Radar point cloud aggregation of dynamic objects with minimized differences

Abstract

Systems and methods for operating a host vehicle. A first detection of a first reflection point from an object is received during a first time frame of a radar. The first position of the first detection and the first Doppler frequency are directions. The first location is updated to a first predicted location in the second time frame using the first Doppler frequency. The updating includes shifting the first detection from the first position to an intermediate position in the second time frame using an object-based component of the first Doppler frequency, and shifting the first detection from the intermediate position to a first predicted position using a vehicle-based component of the first Doppler frequency. The predicted position is aggregated with a second detection of a second reflection point from the object, and the object is detected from the aggregation.

Description

Technical Field

[0001] This subject matter discloses radar detection of moving objects, and in particular, systems and methods for aggregating radar detections across multiple time frames. Background Technology

[0002] Radar can be used to acquire point clouds, including detecting reflections from objects within the radar's field of view. Detection can be used to determine the distance and velocity to the object, as well as the object's shape and / or category. The sparsity of detections in the point cloud leads to inaccurate estimations of object shape and / or category. To counteract the sparse detection density, detections can be aggregated across multiple time frames of the radar. This aggregation typically requires knowledge of the object's relative velocity to the radar. However, objects often have unknown relative velocities. Therefore, this aggregation results in detections becoming scattered over time, making it difficult to distinguish nearby objects and reducing the accuracy of category and shape estimations. Therefore, it is desirable to provide a method for aggregating detections across time frames that maintains the resolution of objects in the environment. Summary of the Invention

[0003] In one exemplary embodiment, a method for operating a master vehicle is disclosed. The method includes receiving a first detection from a first reflection point of an object during a first time frame of a radar; determining a first position and a first Doppler frequency of the first detection; updating the first position to a first predicted position in a second time frame using the first Doppler frequency; receiving a second detection from a second reflection point of the object; and detecting the object from the first predicted position in the second time frame and the second detection. Updating the first position includes: determining an object-based component of the first Doppler frequency for the first detection by removing the influence of the master vehicle's speed from the first Doppler frequency; shifting the first detection from the first position to an intermediate position in the second time frame using the object-based component of the first Doppler frequency; and shifting the first detection from the intermediate position to the first predicted position in the second time frame using a vehicle-based component of the first Doppler frequency.

[0004] In addition to one or more features described herein, the method further includes: receiving a second detection from a second reflection point of an object during a first time frame; determining a second position of the second detection and a second Doppler frequency for the second detection; updating the second position to a second predicted position in a second time frame based on a calculation using the second Doppler frequency; and detecting an object from a first predicted position in the second time frame and a second predicted position in the second time frame.

[0005] In addition to one or more features described herein, the method also includes updating a first predicted position in a second time frame to a second predicted position in a third time frame based on a first calculation using a first Doppler frequency and the speed of the master vehicle obtained in a second time frame.

[0006] In addition to one or more features described herein, the method further includes receiving a second detection in a second time frame, determining a second position of the second detection and a second Doppler frequency for the second detection in the second time frame, and updating the second position to a third predicted position in a third time frame using a second calculation based on the second Doppler frequency.

[0007] In addition to one or more features described herein, object detection also includes determining at least one of the following: the object's location, the object's shape, the object's orientation, and the object's category.

[0008] In addition to one or more features described herein, wherein the first time frame is one of a plurality of time intervals, the method also includes selecting a subset of time frames of the plurality of time intervals using a moving time window.

[0009] In addition to one or more features described herein, the method also includes controlling the master vehicle to navigate relative to an object based on a first predicted position and a second detection.

[0010] In another exemplary embodiment, a system for operating a master vehicle is disclosed. The system includes a processor configured to: receive a first detection from a first reflection point of an object during a first time frame of a radar; determine a first position and a first Doppler frequency of the first detection; update the first position to a first predicted position in a second time frame using the first Doppler frequency; receive a second detection from a second reflection point of the object; and detect an object from the first predicted position in the second time frame and the second detection. Updating the first position includes: determining an object-based component of the first Doppler frequency for the first detection by removing the influence of the master vehicle's speed from the first Doppler frequency; shifting the first detection from the first position to an intermediate position in the second time frame using the object-based component of the first Doppler frequency; and shifting the first detection from the intermediate position to the first predicted position in the second time frame using a vehicle-based component of the first Doppler frequency.

