WO2023246551A1 - 车辆跟踪方法、装置、通信单元及存储介质 - Google Patents

车辆跟踪方法、装置、通信单元及存储介质 Download PDF

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Publication number
WO2023246551A1
WO2023246551A1 PCT/CN2023/099778 CN2023099778W WO2023246551A1 WO 2023246551 A1 WO2023246551 A1 WO 2023246551A1 CN 2023099778 W CN2023099778 W CN 2023099778W WO 2023246551 A1 WO2023246551 A1 WO 2023246551A1
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Prior art keywords
vehicle position
data frame
coordinate system
vehicle
target data
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PCT/CN2023/099778
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English (en)
French (fr)
Inventor
夏树强
刘凡
孟骁
袁伟杰
杨立
韩志强
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中兴通讯股份有限公司
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Publication of WO2023246551A1 publication Critical patent/WO2023246551A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]

Definitions

  • the present application relates to the field of wireless communication network technology, for example, to a vehicle tracking method, device, communication unit and storage medium.
  • Integrated Sensing And Communication emerged as the times require and has great potential to play a key role in Internet of Vehicles communications, such as assisting communication data transmission through sensing algorithms.
  • IPAC Integrated Sensing And Communication
  • most of the perception-assisted communication methods in the related art assume that the vehicle is driving on a straight one-way street.
  • the beam tracking technology based on communication and perception integration currently only performs high-precision kinematic modeling for the communication and perception integrated system under linear motion. , however in actual scenarios the target may move along a curved or irregularly shaped road. At this time, if the tracking is still based on the motion model under straight-line conditions, a very serious model mismatch will occur, resulting in tracking failure.
  • This application provides a vehicle tracking method, device, communication unit and storage medium.
  • the embodiment of the present application provides a vehicle tracking method, including:
  • CCS Curvilinear Coordinate System
  • the measured parameters are used to determine the status information of the target data frame, and the status information of the target data frame includes the corrected vehicle position.
  • An embodiment of the present application also provides a vehicle tracking device, including:
  • the position prediction module is configured to predict the vehicle position in the curve coordinate system at the corresponding moment of the target data frame based on the status information of the historical data frame;
  • a vector determination module configured to determine a beamforming vector according to the vehicle position
  • a vehicle tracking module configured to transmit a beam based on the beamforming vector and receive an echo
  • a state determination module configured to determine state information of the target data frame based on an extended Kalman filter algorithm and the measurement parameters of the echo, where the state information of the target data frame includes the corrected vehicle position.
  • An embodiment of the present application also provides a communication unit, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above vehicle tracking method is implemented.
  • Embodiments of the present application also provide a computer-readable storage medium.
  • a computer program is stored on the computer-readable storage medium. When the program is executed by a processor, the above vehicle tracking method is implemented.
  • Figure 1 is a schematic diagram of the relationship between the Cartesian coordinate system and the curvilinear coordinate system
  • Figure 2 is a flow chart of a vehicle tracking method provided by an embodiment
  • Figure 3 is a flow chart of a vehicle tracking method provided by another embodiment
  • Figure 4 is a schematic diagram of a coordinate transformation provided by an embodiment
  • Figure 5 is a schematic diagram of a beam-based vehicle tracking process provided by an embodiment
  • Figure 6 is a schematic structural diagram of a vehicle tracking device according to an embodiment
  • FIG. 7 is a schematic diagram of the hardware structure of a communication unit provided by an embodiment.
  • a vehicle tracking method which predicts the vehicle position in a curvilinear coordinate system and tracks the vehicle based on the beam, and completes perception-assisted communication in the curvilinear coordinate system. It is not only suitable for linear motion of the vehicle. The scene can meet the tracking needs of vehicle movements of any shape, such as curved or irregular shapes.
  • Figure 1 is a schematic diagram of the relationship between the Cartesian coordinate system and the curvilinear coordinate system provided by an embodiment.
  • Figure 1(a) shows the road model in the three-dimensional Cartesian coordinate system
  • Figure 1(b) decomposes each axis of a point in the Cartesian coordinate system under the CCS.
  • a vehicle when a vehicle is driving on the road, its movement trajectory and speed can be decomposed into components along the direction of the road markings and perpendicular to the directions of the road markings according to the shape of the road.
  • These two directions can be expressed as in Figure 1 In the s direction and n direction, the height of the vehicle center point relative to the road surface is expressed as the q direction.
  • Figure 1(b) shows the expression of its curve.
  • FIG 2 is a flow chart of a vehicle tracking method provided by an embodiment. This method can be applied to communication units such as Road Side Units (RSU).
  • the communication unit is equivalent to the integration of a base station and a radar.
  • the communication unit has the function of sending downlink communication data to the vehicle and tracking the vehicle's position through electromagnetic waves.
  • the method provided by this embodiment includes:
  • Step 110 Predict the vehicle position in the curve coordinate system at the corresponding time of the target data frame based on the status information of the historical data frame.
  • the historical data frame refers to the data frame that has been collected currently
  • the target data frame refers to a certain future data frame that needs to predict the vehicle position.
  • the beam can be sent to the vehicle and the echo can be received, and then through Measuring the echoes corrects predictions of the vehicle's position.
  • the vehicle position of the target data frame can be used to continue to predict the vehicle position of the next frame, and this process is executed cyclically to achieve continuous prediction of the vehicle position and beam tracking.
  • the historical data frame can be recorded as the l-1th data frame.
  • the status information of the historical data frame can be understood as the estimation result of the vehicle's status information (including vehicle position, vehicle speed and other status information) at the corresponding time of the l-1th data frame. , recorded as The status information of the vehicle at the corresponding time of the target data frame (recorded as the l-th data frame) can be estimated using the status information of the l-1th data frame, and we get The status information of the target data frame includes status information such as vehicle position and vehicle speed corresponding to the l-th data frame.
  • a state transition model can be built in advance, which is used to describe the input (i.e. ) and status relationship between each other, thereby using the status information of historical data frames to predict the vehicle Location.
  • the predicted vehicle position refers to the vehicle position under CCS.
  • roads of any shape can be modeled, and are not limited to scenes of straight-line vehicle motion, so that the vehicle position can be accurately represented in various scenarios.
  • Step 120 Determine a beamforming vector according to the vehicle position.
  • Beamforming refers to guiding the radiation direction of a beam to a specific direction.
  • the specific direction is the direction from the communication unit to the predicted vehicle position.
  • the vehicle position under CCS is converted into the vehicle position under the polar coordinate system, and the angle representation of the vehicle position under the polar coordinate system is obtained, which can clarify the direction of the communication unit to the vehicle position, thereby guiding beamforming.
  • Step 130 Send a beam based on the beamforming vector and receive an echo.
  • the beamforming vector based on the communication unit to the vehicle position can guide the communication unit to send a beam to the predicted vehicle position. After the beam encounters the vehicle (the communication receiver), it forms an echo, and the communication unit receives the echo. , and the measurement parameters of the target data frame can be obtained based on the echo.
