Patent Application
20250147169
This application claims the benefit under 35 USC § 119 (a) of Korean Patent Application No. 10-2023-0150134, filed on Nov. 2, 2023, in the Korean Intellectual Property Office, the entire disclosure of which is incorporated herein by reference for all purposes.
The following description relates to a method and apparatus with target velocity measurement.
In traditional radar technology, a radar may be used to measure a velocity of a target by utilizing the Doppler effect. For a stationary object, a radar may measure distance through time delay. For a moving target, the radar may measure a velocity through changes in a signal frequency. Using radar technology, data may be transmitted and received and information on distance and velocity may be calculated through Fourier transform.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
In one general aspect, a method of determining a velocity vector of a target object includes: obtaining pieces of radar data obtained by detecting the target object; based on the pieces of radar data, extracting velocity vectors for respective points that correspond to the detected target object; and calculating the velocity vector of the target object based on the velocity vectors.
The calculating the velocity vector may include arranging starting points of the velocity vectors to a same reference point.
The calculating the velocity vector may further include: generating an approximation circle based on endpoints of the velocity vectors as arranged to the reference point; and calculating the velocity vector of the target object based on the reference point and a feature of the approximation circle.
The calculating the velocity vector of the target object may further include: generating line segments that are orthogonal to the velocity vectors, respectively, wherein the line segments originate at the endpoints of the respective velocity vectors as arranged to the reference point; and calculating the velocity vector of the target object based on intersections of the orthogonal line segments and based on the reference point.
The start point of the velocity vector of the target object may correspond to the reference point and the end point of the velocity vector may correspond to a point that is based on the intersections.
The method may further include: generating a data cube by performing a Fourier transformation on the pieces of radar data to obtain, for each of the pieces of radar data, a distance value, a radial velocity value, and an angle value; and labeling and clustering, as pieces of data about the target object, pieces of data extracted by applying the data cube to a target detector.
The extracting of the velocity vectors ma include: determining first reliabilities of a set of velocity vectors, and selecting the velocity vectors from among the set of velocity vectors based on the velocity vectors having first reliabilities that satisfy a first reliability threshold.
The velocity vectors may be extracted based at least in part on determining that they correspond to a surface of the target vector.
The obtaining the pieces of radar data may include: measuring second reliabilities for the respective pieces of radar data; and selecting, from among the pieces of radar data, pieces of the radar data whose second reliability is greater than a second threshold value, wherein the velocity vectors are extracted based on the selected pieces of radar.
1The pieces of radar data may be obtained from a bounding box provided by a radar device.
The velocity vector of the target object may represent an actual velocity of the target object and may include a vector component other than a line-of-sight vector component.
In another general aspect, an electronic device includes: one or more processors; and memory storing instructions configured to cause the one or more processors to: obtain pieces of radar data obtained by detecting a target object; based on the pieces of radar data, extract velocity vectors for respective points that correspond to the detected target object; and calculate a velocity vector of the target object based on the velocity vectors.
The calculating the velocity vector may include arranging starting points of the velocity vectors to be at a same reference point.
The calculating the velocity vector may further include: generating an approximation circle based on endpoints of the velocity vectors as arranged at the reference point, wherein the circle is fitted to the endpoints; and calculating the velocity vector of the target object based on the reference point and a feature of the approximation circle.
The calculating the velocity vector may further include: finding intersections between lines normal to endpoints of the velocity vectors as arranged at the reference point; and calculating the velocity vector of the target object based on the intersections.
The instructions may be further configured to cause the one or more processors to: generate a data cube by performing Fourier transformation on the pieces of radar data to obtain, for each piece of radar data, a distance value, a radial velocity value, and an angle value; and label, as pieces of data about the target object, and cluster, pieces of data extracted by applying the data cube to a target detector.
The instructions may be further configured to cause the one or more processors to: determine first reliabilities of a set of velocity vectors, and select the velocity vectors from among the set of velocity vectors based on the velocity vectors having first reliabilities that satisfy a first reliability threshold.
The velocity vector for the target object may be determined based on pieces of radar data determined to correspond to a surface of the target object.
The processor may be further configured to measure a second reliability for each of the plurality of pieces of radar data and detect and cluster pieces of radar data, from among the plurality of pieces of radar data, in which the second reliability is greater than or equal to a second threshold value.
