CN116816208B - Vehicle door radar static obstacle recognition enhancement method - Google Patents
Vehicle door radar static obstacle recognition enhancement method Download PDFInfo
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Abstract
The invention relates to a method for identifying and enhancing a static obstacle of a vehicle door radar, which uses detection information in an automatic vehicle door starting process to abandon the traditional method for measuring angles through an antenna array, and deduces angle information of a reflecting point through distance speed information so as to improve the contour identification effect of the obstacle. Meanwhile, speed dimension information is introduced through the movement of the vehicle door, the isolation between different reflection points of the static obstacle is increased, and the detection effect of the traditional antenna array angle measurement method is improved. And the angle information of the reflection points is deduced through the distance speed information, so that the problem that part of the reflection points cannot acquire correct angle information through a traditional angle measurement method is solved. In addition, the invention improves the FOV under the radar vehicle body coordinate system and enhances the data confidence by utilizing the change of the radar visible area under the vehicle body coordinate system in the vehicle door rotation process.
Description
Technical Field
The invention relates to the field of automotive radar application, in particular to a method for identifying and enhancing an exquisite obstacle of a vehicle door radar.
Background
The current automatic opening function of the vehicle door requires corresponding sensors to identify target obstacle information, and the vehicle door is opened to the maximum extent under the condition that the vehicle door is not touched with the obstacle through the obstacle space information provided by the sensors. Millimeter radar has the capability of 3D space detection and speed detection, and becomes a resident sensor in the automatic door opening function, referred to herein as millimeter wave door radar.
However, the millimeter wave vehicle door radar is limited by the cost and size, the number of antenna channels is not large, the physical caliber of the antenna is small, the angular resolution is large, and the shape recognition effect on the target is poor. One of the typical scenarios is that the wall surface target and the metal rod target cannot be distinguished correctly, or the edge contour of the wall surface target cannot be identified.
Specifically, the working logic of the existing millimeter wave vehicle door radar is as follows: (1) vehicle speed 0, door closed state; (2) radar detection and feedback of the openable angle of the vehicle door; (3) the door control module performs a door opening action; (4) execute in place; (5) secondary door opening detection.
The radar detection and feedback process of the openable angle process of the vehicle door comprises the following steps: (1) Performing distance-speed or distance-angle algorithm separation on the data; (2) Performing CFAR processing on the distance-velocity spectrum or the distance-angle spectrum; (3) calculating 4D point cloud information exceeding a threshold reflection point; (4) clustering and tracking point clouds; (5) carrying out contour recognition on the same category of targets; (6) calculating the openable angle of the vehicle door. The processing method for carrying out contour recognition on the same category of targets comprises the following steps: and calculating the spatial position distribution of the targets under a vehicle body coordinate system through the radial distance and the horizontal pitch angle information in the monitored 4D point cloud information, and obtaining contour information.
The method for the door control module to execute the door opening action comprises the following steps: the vehicle door control module receives vehicle door openable angle information; and according to the door opening angle information, the rotation speed, the acceleration and the deceleration of the door control module and the position for executing acceleration and deceleration are programmed at one time.
As shown in fig. 1-3, since the main obstacle in the door and the environment is stationary, a large amount of reflection point information exists in the block with the relative speed of 0, and the distance speed separation process is equivalent to only performing distance separation, and the speed separation is meaningless. After that, angular separation is performed, but since the angular resolution is not fine enough, there are a large number of different distance units in one angular resolution unit, resulting in inaccurate angle measurement results for the different distance units.
And because the angle measurement results of different distance resolution units are inaccurate, the space positions of the reflection points under the vehicle body coordinate system calculated by the distance angle information are also inaccurate. The identification deviation of the planar targets such as corresponding walls, chains, guardrails, road edges and the like is large, even the planar targets are disordered.
Meanwhile, the deceleration information is preset in the command of the door control module, so that when the door rotates, the radar updates the door openable angle information, and all the commands need to be reprogrammed. Therefore, the frequent updating of the openable angle information by the radar can cause the phenomenon of door rotation setback.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention aims to provide a method for enhancing the identification of the exquisite obstacle of the vehicle door radar so as to improve the identification precision of the outline of the obstacle.