  • Step 140 Determine the status information of the target data frame based on the extended Kalman filter algorithm and the measurement parameters of the echo.
  • the status information of the target data frame includes the corrected position of the vehicle.
  • the predicted vehicle position can be corrected, and more accurate status information of the target data frame can be obtained to facilitate subsequent continuous tracking of the vehicle.
  • the EKF algorithm bases both prediction and correction on nonlinear calculations. It can obtain the first-order approximation term based on the linearization process of the first-order Taylor series expansion as the approximate expression form of the state transition model and the observation model; and then through the state transition Iterative updates of models and observation models achieve optimal estimation and real-time correction of forecast data.
  • the predicted vehicle position can be corrected and the status information of the target data frame can be obtained by using the EKF algorithm and the measured parameters of the echo (such as the angle of arrival of the echo, time delay and Doppler frequency shift, etc.) It can be used as the status information of the historical data frame for a new round of prediction, and is used to estimate the status information of the next target data frame (i.e., the l+1th data frame) to achieve continuous prediction and tracking of the vehicle.
  • the EKF algorithm the measured parameters of the echo (such as the angle of arrival of the echo, time delay and Doppler frequency shift, etc.) It can be used as the status information of the historical data frame for a new round of prediction, and is used to estimate the status information of the next target data frame (i.e., the l+1th data frame) to achieve continuous prediction and tracking of the vehicle.
  • This embodiment uses the vehicle position under CCS to decompose the vehicle position and movement trajectory relative to the road markings, and fully utilizes the information provided by the road shape to achieve modeling on arbitrary-shaped roads and beam-based communication vehicle tracking. , more effectively estimate and predict the position of the vehicle to improve the reliability of beam alignment; in addition, by using a predictive form of tracking algorithm, before the start of the l-th data frame (i.e. at the end of the l-1th data frame) A beamforming vector is generated based on the predicted vehicle position. The data in the l-th data frame is sent using this beamforming vector. There is no need to send search results to the vehicle. It does not need to wait for the echo of the vehicle to generate the beamforming vector, so it can save pilot overhead and feedback link resources.
  • Figure 3 is a flow chart of a vehicle tracking method provided by another embodiment. As shown in Figure 3, the method includes:
  • Step 210 Construct a state transition model based on the vehicle position and vehicle speed in the curve coordinate system at the corresponding time of the historical data frame.
  • the vehicle position in the curvilinear coordinate system at the corresponding time of the historical data frame is (s l-1 , n l-1 , q l-1 ), where q l-1 is the position of the vehicle center point relative to the road surface.
  • the height is a fixed value; correspondingly, the vehicle speed in the s-axis and n-axis directions are v s,l-1 and v n,l-1 respectively.
  • the EKF algorithm uses a state transfer model to describe the motion state of the tracked vehicle.
  • the tracked vehicle can be considered to be moving in a straight line at a uniform speed in the s direction and n direction of the CCS.
  • the state transition model can be expressed as: Among them, z s and z n are the systematic errors of the vehicle position coordinates in the s-axis and n-axis directions respectively, z vs and z vn are the systematic errors of the vehicle speed in the s-axis and n-axis directions respectively.
  • Step 220 Input the status information of the historical data frame into the state transition model to obtain the vehicle position in the curve coordinate system at the corresponding time of the target data frame.
  • the status information of the historical data frame includes s l-1 , n l-1 , v s,l-1 and v n,l-1 , which are input to the state transition model and combined with the system error to obtain Get the status information of the target data frame
  • q l is the height of the vehicle center point relative to the road surface, which is equal to q l-1 ; correspondingly,
  • the status information of the target data frame also includes the vehicle speed v s and v n in the s-axis and n-axis directions at the corresponding time of the target data frame.
  • Step 230 Convert the vehicle position into a vehicle position in a Cartesian coordinate system, and convert the vehicle position in a Cartesian coordinate system into a vehicle position in a polar coordinate system to obtain angle prediction information of the vehicle position. .
  • the angle prediction information includes the angle coordinates of the vehicle position in the polar coordinate system. Specifically, through coordinate transformation, the vehicle position under CCS can be converted to the Cartesian coordinate system and then converted to In the polar coordinate system, obtain the angular coordinates of vehicle position prediction
  • Step 240 Construct the beamforming vector according to the angle prediction information.
  • Step 250 Send a beam based on the beamforming vector and receive an echo.
  • the angular coordinates of the predicted vehicle position are used Construct the beamforming vector of the transmitter
  • the ISAC beam is sent using this beamforming vector to deliver the communication data (i.e., the target data frame, i.e., the l-th data frame) and receive the vehicle's echo.
  • Step 260 Measure the echo based on the multi-signal classification algorithm and the matched filtering algorithm to obtain measurement parameters.
  • the Multiple Signal Classification (MUSIC) algorithm can decompose the echo signal to obtain a signal subspace corresponding to the signal component of the echo and an orthogonal noise subspace, and then use this
  • the orthogonality of the two subspaces estimates the measurement parameters of the echo;
  • the matched filtering algorithm is equivalent to performing an autocorrelation operation on the echo signal to maximize the signal-to-noise ratio and accurately measure the echo.
  • the resulting measurement parameters include the angle of arrival of the echo Delay and Doppler shift
  • Step 270 Based on the extended Kalman filter algorithm, the vehicle position is corrected according to the observation model and the measurement parameters to obtain the status information of the target data frame.
  • the EKF algorithm is used to process the measurement parameters and the observation model is used to correct the predicted vehicle position, and finally the status information of the target data frame is output.
  • the observation model is used to describe a set of observation values that can be actually measured (i.e., the measured arrival angle and Delay and Doppler shift ) and the real state of the system that cannot be directly measured (i.e., the real vehicle position (s, n) and vehicle speed (v s , v n ), the real vehicle position and speed can be mapped to the real arrival angle ⁇ l and Delay d l /c and Doppler frequency shift the relationship between.
  • the predicted values are corrected through observed values and observation models.
  • the observation model can be expressed as Among them, ⁇ ⁇ , ⁇ ⁇ are the measurement errors of the arrival angle and time delay respectively, c is the speed of light, v R, l is the radial speed of the vehicle along the direction with the communication unit, ⁇ is the wavelength of the carrier, d l is the distance between the vehicle and the communication unit 2 times the distance between them.
  • the status information of the target data frame can be done
  • the state information of the historical data frame predicted in the new round is used to estimate the state information of the next target data frame (i.e., the l+1th data frame) to achieve continuous prediction and accurate tracking of the vehicle.
  • the method before determining the beamforming vector according to the vehicle position, the method further includes:
  • control points are used to divide the road markings into multiple control point intervals
  • interpolation is performed within the control point interval where the reference point is located based on the cubic spline interpolation method
  • the spline interpolation method is used to interpolate known control points on the road markings to obtain a series of discrete points. These discrete points can be used to smooth the road markings and obtain an expression of a smooth curve.