The instructions may be further configured to cause the one or more processors to obtain pieces of data about the target object in the form of a bounding box provided with the radar data from a radar device.
Other features and aspects will be apparent from the following detailed description, the drawings, and the claims.
Throughout the drawings and the detailed description, unless otherwise described or provided, the same or like drawing reference numerals will be understood to refer to the same or like elements, features, and structures. The drawings may not be to scale, and the relative size, proportions, and depiction of elements in the drawings may be exaggerated for clarity, illustration, and convenience.
The following detailed description is provided to assist the reader in gaining a comprehensive understanding of the methods, apparatuses, and/or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatuses, and/or systems described herein will be apparent after an understanding of the disclosure of this application. For example, the sequences of operations described herein are merely examples, and are not limited to those set forth herein, but may be changed as will be apparent after an understanding of the disclosure of this application, with the exception of operations necessarily occurring in a certain order. Also, descriptions of features that are known after an understanding of the disclosure of this application may be omitted for increased clarity and conciseness.
The features described herein may be embodied in different forms and are not to be construed as being limited to the examples described herein. Rather, the examples described herein have been provided merely to illustrate some of the many possible ways of implementing the methods, apparatuses, and/or systems described herein that will be apparent after an understanding of the disclosure of this application.
The terminology used herein is for describing various examples only and is not to be used to limit the disclosure. The articles “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. As used herein, the term “and/or” includes any one and any combination of any two or more of the associated listed items. As non-limiting examples, terms “comprise” or “comprises,” “include” or “includes,” and “have” or “has” specify the presence of stated features, numbers, operations, members, elements, and/or combinations thereof, but do not preclude the presence or addition of one or more other features, numbers, operations, members, elements, and/or combinations thereof.
Throughout the specification, when a component or element is described as being “connected to,” “coupled to,” or “joined to” another component or element, it may be directly “connected to,” “coupled to,” or “joined to” the other component or element, or there may reasonably be one or more other components or elements intervening therebetween. When a component or element is described as being “directly connected to,” “directly coupled to,” or “directly joined to” another component or element, there can be no other elements intervening therebetween. Likewise, expressions, for example, “between” and “immediately between” and “adjacent to” and “immediately adjacent to” may also be construed as described in the foregoing.
Although terms such as “first,” “second,” and “third”, or A, B, (a), (b), and the like may be used herein to describe various members, components, regions, layers, or sections, these members, components, regions, layers, or sections are not to be limited by these terms. Each of these terminologies is not used to define an essence, order, or sequence of corresponding members, components, regions, layers, or sections, for example, but used merely to distinguish the corresponding members, components, regions, layers, or sections from other members, components, regions, layers, or sections. Thus, a first member, component, region, layer, or section referred to in the examples described herein may also be referred to as a second member, component, region, layer, or section without departing from the teachings of the examples.
Unless otherwise defined, all terms, including technical and scientific terms, used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains and based on an understanding of the disclosure of the present application. Terms, such as those defined in commonly used dictionaries, are to be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the disclosure of the present application and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein. The use of the term “may” herein with respect to an example or embodiment, e.g., as to what an example or embodiment may include or implement, means that at least one example or embodiment exists where such a feature is included or implemented, while all examples are not limited thereto.
Operations 110 to 130 are described as being performed by an electronic device 800 and/or a processor 830 of the electronic device 800 illustrated in
The processor 830 may measure a velocity of a target. Here, “measure” is short for computing a velocity value based on measured data.
In operation 110, the processor 830 may obtain pieces of radar data obtained by detecting the target (e.g., point cloud data). The pieces of radar data are described in detail below with reference to
In operation 120, the processor 830 may extract velocity vectors for respective points in the pieces of radar data for which the target is detected. That is, the target may be detected and velocity vectors of pieces of data corresponding to the target may be extracted.
The processor 830 may obtain a data cube by performing Fourier transformation on the pieces of radar data to obtain a distance value, a radial velocity value, and an angle value of the target (for respective pieces of radar data). The processor 830 may label and cluster pieces of data extracted by applying the data cube to a target detector as pieces of data about the target.
The processor 830 may measure a first reliability to determine whether the velocity vectors are detection results for the target. The processor 830 may detect/select velocity vectors, from among the velocity vectors, in which the first reliability is greater than or equal to a first threshold value.