In order to achieve the above purpose, the invention adopts the following technical scheme:
a method of vehicle door radar stationary obstacle identification enhancement, the method comprising the steps of:
step 1, starting an automatic opening function of a vehicle door, and detecting and calculating an openable angle of the vehicle door by a radar;
step 2, the door control module executes door opening action and feeds back the angle and the angular speed in real time;
step 3, circularly detecting and correcting the openable angle of the vehicle door until the difference value between the latest openable angle of the vehicle door and the opened angle of the vehicle door is smaller than a, wherein a is an angle change value from the start of braking action to the complete stop of the electric door;
step 4, controlling the vehicle door to stop rotating, and executing in-place;
in the step 1 and the step 3, the radar detects and calculates the openable angle of the vehicle door specifically as follows:
step 11, separating the data by a distance-speed algorithm;
step 12, performing CFAR on the distance-velocity spectrum;
step 13, carrying out special clustering treatment on reflection points exceeding a threshold value;
the special clustering method comprises the following steps:
the reflection points connected on the distance-velocity spectrum are the same cluster, the point with the largest amplitude value in the same cluster is the core A, the point with the largest speed in the same cluster is the core B (the reflection point corresponds to 0 degree angle), and other extreme points in the same cluster are the core C;
recording cluster ID, core category, distance and speed information of each reflection point;
step 14, calculating and outputting 4D point cloud information;
step 15, updating and accumulating FOV expansion data:
step 16, conventional clustering and tracking of point clouds;
step 17, carrying out contour recognition on the same category of targets;
and step 18, calculating the openable angle of the vehicle door.
The step 14 specifically comprises the following steps:
acquiring angle information of a core A and a core C by using an original angle measurement algorithm;
then, carrying out angle estimation on each reflection point of the cluster where the core A is positioned; if the core B speed is equal to Vmax, the cluster is considered to be a static cluster, otherwise, the cluster is considered to be a moving cluster; for the static cluster, performing angle estimation on other reflection points in the static cluster through the distance speed information;
when the starting stage of the vehicle door is that the starting angle of the vehicle door is relatively smaller, the reflection points of the core B and the core A are consistent; the calculation formula of the angle delta of the reflection point to be subjected to angle estimation is as follows:
cos(δ)=Vx/Vb（1）
cos(δ)=Ra/Rx（2）
wherein,
vx is the velocity of the reflection point to be angle estimated in the same cluster,
vb is the speed of core B in the same cluster,
ra is the distance of core a in the same cluster,
rx is the distance of the reflection point to be angle estimated in the same cluster;
when the angles delta calculated by the formulas (1) and (2) are consistent, reserving the reflection point, taking delta as the horizontal angle of the reflection point, otherwise discarding the reflection point information;
when the door starting angle is large, the angle of the core A is consistent with the door starting angle, and the angle of the core B is 0 degree;
the delta calculation formula at this time is as follows:
cos(δ+ θ)=Vx/Vb（3）
cos(δ)=Ra/Rx（4）
wherein θ is the opened angle of the vehicle door.
After the angle delta of the reflection point is calculated, the angle delta is deblurred, and the specific steps are as follows:
(a) Calculating to obtain a delta value through a formula (4);
(b) Calculating a Vx/Vb value, and recording the Vx/Vb value as k;
(c) Calculating cos (delta+theta) and cos (-delta+theta) values, which are respectively denoted as p1 and p2;
(d) Comparing |p1-k| with |p2-k|, if |p1-k| is smaller, taking delta, otherwise taking-delta;
taking the smaller value of |p1-k| and |p2-k| and the preset deviation threshold valueComparing, if less than->And reserving the reflection point, taking the corresponding deblurred delta as the horizontal angle of the reflection point, otherwise, discarding the reflection point information.