  • Spline interpolation uses piecewise polynomials for interpolation, that is, by selecting several control points, the road markings are divided into multiple segments to obtain multiple control point intervals. In the smoothing process, a function is determined based on each two adjacent data points, and then the functions of each control point interval are combined into one function, which is a smooth curve function.
  • the cubic spline interpolation method is used, that is, a cubic function is constructed for each control point interval, and the connection point of the piecewise function is ensured to have the properties of 0th order continuous, first order derivative continuous, and second order derivative continuous, thereby obtaining smooth function of the curve.
  • the CCS modeling process for road markings of any shape is as follows:
  • the position information of each reference point in the road markings is obtained by importing map information or analyzing road design drawings.
  • This embodiment does not limit the selection of reference points, such as random selection, equal intervals, or according to road markings.
  • By selecting the curvature radius of the line, etc. by solving the coordinates of each selected reference point in the Cartesian coordinate system, these coordinates can be used to fit the expression of the curve of the road marking line;
  • control points can include the starting point, midpoint and/or end point of the road markings.
  • control points can be selected based on the curvature radius of the road markings. The smaller the curvature radius. The more and denser the control points can be selected for the road section, the more detailed the cubic function corresponding to the road section can be determined and the more accurate curve fitting results can be obtained;
  • the cubic spline interpolation method can be used to use the above-mentioned interpolation parameters to insert multiple points within the control point interval, and then according to the reference point, control point and inserted Points can be used to smooth the road markings, thereby fitting the curve of the road markings.
  • the vehicle position in the Cartesian coordinate system is converted into the vehicle position in the polar coordinate system.
  • Figure 4 is a schematic diagram of a coordinate transformation provided by an embodiment. As shown in Figure 4, for the predicted vehicle position, from the representation ( s , n ) under CCS to the representation (x, y) under the Cartesian coordinate system The conversion can be achieved through the following steps:
  • the projection (s, 0) of (s, n) in CCS about the s direction is integrated with ⁇ x s , y s ⁇ .
  • the projection relationship depends on the existence of the road direction angle ⁇ . Since the n direction is always It is perpendicular to the ⁇ direction, so the vehicle position (x, y) in the Cartesian coordinate system can be obtained based on the value of n, the ⁇ direction and the position of (s, 0).
  • the vehicle position (x, y) in the Cartesian coordinate system is converted into the angular coordinates of the vehicle position in the polar coordinate system. Used to construct beamforming vectors.
  • the polar coordinate system in Figure 4 is centered on the communication unit ⁇ x 0 , y 0 ⁇ , and ⁇ is the angle between the line projected on the ground from the communication unit to the vehicle and the x-axis (can be understood as the azimuth angle); is the angle between the line connecting the communication unit and the vehicle and the xoy plane (that is, the ground plane) (which can be understood as the pitch angle); ⁇ is the angle between the road marking direction of the current vehicle position and the x-axis.
  • FIG. 5 is a schematic diagram of a beam-based vehicle tracking process provided by an embodiment.
  • the purpose of initializing road model information is to establish the correlation between the Cartesian coordinate system, the CCS coordinate system and the polar coordinate system.
  • the vehicle position can be used to determine the beamforming vector, specifically by converting the vehicle position under CCS to the vehicle position under the Cartesian coordinate system, and then converting The vehicle position in the Cartesian coordinate system is converted into the vehicle position in the polar coordinate system, and the angular representation of the vehicle position in the polar coordinate system is obtained. Based on this, a beamforming vector is constructed to send a beam to the vehicle, which then encounters the vehicle. An echo will be generated after the communication receiver; then the echo is measured through MUSIC and matched filtering algorithms. Based on the EKF and measurement parameters, the vehicle position can be corrected to obtain accurate And used for prediction and tracking of the next target data frame.
  • the vehicle tracking method of this embodiment realizes state transition model modeling on arbitrary-shaped roads and perception-assisted communication vehicle tracking based on the state transition model by decomposing the vehicle position and motion trajectory relative to the road markings; and adopts The predictive tracking algorithm is based on the extended Kalman filter algorithm and the echo measurement parameter echo to estimate the status information of the vehicle. It replaces the solution of using pilot to estimate the channel and using uplink feedback to obtain the channel status, which can save pilot overhead and Feedback link resources.
  • this method uses the ISAC method to effectively improve the accuracy of vehicle position tracking and greatly improve the communication stability in high-mobility environments; and By using beams to estimate angle, position and Doppler frequency shift, there is no need to introduce additional radar sensors, which has the advantage of low cost.
  • FIG. 6 is a schematic structural diagram of a vehicle tracking device according to an embodiment. As shown in Figure 6, the vehicle tracking device includes:
  • the position prediction module 310 is configured to predict the vehicle position in the curve coordinate system at the corresponding time of the target data frame based on the status information of the historical data frame;
  • a vehicle tracking module 330 configured to transmit beams and receive echoes based on the beamforming vector
  • the state determination module 340 is configured to determine the state information of the target data frame based on the extended Kalman filter algorithm and the measurement parameters of the echo, where the state information of the target data frame includes the corrected vehicle position.
  • the vehicle tracking device of this embodiment uses the vehicle position in the CCS coordinate system to decompose the vehicle position and movement trajectory relative to the road markings, fully utilizes the information provided by the road shape, and realizes modeling of roads of any shape.
  • the position of the vehicle is more effectively estimated and predicted to improve the reliability of beam alignment; in addition, by using a predictive form of tracking algorithm, the vehicle position is generated based on the prediction before the target data frame begins. Beamforming vector, the data in the target data frame is sent using this beamforming vector, which can save pilot overhead and feedback link resources.
  • the location prediction module 310 includes:
  • a prediction unit is configured to input the state information of the historical data frame into the state transition model to obtain the vehicle position.
  • the vector determination module 320 includes:
  • a coordinate conversion unit configured to convert the vehicle position into a vehicle position in a Cartesian coordinate system, and to convert the vehicle position in a Cartesian coordinate system into a polar coordinate system to obtain the vehicle position.
  • Angle prediction information includes the angular coordinates of the vehicle position in the polar coordinate system;
  • a vector determination unit configured to construct the beamforming vector according to the angle prediction information.
  • the device further includes:
  • An acquisition module configured to acquire the position information of each reference point in the road marking before determining the beamforming vector according to the vehicle position
  • a selection module configured to select a number of control points from the road markings, the control points being used to divide the road markings into multiple control point intervals;
  • the fitting module is configured to fit the curve of the road marking according to the interpolation result corresponding to each of the reference points, and save the interpolation parameters.
  • the coordinate conversion unit is set to:
  • the vehicle position in the Cartesian coordinate system is converted into a vehicle position in the polar coordinate system according to the positional relationship between the polar coordinate center and the curve of the road marking.
  • the device further includes:
  • a measurement module configured to, after receiving the echo, measure the echo based on a multi-signal classification algorithm and a matched filtering algorithm to obtain the measurement parameters, where the measurement parameters include angle of arrival, time delay and Doppler frequency shift.