[0011] In addition to one or more features described herein, the processor is also configured to: receive a second detection during a first time frame; determine a second position of the second detection and a second Doppler frequency for the second detection; update the second position to a second predicted position in the second time frame based on calculations using the second Doppler frequency; and detect an object from a first predicted position in the second time frame and a second predicted position in the second time frame.

[0012] In addition to one or more features described herein, the processor is also configured to update the first predicted position in the second time frame to the second predicted position in the third time frame based on a first calculation using a first Doppler frequency and the speed of the master vehicle obtained in the second time frame.

[0013] In addition to one or more features described herein, the processor is also configured to receive a second detection in a second time frame, determine a second position of the second detection and a second Doppler frequency for the second detection in the second time frame, and update the second position to a third predicted position in a third time frame using a second calculation based on the second Doppler frequency.

[0014] In addition to one or more features described herein, the processor is also configured to detect objects by determining at least one of the following: the object's position, the object's shape, the object's orientation, and the object's category.

[0015] In addition to one or more features described herein, the first time frame is one of multiple time frames of time intervals, and the processor is also configured to use a moving time window to select a subset of time frames of multiple time intervals.

[0016] In addition to one or more features described herein, the processor is also configured to control the master vehicle to navigate relative to objects based on a first predicted position and a second detection.

[0017] In yet another exemplary embodiment, a master vehicle is disclosed. The master vehicle includes a system and a processor for controlling the navigation of the master vehicle. The processor is configured to: receive a first detection from a first reflection point of an object during a first time frame of a radar; determine a first position and a first Doppler frequency of the first detection; update the first position to a first predicted position in a second time frame using the first Doppler frequency; receive a second detection from a second reflection point of the object; detect an object from the first predicted position in the second time frame and the second detection; and control the system to navigate the master vehicle relative to the object. Updating the first position includes: determining an object-based component of the first Doppler frequency for the first detection by removing the influence of the master vehicle's speed from the first Doppler frequency; shifting the first detection from the first position to an intermediate position in the second time frame using the object-based component of the first Doppler frequency; and shifting the first detection from the intermediate position to the first predicted position in the second time frame using a vehicle-based component of the first Doppler frequency.

[0018] In addition to one or more features described herein, the processor is also configured to: receive a second detection in a first time frame; determine a second position of the second detection and a second Doppler frequency for the second detection; update the second position to a second predicted position in the second time frame based on calculations using the second Doppler frequency; and detect an object from the first predicted position in the second time frame and the second predicted position in the second time frame.

[0019] In addition to one or more features described herein, the processor is also configured to update the first predicted position in the second time frame to the second predicted position in the third time frame based on a first calculation using a first Doppler frequency and the master vehicle speed obtained in the second time frame.

[0020] In addition to one or more features described herein, the processor is also configured to receive a second detection in a second time frame, determine a second position of the second detection and a second Doppler frequency for the second detection in the second time frame, and update the second position to a third predicted position in a third time frame using a second calculation based on the second Doppler frequency.

[0021] In addition to one or more features described herein, the processor is also configured to detect objects by determining at least one of the following: the object's position, the object's shape, the object's orientation, and the object's category.

[0022] In addition to one or more features described herein, the first time frame is one of multiple time frames of time intervals, and the processor is also configured to use a moving time window to select a subset of time frames of multiple time intervals.

[0023] The above-described features and advantages, as well as other features and advantages, of this disclosure will become apparent when the following detailed description is read in conjunction with the accompanying drawings. Attached Figure Description

[0024] Other features, advantages, and details appear by way of example only in the following detailed description, which refers to the accompanying drawings, wherein:

[0025] Figure 1 A vehicle with a relevant trajectory planning system is shown;

[0026] Figure 2 This is a diagram illustrating the method disclosed herein for updating the detection position between time frames of a radar;

[0027] Figure 3 This is a diagram illustrating a method for updating multiple detections obtained in the first time frame;

[0028] Figure 4 This is a graph showing the updating of a single detection across multiple time frames;

[0029] Figure 5 This is a graph showing the updates of detections obtained during individual time frames across multiple time frames;

[0030] Figure 6 A flowchart of a method for detecting objects in an illustrative embodiment is shown;

[0031] Figure 7 A plan view of the area containing the first vehicle and the second vehicle is shown;

[0032] Figure 8 A plan view of the detected region, calculated using a conventional temporal aggregation method, is shown; and

[0033] Figure 9 A plan view of the region including the detections calculated using the temporal aggregation method disclosed herein is shown. Detailed Implementation

[0034] The following description is exemplary in nature only and is not intended to limit this disclosure or its application or use. It should be understood that in all the drawings, corresponding reference numerals denote the same or corresponding parts and features.