The FOV extension data update and accumulation is specifically as follows:
acquiring the opened angle of the vehicle door; converting the radar coordinate system FOV into a vehicle body coordinate system FOV; converting the radar coordinate system 4D point cloud data into vehicle body coordinate system 4D point cloud data and storing; carrying out matching confirmation on the overlapping part of the FOV of the front and rear frame vehicle body coordinate systems according to the 4D point cloud data of the vehicle body coordinate systems, and setting the confidence level to be high when the matching is carried out; the non-overlapping portions of the front and rear frame vehicle body coordinate systems FOV are accumulated as vehicle body coordinate system 4D point cloud data, but the confidence level is set to be low.
After the scheme is adopted, the invention has the following beneficial effects:
1. according to the invention, the speed dimension information is introduced by utilizing the speed of starting the vehicle door, so that the information isolation between reflection points is improved, and the angle measurement performance is directly improved.
2. The invention modifies the door control logic, so that the openable angle of the vehicle door can be continuously corrected after the vehicle door is started, and the door is not blocked.
3. The invention indirectly obtains the angles of the reflecting points, the angles of which cannot be accurately estimated by an angle measurement algorithm, by utilizing the relation between the distance speed angles of the reflecting points of the planar static obstacle. And improving the point cloud output information.
4. According to the invention, the change of the radar visible area under the vehicle body coordinate system in the vehicle door rotation process is utilized, the FOV under the radar vehicle body coordinate system is improved, and the data confidence is enhanced.
Drawings
FIG. 1 is a schematic diagram of a prior art wall with common angle resolution units for reflection points of different distance resolution units;
FIG. 2 is a diagram showing the non-differential speed dimensions of different reflection points of a wall in the prior art;
FIG. 3 is a schematic diagram of the separation of different reflection point distance speed and angle information of a wall in the prior art;
FIG. 4 is a schematic diagram showing the difference in speed dimensions of different reflection points of the wall according to the present invention;
FIG. 5 is a schematic diagram of the distance, speed and angle information isolation of different reflection points of the wall body according to the present invention;
FIG. 6 is a schematic diagram showing the relative velocity relationship between a door radar and a wall reflection point in the motion state of a vehicle door according to the present invention;
FIG. 7 is a diagram showing changes in FOV of door radar in the motion state of a vehicle door according to the present invention;
FIG. 8 is a schematic view of different core reflection points of a wall body under a small door opening angle;
FIG. 9 is a schematic diagram of the present invention showing the blurring of the angle of the reflecting point of the wall to be measured at a wide angle of the door;
FIG. 10 is a diagram showing two schematic views of the present invention for the blurring of the angle of the reflecting point of the wall to be measured at a large door opening angle.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosed aspects may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The invention discloses a method for identifying and enhancing a static obstacle of a vehicle door radar, which uses detection information in the starting process of an automatic vehicle door to abandon the traditional method for measuring angles through an antenna array, and deduces angle information of a reflecting point through distance speed information so as to improve the contour identification effect of the obstacle.
The relative speed of the static obstacle detected in the static state of the vehicle door is 0, and the speed dimension separation is meaningless. The relative speed of the static obstacle detected in the moving state of the vehicle door is not 0, and the relative speeds of different angles are different. Therefore, as shown in fig. 4-5, the invention introduces a velocity component, increases the isolation between the distance resolution units, and reduces the information coupling in the same angle resolution unit through the separation of the velocity dimension.
During the door starting process, a relative speed exists between the door and the stationary object, and the relative speed has the following relation with the door rotating speed and the reflection point azimuth angle:
Vmax=ω* L
V1=Vmax * cos(θ)
wherein ω is the angular velocity of the door movement, and L is the distance from the door rotation axis to the center of the radar receiving antenna; vmax is the linear velocity of the movement of the vehicle door corresponding to the central position of the antenna, and when the radar mounting surface is parallel to the vehicle door, the linear velocity is the relative velocity detected in the radar 0-degree angular direction, as shown in fig. 6. The corresponding relationship between the speed and the angle of the reflection point can be obviously seen from the formula.