  • the status determination module 340 is configured as:
  • the vehicle position is corrected according to the observation model and the measurement parameters to obtain the status information of the target data frame.
  • the vehicle tracking device proposed in this embodiment and the vehicle tracking method proposed in the above embodiment belong to the same inventive concept.
  • Technical details that are not described in detail in this embodiment can be found in any of the above embodiments, and this embodiment is equipped with and executes the vehicle tracking method. Same beneficial effects.
  • FIG. 7 is a schematic diagram of the hardware structure of a communication unit provided by an embodiment.
  • the communication unit provided by the present application includes a processor 410 and a memory 420;
  • the processor 410 in the communication unit may be one or more, one processor 410 is taken as an example in Figure 7;
  • the memory 420 is configured to store one or more programs;
  • the one or more The program is executed by the one or more processors 410, so that the one or more processors 410 implement the vehicle tracking method as described in the embodiment of this application.
  • the communication unit also includes: a communication device 430, an input device 440, and an output device 450.
  • the processor 410, memory 420, communication device 430, input device 440 and output device 450 in the communication unit can be connected through a bus or other means.
  • connection through a bus is taken as an example.
  • the input device 440 may be used to receive input numeric or character information and generate key signal input related to user settings and function control of the communication unit.
  • the output device 450 may include a display device such as a display screen.
  • Communication device 430 may include a receiver and a transmitter.
  • the communication device 430 is configured to perform information transceiver communication according to the control of the processor 410 .
  • the memory 420 can be configured to store software programs, computer-executable programs and modules, such as program instructions/modules corresponding to the vehicle tracking method described in the embodiments of the present application (for example, the location in the vehicle tracking device prediction module 310, vector determination module 320, vehicle tracking module 330, and status determination module 340).
  • the memory 420 may include a program storage area and a storage data area, where the program storage area may store an operating system and an application program required for at least one function; the storage data area may store data created according to the use of the communication unit, and the like.
  • the memory 420 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • the memory 420 may further include memory located remotely relative to the processor 410, and these remote memories may be connected to the communication unit through a network. Examples of the above-mentioned networks include but are not limited to the Internet, intranets, local area networks, mobile communication networks and combinations thereof.
  • An embodiment of the present application also provides a storage medium, the storage medium stores a computer program, and when the computer program is executed by a processor, any of the vehicle tracking methods described in the embodiments of the present application is implemented.
  • This method includes:
  • the status information of the target data frame is determined, and the status information of the target data frame includes the corrected position of the vehicle.
  • the computer storage medium in the embodiment of the present application may be any combination of one or more computer-readable media.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but is not limited to: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard drives, random access memory (RAM), read-only memory (Read Only Memory, ROM), Erasable Programmable Read Only Memory (EPROM), flash memory, optical fiber, portable CD-ROM, optical storage device, magnetic storage device, or any suitable combination of the above .