[0035] According to an exemplary embodiment, Figure 1 A vehicle 10 with a relevant trajectory planning system 100 is shown. Typically, the trajectory planning system 100 determines the trajectory planning for autonomous driving of the vehicle 10. The vehicle 10 typically includes a chassis 12, a body 14, front wheels 16, and rear wheels 18. The body 14 is mounted on the chassis 12 and substantially surrounds the components of the vehicle 10. The body 14 and the chassis 12 may together form a frame. The front wheels 16 and the rear wheels 18 are each rotatably connected to the chassis 12 near a corresponding corner of the body 14.

[0036] In various embodiments, vehicle 10 is an autonomous vehicle, and trajectory planning system 100 is incorporated into the autonomous vehicle. For example, an autonomous vehicle is a vehicle that is automatically controlled to transport passengers from one location to another. In the illustrated embodiment, vehicle 10 is described as a passenger car, but it should be understood that any other means of transportation, including motorcycles, trucks, sports utility vehicles (SUVs), recreational vehicles (RVs), ships, aircraft, etc., may also be used. In exemplary embodiments, the autonomous vehicle is a so-called Level 4 or Level 5 automation system. Level 4 system means "high automation," referring to the driving mode-specific performance of the automated driving system in all aspects of a dynamic driving task, even if the human driver does not respond appropriately to intervention requests. Level 5 system means "full automation," referring to the full-time performance of the automated driving system in all aspects of a dynamic driving task under all road and environmental conditions that a human driver can manage.

[0037] As shown in the figure, an autonomous vehicle typically includes a propulsion system 20, a drivetrain 22, a steering system 24, a braking system 26, a sensor system 28, an actuator system 30, at least one data storage device 32, at least one controller 34, and a communication system 36. In various embodiments, the propulsion system 20 may include an internal combustion engine, an electric motor such as a traction motor, and / or a fuel cell propulsion system. The drivetrain 22 is configured to transmit power from the propulsion system 20 to the front wheels 16 and the rear wheels 18 according to a selectable speed ratio. According to various embodiments, the drivetrain 22 may include a stepped automatic transmission, a continuously variable transmission (CVT), or other suitable transmission. The braking system 26 is configured to provide braking torque to the front wheels 16 and the rear wheels 18. In various embodiments, the braking system 26 may include friction brakes, brake-by-wire brakes, regenerative braking systems such as electric motors, and / or other suitable braking systems. The steering system 24 affects the position of the front wheels 16 and the rear wheels 18. Although depicted as including a steering wheel for illustrative purposes, in some embodiments contemplated within the scope of the invention, the steering system 24 may not include a steering wheel.

[0038] Sensor system 28 includes one or more sensing devices 40a-40n that sense observable conditions of the external and / or internal environment of vehicle 10. Sensing devices 40a-40n may include, but are not limited to, radar, lidar, global positioning system, optical camera, thermal camera, ultrasonic sensor, and / or other sensors. Sensor system 28 may also include dynamic sensors for measuring one or more dynamic parameters of the vehicle. Exemplary dynamic sensors include an inertial measurement unit (IMU) for measuring the vehicle's three-dimensional acceleration, a steering angle sensor, a torque sensor, a yaw rate sensor, wheel speed sensors, etc.

[0039] The actuator system 30 includes one or more actuator devices 42a-42n that control one or more vehicle features, such as, but not limited to, the propulsion system 20, the transmission system 22, the steering system 24, and the braking system 26. In various embodiments, the vehicle features may also include interior and / or exterior vehicle features, such as, but not limited to, doors, trunks, and cabin features, such as air conditioning, music, lighting, etc. (not shown).