The processor 830 may determine whether the detection results for the target are pieces of data corresponding to a surface of the target. The reliability of the velocity vector is described with reference to
In operation 130, the processor 830 may calculate the velocity of the target based on a predetermined method and the velocity vectors.
For example, the predetermined method may be a circle-fitting method. The predetermined method, e.g., the circle-fitting method, may involve arranging starting points of the velocity vectors with respect to a reference point. The predetermined method may generate an approximation circle based on endpoints of the velocity vectors (as arranged with respect to the reference point) and may calculate the velocity vector of the target based on the reference point and a parameter of the approximation circle.
In another example, the predetermined method may be an orthogonal line segment intersection method. The predetermined method, e.g., the orthogonal line segment method, may involve generating line segments that are orthogonal to the velocity vectors, respectively, at the endpoints of the velocity vectors (as arranged with respect to the reference point) and may calculate the velocity of the target based on intersections of the orthogonal line segments and the reference point.
The description provided with reference to
A radar 270 may transmit a signal through a transmitter and may receive the signal as returned from an object through a receiver. In the case of a stationary object, the processor 830 may calculate a distance to the object by measuring the time taken by the transmitted signal return from the object (which can be used to find the time taken for the transmitted signal to reach the object). However, when the object is moving, the frequency of the received return signal changes due to the Doppler effect, and the processor 830 may measure a velocity of a target 210 using the changed frequency. For example, the radar 270 may typically measure the velocity of the target 210 using frequency modulated continuous-wave (FMCW) technology. An FMCW radar (e.g., the radar 270) may obtain multiple pieces of signal data by repeating signal-transmission and signal-reception processes at a predetermined interval. The processor 830 may calculate a distance and a speed value by applying Fourier transform to multiple pieces of signal data.
The radar 270 may have multiple transmitters and receivers. A transmitter-receiver pair may be referred to as a channel. Phase differences between the received signals of the respective channels may depend on their respective physical distances to the target 210. The processor 830 may estimate an angle between the target 210 and the radar 270 using the phase differences of the received signals. More specifically, the radar 270 may store the received signals as digital signals, which may be converted to digital from received analog signals by an analog to digital converter (ADC). In addition, when storing the received digital signals, the processor 830 may arrange the stored received signals in an order corresponding to their transmission order. Furthermore, the processor 830 may arrange the pieces of data (e.g., 3D points) once more based on the distance value according to the channel to finally obtain a raw data cube (e.g., a point cloud). The processor 830 may convert the raw data cube into a final data cube that includes additional information (e.g., information on the distances, velocities, and angles of respective points) by applying Fourier transform to each axis of the raw data cube. The processor 830 may apply the final data cube to a target detection algorithm (e.g., a constant false alarm rate (CFAR)). The target detection algorithm may, based on the final data cube, extract a potential target (e.g., the target 210) and may also obtain distance and speed information of the target 210.
Referring to
The description of
Referring to
The radar 270 may obtain the pieces of data of the radar 270 from a surface of the target 310. In the example of
The description of
Referring to
The processor 830 may perform circle-fitting to generate an approximation circle 410 that passes through (or is close to) endpoints of the plurality of radial velocity vectors 251 to 253 arranged with respect to the reference point 400. The approximation circle 410 may include the endpoints of the plurality of radial velocity vectors 251 to 253. In an ideal case, the circle generated based on the radial velocity vectors (having been extracted from pieces of radar data about the velocity of the target 210) may be a circle that includes all endpoints of the radial velocity vectors. However, since the pieces of radar data measured for the actual target 210 and the extracted radial velocity vectors may include errors, the processor 830 may generate a circle that is an approximation, i.e., the approximation circle 410 to accommodate the errors. In the generated approximation circle 410, the processor 830 may determine a direction passing through the reference point 400 and the center of the approximation circle 410 and may use that direction as a direction of the actual velocity 230 of the target 210. In addition, the diameter of the approximation circle 410 may be used as the magnitude of the actual velocity 230 of the target 210.