  • a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to: electromagnetic signals, optical signals, or any suitable combination of the above.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device .
  • Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • any appropriate medium including but not limited to: wireless, wire, optical cable, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • Computer program code for performing operations of the present application may be written in one or more programming languages, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional A procedural programming language, such as the "C" language or similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (such as an Internet service provider through the Internet). connect).
  • LAN local area network
  • WAN wide area network
  • Internet service provider such as an Internet service provider through the Internet. connect
  • user terminal covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser or a vehicle-mounted mobile station.
  • the various embodiments of the present application may be implemented in hardware or special purpose circuitry, software, logic, or any combination thereof.
  • some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device, although the application is not limited thereto.
  • Embodiments of the present application may be implemented by a data processor of the mobile device executing computer program instructions, for example in a processor entity, or by hardware, or by a combination of software and hardware.
  • Computer program instructions may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or written in any combination of one or more programming languages source code or object code.
  • ISA Instruction Set Architecture
  • Any block diagram of a logic flow in the figures of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions.
  • Computer programs can be stored on memory.
  • the memory can be of any type suitable for the local technical environment and can be implemented using any suitable data storage technology, such as, but not limited to, read-only memory (ROM), random access memory (Random Access Memory, RAM), optical Memory devices and systems (Digital Video Disc (DVD) or Compact Disk (CD)), etc.
  • Computer-readable media may include non-transitory storage media.
  • the data processor may be any device suitable for the local technical environment Types, such as but not limited to general-purpose computers, special-purpose computers, microprocessors, digital signal processors (Digital Signal Processing, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable logic devices (Field-Programmable Gate Array) , FGPA) and processors based on multi-core processor architecture.
  • DSP Digital Signal Processing
  • ASIC Application Specific Integrated Circuit
  • FGPA programmable logic devices

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Abstract

本申请提供一种车辆跟踪方法、装置、通信单元及存储介质。该方法根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置；根据所述车辆位置确定波束成形向量；基于所述波束成形向量发送波束并接收回波；基于扩展卡尔曼滤波算法和所述回波的测量参数，确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。

Description

车辆跟踪方法、装置、通信单元及存储介质 技术领域
本申请涉及无线通信网络技术领域，例如涉及一种车辆跟踪方法、装置、通信单元及存储介质。
背景技术
在5G的低延时(低于100毫秒)和高定位精度(厘米级)的强大驱动力下，车联网等技术迸发了新的生命力。例如，相关技术中的工厂物流需要通过第三方进行物流运输，但缺乏对第三方车辆的有效监管和实时跟踪。作为智慧工厂的重要环节之一，智能物流运输通过与5G车联网技术结合，可以实现物流轨迹的精细化和动态化管理，从而有效地提升工厂效率。在技术上实现这一目标，主要依靠路边基站与物流车辆的通信和跟踪。
对更大带宽、更高速率的追求使得感知和通信走向融合并最终实现一体化成为主流趋势。在这种背景下，通信感知一体化(Integrated Sensing And Communication，ISAC)应运而生，并有极大的潜力在车联网通信中扮演关键角色，例如通过感知算法来辅助通信数据传输。然而相关技术中的感知辅助通信的方法大多假设车辆行驶在平直的单行道上，基于通信感知一体化的波束跟踪技术目前仅针对直线运动下的通信感知一体化***进行了高精度运动学建模，然而实际场景下目标可能沿着弯曲形状或不规则形状的道路运动。此时，若仍然按照直线条件下的运动模型进行跟踪则会产生非常严重的模型失配，从而导致跟踪失败。
发明内容
本申请提供一种车辆跟踪方法、装置、通信单元及存储介质。
本申请实施例提供一种车辆跟踪方法，包括：
根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系(Curvilinear Coordinate System，CCS)下的车辆位置；
根据所述车辆位置确定波束成形向量；
基于所述波束成形向量发送波束并接收回波；
基于扩展卡尔曼滤波(Extended Kalman Filtering，EKF)算法和所述回波 的测量参数，确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
本申请实施例还提供了一种车辆跟踪装置，包括：
位置预测模块，设置为根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置；
向量确定模块，设置为根据所述车辆位置确定波束成形向量；
车辆跟踪模块，设置为基于所述波束成形向量发送波束并接收回波；