[0040] The controller 34 includes at least one processor 44 and a computer-readable storage device or medium 46. The processor 44 can be any custom or commercially available processor, central processing unit (CPU), graphics processing unit (GPU), auxiliary processor among multiple processors associated with the controller 34, semiconductor-based microprocessor (in the form of a microchip or chipset), macroprocessor, any combination thereof, or any device typically used to execute instructions. The computer-readable storage device or medium 46 can include volatile and non-volatile memory, such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a permanent or non-volatile memory that can be used to store various operational variables when the processor 44 is powered off. The computer-readable storage device or medium 46 can be implemented using any of a variety of known storage devices, such as PROM (programmable read-only memory), EPROM (electrical PROM), EEPROM (electrically erasable PROM), flash memory, or any other electrical, magnetic, optical, or combined storage device capable of storing data, some of which represent executable instructions used by the controller 34 in controlling the vehicle 10.

[0041] The instructions may include one or more separate programs, each comprising an ordered list of executable instructions for implementing logical functions. When executed by processor 44, these instructions receive and process signals from sensor system 28, execute logic, calculations, methods, and / or algorithms for automatically controlling components of vehicle 10, and generate control signals to actuator system 30 to automatically control components of vehicle 10 based on that logic, calculations, methods, and / or algorithms. Although Figure 1 Only one controller 34 is shown, but embodiments of vehicle 10 may include any number of controllers 34 that communicate via any suitable communication medium or combination of communication media and cooperate to process sensor signals, execute logic, calculations, methods and / or algorithms, and generate control signals to automatically control the features of vehicle 10.

[0042] In various embodiments, one or more instructions of controller 34 are included in trajectory planning system 100, and when executed by processor 44, determine the aggregation of radar cloud points or the detection of reflection points from one or more objects obtained by radar during a first time frame, update the detection to subsequent time frames to maintain the resolution of one or more objects, detect objects from the aggregation of detections, and control the operation of the vehicle, for example by controlling one or more of a steering system, actuator system, braking system, etc., to navigate the vehicle relative to the objects.

[0043] Communication system 36 is configured to communicate wirelessly with other entities 48, such as, but not limited to, other vehicles (“V2V” communication), infrastructure (“V2I” communication), remote systems, Global Positioning Satellite (GPS), map servers, and / or personal devices. In an exemplary embodiment, communication system 36 is a wireless communication system configured to communicate using the IEEE 802.11 standard or via a wireless local area network (WLAN) using cellular data communication. However, additional or alternative communication methods, such as Dedicated Short Range Communication (DSRC) channels, are also contemplated within the scope of this disclosure. A DSRC channel refers to a one-way or two-way short-to-medium range wireless communication channel specifically designed for automotive applications, along with a corresponding set of protocols and standards.

[0044] Figure 2 Figure 200 illustrates the method disclosed herein for updating the detected position between time frames of a radar. Figure 200 is shown in a plan view. The main vehicle 202 includes a radar 204. The main vehicle 202 may be stationary or moving at a main vehicle speed v. e Movement. Radar 204 acquires detections of reflection points from an object in one or more time frames. For illustrative purposes, a first time frame 206 (T=1) and a second time frame 208 (T=2) are shown. During the time interval T between the first time frame 206 and the second time frame 208, the object may move relative to radar 204 and the host vehicle 202. For illustrative purposes, the second time frame 208 is shown as being closer to the host vehicle 202 than the first time frame 206, to illustrate the relative motion between the host vehicle and the object. The method disclosed herein updates the position of the detections acquired from the object in an earlier time frame (e.g., the first time frame 206) to estimate the predicted position in a subsequent time frame (e.g., the second time frame 208). The method also includes the possibility of aggregating the predicted positions of the detections with those acquired in subsequent time frames. The aggregated detections may be processed to estimate one or more of the following: the object's position, the object's shape, the object's orientation, and the object's category.

