] As an optional solution, the above-mentioned acquisition unit also includes a fourth determination module, which is used to determine that the driving state information of the vehicle object does not meet the target measurement point conditions when the road shape information indicates that the vehicle is in a non-straight driving state; when the road shape information indicates that the vehicle is in a straight driving state and the point trace processing identifier is a first value, determine that the driving state information of the vehicle object meets the target measurement point conditions; when the road shape information indicates that the vehicle is in a straight driving state and the weighted sum of the first point trace number and the historical point trace number is greater than a reference number threshold, determine that the driving state information of the vehicle object meets the target measurement point conditions.

[0009] As an optional scheme, the above-mentioned candidate determination unit includes: a candidate acquisition module, which is used to input multiple candidate point trace objects in the candidate point trace set into the first angle measurement module to obtain the azimuth index identifiers corresponding to each of the multiple candidate point trace objects; obtain the boundary information of the area of ​​interest matching the current frame; and perform key screening operations on the candidate point trace set according to the boundary information of the area of ​​interest and the azimuth index identifiers corresponding to each of the multiple candidate point trace objects.

[0010] As an optional scheme, the above-mentioned candidate acquisition module is also used to determine the boundary information of the area of ​​interest based on the position description array and offset angle of the boundary point traces in the historical point trace set, wherein the position description array is used to indicate the association relationship between the azimuth index identifier and the distance unit index identifier; determine the reference distance unit index identifier in the boundary information of the area of ​​interest that matches the azimuth index identifier of the candidate point trace object, and the reference azimuth index identifier that matches the reference distance unit index identifier; when the azimuth index identifier of the candidate point trace object does not match the reference azimuth index identifier, determine to delete the candidate point trace object.

[0011] As an optional scheme, the above-mentioned second acquisition module also includes a fifth determination module, which is used to determine the boundary information of the area of ​​interest according to the position corresponding to the target enclosure object when the driving environment of the vehicle object includes the target enclosure object; and to obtain the preset boundary information of the area of ​​interest when the driving environment of the vehicle object does not include the target enclosure object.

[0012] As an optional scheme, the above-mentioned second acquisition module includes a sixth determination module for determining the point trace processing identifier of the current frame as the first value when the ratio between the first target number of the reference point trace object and the second point trace number of the second point trace object is greater than the first threshold, wherein the reference point trace object is a point trace outside the boundary of the area of ​​interest, and the first target number is used to indicate the number of static points; when the ratio between the second target number of the reference point trace object and the second point trace number of the second point trace object is greater than the second threshold, the point trace processing identifier of the current frame is determined as the first value.

[0013] As an optional scheme, the above-mentioned sixth determination module includes a counting module, which is used to modify the value of the inter-frame counter matching the current frame to a first counting value, wherein the first counting value is greater than 0, and the value of the inter-frame counter is used to indicate the number of frames for performing the first filtering operation; when the driving status information of the vehicle object does not meet the target measurement point conditions, the first counting value matching the inter-frame counter is reduced by 1.

[0014] As an optional scheme, the above-mentioned first determination unit is also used to obtain a first processing description parameter and a second processing description parameter that match the historical point trace set, wherein the first processing description parameter is used to indicate the point trace utilization rate of the historical point trace set in the previous frame processing cycle, and the second processing description parameter is used to indicate the set processing efficiency of the historical point trace set in the previous frame processing cycle; the second quantity threshold is determined based on the first processing description parameter and the second processing description parameter.

[0015] According to another aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method for determining a point trace set.

[0016] According to another aspect of an embodiment of the present application, there is also provided an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining a point trace set through the computer program.

[0017] In the above-mentioned embodiment of the present application, the driving state information of the self-vehicle object and the first point trace set collected by the self-vehicle object in the current frame are obtained, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm; when the driving state information of the self-vehicle object meets the target measurement point condition and the number of first points of the first point trace object is greater than a first quantity threshold, a first screening operation is performed on the first point trace set to obtain a candidate point trace set, wherein the number of candidate points of the candidate point trace objects included in the candidate point trace set is less than or equal to a second quantity threshold, and the second quantity threshold is greater than the first quantity threshold; A key screening operation is performed on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for object recognition operation; when the driving state information of the vehicle object does not meet the target measurement point condition and the number of first point traces of the first point trace object is greater than the first number threshold, a second screening operation is performed on the first point trace set to obtain a third point trace set, wherein the number of third point traces of the third point trace object included in the third point trace set is less than or equal to the first number threshold, and the third point trace set is used for object recognition operation.

[0018] Through the above-mentioned implementation mode of the present application, the driving state information of the self-vehicle object and the first point trace set collected by the self-vehicle object in the current frame are first obtained; then, when the driving state information of the self-vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first number threshold, the first point trace set is subjected to a first screening operation to obtain a candidate point trace set, and the output point trace threshold limit is adjusted and amplified according to the self-vehicle state and the number of collected detection points, and the long-distance interested point traces are released; in addition, a key screening operation is performed on the candidate point trace set to obtain a second point trace set, and the uninteresting points are screened and deleted, thereby improving the efficiency and accuracy of object recognition; and, when the driving state information of the self-vehicle object does not meet the target measurement point condition and the number of first point traces of the first point trace object is greater than the first number threshold, the first point trace set is subjected to a second screening operation to obtain a third point trace set, and the output point trace number threshold is limited in consideration of the memory space limitation and the processing capacity, thereby improving the processing efficiency of the point traces, and solving the technical problem that the point trace set cannot be accurately and efficiently determined in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 is a schematic diagram of an application environment of an optional method for determining a point trace set according to an embodiment of the present application;

[0021] Figure 2 is a flowchart of an optional method for determining a point trace set according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of an optional method for determining a point trace set according to an embodiment of the present application;

[0023] Figure 4 is a schematic diagram of another optional method for determining a point trace set according to an embodiment of the present application;

[0024] Figure 5 is a schematic diagram of another optional method for determining a point trace set according to an embodiment of the present application;

[0025] Figure 6 is a flowchart of another optional method for determining a point trace set according to an embodiment of the present application;

[0026] Figure 7 is a flowchart of another optional method for determining a point trace set according to an embodiment of the present application;

[0027] Figure 8 is a flowchart of another optional method for determining a point trace set according to an embodiment of the present application;

[0028] Fig. 9 is a schematic diagram of a device for determining a point trace set according to an embodiment of the present application;

[0029] Fig.10 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] According to one aspect of an embodiment of the present application, a method for determining a point trace set is provided. Optionally, the method for determining a point trace set can be applied to, but is not limited to, Figure 1 In the hardware environment shown. Optionally, the method for determining the point trace set provided in the present application can be applied to a vehicle terminal. Figure 1 A side view of a vehicle terminal 101 is shown, and the vehicle terminal 101 can travel on a travel surface 113. The vehicle terminal 101 includes an onboard navigation system 103, a memory 102 storing a digitized road map 104, a space monitoring system 117, a vehicle controller 109, a GPS (Global Positioning System) sensor 110, an HMI (Human / Machine Interface) device 111, and also includes an autonomous controller 112 and a telematics controller 114.

[0033] In one embodiment, the space monitoring system 117 includes one or more space sensors and systems, which are used to monitor the visible area 105 in front of the vehicle terminal 101. The space monitoring system 117 also includes a space monitoring controller 118; the space sensors used to monitor the visible area 105 include a laser radar sensor 106, a radar sensor 107, a camera 108, etc. The space monitoring controller 118 can be used to generate data related to the visible area 105 based on the data input from the space sensor. The space monitoring controller 118 can determine the linear range, relative speed and trajectory of the vehicle terminal 101 based on the input from the space sensor, for example, determine the current speed of the vehicle and the relative speed compared to the front vehicle. The space sensor of the vehicle terminal space monitoring system 117 may include an object positioning sensing device, and the object positioning sensing device may include a range sensor, which can be used to locate the front object such as the front vehicle object.

[0034] The camera 108 is advantageously mounted and positioned on the vehicle terminal 101 in a position that allows capturing an image of the visible area 105, wherein at least a portion of the visible area 105 includes a portion of the travel surface 113 in front of the vehicle terminal 101 and including the trajectory of the vehicle terminal 101. The visible area 105 may also include the surrounding environment. Other cameras may also be used, for example, including a second camera disposed on the rear or side portion of the vehicle terminal 101 to monitor the rear of the vehicle terminal 101 and one of the right or left sides of the vehicle terminal 101.