状态确定模块，设置为基于扩展卡尔曼滤波算法和所述回波的测量参数，确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
本申请实施例还提供了一种通信单元，包括：存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现上述的车辆跟踪方法。
本申请实施例还提供了一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，该程序被处理器执行时实现上述的车辆跟踪方法。
附图说明
图1为笛卡尔坐标系与曲线坐标系的关系示意图；
图2为一实施例提供的一种车辆跟踪方法的流程图；
图3为另一实施例提供的一种车辆跟踪方法的流程图；
图4为一实施例提供的一种坐标转换的示意图；
图5为一实施例提供的一种基于波束的车辆跟踪过程的示意图；
图6为一实施例提供的一种车辆跟踪装置的结构示意图；
图7为一实施例提供的一种通信单元的硬件结构示意图。
具体实施方式
下面结合附图和实施例对本申请进行说明。可以理解的是，此处所描述的具体实施例仅仅用于解释本申请，而非对本申请的限定。需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互任意组合。另外还需要说明的是，为了便于描述，附图中仅示出了与本申请相关的部分而非全部结构。
在本申请实施例中，提供一种车辆跟踪方法，对在曲线坐标系下的车辆位置进行预测并基于波束对车辆进行跟踪，在曲线坐标系下完成感知辅助通信，不仅适用于车辆直线运动的场景，能够满足任意形状如弯曲或不规则形状等的车辆运动的跟踪需求。
图1为一实施例提供的笛卡尔坐标系与曲线坐标系的关系示意图。如图1所示，图1(a)中示出了三维的笛卡尔坐标系下的道路模型，图1(b)中对笛卡尔坐标系下的一个点在CCS下的各轴进行分解。具体的，车辆在道路上行驶时，可以按照道路的形状将其运动轨迹与运动速度分解为沿道路标线方向以及垂直于道路标线方向的分量，这两个方向可表示为图1中的s方向和n方向，车辆中心点相对于路面的高度表示为q方向。这三个方向在笛卡尔坐标系中相互正交，故可以表示为图1(b)的形式。图1(a)中的道路标线在笛卡尔坐标系下可以采取插值拟合的方式确定其曲线的表达式。
图2为一实施例提供的一种车辆跟踪方法的流程图。该方法可应用于通信单元，例如路边单元(Road Side Unit，RSU)。通信单元相当于基站和雷达的一体化，通信单元具有向车辆发送下行链路的通信数据以及通过电磁波跟踪车辆位置的功能。如图2所示，本实施例提供的方法包括：
步骤110，根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置。
本实施例中，历史数据帧指当前已经采集到的数据帧，目标数据帧指需要预测车辆位置的未来的某一数据帧，根据预测的车辆位置可向车辆发送波束并接收回波，然后通过对回波进行测量可以修正车辆位置的预测结果。在此基础上，可以利用目标数据帧的车辆位置继续预测下一帧的车辆位置，循环执行此过程，以实现对车辆位置持续的预测以及波束跟踪。
历史数据帧可以记为第l-1个数据帧，历史数据帧的状态信息可以理解为对第l-1个数据帧对应时刻车辆的状态信息(包括车辆位置以及车速等状态信息)的估计结果，记为利用第l-1个数据帧的状态信息可对目标数据帧(记为第l个数据帧)对应时刻车辆的状态信息进行估计，得到目标数据帧的状态信息包括第l个数据帧对应的车辆位置以及车速等状态信息。在预测车辆位置时，可以预先构建状态转移模型，状态转移模型用于描述输入(即)和状态之间的关系，从而利用历史数据帧的状态信息预测车辆 位置。
本实施例中，预测得到的车辆位置指的是CCS下的车辆位置。利用CCS坐标系下的车辆位置，可为任意形状的道路建模，而不局限于车辆直线运动的场景，从而在各种场景下都可准确表示出车辆位置。
步骤120，根据所述车辆位置确定波束成形向量。
波束成形是指将波束的辐射方向引导至特定方向，本实施例中，特定方向即为通信单元到预测得到的车辆位置的方向。具体的，将CCS下的车辆位置转换为极坐标系下的车辆位置，得到车辆位置在极坐标系下的角度表示，可以明确通信单元到车辆位置的方向，从而引导波束成形。
步骤130，基于所述波束成形向量发送波束并接收回波。
本实施例中，基于通信单元到车辆位置的波束成形向量可引导通信单元向预测的车辆位置发送波束，该波束再遇到车辆(的通信接收机)后形成回波，通信单元接收该回波，并可根据回波获得目标数据帧的测量参数。
步骤140，基于扩展卡尔曼滤波算法和所述回波的测量参数，确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
本实施例中，通过对回波进行测量，可以修正预测得到的车辆位置，也可得到更加准确的目标数据帧的状态信息，便于后续对车辆的持续跟踪。其中，EKF算法将预测和修正都建立在非线性计算基础上，可以基于一阶泰勒级数展开的线性化处理得到一阶近似项作为状态转移模型和观测模型的近似表述形式；然后通过状态转移模型和观测模型的迭代更新，实现对预测数据的最优估计和实时修正。本实施例中，利用EKF算法和对回波的测量参数(例如回波的到达角、时延和多普勒频移等)，可以对预测的车辆位置进行修正并得到目标数据帧的状态信息可作为新一轮预测的历史数据帧的状态信息，用于对下一目标数据帧(即第l+1个数据帧)的状态信息进行估计，实现对车辆的持续预测和跟踪。
本实施例通过利用CCS下的车辆位置，进行车辆位置与运动轨迹相对于道路标线的分解，充分利用道路形状提供的信息，实现了在任意形状道路下的建模以及基于波束的通信车辆跟踪，更有效地估计和预测车辆的位置以提升波束对准的可靠性；此外，通过使用预测形式的跟踪算法，在第l个数据帧开始前(即在第l-1个数据帧的结尾)就根据预测得到的车辆位置生成波束成形向量，第l个数据帧中的数据均采用该波束成行向量进行发送，不需要向车辆发送搜 索导频，也不需要等待车辆的回波即可生成波束成形向量，因此能够节省导频开销和反馈链路资源。
图3为另一实施例提供的一种车辆跟踪方法的流程图。如图3所示，该方法包括：
步骤210，根据历史数据帧对应时刻的曲线坐标系下的车辆位置以及车速构建状态转移模型。
本实施例中，历史数据帧对应时刻的曲线坐标系下的车辆位置为(sl-1，nl-1，ql-1)，其中，ql-1为车辆中心点相对于路面的高度，为一个定值；相应的，在s轴和n轴方向上的车速分别为vs,l-1和vn,l-1。EKF算法使用状态转移模型描述被跟踪的车辆的运动状态，被跟踪的车辆可被认为在CCS的s方向和n方向上各自进行匀速直线运动，但由于实际运动并不一定完全符合该运动状态，因此在状态转移模型中包含***误差。具体的，状态转移模型可以表示为：其中，zs和zn分别为车辆位置坐标在s轴和n轴方向上的***误差，zvs和zvn分别为车速在s轴和n轴方向上的***误差。
步骤220，将历史数据帧的状态信息输入至状态转移模型，得到在目标数据帧对应时刻的曲线坐标系下的车辆位置。
本实施例中，历史数据帧的状态信息包括sl-1、nl-1、vs,l-1和vn,l-1，将其输入至状态转移模型并与***误差结合，可得到目标数据帧的状态信息其中包括目标数据帧对应时刻的曲线坐标系下的车辆位置(sl，nl)，需要说明的是，ql为车辆中心点相对于路面的高度，与ql-1相等；相应的，目标数据帧的状态信息还包括目标数据帧对应时刻在s轴和n轴方向上的车速vs和vn。
步骤230，将所述车辆位置转换为笛卡尔坐标系下的车辆位置，并将所述笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置，得到所述车辆位置的角度预测信息。
本实施例中，角度预测信息包括极坐标系下的车辆位置的角度坐标。具体的，通过坐标转换，可以将CCS下的车辆位置转换至笛卡尔坐标系进而转换至 极坐标系下，获得车辆位置预测的角度坐标
步骤240，根据角度预测信息构建所述波束成形向量。
步骤250，基于所述波束成形向量发送波束并接收回波。
本实施例中，使用预测的车辆位置的角度坐标构建发送端的波束成形向量使用该波束成形向量发送ISAC波束，以传递通信数据(即目标数据帧，也即第l个数据帧)并接收车辆的回波。
步骤260，基于多信号分类算法和匹配滤波算法对回波进行测量，得到测量参数。
本实施例中，多信号分类(Multiple Signal classification，MUSIC)算法可以将回波信号进行分解，得到基于与回波的信号分量相对应的信号子空间和相正交的噪声子空间，然后利用这两个子空间的正交性估计回波的测量参数；匹配滤波算法相当于对回波的信号进行自相关运算，以将信噪比的最大化，从而准确地对回波进行测量。得到的测量参数包括回波的到达角时延和多普勒频移
步骤270，基于扩展卡尔曼滤波算法，根据观测模型和所述测量参数对所述车辆位置进行修正，得到所述目标数据帧的状态信息。
本实施例中，使用EKF算法处理测量参数并利用观测模型对预测得到的车辆位置进行修正，最终输出目标数据帧的状态信息其中包括修正后的车辆位置。其中，观测模型用于描述一组可以实际测量的观测值(即测量得到的到达角和时延和多普勒频移)和不可直接测量的***真实状态(即真实的车辆位置(s，n)和车速(vs，vn)，真实的车辆位置和车速可以映射为真实的到达角θl和时延dl/c和多普勒频移的之间的关系。在EKF处理测量参数的过程中，通过观测值和观测模型对预测值进行修正。观测模型可以表示为其中，ωθ、ωτ分别为对到达角和时延的测量误差，c为光速，vR,l为车辆沿着其与通信单元方向的径向速度，λ是载波的波长，dl为车辆与通信单元之间的距离的2倍。通过根据观测模型和测量参数对车辆位置进行修正，目标数据帧的状态信息可作 为新一轮预测的历史数据帧的状态信息，用于对下一目标数据帧(即第l+1个数据帧)的状态信息进行估计，实现对车辆的持续预测和准确跟踪。
在一实施例中，在根据车辆位置确定波束成形向量之前，还包括：