[0045] A first detection 210 (i.e., a first reflection point) is obtained in the first time frame (T=1). The first detection 210 is located at a first position p1 relative to the host vehicle 202, where p1 = (x1, y1, z1). Typically, at the same time as determining the position p1, a first Doppler frequency f1 of the first detection 210 is measured at the radar 204. The first detection 210 is updated to the second time frame (T=2) using the first Doppler frequency. The update involves a multi-step process. In the first step, the first Doppler frequency f1 associated with the first detection 210 is obtained. The Doppler frequency can be divided into a first component caused by the object velocity (based on the object component) and a second component caused by the host vehicle velocity (based on the vehicle component). Based on the velocity v of the host vehicle 202... eThe object-based component of the Doppler frequency is calculated from the first Doppler frequency. Specifically, the object-based component of the Doppler frequency is calculated by removing the influence of the main vehicle speed from the Doppler frequency. Generally, for the i-th detection, the object-based component of the Doppler frequency is calculated as shown in equation (1):

[0046]

[0047] Among them, f i It is the Doppler frequency of the i-th detection. p is the object-based Doppler frequency of the i-th detection. i It is the position coordinate of the i-th detection in the n-th time frame, v e T It is the transpose of the velocity vector of the main vehicle 202, and λ is the radar wavelength of the radar 204. The velocity v of the main vehicle 202... e This can be obtained from the vehicle's speedometer or any other suitable device (such as GPS). Alternatively, the speed v of the main vehicle... e It can be estimated using radar.

[0048] In the second step, the object-based component of the Doppler frequency is used to shift the detection position to an intermediate position 212 for detection in the second time frame (T=2). The calculation of the intermediate position 212 for the i-th detection can generally be described as shown in equation (2):

[0049]

[0050] in, It is the middle position of the i-th detection in the (n+1)-th time frame (e.g., the second time frame 208), p i It is the original location of the detection (p) i =(x i ,y i ,z i T is the duration between the nth time frame (e.g., the first time frame) and the (n+1)th time frame (e.g., the second time frame). The shift from the original position to the intermediate position is indicated by the first shift vector 214. The first shift vector 214 is oriented along the radial line 216 between the first detection 210 and the radar 204.

[0051] In the third step, a first predicted position 218 for detection in the second time frame is determined by the vehicle component shift intermediate position 212 based on Doppler frequency. The vehicle component shift intermediate position based on Doppler frequency is based on the velocity v of the main vehicle 202. e The first predicted position 218 for detection is calculated from the middle position 212. For the i-th detection, it is usually described as shown in equation (3):

[0052]

[0053] in, It is the predicted position of the i-th detection in the (n+1)-th time frame (e.g., the second time frame 208). It is the middle position 212 of the i-th detection in the (n+1)-th time frame (e.g., the second time frame 208). The adjustment of the Doppler frequency based on the vehicle component caused by the speed of the main vehicle 202 is shown by the second shift vector 220.

[0054] Figure 3 This is a schematic diagram 300 illustrating a method for updating multiple detections obtained in the first time frame 206. A first detection 210 (p1) and a second detection 302 (p2) as shown in the first time frame 206 are obtained. The second detection 302 is a detection of the second reflection of the object. A first Doppler frequency f1 is associated with the first detection 210, and a second Doppler frequency f2 is associated with the second detection 302. Based on the first Doppler frequency f1, the first detection 210 is updated to the first predicted position 218 using the calculations discussed in equations (1)-(3). Furthermore, based on the second Doppler frequency f2, the second detection 302 is updated to the second prediction position 310 using the calculations discussed in equations (1)-(3). The update process disclosed herein uses a third shift vector 306 along a second radial line 308 extending between radar 204 and the second detector 302 (p2) to move the second detector 302 (p2) to a second intermediate position 304. The process then proceeds to the second intermediate position 304 by using the speed of the main vehicle 202 (as shown by the fourth shift vector 312). Add adjustments to calculate the second predicted position 310.

[0055] Figure 4 Figure 400 illustrates the updating of a single detection across multiple time frames. For illustrative purposes, a first time frame 206, a second time frame 208, and a third time frame 402 are shown. A first detection 210 is obtained during the first time frame 206. Based on calculations using the relevant Doppler frequency (i.e., f1), this paper... Figure 2 The disclosed update method is used to calculate the first predicted position 218 of the detection in the second time frame 208. First predicted position 218 It was then updated to the second prediction position 404. The same calculations are used for detection in the third time frame 402. The calculation for determining the second predicted position 404 from the first predicted position 218 is based on the Doppler frequency f1 obtained in the first time frame 206 and the velocity obtained in the second time frame 208. For subsequent time frames, the predicted position is calculated using the original Doppler frequency (obtained in the first time frame) and the velocity of the main vehicle 202 obtained in the immediately preceding time frame.