[0035] The autonomous controller 112 is configured to implement autonomous driving or advanced driver assistance system (ADAS) vehicle terminal functionality, and it should also be noted that it can also be applied to radar sensors in this embodiment. Such functionality may include a vehicle terminal onboard control system that can provide a certain level of driving automation. Driving automation may include a series of dynamic driving and vehicle terminal operations. Driving automation may include a certain level of automatic control or intervention involving a single vehicle terminal function (e.g., steering, acceleration and / or braking). For example, the above-mentioned autonomous controller can be used to generate a vehicle driving scenario of a vehicle terminal by performing the following steps:

[0036] S102, obtaining driving state information of the vehicle object and a first point trace set collected by the vehicle object in the current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm;

[0037] S104, when the driving state information of the vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a first screening operation on the first point trace set to obtain a candidate point trace set, wherein the number of candidate point traces of the candidate point trace object included in the candidate point trace set is less than or equal to the second quantity threshold, and the second quantity threshold is greater than the first quantity threshold;

[0038] S106, performing a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for the object recognition operation;

[0039] S108, when the driving state information of the vehicle object does not meet the target measurement point conditions and the number of first points of the first point object is greater than the first quantity threshold, a second screening operation is performed on the first point set to obtain a third point set, wherein the number of third points of the third point object included in the third point set is less than or equal to the first quantity threshold, and the third point set is used for the object recognition operation.

[0040] The HMI device 111 provides human-machine interaction for the purpose of guiding the operation of the infotainment system, GPS (Global Positioning System) sensor 110, onboard navigation system 103 and the like, and includes a controller. The HMI device 111 monitors operator requests and provides the operator with status, service and maintenance information of the vehicle terminal system. The HMI device 111 communicates with multiple operator interface devices and / or controls the operation of multiple operator interface devices. The HMI device 111 may also communicate with one or more devices that monitor biometric data associated with the vehicle terminal operator. For simplicity of description, the HMI device 111 is depicted as a single device, but in the embodiments of the system described herein, it may be configured as multiple controllers and associated sensing devices.

[0041] Operator controls may be included in the passenger compartment of the vehicle terminal 101, and may include, by way of non-limiting example, a steering wheel, an accelerator pedal, a brake pedal, and an operator input device, which is an element of the HMI device 111. The operator controls enable a vehicle terminal operator to interact with the operating vehicle terminal 101 and direct the operation of the vehicle terminal 101 to provide passenger transportation.

[0042] The onboard navigation system 103 uses the digitized road map 104 for the purpose of providing navigation support and information to the vehicle terminal operator. The autonomous controller 112 uses the digitized road map 104 for the purpose of controlling the autonomous vehicle terminal operation or ADAS vehicle terminal functions.

[0043] The vehicle terminal 101 may include a telematics controller 114, which includes a wireless telematics communication system capable of communicating outside the vehicle terminal (including communicating with a communication network 115 having wireless and wired communication capabilities). The wireless telematics communication system includes an off-board server 116 capable of short-range wireless communication with a mobile terminal.

[0044] As an optional implementation, Figure 2 As shown, the method for determining the point trace set can be executed by an electronic device, such as Figure 1 The user device or server shown in the figure comprises:

[0045] S202, obtaining driving state information of the vehicle object and a first point trace set collected by the vehicle object in the current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm;

[0046] S204, when the driving state information of the self-vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a first screening operation on the first point trace set to obtain a candidate point trace set, wherein the number of candidate point traces of the candidate point trace object included in the candidate point trace set is less than or equal to the second quantity threshold and greater than or equal to the first quantity threshold, and the second quantity threshold is greater than the first quantity threshold;

[0047] S206, performing a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for the object recognition operation;

[0048] S208, when the driving state information of the vehicle object does not meet the target measurement point conditions and the number of first points of the first point object is greater than the first quantity threshold, a second screening operation is performed on the first point set to obtain a third point set, wherein the number of third points of the third point object included in the third point set is less than or equal to the first quantity threshold, and the third point set is used for object recognition operation.

[0049] In the above step S202, the driving state information of the vehicle object and the first point trace set collected by the vehicle object in the current frame are obtained. Specifically, the above driving state information includes but is not limited to the driving state of the vehicle object in the current frame, such as straight line, turning, acceleration, etc., and the full point state of the detection points collected in the previous frame. It can be understood that the radar system will set a maximum number of output points per frame when designing, which is usually to balance the real-time processing speed, memory usage and signal processing complexity. Specifically, in some complex or special scenarios, such as when the vehicle passes through an area with extremely high reflectivity (such as a metal fence, tunnel wall), the radar will receive an abnormally large number of echo signals, resulting in a surge in the number of detected points, thereby triggering an "abnormal full point state".

[0050] The first point trace set may be a set of all point traces monitored according to the radar echo signal, and the first point trace object may be a detection point obtained by performing constant false alarm detection (CFAR) on a range-speed RV diagram generated according to the radar echo signal and filtering out a portion of noise points.

[0051] In the above step S204, when the driving state information of the vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, a first screening operation is performed on the first point trace set to obtain a candidate point trace set, wherein the number of candidate point traces of the candidate point trace object included in the candidate point trace set is less than or equal to the second quantity threshold, and the second quantity threshold is greater than the first quantity threshold.

[0052] As an alternative implementation, the above target detection condition can be that the ego vehicle object in the current frame is driving straight, the number of the first echoes is greater than the first number threshold (i.e., NR1 > Nmax0), and the above first number threshold can be a threshold set according to requirements, triggering the first screening operation, for example, it can be a point releasing operation, and setting the number of outputtable echoes to be less than Nmax1 (the second number threshold), where Nmax0 < Nmax1, and the number of detection points in the obtained candidate echo set is relaxed to be within the range less than Nmax1.

[0053] Further, in the above step S206, a key screening operation is performed on the candidate echo set to obtain a second echo set. The second echo set includes second echo objects, and the number of second echoes is less than or equal to the first number threshold. The second echo set is used for object recognition operations. Optionally, the above key screening operation can be to determine the region of interest, and delete the detection points outside the region boundary that are not of interest according to the region of interest information to obtain the second echo set. The total number of points M of the second echo set used for object recognition operations is less than or equal to Nmax0, that is, the number of points M entering the angle measurement module < = Nmax0. The above object recognition operation can be used to recognize roads, lane lines, obstacle positions, and the motion states of traffic participants, or to judge whether to trigger warning information, etc. by calculating the relative speed and distance from the vehicle ahead.

[0054] The following gives a complete description of the above process in an alternative implementation:

[0055] S1. Obtain the echo signal of the current frame (D1 frame). The radar echo signal can be generated by either general linear frequency modulation or stepped frequency continuous wave radar, that is, the radar emits electromagnetic waves to the target of interest, and then receives the echo signal reflected from the target of interest.

[0056] S2. Process the echo signal of the current frame (D1 frame) to obtain a range-velocity map. By performing Fourier transform processing on the echo signal in the range and Doppler dimensions respectively, a range-velocity map (RV map) of the echo signal is obtained. The RV map contains the range-velocity information of all target points and clutter points in the current environment.

[0057] S3. Output the number of target points detected by CFAR in the current frame (D1 frame), and their corresponding range-Doppler cell indices. According to the range-velocity RV map generated by the radar echo signal, constant false alarm rate detection (CFAR) is normally performed. Generally, the points that can pass the CFAR detection are considered target points, and the points that do not pass the CFAR are considered clutter points. However, due to the complexity of the road environment, even the points that pass the CFAR detection contain clutter points that are not of interest. Here, the number of points NR1 detected by CFAR is initially output, as well as the corresponding range cell index RIdx and DopIdx.

[0058] S4. Determine whether to trigger the temporary point release mechanism by combining the flag_0 of the previous frame (D0 frame) and the information of the current frame (D1 frame), and confirm whether to relax the limit on the number of traces entering the channel matching, distance, and speed calculation module. The specific judgment logic is as follows:

[0059] When the host vehicle is in a straight-line motion state, enter the temporary point release judgment module; otherwise, do not trigger the temporary point release mechanism, the limit on the number of traces remains the original value Nmax0, and the frame counter NumCnt is decremented by 1 (i.e., U - 1).

[0060] Temporary point release judgment module: If condition 1) the number of traces NR1 passing the CFAR detection in the current frame is greater than N_Raw1 and the flag of the previous frame = 1, or condition 2) the weighted sum of the number of traces (NR1 and NR0) passing the CFAR detection in the current frame and the previous frame is greater than N_Raw2, then trigger the temporary point release mechanism to relax the limit on the number of traces to Nmax1; otherwise, the limit on the number of traces remains the original value Nmax0, and the frame counter NumCnt is decremented by 1 (i.e., U - 1).

[0061] Among them, N_Raw1 and N_Raw2 are the number of points detected by CFAR statistics based on the abnormal full-point state, which are positive integer constants that can be dynamically adjusted according to the actual situation, and generally N_Raw2 > N_Raw1. It can be the results of Nraw1 and Nraw2 obtained by comparing the full-point and non-full-point states through a histogram at the matlab end (the conclusion is that the results in the non-full-point state are generally smaller than those in the full-point state), and the minimum value and median in the full-point state are taken as Nraw1 and Nraw2 respectively. flag_0 is a flag bit indicating whether the data of the previous frame is in an abnormal full-point state, and its calculation method is the same as that of the current frame flag_1 described in step 8.

[0062] S5. Calculate the distance and speed information of the N detected points output after S4. According to the distance unit RIdx and Doppler unit VIdx information of the target point, calculate and output the distance R and speed V information of all points. In the normal mode, the number of detection points N entering this module < Nmax0, and after successfully triggering the temporary point release mechanism, the number of points N entering this module < Nmax1, where Nmax0 < Nmax1. In addition, due to the existence of the limit on the number of traces, the number of points output in this step is generally less than or equal to the number of points detected by CFAR, that is, N <= NR1.