获取道路标线中的每个参考点的位置信息；
从道路标线中选取若干控制点，控制点用于将道路标线划分为多个控制点区间；
对于各参考点，基于三次样条插值法在参考点所处的控制点区间内进行插值；
根据各参考点对应的插值结果拟合得到道路标线的曲线，并保存插值参数。
本实施例中采用样条插值法，利用已知的控制点在道路标线上进行插值得到一系列离散的点，用这些离散的点可以平滑道路标线，得到一个平滑曲线的表达式。样条插值是使用分段多项式进行插值，即通过选取若干控制点，将道路标线划分为多段，得到多个控制点区间。在平滑过程中，根据每两个相邻的数据点确定一段函数，然后再将各个控制点区间的函数结合成一个函数，该函数即为光滑的曲线函数。具体的，采用三次样条插值法，即对于每个控制点区间构造一个三次函数，并且保证分段函数的衔接处具有0阶连续、一阶导数连续、二阶导数连续的性质，从而得到光滑的曲线的函数。
本实施例中，对任意形状的道路标线进行CCS的建模过程如下：
通过导入地图信息或分析道路设计图纸的方式，获取道路标线中的每个参考点的位置信息，本实施例对参考点的选取不做限定，例如随机选取、等间隔选取、或者根据道路标线的曲率半径等选取，通过求解所选取的各参考点在笛卡尔坐标系下的坐标，利用这些坐标可拟合得到道路标线的曲线的表达式；
从道路标线中选取若干个控制点，例如，控制点可以包括道路标线的起点、中点和/或终点等，又如，可以根据道路标线的曲率半径选取控制点，曲率半径越小的路段选取的控制点可以越多、越密集，从而更细致地确定该路段对应的三次函数，得到更准确的曲线的拟合结果；
根据各参考点在笛卡尔坐标系下的坐标，可使用三次样条插值法利用上述的插值参数，在其所处的控制点区间内***多个点，然后根据参考点、控制点以及***的点可以对道路标线做平滑处理，从而拟合得到道路标线的曲线，其表达式为：
xs＝ai,3r3+ai,2r2+ai,1r1+ai,0
ys＝bi,3r3+bi,2r2+bi,1r1+bi,0
s＝ci,3r3+ci,2r2+ci,1r1+ci,0
其中，(xs,ys)为道路标线上的任一参考点在笛卡尔坐标系中的位置，i表示该参考点处于第i个控制点区间(即第i个和第i+1个控制点之间)，s为控制点区间中的起点(即第i个控制点)到该参考点之间的沿道路标线方向的曲线的长度，ai,k、bi,k、ci,k(k＝1,2,3)均为在第i个控制点区间内进行三次样条插值的插值参数。
在一实施例中，将车辆位置转换为笛卡尔坐标系下的车辆位置，并将笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置，包括：
确定车辆位置所处的控制点区间；
根据车辆位置所处的控制点区间、控制点区间中的起点到车辆位置向道路标线的投影之间对应的曲线的长度，以及插值参数确定插值方程的自变量值；
根据插值方程的自变量值和插值参数确定笛卡尔坐标系下的车辆在道路标线上的投影位置以及道路方向角；
根据投影位置和道路方向角确定笛卡尔坐标系下的车辆位置；
根据极坐标中心与道路标线的曲线之间的位置关系将笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置。
图4为一实施例提供的一种坐标转换的示意图，如图4所示，对于预测的车辆位置，从CCS下的表示(s，n)向笛卡尔坐标系下的表示(x,y)的转换可以通过如下步骤实现：
1)判定该车辆位置在道路标线上处于三次样条插值的第几个控制点区间(即确定i)；
2)根据s＝ci,3r3+ci,2r2+ci,1r1+ci,0求解插值方程的自变量r；
3)将求解的r代入xs＝ai,3r3+ai,2r2+ai,1r1+ai,0和ys＝bi,3r3+bi,2r2+bi,1r1+bi,0，得到该车辆位置在道路标线上的投影位置(xs,ys)；
4)将求解的r代入求解道路方向角α；
5)根据x＝xs-n sinα和y＝ys+n cosα求解笛卡尔坐标系下的车辆位置(x,y)。
本实施例中，(s，n)在CCS中关于s方向的投影(s，0)与{xs，ys}是一体的，投影关系依赖于道路方向角α的存在，由于n方向永远与α方向垂直，因此根据n的数值和α方向以及(s，0)的位置可以得到笛卡尔坐标系下的车辆位置(x,y)。
在此基础上，根据极坐标中心与道路标线的曲线之间的位置关系将笛卡尔坐标系下的车辆位置(x,y)转换为车辆位置在极坐标系下的角度坐标用于构建波束成形向量。图4中的极坐标系是以通信单元{x0，y0}为中心，θ为通信单元到车辆连线在地面投影的线与x轴的夹角(可以理解为方位角)；为通信单元与车辆的连线与xoy平面(即地平面)的夹角(可以理解为俯仰角)；α为当前车辆位置的道路标线方向相对于x轴的夹角。
图5为一实施例提供的一种基于波束的车辆跟踪过程的示意图。如图5所示，道路模型信息初始化的目的是建立笛卡尔坐标系、CCS坐标系以及极坐标系之间的关联关系。利用状态转移模型，可以根据历史数据帧的状态信息对目标数据帧对应时刻车辆的状态信息进行预测，中包括第l个数据帧对应的车辆位置以及车速等状态信息，其中，车辆位置可用于确定波束成形向量，具体是通过将CCS下的车辆位置转换为笛卡尔坐标系下的车辆位置，再将笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置，得到车辆位置在极坐标系下的角度表示，据此构建波束成形向量，从而向车辆发送波束，该波束再遇到车辆的通信接收机后会产生回波；然后通过MUSIC和匹配滤波算法对回波进行测量，基于EKF和测量参数可对车辆位置进行修正，得到准确的并用于对下一个目标数据帧的预测和跟踪。
本实施例的车辆跟踪方法，通过将车辆位置与运动轨迹相对于道路标线进行分解，实现了在任意形状道路下的状态转移模型建模以及基于状态转移模型的感知辅助通信车辆跟踪；并且采用预测形式的跟踪算法，基于扩展卡尔曼滤波算法以及回波的测量参数回波估计车辆的状态信息，代替了使用导频估计信道和使用上行的反馈获取信道状态的方案，能够节省导频开销和反馈链路资源。此外，对于任意道路形状或车速场景中车辆方位角的变化多样且变化较快，该方法利用ISAC方法，可以有效提升车辆位置跟踪的精度，极大提升在高移动环境下的通信稳定性；并且通过利用波束进行角度、位置与多普勒频移的估计，无需引入额外的雷达传感器，具有成本低的优势。
本申请实施例还提供一种车辆跟踪装置。图6为一实施例提供的一种车辆跟踪装置的结构示意图。如图6所示，所述车辆跟踪装置包括：
位置预测模块310，设置为根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置；
向量确定模块320，设置为根据所述车辆位置确定波束成形向量；
车辆跟踪模块330，设置为基于所述波束成形向量发送波束并接收回波；
状态确定模块340，设置为基于扩展卡尔曼滤波算法和所述回波的测量参数，确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
本实施例的车辆跟踪装置，通过利用CCS坐标系下的车辆位置，进行车辆位置与运动轨迹相对于道路标线的分解，充分利用道路形状提供的信息，实现了在任意形状道路下的建模以及基于波束的通信车辆跟踪，更有效地估计和预测车辆的位置以提升波束对准的可靠性；此外，通过使用预测形式的跟踪算法，在目标数据帧开始前就根据预测得到的车辆位置生成波束成形向量，目标数据帧中的数据均采用该波束成行向量进行发送，能够节省导频开销和反馈链路资源。
在一实施例中，位置预测模块310，包括：
构建单元，设置为根据所述历史数据帧对应时刻的曲线坐标系下的车辆位置以及车速构建状态转移模型；
预测单元，设置为将所述历史数据帧的状态信息输入至所述状态转移模型，得到所述车辆位置。
在一实施例中，向量确定模块320，包括：
坐标转换单元，设置为将所述车辆位置转换为笛卡尔坐标系下的车辆位置，并将所述笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置，得到所述车辆位置的角度预测信息，所述角度预测信息包括所述极坐标系下的车辆位置的角度坐标；
向量确定单元，设置为根据所述角度预测信息构建所述波束成形向量。
在一实施例中，该装置还包括：
获取模块，设置为在根据所述车辆位置确定波束成形向量之前，获取道路标线中的每个参考点的位置信息；
选取模块，设置为从所述道路标线中选取若干控制点，所述控制点用于将所述道路标线划分为多个控制点区间；
插值模块，设置为对于各所述参考点，基于三次样条插值法在所述参考点所处的控制点区间内进行插值，
拟合模块，设置为根据各所述参考点对应的插值结果拟合得到所述道路标线的曲线，并保存插值参数。
在一实施例中，坐标转换单元，设置为：
确定所述车辆位置所处的控制点区间；
根据所述车辆位置所处的控制点区间、控制点区间的起点到所述车辆位置向道路标线的投影之间对应的所述曲线的长度，以及所述插值参数确定插值方程的自变量值；
根据所述插值方程的自变量值和所述插值参数确定所述笛卡尔坐标系下的车辆在道路标线上的投影位置以及道路方向角；
根据所述投影位置和所述道路方向角确定所述笛卡尔坐标系下的车辆位置；
根据极坐标中心与所述道路标线的曲线之间的位置关系将所述笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置。
在一实施例中，该装置还包括：
测量模块，设置为在接收回波之后，基于多信号分类算法和匹配滤波算法对所述回波进行测量，得到所述测量参数，所述测量参数包括到达角、时延和多普勒频移。
在一实施例中，状态确定模块340，设置为：
基于扩展卡尔曼滤波算法，根据观测模型和所述测量参数对所述车辆位置进行修正，得到所述目标数据帧的状态信息。
本实施例提出的车辆跟踪装置与上述实施例提出的车辆跟踪方法属于同一发明构思，未在本实施例中详尽描述的技术细节可参见上述任意实施例，并且本实施例具备与执行车辆跟踪方法相同的有益效果。