[0056] Figure 5 Figure 500 illustrates updating detections obtained during individual time frames across multiple time frames. For illustrative purposes, a first time frame 206, a second time frame 208, and a third time frame 402 are shown. A first detection 210 is obtained during the first time frame 206. (The text then continues with further details about updating detections obtained during individual time frames.) Figure 2 The disclosed update method is used to calculate the first predicted position 218 of the first detection in the second time frame 208 using the relevant Doppler frequency (i.e., f1). In the second time frame 208, radar 204 acquires the second detection 502. The second Doppler frequency f2 is associated with the second detection 502. The second detection 502 has a position p2 in the second time frame 208.

[0057] Based on the calculation using the first Doppler frequency f1 and the main vehicle speed obtained in the second time frame 208, the first predicted position 218 in the second time frame 208 is determined. Updated to the second predicted position 404 in the third time frame 402. Using the second Doppler frequency f2 obtained in the second time frame and the speed of the main vehicle 202 obtained in the second time frame, the second position p2 of the second detection 502 in the second time frame 208 is updated to the third predicted position 504 in the third time frame.

[0058] Figure 6 A flowchart 600 of a method for detecting an object is shown in an illustrative embodiment. In block 602, one or more radar detections of a reflection point from the object are obtained in the nth time frame (e.g., in the first time frame 206). In block 604, the one or more radar detections in the nth time frame are aggregated with one or more radar detections from a previous (e.g., the (n-1)th) time frame. This aggregation may include radar detections within a subset of time frames across multiple time intervals, wherein the subset is selected using a moving time window associated with the latest time window of the multiple time frames. Thus, radar detections from the (nm)th time frame to the nth time frame, where n is the current time frame and m represents the duration of the moving time window. Furthermore, if n = 1 (i.e., no previous detections), the aggregation step in block 604 can be skipped or aggregation can be performed using an empty set.

[0059] The method proceeds from block 604 to block 606. In block 606, by using the Doppler frequency f associated with the corresponding detection... i After removing the influence of the main vehicle speed, the Doppler frequency associated with each detection is obtained. Based on object components. In box 608, Doppler frequency. Object component-based methods are used to determine the intermediate position in the next (e.g., the (n+1)th) time frame for each detection. In box 610, from the middle position The predicted location is calculated using the speed of the main vehicle. From box 610, the process can return to box 604, where the predicted location of the detection in the new frame is aggregated or merged with the new radar detection obtained in the new frame.

[0060] Furthermore, the predicted position in box 610 can be used for subsequent calculations. These subsequent calculations include determining the object's position or orientation, determining the object's shape, determining the object's orientation, classifying the object, and controlling the vehicle to perform one or more of one or more maneuvers relative to the object. Aggregated detection improves the resolution of the radar image of the object, thereby providing increased capabilities for maneuvering the master vehicle relative to the object.

[0061] Figure 7 A plan view 700 showing the area containing the first vehicle 702 and the second vehicle 704 is shown. A graph with x-axis and y-axis is shown. The positions of the first vehicle 702 and the second vehicle 704 are shown at multiple time frames. The second vehicle 740 is moving along the y-axis. The first vehicle 702 moves into the space vacated by the second vehicle 704. All radar detections from the first vehicle 702 and the second vehicle 704 are mixed together to form a detection cloud that cannot be used to distinguish the vehicles from each other.

[0062] Figure 8 A plan view 800 is shown, including the region of detection calculated using a conventional time aggregation method. This region includes a single set of detections 802 that cannot be used to distinguish between the first vehicle 702 and the second vehicle 704.

[0063] Figure 9 A plan view 900 is shown, including a region of detection calculated using the time aggregation method disclosed herein. This region includes a first group 902 of detections and a second group 904 of detections. The first group 902 and the second group 904 are distinguishable from each other and can therefore be used to distinguish between the first vehicle 702 and the second vehicle 704.

[0064] The terms “a” and “an” do not indicate a limitation of quantity, but rather that at least one of the referenced items is present. The term “or” means “and / or”, unless the context clearly indicates otherwise. A reference to “an aspect” throughout the specification means that a particular element (e.g., feature, structure, step, or characteristic) described in connection with that aspect is included in at least one aspect described herein and may or may not be present in other aspects. Furthermore, it should be understood that the described elements may be combined in any suitable manner across the aspects.