[0063] Among them, linear frequency modulation: R = RIdx*Rres; V = VIdx*Vres; step frequency: R = RIdx*Rres-fc*VIdx*Vres / k; V = VIdx*Vres-Δf*RIdx*Rres / fc / PRT; Rres is the distance resolution, Vres is the velocity resolution, fc is the radar center frequency, Δf is the frequency step between adjacent chirp signals, k is the frequency modulation slope, and PRT is the chirp signal pulse repetition period.

[0064] S6, judging whether to trigger the region of interest screening module based on the number of detection points N, and marking the points outside the boundary of interest. If the number of detection points N is greater than Nmax0, that is, if S2 successfully triggers the temporary point release mechanism, the region of interest screening module is triggered, otherwise it jumps directly to S7.

[0065] Interested region screening module: N detection points first enter the channel calibration and FFT angle measurement module, output the azimuth index number azimIdx corresponding to the point trace, and save the FFT angle measurement result. Then, according to the relationship between the distance unit RIdx corresponding to the boundary point azimIdx in multi-frame statistics, the points outside the boundary of the interested region are marked as invalid points (InvalidFlag = 1). Among them, this module needs to perform external regularity statistics, input the boundary of the interested region and the compensation value of the installation angle change.

[0066] Calculation of the boundary of the region of interest: Since the scenes of missed detection of the target in the region of interest found so far mostly occur when the vehicle is driving straight through an environment with strong reflective targets such as large fences, the number of target points at close range and the distance at far range are too many, exceeding the limit of the number of traces Nmax0, resulting in the inability to detect moving targets at medium and long distances. By limiting the horizontal and vertical positions of the output traces in the Cartesian coordinate system, the memory occupation of the close-range uninteresting points can be released, which can be used to output the target points of interest at medium and long distances. The calculation of the horizontal and vertical positions (xpos and ypos) involves the calculation of angles and the conversion of the coordinate system, which will increase the additional running time. In addition, it can be found from the following formula that after the azimIdx and RIdx of a trace are determined, its corresponding xpos and ypos are also determined. Therefore, here we directly count the azimIdx-RIdx relationship of the boundary points based on the accumulated boundaries of interest in multiple frames to limit the marking boundaries.

[0067] Lateral position: xpos=R*cos(θ)+longoffset, longitudinal position: ypos=R*sin(θ)+latoffset, where θ=azimuth0+yawAngle0, azimuth0 is the point azimuth calculated by the azimuth index azimIdx0, R is the target distance, longoffset is half the vehicle length, latoffset is half the vehicle width, and yawAngle0 is the radar installation angle used for boundary statistics.

[0068] In addition, considering the action areas of special functional scenarios such as RCTA (rear cross traffic alert system) and FCTA (front cross traffic alert system), the points in the close-range designated area are also relaxed, and finally the boundary of the area of ​​interest in the Cartesian coordinate system is generated as follows Figure 3 As shown, the corresponding generated azimIdx-RIdx boundary of interest is as follows Figure 4 The final output of the boundary of interest gARIdx is a one-dimensional array, which represents the relationship between the angle index and the distance index of the boundary points of the region of interest. The size of the array is determined by the selected angle measurement step azi_step.

[0069] Compensation for the effects of changes in installation position and installation angle: When the radar installation angle yawAngle changes, it will affect θ, causing the boundary of the area of ​​interest written in the Cartesian coordinate system to shift (such as Figure 5 As shown in the figure), the corresponding azimIdx-RIdx boundary will also be offset, so the deviation caused by the installation angle needs to be compensated before comparing the boundary values. The specific compensation formula is as follows.

[0070] azimIdx=azimIdx0+(yawAngle-yawAngle0) / azi_step; wherein yawAngle is the radar installation angle after the change, and azi_step is the angle measurement step (unit: °).

[0071] S7. Enter different angle measurement modules according to the number of detection points N, and output the number of points M entering the angle measurement module. If the number of points N is less than or equal to Nmax0, that is, the temporary point release mechanism is not triggered, then all points directly enter the original angle measurement module, otherwise, only unmarked points (InvalidFlag=0) are allowed to enter the new angle measurement module, and the total number of points M entering should be less than or equal to Nmax0, so the number of points entering the angle measurement module M<=Nmax0. In addition, the original angle measurement module includes sub-modules such as channel calibration, FFT angle measurement, DBF angle measurement and high-resolution angle measurement, but in S6, if the region of interest screening module is triggered, the channel calibration and FFT angle measurement have been performed and the corresponding results have been saved, so the new angle measurement module here only includes the two main sub-modules of DBF angle measurement and high-resolution angle measurement.

[0072] S8. Output the flag bit flag_1 representing the full state of the D1 frame according to the information of the current frame (D1 frame). When the vehicle is in a straight-moving state, enter the flag assignment judgment module, otherwise directly output flag_1=0. In addition, if the value of the inter-frame counter (NumCnt) is greater than 0, it will not enter the flag assignment judgment, but directly output flag_1=1.

[0073] Flag assignment judgment module: If the number of points M output by the D1 frame is greater than c1*Nmax0, or the number of points N entering the angle measurement module is greater than or equal to Nmax0, then the number of points n1 in the uninterested area in the Cartesian coordinate system is counted. If n1 is greater than c2*N, or the number of points marked as uninteresting is greater than c3*N, then the current frame is considered to be in an abnormally full-point state, and flag_1 is set to 1 and the inter-frame counter NumCnt is set to U. Otherwise, flag_1 is output to 0 and NumCnt is cleared.

[0074] Among them, flag_i (i = 0, 1, 2, ..., Nf) is the flag bit representing the full-point state, Nf indicates that the radar frame number is any integer greater than or equal to 0, U indicates the number of frames that directly set flag_i = 1 without entering the flag assignment judgment (arbitrarily set a positive integer), c1, c2, c3 respectively represent the weight coefficient value range [0, 1], and the specific value can be adjusted according to the statistical value under the abnormal full-point state of the system.

[0075] S9, the flag_i value outputted by S8 is given to the next frame (frame D2). The flag_i value outputted by S8 is passed to the next frame data, and participates in the temporary placement point judgment described in S4, and then continues to execute S1 to S9 in a loop until the data processing of all frames is completed.

[0076] Flowchart Figure 6 The above process is explained as follows:

[0077] S602, acquiring the echo signal of the current frame radar, generating a range-velocity spectrogram according to the echo signal, outputting the points that pass the constant false alarm detection, and counting the number of output points;

[0078] S604, judging whether to trigger the temporary point release mechanism by combining the abnormal full point mark bit reserved in the previous frame, the number of constant false alarm output points and the constant false alarm output points of the current frame, and if triggered, relaxing the limit on the number of point traces and performing a subtraction operation on the inter-frame counter;

[0079] S606, judging whether to trigger the region of interest screening module according to the number of point traces, if triggered, marking the points outside the boundary of the region of interest as invalid points, and then confirming whether to allow entering the angle measurement module according to the mark, otherwise all points directly enter the angle measurement module;

[0080] S608, judging whether the current frame is in an abnormal full-dot state according to the value of the inter-frame counter or the number and position distribution of the dots, outputting an abnormal full-dot mark to participate in the temporary dot placement judgment of the next frame, and reassigning the inter-frame counter.

[0081] Optionally, in the above step S208, when the driving status information of the vehicle object does not meet the target measurement point conditions and the number of first points of the first point object is greater than the first quantity threshold, a second screening operation is performed on the first point set to obtain a third point set, wherein the number of third points of the third point object included in the third point set is less than or equal to the first quantity threshold, and the third point set is used for object recognition operations.

[0082] As an optional implementation, in another case, for example, the vehicle is not in a straight-line driving state, the first point trace quantity is greater than the first quantity threshold (i.e., NR1>Nmax0), and the above-mentioned first quantity threshold may be the threshold of the maximum number of detection points that can be currently processed. It can be understood that under this implementation, the memory occupancy of the first point trace quantity detection points exceeds the threshold range, and then the second screening operation is performed, specifically, it may be a point reduction operation, that is, the third point trace quantity of the third point trace object included in the third point trace set obtained after the second screening operation is less than or equal to the first quantity threshold.

[0083] The above process is described below in an optional implementation manner:

[0084] S1. Evaluate the frame duration, memory conditions, and radar performance requirements to determine the maximum number of temporary points Nmax and the maximum number of points Mmax for the final output. Set different numbers of points, count the time required for all processing modules of the point traces under different numbers of points, and record the maximum number of points that meet the time requirements of a single frame as the maximum number of points Nmax1. At the same time, evaluate the remaining memory space. If the memory occupied by Nmax1 points exceeds the existing memory space limit, reduce the maximum number of points to Nmax based on memory usage, otherwise confirm that Nmax = Nmax1. Combine radar performance and customer needs to jointly determine the maximum number of output points Mmax of the radar, where Mmax <= Nmax.