The term “circle” and “approximate circle”, as used herein refers to a family of round shapes that may have an approximate center or multiple approximate centers. The shapes are generally round, but may be somewhat circular, somewhat oval, or the like. In the context of this disclosure, the “ideal case” circle refers to an “exact circle”, i.e., a geometric circle. Fundamentally, using two velocity vectors without error can generate an exact circle, and based on this circle, the actual velocity vectors can be derived. However, in the case of an approximate circle, as noted, this encompasses oval and similar round shapes. Even with approximate circles, it's possible to create an “exact circle” by considering errors to extract the actual velocities. However, if an elliptical approximate circle is generated, two centers are formed. Therefore, the actual velocity can be determined by the velocity vector that passes through the midpoint of these two centers and a reference point. The endpoint of this velocity vector would be the intersection point with the ellipse. Similarly, even if a circular shape with multiple centers is formed, the actual velocity can be determined by the velocity vector passing through the midpoint of these centers (e.g., a cluster's center) and a reference point.
As noted above, in one example implementation, the processor 830 may perform an orthogonal line segment intersection method. This may involve calculating an intersection of line segments that are orthogonal to the endpoints of the respective radial velocity vectors 251 to 253 (as arranged with respect to the reference point 400). These orthogonal line segments may intersect at an intersection point 420. In an ideal case, all of the orthogonal line segments intersect the intersection point 420. However, since the extracted radial velocity vectors may have error components (due to errors in the pieces of radar data measured for the actual target 210) it may not be possible to generate an orthogonal line segment intersection point 420 through which all of the orthogonal line segments intersect. Rather, the processor 830 may generate multiple intersection points of the orthogonal segments. To handle the multiple intersection points, the processor 830 may use a center-of-mass method. That is, the processor 830 may find a center of mass of the coordinates of the generated intersection points and use the center of mass coordinate as the intersection point 420. Or, the processor may select, as the intersection point 420, from among the intersection points, the coordinates of an intersection point with the highest distribution according to a distribution of the intersection points. Regardless of how the orthogonal line intersection point 420 is generated, the processor 830 may set an actual velocity vector (representing the actual velocity 230 of the target 210) as the vector with (i) the reference point 400 as a starting point and (ii) the determined orthogonal line segment intersection point 420 as an endpoint.
The description of
The processor 830 may collect pieces of radar data using a radar. The processor 830 may measure reliabilities for the respective pieces of radar data and may detect and cluster pieces of radar data, from among the plurality of pieces of radar data, whose respective second reliabilities are greater than or equal to a second threshold value.
To elaborate, the processor 830 may collect detection results 530 (e.g., pieces of data in point cloud format) for a truck 510 using a radar. The processor 830 may determine the second reliabilities for the respective pieces/points in the detection results 530.
For example, the processor 830 may normalize distribution of the detection results 530 according to the second reliabilities. Specifically, the processor 830 may set (e.g., normalize) the second reliabilities to have the normal distribution, and may set the second reliabilities to decrease for each difference in standard deviation ( ) on the normal distribution (e.g., for a part in which the standard deviation differs from the average by more than or equal to 20, the second reliability may be set to 0.2). The processor 830 may assign the second reliabilities to the respective points/pieces in the detection results 530 and may then detect/select, from among the detection results 530, pieces/points of the detection results whose second reliability is greater than or equal to the second threshold value (e.g., 0.3). To summarize, as shown in
The description of
Referring to
For example, the processor 830 may normalize distribution of magnitudes of the velocity vectors. The processor 830 may set the first reliabilities of respective velocity vectors at an average to 1 (e.g., a normal distribution) and may set the first reliabilities to decrease for each difference in standard deviation (σ) on a normal distribution (e.g., for a part in which the standard deviation differs from the average by more than or equal to 20, the first reliability may be set to 0.1). The processor 830 may assign the first reliabilities to the respective velocity vectors and may detect/select, from among the velocity vectors, the velocity vectors whose first reliabilities are greater than or equal to a first threshold value (e.g., 0.2) among the velocity vectors. In sum, as shown in
In addition, the processor 830 may determine whether the velocity vectors determined to have high reliability correspond to a surface of the target. Since the processor 830 has measured the reliabilities based on the magnitudes of the velocity vectors, when the coordinate of a starting point of a velocity vector actually measured is apart from the coordinates of starting points of other velocity vectors, at more than a predetermined level, then the velocity vector may be determined not to correspond to the surface of the target. Accordingly, the processor 830 may only extract, from among the velocity vectors with high reliability, the velocity vectors based on pieces of data measured on the surface of the target.