Speed resolution in LFMCW vehicle radar regimeObtained by
Where c is the speed of light, fc is the carrier frequency, T is the single chirp period, and M is the chirp number.
From the above equation, the speed resolution is only related to the total chirp duration (TM), and theoretically the speed resolution can be made infinitely small (time sacrifice is required).
The velocity components of the different range resolution units can be accurately identified. In practical application, parameters such as omega, L, vres, vmax, chirp number and the like should be reasonably set, so that the phenomenon is complete and easy to distinguish. Therefore, the invention estimates the stationary target angle reversely by the distance and the speed information.
As shown in fig. 7, the radar antenna FOV remains unchanged, and as the door rotates, its FOV changes relative to the original position. Therefore, the radar effective FOV is expanded through the accumulation of the vehicle door rotation information, and the purposes of improving the radar effective FOV and enhancing the confidence coefficient are achieved through the matching and accumulation of detection data at different times.
The method specifically comprises the following steps:
step 1, a radar detects and feeds back an openable angle of a vehicle door when an automatic opening function of the vehicle door is started (generally, the vehicle door is started when the vehicle speed is 0 and the vehicle door is in a closed state).
And 2, the door control module executes door opening action and feeds back the angle and the angular speed in real time.
And 3, circularly detecting and correcting the openable angle of the vehicle door until the difference value between the latest openable angle of the vehicle door and the opened angle of the vehicle door is smaller than a, wherein a is an angle change value from the start of the braking action of the electric door to the complete stop, and a is a preset value, and the value is set by a user according to the specific performance of the electric door.
And 4, controlling the vehicle door to stop rotating, and executing in-place.
The above steps 1 and 3 are the same, and are all performed with radar detection and calculation of the openable door angle, except that part of the data used in calculating the openable door angle in step 3 is calculated in step 1.
As shown in fig. 7-10, the radar detection and calculation of openable doors is as follows:
step 11, separating the data by a distance-speed algorithm;
step 12, performing CFAR on the distance-velocity spectrum;
step 13, clustering the reflection points exceeding the threshold value;
the reflection points connected on the distance-speed spectrum are the same cluster, the point with the largest amplitude value in the same cluster is core A (the vertical reflection point of the wall surface can ensure the correct angle measurement result), the point with the largest speed in the same cluster is core B (the reflection point corresponds to 0 degree angle), and other extreme points in the same cluster are core C (the correct angle measurement result can ensure the correct angle measurement result);
cluster ID, core class, distance, and speed information of each reflection point are recorded.
Step 14, calculating and outputting 4D point cloud information; the 4D point cloud information at least comprises normal radar information such as distance, speed, angle, amplitude and the like.
Specifically, the angle information of the core A and the core C is obtained by using an original angle measurement algorithm.
And then, carrying out angle estimation on each reflection point of the cluster where each core A is located. If the core B speed is equal to Vmax, then the cluster is considered a stationary cluster, otherwise a moving cluster. The conventional processing method is adopted for the motion cluster, and the redundant description is omitted herein. And for the static cluster, performing angle estimation on other reflection points in the static cluster through the distance speed information. The method comprises the following steps:
when the door is started in the initial stage, that is, when the door starting angle is relatively small, the reflecting point of the core B is consistent with that of the core A.
If the reflection point to be solved is other reflection points of the wall body, the angle delta of the reflection point is related to the core A and the core B, the reflection point is further ensured to be a true and effective reflection point of the wall body through the correlation, and the possibility that the reflection point is an interference or a side lobe is eliminated.
The calculation formula of the angle delta of the reflection point to be subjected to angle estimation is as follows:
cos(δ)=Vx/Vb（1）
cos(δ)=Ra/Rx（2）
wherein Vx is the speed of the reflection point to be angle-estimated in the same cluster, vb is the speed of the core B in the same cluster, ra is the distance of the core a in the same cluster, and Rx is the distance of the reflection point to be angle-estimated in the same cluster.