[0085] S2, obtain the radar echo signal of the current frame and output the target point of the current frame. Perform constant false alarm detection (CFAR) on the echo signal of the current frame, and normally output all target points that pass the CFAR detection, and record the number of points NR1 that pass the CFAR detection in the current frame.

[0086] S3. Calculate the frame duration T0 of the current frame according to the input clock counter value and the CPU operating frequency. The frame duration of the previous frame can be obtained by multiplying the clock counter difference between the current frame and the previous frame by the CPU operating frequency. Since the frame duration is at the millisecond level, it is considered that the point trace characteristics in two consecutive frames change little, and the frame duration between the corresponding frames also changes little, that is, the calculated frame duration of the previous frame data can be recorded as the frame duration of the current frame.

[0087] S4. Calculate the ratio of the frame duration of the current frame to the theoretical maximum frame duration. According to the frame duration of the current frame obtained in S1, calculate its ratio to the theoretical maximum frame duration.

[0088] S5, determine the number of points Nm in the current frame based on the ratio of the proportion of uninteresting points in the previous frame to the frame duration of the current frame, and the number of points NR1 detected by the current frame through CFAR. Assuming that the number of points is divided into m blocks, the value δ obtained by weighted summation of the point proportion and the frame duration ratio is m When in different intervals, the number of points Nm in the current frame is also assigned different values ​​accordingly.

[0089] δ m =α 1 *p before / M before +α 2 *T now / T max , where α 1 , α 2 are any coefficients less than 1 and α 1 +α 2 =1, p before / M before Indicates the ratio of the number of uninteresting points in the previous frame to the total number of points output in the previous frame, T now / T max Indicates the ratio of the current frame duration to the theoretical maximum frame duration. In addition, the number of points here should be less than the maximum number of points Nmax determined in S1, and greater than the maximum number of output points Mmax determined according to the radar performance requirements, that is, Mmax<=Nm <Nmax。

[0090] S6, perform distance and speed measurement calculations on all Nm output points, and finally output Mnow points to enter the angle measurement module. Perform distance and speed measurement calculations on all points released by S5 to obtain the distance and speed information of the target. Before entering the angle measurement module, delete the points outside the boundary of the area of ​​interest according to the point trace characteristics to ensure that only Mnow points enter the angle measurement module. Here, Mnow should be less than or equal to Mmax.

[0091] S7. Dynamically determine the boundary of the region of interest based on the point characteristics of the current frame data, and count the number of uninteresting points outside the boundary of interest. It is generally believed that full points are likely to appear in scenes such as fences and iron walls. Therefore, the fence / iron wall is considered as the boundary of the region of interest. The boundary position is determined by grid division, and the number of uninteresting points outside the boundary pnow is counted.

[0092] According to the distribution of stationary points, the boundary is identified by grid division. First, based on the Cartesian coordinate system of the vehicle, the stationary points are grouped by grid in the horizontal position, and the two groups with the largest number of points in the horizontal grid are recorded. Then, the distribution of these two groups in the vertical position is counted. If the density of the point distribution in the vertical position meets certain conditions, the maximum value of the horizontal position of the two groups of points is recorded as the horizontal boundary value of the area of ​​interest.

[0093] S8, calculate the proportion of uninteresting points in the current frame, which is used to participate in the point placement judgment of the next frame. The uninteresting points in the current frame are divided by the total number of output points to obtain the proportion of uninteresting points in the current frame pnow / Mnow.

[0094] Flowchart Figure 7 The above process is explained as follows:

[0095] S702, evaluating the frame duration and memory conditions to determine the maximum number of points in the system;

[0096] S704, obtaining the echo signal of the current frame and performing constant false alarm detection normally;

[0097] S706, obtaining and calculating the echo signal processing time of the current frame, and calculating the ratio of the current frame time to the theoretical frame time;

[0098] S708, determining the number of points to be placed in the current frame by combining the frame duration ratio of the current frame and the percentage of points outside the boundary of interest of the previous frame;

[0099] S710, dynamically determine the boundary of the region of interest of the current frame and count the number of points outside the boundary, and calculate the proportion of uninteresting points in the current frame for participating in the point placement judgment of the next frame.

[0100] Through the above-mentioned implementation mode of the present application, the driving state information of the self-vehicle object and the first point trace set collected by the self-vehicle object in the current frame are first obtained; then, when the driving state information of the self-vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first number threshold, the first point trace set is subjected to a first screening operation to obtain a candidate point trace set, and the output point trace threshold limit is adjusted and amplified according to the self-vehicle state and the number of collected detection points, and the long-distance interested point traces are released; in addition, a key screening operation is performed on the candidate point trace set to obtain a second point trace set, and the uninteresting points are screened and deleted, thereby improving the efficiency and accuracy of object recognition; and, when the driving state information of the self-vehicle object does not meet the target measurement point condition and the number of first point traces of the first point trace object is greater than the first number threshold, the first point trace set is subjected to a second screening operation to obtain a third point trace set, and the output point trace number threshold is limited in consideration of the memory space limitation and the processing capacity, thereby improving the processing efficiency of the point traces, and solving the technical problem that the point trace set cannot be accurately and efficiently determined in the prior art.

[0101] In an optional implementation, obtaining the driving state information of the vehicle object includes at least one of the following:

[0102] Method 1: Obtaining the road shape information where the vehicle object is located;

[0103] Method 2: obtaining a point trace processing identifier that matches the historical point trace set collected in the previous frame, wherein the point trace processing identifier is used to indicate a processing method for the first point trace set in the current frame;

[0104] Method three: obtain the number of historical point traces of the historical point trace objects included in the historical point trace set.

[0105] In the above-mentioned method 1, the above-mentioned road shape information is used to indicate whether the road of the self-vehicle object in the current frame is a straight line, and then determine whether the self-vehicle object is in a straight driving state. By judging the driving state of the vehicle, further, for example, in a straight driving scene, since dynamic targets may pose a direct threat to driving safety, in order to ensure that key targets are not missed, the radar system needs to adopt a point release strategy to give priority to the point trace output of dynamic or long-distance targets, that is, it is necessary to further perform a screening operation on static or close-range target point traces.

[0106] In the above-mentioned method 2, for example, the number of detection points outside the boundary of the area of ​​interest in the historical point trace set collected in the previous frame is obtained, and when the ratio of the number of uninteresting points to the total number of points is greater than a preset threshold, the above-mentioned point trace processing mark is determined to be an abnormal full point state, and then according to the abnormal identification state, it can be determined that the points that need to be output for object recognition in the current frame need to be released, and more detection points that meet the needs are released, and then the points of no interest are further deleted during area screening, so as to improve the accuracy of the determined point trace set.

[0107] In the above-mentioned method three, the number of historical point traces of the above-mentioned historical point trace object can be the number of point traces in the uninterested area in the Cartesian coordinate system in the point trace output of the previous frame, or the number of points marked as uninterested. It should be noted that when the radar detects the points and converts them into the Cartesian coordinate system, the dynamic target and the static target can be distinguished based on the speed information of the point trace (i.e., the V coordinate). The points of uninterest in the above-mentioned Cartesian coordinate system are specifically static points outside the area of ​​interest. The points marked as uninterested can be specifically determined based on the relationship between the distance unit RIdx corresponding to the boundary point azimIdx corresponding to the boundary point trace output of multiple frames, and then the points outside the area of ​​interest. When the ratio of the uninterested points determined according to the above method to the total number of output points is greater than the preset threshold, it can be judged that the frame belongs to the abnormal full point state, and the marked state value can be used in the next frame to determine whether to trigger the screening operation.

[0108] In addition, in this embodiment, the use of method one, method two and method three can be arbitrarily combined. For example, taking the combination of method two and method three as an example, a point trace processing identifier that matches the historical point trace set collected in the previous frame is obtained, and the point trace processing identifier is in a normal state. The probability of performing special processing on the point release threshold of the current frame is 0.3, and the weight value corresponding to this condition is R1; the number of historical point traces of the historical point trace objects included in the historical point trace set is obtained, for example, the ratio of the number of uninteresting points in the previous frame to the total number of output points is greater than a preset threshold, and the probability of triggering the adjustment operation of the point release threshold of the current frame is 0.8, and the weight value corresponding to this condition is R2. The judgment results of the two judgment conditions are weighted averaged to obtain the result W. When W is greater than the preset threshold, it can be determined to perform a screening operation. Otherwise, no special processing is required.

[0109] Through the above implementation of the present application, the driving status information including road shape information, the point trace processing identifier matched by the historical point trace set collected in the previous frame, and the number of historical point traces of the historical point trace object are obtained, and the trigger conditions for entering the screening operation are designed from different angles. By considering the current road conditions, historical point trace processing strategy and the distribution trend of the target point trace, it is ensured that the radar can intelligently adjust its detection and processing process to maintain the best performance and functional stability in various driving environments. Especially in complex and rapidly changing traffic scenes, the execution accuracy of the screening operation is improved, and entering the screening operation under specific conditions improves the efficiency of determining the point trace set.