[0065] When an element, such as a layer, film, region, or substrate, is referred to as being "on" another element, it can be directly on the other element, or there may be intermediate elements present. Conversely, when an element is referred to as being "directly on" another element, there are no intermediate elements present.

[0066] Unless otherwise stated herein, all test standards are the most recent valid standards up to the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which the test standard appears.

[0067] Unless otherwise defined, the 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.

[0068] While the foregoing disclosure has been described with reference to exemplary embodiments, those skilled in the art will understand that various changes can be made and equivalents can replace its elements without departing from its scope. Furthermore, many modifications can be made to adapt particular situations or materials to the teachings of this disclosure without departing from its essential scope. Therefore, it is intended that this disclosure be limited to the specific embodiments disclosed, but will include all embodiments falling within its scope.

Claims

1. A method for operating a master vehicle, comprising: Receive the first detection from the first reflection point of the object during the first time frame of the radar; Determine the first position and the first Doppler frequency of the first detection; The first position is updated to the first predicted position in the second time frame using the first Doppler frequency, wherein the update includes: By removing the influence of the main vehicle speed from the first Doppler frequency, the object-based component of the first Doppler frequency used for the first detection is determined from the first Doppler frequency; The first detection is shifted from the first position to the middle position in the second time frame using the object component based on the first Doppler frequency; The first detection is shifted from the intermediate position to the first predicted position in the second time frame using the vehicle component based on the first Doppler frequency; Receive a second detection from the second reflection point of the object; and The object is detected from the first predicted position in the second time frame and the second detection.

2. The method according to claim 1, further comprising: During the first time frame, a second detection is received from the second reflection point of the object; Determine the second position of the second detection and the second Doppler frequency used for the second detection; Based on the calculation using the second Doppler frequency, the second position is updated to the second predicted position in the second time frame; as well as Detect objects from the first predicted position in the second time frame and the second predicted position in the second time frame.

3. The method of claim 1, further comprising updating the first predicted position in the second time frame to the second predicted position in the third time frame based on a first calculation using the first Doppler frequency and the main vehicle speed obtained in the second time frame.

4. The method of claim 3, further comprising receiving the second detection within the second time frame, determining a second position of the second detection and a second Doppler frequency for the second detection within the second time frame, and updating the second position to a third predicted position in the third time frame using a second calculation based on the second Doppler frequency.

5. The method according to claim 1, wherein, The first time frame is one of multiple time frames with time intervals, and also includes a subset of time frames with multiple time intervals selected using a moving time window.

6. A system for operating a master vehicle, comprising: The processor is configured as follows: Receive the first detection from the first reflection point of the object during the first time frame of the radar; Determine the first position and the first Doppler frequency of the first detection; The first position is updated to the first predicted position in the second time frame using the first Doppler frequency, wherein the update includes: By removing the influence of the main vehicle speed from the first Doppler frequency, the object-based component of the first Doppler frequency used for the first detection is determined from the first Doppler frequency; The first detection is shifted from the first position to the middle position in the second time frame using the object component based on the first Doppler frequency; The first detection is shifted from the intermediate position to the first predicted position in the second time frame using the vehicle component based on the first Doppler frequency; Receive a second detection from the second reflection point of the object; and The object is detected from the first predicted position in the second time frame and the second detection.

7. The system according to claim 6, wherein, The processor is also configured to: The second detection is received during the first time frame; Determine the second position of the second detection and the second Doppler frequency used for the second detection; Based on the calculation using the second Doppler frequency, the second position is updated to the second predicted position in the second time frame; as well as Detect objects from the first predicted position in the second time frame and the second predicted position in the second time frame.

8. The system according to claim 6, wherein, The processor is also configured to update the first predicted position in the second time frame to the second predicted position in the third time frame based on a first calculation using the first Doppler frequency and the main vehicle speed obtained in the second time frame.

9. The system according to claim 8, wherein, The processor is further configured to receive the second detection within the second time frame, determine a second position of the second detection and a second Doppler frequency for the second detection within the second time frame, and update the second position to a third predicted position in the third time frame using a second calculation based on the second Doppler frequency.

10. The system according to claim 6, wherein, The first time frame is one of a plurality of time intervals, and the processor is further configured to select a subset of the time frames of the plurality of time intervals using a moving time window.

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