When the angles delta calculated by the formulas (1) and (2) are consistent, the reflection point is reserved, delta is used as the horizontal angle of the reflection point, and otherwise, the reflection point information is discarded.
Note that the delta calculated at this time has positive and negative ambiguity, which is not completely correct, and the data confidence should be set low, so that the problem of angular ambiguity needs to be further solved.
When the door actuation angle is large (no more than half the main beam width at maximum), the angle of core a should coincide with the door actuation angle and the angle of core B should be 0 °.
The delta calculation formula at this time is as follows:
cos(δ+ θ)=Vx/Vb（3）
cos(δ)=Ra/Rx（4）
where θ is the door open angle, and since θ is known to be definite, the delta angle ambiguity can be further resolved by the above equation. The deblurring method is as follows:
(a) Calculating a delta value (with + -ambiguity) by the formula (4);
(b) Calculating a Vx/Vb value, and recording the Vx/Vb value as k;
(c) Calculating cos (delta+theta) and cos (-delta+theta) values, which are respectively denoted as p1 and p2;
(d) Comparing |p1-k| with |p2-k|, if |p1-k| is smaller, taking delta, otherwise taking-delta;
(e) Taking the smaller value of |p1-k| and |p2-k| and the preset deviation threshold valueComparison (theoretically 0, actually a smaller value) if less than +.>And reserving the reflection point, taking the corresponding deblurred delta as the horizontal angle of the reflection point, otherwise, discarding the reflection point information.
And step 15, updating and accumulating FOV extension data.
And step 16, conventionally clustering and tracking the point cloud.
And step 17, carrying out contour recognition on the same category of targets.
And step 18, calculating the openable angle of the vehicle door.
The method adopted in the steps 16, 17 and 18 is not different from the existing calculation method, so the invention will not be described in detail.
In the step 15, the FOV extension data updating and accumulating are specifically as follows:
acquiring the opened angle of the vehicle door; converting the radar coordinate system FOV into a vehicle body coordinate system FOV; converting the radar coordinate system 4D point cloud data into vehicle body coordinate system 4D point cloud data and storing; carrying out matching confirmation on the overlapping part of the FOV of the front and rear frame vehicle body coordinate systems according to the 4D point cloud data of the vehicle body coordinate systems, and setting the confidence level to be high when the matching is carried out; the non-overlapping portions of the front and rear frame vehicle body coordinate systems FOV are accumulated as vehicle body coordinate system 4D point cloud data, but the confidence level is set to be low.
The invention has the following beneficial effects:
1. according to the invention, the speed dimension information is introduced by utilizing the speed of starting the vehicle door, so that the information isolation between reflection points is improved, and the angle measurement performance is directly improved.
2. The invention modifies the door control logic, so that the openable angle of the vehicle door can be continuously corrected after the vehicle door is started, and the door is not blocked.
3. According to the invention, the angles of the reflecting points, which cannot be accurately estimated by an angle measurement algorithm, are indirectly obtained by utilizing the relation between the distance speed angles of the reflecting points of the planar static obstacle, so that the point cloud output information is improved.
4. According to the invention, the change of the radar visible area under the vehicle body coordinate system in the vehicle door rotation process is utilized, the FOV under the radar vehicle body coordinate system is improved, and the data confidence is enhanced.
The present invention also provides a computer-readable medium that may be contained in the electronic device described in the above embodiments; or may exist alone without being incorporated into the electronic device.