[0110] In an optional implementation, the driving state information includes road shape information, point trace processing identifier and point trace quantity. After the driving state information of the vehicle object is obtained, it also includes:

[0111] S1, when the road shape information indicates that the ego vehicle is in a non-straight driving state, determining that the driving state information of the ego vehicle object does not meet the target measurement point condition;

[0112] S2, when the road shape information indicates that the vehicle is in a straight-line driving state and the point trace processing identifier is a first value, determining that the driving state information of the vehicle object meets the target measurement point condition;

[0113] S3, when the road shape information indicates that the vehicle is in a straight-line driving state, and the weighted sum of the first point trace quantity and the historical point trace quantity is greater than a reference quantity threshold, determine that the driving state information of the vehicle object meets the target measurement point condition.

[0114] In the above step S1, when the road shape information indicates that the vehicle is in a non-straight driving state, the target measurement point conditions are not met. For example, when the vehicle turns or changes lanes, the relative position and importance of close-range static targets (such as roadside facilities, walls, etc.) may change. If the point release strategy is still implemented at this time and the limit on the number of point traces is relaxed, it may lead to excessive attention to these static targets that no longer pose a threat, thereby increasing the risk of false alarms and affecting the performance and reliability of the driving assistance system.

[0115] In the above step S2, when the road shape information indicates that the vehicle is in a straight-line driving state and the point trace processing flag is a first value, it is determined that the driving state information of the vehicle object meets the target measurement point condition.

[0116] For example, when the vehicle is traveling in a straight line, and the ratio of the number of traces outside the area of ​​interest in the detection point output of the previous frame to the total number of traces output is greater than a certain ratio, resulting in the trace processing identifier of the previous frame being the first value (the first value corresponds to the abnormal full point state), it is determined that the target measurement point condition is met. It should be noted that when the number of traces initially output in the current frame is greater than the preset threshold value N_Raw1, the point release operation can be further judged according to the target measurement point condition in combination with the form state and the full point state of the previous frame. If the number of traces output according to the radar echo signal does not exceed the initial set point release limit, all can directly enter the angle measurement module.

[0117] Optionally, in the above step S3, when the road shape information indicates that the vehicle is in a straight-line driving state, and the number of points and the weighted sum of the number of points is greater than the reference number threshold, it is determined that the driving state information of the vehicle object meets the target measurement point condition. The above number of historical points can be the number of points NR0 detected by CFAR in the previous frame.

[0118] As an optional implementation, when the vehicle is in a straight-line driving state and the weighted sum of the number of points (NR1 and NR0) detected by CFAR in the current frame and the previous frame is greater than N_Raw2, the temporary point release mechanism is triggered to relax the limit on the number of points to Nmax1. This is just an example.

[0119] Through the above-mentioned implementation methods recorded in this application, the target measurement point conditions are further clarified. If the vehicle is not in a straight-line driving state, the algorithm will not trigger the point release mechanism, avoiding unnecessary relaxation of the point trace number limit when there are a large number of point traces in the close-range area, thereby maintaining the radar's sensitivity and response speed to changes in the close-range environment; using the abnormal full point state detected in the previous frame as the target measurement point condition is helpful to ensure continuous monitoring and tracking of medium and long-range targets in a complex environment. The judgment method based on multi-frame data can filter out erroneous points caused by short-term environmental changes (such as temporary occlusion), ensuring that the point trace number limit is relaxed only when an abnormally high number of point traces is detected in multiple consecutive frames.

[0120] In an optional implementation, a key screening operation is performed on the candidate point set, including:

[0121] S1, inputting multiple candidate point trace objects in the candidate point trace set into a first angle measurement module to obtain azimuth index identifiers corresponding to each of the multiple candidate point trace objects;

[0122] S2, obtaining the boundary information of the region of interest matching the current frame;

[0123] S3, performing a key screening operation on the candidate point set according to the boundary information of the region of interest and the azimuth index identifiers corresponding to each of the plurality of candidate point objects.

[0124] In the above step S1, multiple candidate point trace objects in the candidate point trace set are input into the first angle measurement module to obtain the azimuth index identifiers corresponding to the multiple candidate point trace objects; the above first angle measurement module can specifically be an FFT angle measurement module, which is used to output the azimuth index number azimIdx corresponding to the point trace.

[0125] Further in step S2, the boundary information of the region of interest matching the current frame is obtained; as an optional implementation, for each historical reference frame, the system can determine the corresponding historical region of interest boundary through static point analysis and grid division, and record these boundary information; analyze the distribution trend of these historical region of interest boundaries, and identify the law of boundary change; for example, according to road conditions such as urban roads, highway environments, rural or wild road environments, etc. Then, the division of the region of interest can be determined according to the road classification of the current frame.

[0126] Taking urban roads as an example, the radar system records multiple historical reference frames of urban roads, which contain a large number of dynamic points (vehicles, pedestrians) and static points (buildings, street lights); based on the point data output by the historical reference frame, the system identifies the boundary positions of static obstacles (such as buildings, street lights, and curbstones) that may exist on both sides of the urban roads, and uses them as the boundaries of the area of ​​interest. On urban roads, the area at a certain distance ahead is usually focused on to ensure that vehicles and pedestrians ahead are detected in time. The radar system sets the area of ​​interest for urban roads to 100-200 meters directly in front of the vehicle, with a width covering all lanes. Therefore, when the current frame is an urban road, the setting method of the area of ​​interest corresponding to the historical frame of the above-mentioned urban road can be obtained to determine the area of ​​interest that matches the current frame.

[0127] Optionally, in the above step S3, a key screening operation is performed on the candidate point set according to the boundary information of the region of interest and the azimuth index identifiers corresponding to each of the plurality of candidate point objects. The above boundary information of the region of interest includes but is not limited to the azimuth index identifier corresponding to the boundary point and the distance unit (RIdx) matching the boundary point.

[0128] It should be noted that linear frequency modulation: R = RIdx*Rres; V = VIdx*Vres; step frequency: R = RIdx*Rres-fc*VIdx*Vres / k; V = VIdx*Vres-Δf*RIdx*Rres / fc / PRT; Rres is the distance resolution, Vres is the velocity resolution, fc is the radar center frequency, Δf is the frequency step between adjacent chirp signals, k is the frequency modulation slope, and PRT is the chirp signal pulse repetition period.

[0129] And, the lateral position: xpos = R*cos(θ)+longoffset, the longitudinal position: ypos = R*sin(θ)+latoffset, where θ = azimuth0+yawAngle0, azimuth0 is the point azimuth calculated by the azimuth index azimIdx0, R is the target distance, longoffset is half the length of the vehicle, latoffset is half the width of the vehicle, and yawAngle0 is the radar installation angle used for boundary statistics. That is, based on the output azimuth index number azimIdx and the distance unit (RIdx), the position coordinates of the detection point can be determined. Based on the azimIdx-RIdx of the boundary point, the candidate point set can be screened.

[0130] Through the above implementation methods recorded in this application, multiple candidate point trace objects in the candidate point trace set are input into the first angle measurement module to obtain the azimuth index identifiers corresponding to each of the multiple candidate point trace objects; obtain the boundary information of the region of interest that matches the current frame; and perform key screening operations on the candidate point trace set based on the boundary information of the region of interest and the azimuth index identifiers corresponding to each of the multiple candidate point trace objects. In a complex driving environment, such as a road section with a large number of barriers or reflectors, the radar may receive many points from short-range areas of no interest. These points may occupy the radar's memory space, resulting in the inability to output target points in medium and long-range areas of interest normally. Through ROI boundary screening, it can be ensured that even when this happens, medium and long-range targets of interest are given priority, avoiding false alarms or omissions caused by full memory points, and improving the accuracy of point trace set determination.

[0131] In an optional implementation, a key screening operation is performed on the candidate point set according to the boundary information of the region of interest and the azimuth index identifiers corresponding to each of the plurality of candidate point objects, including:

[0132] S1, determining the boundary information of the region of interest according to the position description array and the offset angle of the boundary point traces in the historical point trace set, wherein the position description array is used to indicate the association relationship between the azimuth index identifier and the distance unit index identifier;

[0133] S2, determining a reference distance unit index identifier that matches the azimuth index identifier of the candidate point trace object in the boundary information of the region of interest, and a reference azimuth index identifier that matches the reference distance unit index identifier;

[0134] S3: When the azimuth index identifier of the candidate point trace object does not match the reference azimuth index identifier, determine to delete the candidate point trace object.

[0135] In the above step S1, the boundary information of the region of interest is determined according to the position description array and the offset angle of the boundary point traces in the historical point trace set, wherein the position description array is used to indicate the association relationship between the azimuth index identifier and the distance unit index identifier.