The above-described method of measuring the first reliabilities according is only one example and the general technique is not limited to using the normal distribution for the magnitude of the velocity vector; and various other techniques may be used.
Referring to
In the above-described process, the electronic device 800 is described as measuring the velocity of the target using a single radar, but the electronic device 800 may measure the velocity of the target using multiple radars. That is, the electronic device 800 may measure velocities of multiple targets using the multiple radars or may measure a velocity vector of a single target.
The processor 830 may obtain pieces (e.g., 3D points) of data about the target in the form of a bounding box from a high-resolution radar device. In the case of the high-resolution radar device other than an existing radar, a clustering process of the existing radar may be omitted by outputting a final result in the form of a bounding box. In such a case, the velocity of the target may be measured through the method presented in the above-described process using pieces of radar data existing in the bounding box.
Referring to
The output device 870 may display a user interface that receives a user input related to target velocity measurement states and operation provided by the processor 830. Moreover, the target velocity measurement may be used for vehicle functions such as autonomous/assisted driving, driving condition analysis, and so forth.
The memory 850 may store pieces of data related to target velocity measurement performed by the processor 830 and pieces of data obtained from a radar and a communicator. In addition, the memory 850 may store a variety of pieces of information generated by the processing process of the processor 830 described above. Furthermore, the memory 850 may store a variety of data and programs. The memory 850 may include a volatile memory or a non-volatile memory. The memory 850 may include a large-capacity storage medium such as a hard disk to store a variety of pieces of data.
In addition, the processor 830 may perform the at least one method described with reference to
The processor 830 may execute a program (in the form of processor-executable instructions) and control the electronic device 800. Program code to be executed by the processor 830 may be stored in the memory 850.
The computing apparatuses, the vehicles, the electronic devices, the processors, the memories, the sensors/radars, the vehicle/operation function hardware, the displays, the information output system and hardware, the storage devices, and other apparatuses, devices, units, modules, and components described herein with respect to
The methods illustrated in
Instructions or software to control computing hardware, for example, one or more processors or computers, to implement the hardware components and perform the methods as described above may be written as computer programs, code segments, instructions or any combination thereof, for individually or collectively instructing or configuring the one or more processors or computers to operate as a machine or special-purpose computer to perform the operations that are performed by the hardware components and the methods as described above. In one example, the instructions or software include machine code that is directly executed by the one or more processors or computers, such as machine code produced by a compiler. In another example, the instructions or software includes higher-level code that is executed by the one or more processors or computer using an interpreter. The instructions or software may be written using any programming language based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions herein, which disclose algorithms for performing the operations that are performed by the hardware components and the methods as described above.
The instructions or software to control computing hardware, for example, one or more processors or computers, to implement the hardware components and perform the methods as described above, and any associated data, data files, and data structures, may be recorded, stored, or fixed in or on one or more non-transitory computer-readable storage media.
Examples of a non-transitory computer-readable storage medium include read-only memory (ROM), random-access programmable read only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), flash memory, a card type memory such as multimedia card micro or a card (for example, secure digital (SD) or extreme digital (XD)), magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device that is configured to store the instructions or software and any associated data, data files, and data structures in a non-transitory manner and provide the instructions or software and any associated data, data files, and data structures to one or more processors or computers so that the one or more processors or computers can execute the instructions. In one example, the instructions or software and any associated data, data files, and data structures are distributed over network-coupled computer systems so that the instructions and software and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by the one or more processors or computers.
While this disclosure includes specific examples, it will be apparent after an understanding of the disclosure of this application that various changes in form and details may be made in these examples without departing from the spirit and scope of the claims and their equivalents. The examples described herein are to be considered in a descriptive sense only, and not for purposes of limitation. Descriptions of features or aspects in each example are to be considered as being applicable to similar features or aspects in other examples. Suitable results may be achieved if the described techniques are performed in a different order, and/or if components in a described system, architecture, device, or circuit are combined in a different manner, and/or replaced or supplemented by other components or their equivalents.
Therefore, in addition to the above disclosure, the scope of the disclosure may also be defined by the claims and their equivalents, and all variations within the scope of the claims and their equivalents are to be construed as being included in the disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 10-2023-0150134 | Nov 2023 | KR | national |