The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the methods described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (3)
1. A vehicle door radar static obstacle recognition enhancing method is characterized in that: the method comprises the following steps:
step 1, starting an automatic opening function of a vehicle door, and detecting and calculating an openable angle of the vehicle door by a radar;
step 2, the door control module executes door opening action and feeds back the angle and the angular speed in real time;
step 3, circularly detecting and correcting the openable angle of the vehicle door until the difference value between the latest openable angle of the vehicle door and the opened angle of the vehicle door is smaller than a, wherein a is an angle change value from the start of braking action to the complete stop of the electric door;
step 4, controlling the vehicle door to stop rotating, and executing in-place;
in the step 1 and the step 3, the radar detects and calculates the openable angle of the vehicle door specifically as follows:
step 11, separating the data by a distance-speed algorithm;
step 12, performing CFAR on the distance-velocity spectrum;
step 13, carrying out special clustering treatment on reflection points exceeding a threshold value;
the special clustering method comprises the following steps:
the reflection points connected on the distance-velocity spectrum are the same cluster, the point with the largest amplitude value in the same cluster is the core A, the point with the largest speed in the same cluster is the core B, and other extreme points in the same cluster are the core C;
recording cluster ID, core category, distance and speed information of each reflection point;
step 14, calculating and outputting 4D point cloud information;
step 15, updating and accumulating FOV expansion data:
step 16, conventional clustering and tracking of point clouds;
step 17, carrying out contour recognition on the same category of targets;
step 18, calculating the openable angle of the vehicle door;
the step 14 specifically comprises the following steps:
acquiring angle information of a core A and a core C by using an original angle measurement algorithm;
then, carrying out angle estimation on each reflection point of the cluster where the core A is positioned; if the core B speed is equal to Vmax, the cluster is considered to be a static cluster, otherwise, the cluster is considered to be a moving cluster; for the static cluster, performing angle estimation on other reflection points in the static cluster through the distance speed information; wherein, vmax is the linear velocity of the movement of the vehicle door corresponding to the central position of the antenna;
when the starting stage of the vehicle door is that the starting angle of the vehicle door is relatively smaller, the reflection points of the core B and the core A are consistent; the calculation formula of the angle delta of the reflection point to be subjected to angle estimation is as follows:
cos(δ)=Vx/Vb（1）
cos(δ)=Ra/Rx（2）
wherein,
vx is the velocity of the reflection point to be angle estimated in the same cluster,
vb is the speed of core B in the same cluster,
ra is the distance of core a in the same cluster,
rx is the distance of the reflection point to be angle estimated in the same cluster;
when the angles delta calculated by the formulas (1) and (2) are consistent, reserving the reflection point, taking delta as the horizontal angle of the reflection point, otherwise discarding the reflection point information;
when the door starting angle is large, the angle of the core A is consistent with the door starting angle, and the angle of the core B is 0 degree;
the delta calculation formula at this time is as follows:
cos(δ+ θ)=Vx/Vb（3）
cos(δ)=Ra/Rx（4）
wherein θ is the opened angle of the vehicle door.
2. The vehicle door radar stationary obstacle recognition enhancing method according to claim 1, wherein: after the angle delta of the reflection point is calculated, the angle delta is deblurred, and the specific steps are as follows:
(a) Calculating to obtain a delta value through a formula (4);
(b) Calculating a Vx/Vb value, and recording the Vx/Vb value as k;
(c) Calculating cos (delta+theta) and cos (-delta+theta) values, which are respectively denoted as p1 and p2;
(d) Comparing |p1-k| with |p2-k|, if |p1-k| is smaller, taking delta, otherwise taking-delta;
taking the smaller value of |p1-k| and |p2-k| and the preset deviation threshold valueComparing, if less than->And reserving the reflection point, taking the corresponding deblurred delta as the horizontal angle of the reflection point, otherwise, discarding the reflection point information.
3. The vehicle door radar stationary obstacle recognition enhancing method according to claim 1, wherein: the FOV extension data update and accumulation is specifically as follows:
acquiring the opened angle of the vehicle door; converting the radar coordinate system FOV into a vehicle body coordinate system FOV; converting the radar coordinate system 4D point cloud data into vehicle body coordinate system 4D point cloud data and storing; carrying out matching confirmation on the overlapping part of the FOV of the front and rear frame vehicle body coordinate systems according to the 4D point cloud data of the vehicle body coordinate systems, and setting the confidence level to be high when the matching is carried out; the non-overlapping portions of the front and rear frame vehicle body coordinate systems FOV are accumulated as vehicle body coordinate system 4D point cloud data, but the confidence level is set to be low.
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| Publication number | Priority date | Publication date | Assignee | Title |
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