[0136] Specifically, each detection point input FFT angle measurement module will get the corresponding azimuth index azimIdx, and after Fourier transform processing of the echo signal in distance and Doppler dimension, the distance unit index identifier RIdx and VIdx of the detection point can be obtained. The azimIdx-RIdx relationship of the boundary points in the historical point set can be statistically analyzed to define the marking boundary; in addition, when the above-mentioned offset angle (that is, the radar installation angle yawAngle changes, it will affect θ, causing the boundary of the area of ​​interest written in the Cartesian coordinate system to shift), the corresponding azimIdx-RIdx boundary will also shift, so the deviation caused by the installation angle needs to be compensated before the boundary value comparison is performed. The specific compensation formula is as follows: azimIdx = azimIdx0 + (yawAngle-yawAngle0) / azi_step; where yawAngle is the radar installation angle after the change, and azi_step is the angle measurement step (unit °). According to the above steps, the boundary information of the region of interest can be corrected. Specifically, the one-dimensional array gARIdx is corrected. The one-dimensional array gARIdx represents the relationship between the angle index and the distance index of the boundary points of the region of interest.

[0137] Further in the above steps S2-S3, the reference distance unit index identifier that matches the azimuth index identifier of the candidate point object in the boundary information of the area of ​​interest and the reference azimuth index identifier that matches the reference distance unit index identifier are determined; if the azimuth index identifier of the candidate point object does not match the reference azimuth index identifier, it is determined to delete the candidate point object.

[0138] As an optional implementation, for example, the maximum distance unit RmaxIdx of the radar is 200, the maximum azimuth index AmaxIdx is 360 (corresponding to 360° omnidirectional coverage), and the region of interest is from 50 to 200 distance units in distance, and from 0° to 120° and 240° to 360° in azimuth (i.e., the left and right sides in front of the radar). The boundary information of this region of interest is represented by a one-dimensional array gARIdx, such as: the first half of gARIdx (from index 50 to index 169) stores the boundary values ​​of azimuth 0° to 120°, that is, the left boundary value of each distance unit is azimIdx=0, and the right boundary value is azimIdx=120. The second half of gARIdx (starting from index 201 to index 319) stores the boundary values ​​of the azimuth angle from 240° to 360°, that is, the left boundary value of each distance unit is azimIdx=240, and the right boundary value is azimIdx=360.

[0139] Assume that there is a candidate point object with a (RIdx, azimIdx) value of (100, 80). The azimuth boundary value corresponding to RIdx=100 is found in the gARIdx array. For example, gARIdx

[100] is the left boundary value azimIdx=0, and gARIdx[100+1] (i.e., gARIdx

[101] ) is the right boundary value azimIdx=120. It should be noted that, considering the continuity and storage efficiency of the gARIdx array, the left boundary value and the right boundary value are usually stored adjacent to each other.

[0140] According to the above steps, if the azimIdx value 80 of the candidate point object is within the left boundary value 0 and the right boundary value 120, then the candidate point object is determined to be within the region of interest, otherwise the candidate point object outside the region of interest needs to be deleted.

[0141] Through the above implementation methods recorded in this application, the boundaries of the region of interest are dynamically adjusted according to different driving scenarios, so that high target detection performance can be maintained in various driving environments, and the adaptability and flexibility of the radar system are enhanced. In addition, the ROI boundary screening not only limits the range of radar processing points, but also improves the accuracy of point trace azimuth measurement by compensating for the influence of the radar installation angle. This means that the system can more accurately identify which points are located in the region of interest, so as to perform more in-depth processing and analysis on these points, and improve the detection accuracy of actual targets.

[0142] In an optional implementation, obtaining boundary information of a region of interest matching the current frame includes:

[0143] S1, when the driving environment of the vehicle object includes a target enclosure object, determining boundary information of the region of interest according to a position corresponding to the target enclosure object;

[0144] S2: when the driving environment of the vehicle object does not include the target enclosure object, obtain preset boundary information of the region of interest.

[0145] In the above step S1, when the driving environment of the vehicle object includes a target enclosure object, the boundary information of the region of interest is determined according to the position corresponding to the target enclosure object. The target enclosure object can be a large-scale blocking object such as a fence or a sheet metal wall. The boundary information of the region of interest is specifically the maximum value of the horizontal coordinate of the region of interest. Specifically, the boundary can be identified by grid division based on the distribution of stationary points. First, based on the Cartesian coordinate system of the vehicle, the stationary points are grouped by grid from the horizontal position, and the two groups with the largest number of points in the horizontal grid are recorded. Then, the distribution of these two groups in the vertical position is counted. If the density of the point distribution in the vertical position meets certain conditions, the maximum value of the horizontal position of these two groups of points is recorded as the horizontal boundary value of the region of interest.

[0146] In the above step S2, when the driving environment of the vehicle object does not include the target enclosure object, the preset boundary information of the region of interest is obtained. Optionally, the region of interest can be preset as a medium and long distance range in front of the vehicle according to vehicle safety and assisted driving requirements, and the boundary information of the region of interest can be preset to be between 50 meters and 200 meters, and the azimuth covers 0° to 120° and 240° to 360°, which is only an example.

[0147] Through the above implementation described in the present application, the scope of the area of ​​interest is dynamically determined according to the current road conditions, which improves the accuracy of screening out points of no interest, thereby improving the accuracy of the point set for object recognition operations.

[0148] In an optional implementation, after performing a key screening operation on the candidate point trace set to obtain the second point trace set, at least one of the following is further included:

[0149] Method 1: when the ratio between the first target number of the reference point trace object and the second point trace number of the second point trace object is greater than the first threshold, the point trace processing identifier of the current frame is determined to be the first value, wherein the reference point trace object is a point trace outside the boundary of the region of interest, and the first target number is used to indicate the number of static point traces;

[0150] Method 2: when the ratio between the second target quantity of the reference point trace object and the second point trace quantity of the second point trace object is greater than the second threshold, the point trace processing flag of the current frame is determined to be the first value.

[0151] Optionally, in the first method, the first target number of the reference point track object can be the number of static points outside the boundary of the region of interest obtained by calculating the horizontal and vertical coordinate information (xypos) of the points in the Cartesian coordinate system. It should be noted that the Cartesian coordinate system calculation can distinguish between dynamic and static points. In this method, the ratio of the number of static points of no interest outside the region of interest to the number of points entering the angle measurement module is calculated, and when the ratio result is greater than the first threshold, it is determined to be in an abnormal full point state. Specifically, the first value can be used to determine that the corresponding frame image is in an abnormal full point state.

[0152] In the above-mentioned method 2, the second target number of reference point track objects can be the number of points outside all areas of interest determined by generating the azimIdx-RIdx boundary of interest through multiple historical frames, and calculating the ratio of the number of points marked as not interested outside the area of ​​interest to the number of points entering the angle measurement module. When the ratio result is greater than the second threshold, it is determined to be in an abnormal full point state.

[0153] That is, if the number of points M output by the current frame (D1 frame) is greater than c1*initial preset output threshold, or the number of points N entering the angle measurement module is greater than or equal to the initial preset output threshold, then the number of points n1 in the area of ​​no interest in the Cartesian coordinate system is counted. If n1 is greater than c2*N, or the number of points marked as not interested is greater than c3*N, then the current frame is considered to be in an abnormally full-point state.

[0154] Through the above implementation methods recorded in this application, the full point state is judged according to the ratio of the number of points of no interest to the number of input points, so that the millimeter wave radar system can effectively monitor the distribution of points in the current frame and promptly detect abnormal full point states. This monitoring mechanism helps the system to quickly adjust its processing strategy when the number of points increases abnormally, avoiding processing delays or performance degradation caused by this. By setting different trigger conditions to adjust the release limit in different situations, it can ensure that all potential dynamic targets can be detected, and improve the system's adaptability and response speed to complex environments; and then the processing of static or irrelevant points can be reduced by marking the points in the area of ​​interest, reducing the false alarm rate and improving the accuracy of determining the point set.

[0155] In an optional implementation, after the point trace processing flag of the current frame is determined to be the first value, the method further includes:

[0156] S1, modifying the value of the inter-frame counter matching the current frame to a first count value, wherein the first count value is greater than 0, and the value of the inter-frame counter is used to indicate the number of frames for performing a first filtering operation;

[0157] S2: when the driving state information of the vehicle object does not meet the target measurement point condition, the first count value matched with the inter-frame counter is reduced by 1.

[0158] In the above steps S1-S2, the specific value of the first count value can be determined according to the application scenario, and the value NumCnt of the inter-frame counter is set to U. In the next U frame, even if the judgment condition of the full point state is no longer met, the system will automatically relax the limit on the number of points, allowing more points to enter the subsequent processing stage, which is helpful in ensuring that the detection of medium and long-range targets of interest will not be affected by the memory usage of short-range uninteresting points in complex environments (such as roads with a large number of barriers or reflectors).

[0159] Further, when NumCnt is reduced to 0, the system will re-judge the full-point status based on the number of points and the full-point mark flag to decide whether to continue to trigger the point release mechanism or restore the normal point number limit.

[0160] For example, after the first judgment triggers the filtering operation, the inter-frame counter value is set to 3 (assuming it corresponds to the D2 frame), and the filtering operation is directly performed from the D3 frame to the D5 frame. For example, the output point trace restriction is relaxed. If the D6 frame determines that the target measurement point conditions are not met based on the vehicle driving state (for example, the vehicle is in a non-straight driving state), the NumCnt value is reduced by 1 until the filtering operation is exited. The above implementation method reduces the number of times the flag judgment abnormal full point state is entered, thereby improving the efficiency of determining the point trace set.

[0161] In an optional implementation, when the driving state information of the vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first number threshold, a first screening operation is performed on the first point trace set to obtain a candidate point trace set, and the method further includes:

[0162] S1, obtaining a first processing description parameter and a second processing description parameter matching a historical point trace set, wherein the first processing description parameter is used to indicate the point trace utilization rate of the historical point trace set in the previous frame processing cycle, and the second processing description parameter is used to indicate the set processing efficiency of the historical point trace set in the previous frame processing cycle;

[0163] S2: Determine a second quantity threshold according to the first processing description parameter and the second processing description parameter.

[0164] In the above steps S1-S2, a first processing description parameter and a second processing description parameter matching the historical point trace set are obtained, wherein the first processing description parameter is used to indicate the point trace utilization rate of the historical point trace set in the previous frame processing cycle, and the second processing description parameter is used to indicate the set processing efficiency of the historical point trace set in the previous frame processing cycle; the second quantity threshold is determined based on the first processing description parameter and the second processing description parameter.

[0165] As an optional implementation, the weighted average value δ of the first description parameter and the second description parameter may be calculated. m =α 1 *p before / M before +α 2 *T now / T max , where p before / M before Indicates the ratio of the number of uninteresting points in the previous frame to the total number of points output in the previous frame (the first description parameter), T now / T max Indicates the ratio of the current frame duration to the theoretical maximum frame duration (the first description parameter). 1 , α 2 are any coefficients less than 1 and α 1 +α 2 =1, the specific parameter value setting can be dynamically adjusted according to radar performance and test needs.

[0166] Furthermore, for example, there is δ 0 <δ 1 <δ 2 <δ 3 , and there is a matching relationship: if the weighted average is in the first candidate parameter interval (δ 0 <δ m <δ 1 ), the number of candidate points is N0; if the weighted average is in the second candidate parameter interval (δ 1 <δ m <δ 2 ), the number of candidate points is N1; if the weighted average is in the third candidate parameter interval (δ 2 <δ m <δ 3 ), the number of candidate points corresponding to the above is N2, where N0 <N1<N2。

[0167] That is, when the weighted average value is in the first candidate parameter interval, the second quantity threshold is determined to be N0; when the weighted average value is in the second candidate parameter interval, the second quantity threshold is determined to be N1; when the weighted average value is in the third candidate parameter interval, the second quantity threshold is determined to be N2. This is only an example and no specific limitation is imposed on the setting of the numerical value.

[0168] Through the above-mentioned implementation methods recorded in the present application, it is possible to determine the corresponding second quantity threshold value according to different intervals, and consider the proportion of uninteresting points in the previous frame. The radar system can intelligently judge the existence of static obstacles or clutter in the current environment, so that in the processing of the current frame, the maximum number of points can be appropriately adjusted to avoid resources being occupied by unnecessary points; considering the proportion of frame duration can monitor and respond to environmental changes, adjust the number of points, and avoid too long a frame period.

[0169] The following is as follows Figure 8 The specific implementation shown is used to illustrate this solution:

[0170] S802, initialize the inter-frame counter NumCnt=0;

[0171] S804, echo signal acquisition and CFAR detection, outputting the range Doppler units of all detection points;

[0172] S806, combining the previous frame fiag and the current frame information to determine whether to trigger the temporary placement determination module, if not triggered, set the inter-frame counter NumCnt-1 (when NumCnt>0);

[0173] S808, calculating the distance and speed of all detection points;

[0174] S810, number of detection points>Nmax0?

[0175] If the number of detection points>Nmax0, the point release strategy is entered, and the number of output detection points is greater than the original output threshold Nmax0. Enter the following steps to screen the points of interest through the boundary of the region of interest, S812, channel calibration and FFT angle measurement, output and save the FFT angle measurement results azimIdx of all detection points; otherwise, execute S818, if there are invalid points marked, they should be removed, and the total number of points entering the angle measurement module should be less than Nmax0.

[0176] S814, compensate for the influence of the installation angle azimIdx;

[0177] S816, according to the azimIdx-Ridx relationship of the boundary of interest, mark the points outside the boundary as invalid points;

[0178] S818, if there are invalid points marked, they should be removed, and the total number of points entering the angle measurement module should be less than Nmax0;

[0179] S820, NumCnt>0? If NumCnt>0, execute S820-1, flag=1; S820-2, flag NumCnt; that is, continue to judge the trigger condition of the next frame according to flag and NumCnt.

[0180] S822, comprehensively consider the invalid points and total points of the current frame, output the global variable flag to participate in the next frame judgment, and if flag = 1, then NumCnt = U;

[0181] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0182] According to another aspect of the embodiment of the present application, a device for determining a point trace set for implementing the above-mentioned method for determining a point trace set is also provided. Fig. 9 As shown, the device comprises:

[0183] An acquisition unit 902 acquires driving state information of the vehicle object and a first point trace set collected by the vehicle object in a current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm;

[0184] The first determining unit 904 performs a first screening operation on the first point trace set to obtain a candidate point trace set when the driving state information of the vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, wherein the number of candidate point traces of the candidate point trace object included in the candidate point trace set is less than or equal to the second quantity threshold, and the second quantity threshold is greater than the first quantity threshold;

[0185] A second determining unit 906 performs a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for the object recognition operation;

[0186] The third determination unit 908 performs a second screening operation on the first point trace set to obtain a third point trace set when the driving state information of the vehicle object does not meet the target measurement point condition and the number of first points of the first point trace object is greater than the first quantity threshold, wherein the number of third points of the third point trace object included in the third point trace set is less than or equal to the first quantity threshold, and the third point trace set is used for the object recognition operation.

[0187] Optionally, the above-mentioned acquisition unit 902 is also used to obtain the road shape information of the vehicle object; obtain a point trace processing identifier that matches the historical point trace set collected in the previous frame, wherein the point trace processing identifier is used to indicate the processing method of the first point trace set in the current frame; obtain the number of historical point traces of the historical point trace object included in the historical point trace set.

[0188] Optionally, the acquisition unit 902 further includes a fourth determination module, which is used to determine that the driving state information of the vehicle object does not meet the target measurement point condition when the road shape information indicates that the vehicle is in a non-straight driving state; determine that the driving state information of the vehicle object meets the target measurement point condition when the road shape information indicates that the vehicle is in a straight driving state and the point trace processing identifier is a first value; determine that the driving state information of the vehicle object meets the target measurement point condition when the road shape information indicates that the vehicle is in a straight driving state and the weighted sum of the first point trace number and the historical point trace number is greater than a reference number threshold.

[0189] Optionally, the above-mentioned second determination unit 906 includes: a second acquisition module, used to input multiple candidate point trace objects in the candidate point trace set into the first angle measurement module to obtain the azimuth index identifiers corresponding to each of the multiple candidate point trace objects; obtain the boundary information of the area of ​​interest matching the current frame; and perform key screening operations on the candidate point trace set according to the boundary information of the area of ​​interest and the azimuth index identifiers corresponding to each of the multiple candidate point trace objects.

[0190] Optionally, the second acquisition module is also used to determine the boundary information of the area of ​​interest based on the position description array and offset angle of the boundary points in the historical point set, wherein the position description array is used to indicate the association between the azimuth index identifier and the distance unit index identifier; determine the reference distance unit index identifier that matches the azimuth index identifier of the candidate point object in the boundary information of the area of ​​interest, and the reference azimuth index identifier that matches the reference distance unit index identifier; if the azimuth index identifier of the candidate point object does not match the reference azimuth index identifier, determine to delete the candidate point object.

[0191] Optionally, the above-mentioned second acquisition module also includes a fifth determination module, which is used to determine the boundary information of the area of ​​interest according to the position corresponding to the target enclosure object when the driving environment of the vehicle object includes the target enclosure object; and to obtain the preset boundary information of the area of ​​interest when the driving environment of the vehicle object does not include the target enclosure object.

[0192] Optionally, the above-mentioned second acquisition module includes a sixth determination module for determining the point trace processing identifier of the current frame as the first value when the ratio between the first target number of the reference point trace object and the second point trace number of the second point trace object is greater than the first threshold, wherein the reference point trace object is a point trace outside the boundary of the area of ​​interest, and the first target number is used to indicate the number of static points; when the ratio between the second target number of the reference point trace object and the second point trace number of the second point trace object is greater than the second threshold, the point trace processing identifier of the current frame is determined as the first value.

[0193] Optionally, the above-mentioned sixth determination module includes a counting module, which is used to modify the value of the inter-frame counter matching the current frame to a first counting value, wherein the first counting value is greater than 0, and the value of the inter-frame counter is used to indicate the number of frames for performing the first filtering operation; when the driving status information of the vehicle object does not meet the target measurement point conditions, the first counting value matching the inter-frame counter is reduced by 1.

[0194] Optionally, the first determination unit 904 is also used to obtain a first processing description parameter and a second processing description parameter that match the historical point trace set, wherein the first processing description parameter is used to indicate the point trace utilization rate of the historical point trace set in the previous frame processing cycle, and the second processing description parameter is used to indicate the set processing efficiency of the historical point trace set in the previous frame processing cycle; the second quantity threshold is determined based on the first processing description parameter and the second processing description parameter.

[0195] For a specific embodiment, reference may be made to the example shown in the method for determining a point trace set described above, which will not be described in detail in this example.

[0196] Among them, the memory 1002 can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the point trace set in the embodiment of the present invention. The processor 1004 executes various functional applications and data processing by running the software programs and modules stored in the memory 1002, that is, realizing the above-mentioned method for determining the point trace set. The memory 1002 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1002 may further include a memory remotely located relative to the processor 1004, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 1002 can be specifically, but not limited to, used to store file information such as target files. As an example, such as Fig.10 As shown, the memory 1002 may include, but is not limited to, the acquisition unit 902, the first determination unit 904, the second determination unit 906, and the third determination unit 908 in the device for determining the point trace set. In addition, other module units in the device for determining the point trace set may also be included but are not limited to, which will not be repeated in this example.

[0197] Optionally, the transmission device 1006 is used to receive or send data via a network. Specific examples of the above-mentioned network may include a wired network and a wireless network. In one example, the transmission device 1006 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 1006 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0198] In addition, the electronic device mentioned above further includes: a display 1008, and a connection bus 1010, which is used to connect various module components in the electronic device mentioned above.

[0199] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0200] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0201] It should be noted that the computer system of the electronic device is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0202] In particular, according to an embodiment of the present application, the process described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program contains a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions defined in the system of the present application are executed.

[0203] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0204] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0205] S1, obtaining driving state information of the vehicle object and a first point trace set collected by the vehicle object in the current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm;

[0206] S2, when the driving state information of the self-vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than the first quantity threshold, performing a first screening operation on the first point trace set to obtain a candidate point trace set, wherein the number of candidate point traces of the candidate point trace object included in the candidate point trace set is less than or equal to the second quantity threshold, and the second quantity threshold is greater than the first quantity threshold;

[0207] S3, performing a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for the object recognition operation;

[0208] S4, when the driving state information of the vehicle object does not meet the target measurement point conditions and the number of first points of the first point object is greater than the first quantity threshold, a second screening operation is performed on the first point set to obtain a third point set, wherein the number of third points of the third point object included in the third point set is less than or equal to the first quantity threshold, and the third point set is used for the object recognition operation.

[0209] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the electronic device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0210] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0211] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to execute all or part of the steps of the methods of each embodiment of the present application.

[0212] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0213] In the several embodiments provided in the present application, it should be understood that the disclosed user equipment can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0214] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0215] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0216] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining a point trace set, characterized in that: include: Acquire driving state information of the vehicle object and a first point trace set collected by the vehicle object in the current frame, wherein the first point trace object in the first point trace set is a radar point trace object detected by constant false alarm; When the driving state information of the self-vehicle object meets the target measurement point condition and the number of first point traces of the first point trace object is greater than a first quantity threshold, a first screening operation is performed on the first point trace set to obtain a candidate point trace set, wherein the number of candidate point traces of the candidate point trace object included in the candidate point trace set is less than or equal to a second quantity threshold, and the second quantity threshold is greater than the first quantity threshold; Performing a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for object recognition operation; When the driving state information of the self-vehicle object does not meet the target measurement point condition and the number of the first points of the first point object is greater than the first quantity threshold, a second screening operation is performed on the first point set to obtain a third point set, wherein the number of third points of the third point object included in the third point set is less than or equal to the first quantity threshold, and the third point set is used for object recognition operation.

2. The method according to claim 1, characterized in that: Obtaining the driving status information of the vehicle object includes at least one of the following: Obtaining road shape information on which the vehicle object is located; Acquire a point trace processing identifier that matches the historical point trace set collected in the previous frame, wherein the point trace processing identifier is used to indicate a processing method for the first point trace set in the current frame; Gets the number of historical points in the historical point collection.

3. The method according to claim 2, characterized in that After obtaining the driving state information of the vehicle object, the method further includes: When the road shape information indicates that the vehicle is in a non-straight driving state, determining that the driving state information of the vehicle object does not meet the target measurement point condition; When the road shape information indicates that the vehicle is in a straight-line driving state and the point trace processing identifier is a first value, determining that the driving state information of the vehicle object meets the target measurement point condition; When the road shape information indicates that the vehicle is in a straight-line driving state, and a weighted sum of the first point trace quantity and the historical point trace quantity is greater than a reference quantity threshold, it is determined that the driving state information of the vehicle object meets the target measurement point condition.

4. The method according to claim 1, characterized in that: The key screening operation on the candidate point set includes: Inputting the plurality of candidate point trace objects in the candidate point trace set into a first angle measurement module to obtain azimuth index identifiers corresponding to the plurality of candidate point trace objects; Acquire boundary information of a region of interest matching the current frame; According to the boundary information of the region of interest and the azimuth index identifiers corresponding to each of the plurality of candidate point trace objects, a key screening operation is performed on the candidate point trace set.

5. The method according to claim 4, characterized in that The step of performing a key screening operation on the candidate point set according to the boundary information of the region of interest and the azimuth index identifiers corresponding to each of the plurality of candidate point objects comprises: Determine the boundary information of the region of interest according to the position description array and the offset angle of the boundary point traces in the historical point trace set, wherein the position description array is used to indicate the association relationship between the azimuth index identifier and the distance unit index identifier; Determine a reference distance unit index identifier that matches the azimuth index identifier of the candidate point object in the region of interest boundary information, and a reference azimuth index identifier that matches the reference distance unit index identifier; if the azimuth index identifier of the candidate point object does not match the reference azimuth index identifier, determine to delete the candidate point object.

6. The method according to claim 4, characterized in that The acquiring boundary information of the region of interest matching the current frame includes: In a case where the driving environment of the vehicle object includes a target enclosure object, determining the boundary information of the region of interest according to a position corresponding to the target enclosure object; When the driving environment of the vehicle object does not include a target enclosure object, the preset boundary information of the region of interest is obtained.

7. The method according to claim 4, characterized in that After performing a key screening operation on the candidate point trace set to obtain a second point trace set, the method further includes at least one of the following: When the ratio between the first target number of the reference point trace object and the second point trace number of the second point trace object is greater than a first threshold, determining the point trace processing identifier of the current frame as a first value, wherein the reference point trace object is a point trace outside the boundary of the region of interest, and the first target number is used to indicate the number of static point traces; When the ratio between the second target number of the reference point trace object and the second point trace number of the second point trace object is greater than a second threshold, the point trace processing identifier of the current frame is determined to be the first value.

8. The method according to claim 7, characterized in that After determining the trace processing identifier of the current frame as the first value, the method further includes: Modify the value of the inter-frame counter matching the current frame to a first count value, wherein the first count value is greater than 0, and the value of the inter-frame counter is used to indicate the number of frames for performing the first filtering operation; When the driving state information of the vehicle object does not satisfy the target measurement point condition, the first count value matched with the inter-frame counter is reduced by 1.

9. The method according to claim 1, characterized in that: When the driving state information of the vehicle object meets the target measurement point condition and the number of first traces of the first trace object is greater than a first number threshold, performing a first screening operation on the first trace set to obtain a candidate trace set further includes: Acquire a first processing description parameter and a second processing description parameter that match the historical point trace set, wherein the first processing description parameter is used to indicate the point trace utilization rate of the historical point trace set in the previous frame processing cycle, and the second processing description parameter is used to indicate the set processing efficiency of the historical point trace set in the previous frame processing cycle; The second quantity threshold is determined according to the first process description parameter and the second process description parameter.

10. A device for determining a point trace set, characterized in that: include: An acquisition unit is configured to acquire driving state information of a self-vehicle object and a first point trace set collected by the self-vehicle object in a current frame, wherein a first point trace object in the first point trace set is a radar point trace object detected by constant false alarm; A first determining unit, when the driving state information of the self-vehicle object meets the target measuring point condition and the number of first point traces of the first point trace object is greater than a first quantity threshold, performs a first screening operation on the first point trace set to obtain a candidate point trace set, wherein the number of candidate point traces of the candidate point trace objects included in the candidate point trace set is less than or equal to a second quantity threshold, and the second quantity threshold is greater than the first quantity threshold; A second determining unit performs a key screening operation on the candidate point trace set to obtain a second point trace set, wherein the number of second point traces of the second point trace object included in the second point trace set is less than or equal to the first number threshold, and the second point trace set is used for object recognition operation; The third determining unit performs a second screening operation on the first point trace set to obtain a third point trace set when the driving state information of the self-vehicle object does not meet the target measuring point condition and the number of the first points of the first point trace object is greater than the first number threshold, wherein the number of the third points of the third point trace object included in the third point trace set is less than or equal to the first number threshold, and the third point trace set is used for object recognition operation.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 9 when executed by an electronic device.

12. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 9 through the computer program.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 9 are implemented.

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