本申请实施例还提供了一种通信单元，图7为一实施例提供的一种通信单元的硬件结构示意图，如图7所示，本申请提供的通信单元，包括处理器410以及存储器420；该通信单元中的处理器410可以是一个或多个，图7中以一个处理器410为例；存储器420配置为存储一个或多个程序；所述一个或多个 程序被所述一个或多个处理器410执行，使得所述一个或多个处理器410实现如本申请实施例中所述的车辆跟踪方法。
通信单元还包括：通信装置430、输入装置440和输出装置450。
通信单元中的处理器410、存储器420、通信装置430、输入装置440和输出装置450可以通过总线或其他方式连接，图7中以通过总线连接为例。
输入装置440可用于接收输入的数字或字符信息，以及产生与通信单元的用户设置以及功能控制有关的按键信号输入。输出装置450可包括显示屏等显示设备。
通信装置430可以包括接收器和发送器。通信装置430设置为根据处理器410的控制进行信息收发通信。
存储器420作为一种计算机可读存储介质，可设置为存储软件程序、计算机可执行程序以及模块，如本申请实施例所述车辆跟踪方法对应的程序指令/模块(例如，车辆跟踪装置中的位置预测模块310、向量确定模块320、车辆跟踪模块330和状态确定模块340)。存储器420可包括存储程序区和存储数据区，其中，存储程序区可存储操作***、至少一个功能所需的应用程序；存储数据区可存储根据通信单元的使用所创建的数据等。此外，存储器420可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储器420可进一步包括相对于处理器410远程设置的存储器，这些远程存储器可以通过网络连接至通信单元。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
本申请实施例还提供一种存储介质，所述存储介质存储有计算机程序，所述计算机程序被处理器执行时实现本申请实施例中任一所述的车辆跟踪方法。该方法，包括：
根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置；
根据所述车辆位置确定波束成形向量；
基于所述波束成形向量发送波束并接收回波；
基于扩展卡尔曼滤波算法和所述回波的测量参数，确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
本申请实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是，但不限于：电、磁、光、电磁、红外线、或半导体的***、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(Random Access Memory，RAM)、只读存储器(Read Only Memory，ROM)、可擦式可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、闪存、光纤、便携式CD-ROM、光存储器件、磁存储器件、或者上述的任意合适的组合。计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行***、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于：电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行***、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、无线电频率(Radio Frequency，RF)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言，诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言，诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络，包括局域网(LAN)或广域网(WAN)，连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
以上所述，仅为本申请的示例性实施例而已，并非用于限定本申请的保护 范围。
本领域内的技术人员应明白，术语用户终端涵盖任何适合类型的无线用户设备，例如移动电话、便携数据处理装置、便携网络浏览器或车载移动台。
一般来说，本申请的多种实施例可以在硬件或专用电路、软件、逻辑或其任何组合中实现。例如，一些方面可以被实现在硬件中，而其它方面可以被实现在可以被控制器、微处理器或其它计算装置执行的固件或软件中，尽管本申请不限于此。
本申请的实施例可以通过移动装置的数据处理器执行计算机程序指令来实现，例如在处理器实体中，或者通过硬件，或者通过软件和硬件的组合。计算机程序指令可以是汇编指令、指令集架构(Instruction Set Architecture，ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码。
本申请附图中的任何逻辑流程的框图可以表示程序步骤，或者可以表示相互连接的逻辑电路、模块和功能，或者可以表示程序步骤与逻辑电路、模块和功能的组合。计算机程序可以存储在存储器上。存储器可以具有任何适合于本地技术环境的类型并且可以使用任何适合的数据存储技术实现，例如但不限于只读存储器(Read-Only Memory，ROM)、随机访问存储器(Random Access Memory，RAM)、光存储器装置和***(数码多功能光碟(Digital Video Disc，DVD)或光盘(Compact Disk，CD)等。计算机可读介质可以包括非瞬时性存储介质。数据处理器可以是任何适合于本地技术环境的类型，例如但不限于通用计算机、专用计算机、微处理器、数字信号处理器(Digital Signal Processing，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程逻辑器件(Field-Programmable Gate Array，FGPA)以及基于多核处理器架构的处理器。
通过示范性和非限制性的示例，上文已提供了对本申请的示范实施例的详细描述。但结合附图和权利要求来考虑，对以上实施例的多种修改和调整对本领域技术人员来说是显而易见的，但不偏离本申请的范围。因此，本申请的恰当范围将根据权利要求确定。

Claims (10)

  1. 一种车辆跟踪方法，包括：
    根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置；
    根据所述车辆位置确定波束成形向量；
    基于所述波束成形向量发送波束并接收回波；
    基于扩展卡尔曼滤波算法和所述回波的测量参数确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
  2. 根据权利要求1所述的方法，其中，所述根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置，包括：
    根据所述历史数据帧对应时刻的曲线坐标系下的车辆位置以及车速构建状态转移模型；
    将所述历史数据帧的状态信息输入至所述状态转移模型，得到目标数据帧对应时刻的曲线坐标系下的车辆位置。
  3. 根据权利要求1所述的方法，其中，所述根据所述车辆位置确定波束成形向量，包括：
    将所述车辆位置转换为笛卡尔坐标系下的车辆位置，并将所述笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置，得到所述车辆位置的角度预测信息，所述角度预测信息包括所述极坐标系下的车辆位置的角度坐标；
    根据所述角度预测信息构建所述波束成形向量。
  4. 根据权利要求3所述的方法，在所述根据所述车辆位置确定波束成形向量之前，还包括：
    获取道路标线中的多个参考点中的每个参考点的位置信息；
    从所述道路标线中选取多个控制点，所述控制点用于将所述道路标线划分为多个控制点区间；
    对于所述每个参考点，基于三次样条插值法在所述参考点所处的控制点区间内进行插值；
    对所述多个参考点对应的插值结果拟合得到所述道路标线的曲线，并保存插值参数。
  5. 根据权利要求4所述的方法，其中，所述将所述车辆位置转换为笛卡尔坐标系下的车辆位置，并将所述笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置，包括：
    确定所述车辆位置所处的控制点区间；
    根据所述车辆位置所处的控制点区间、所述控制点区间的起点到所述车辆位置向道路标线的投影之间对应的所述曲线的长度，以及所述插值参数确定插值方程的自变量值；
    根据所述插值方程的自变量值和所述插值参数确定所述笛卡尔坐标系下车辆在道路标线上的投影位置以及道路方向角；
    根据所述投影位置和所述道路方向角确定所述笛卡尔坐标系下的车辆位置；
    根据极坐标中心与所述道路标线的曲线之间的位置关系将所述笛卡尔坐标系下的车辆位置转换为极坐标系下的车辆位置。
  6. 根据权利要求1所述的方法，在所述接收回波之后，还包括：
    基于多信号分类算法和匹配滤波算法对所述回波进行测量，得到所述测量参数，所述测量参数包括到达角、时延和多普勒频移。
  7. 根据权利要求1所述的方法，其中，所述基于扩展卡尔曼滤波算法和所述回波的测量参数，确定所述目标数据帧的状态信息，包括：
    基于扩展卡尔曼滤波算法，根据观测模型和所述测量参数对所述车辆位置进行修正，得到所述目标数据帧的状态信息。
  8. 一种车辆跟踪装置，包括：
    位置预测模块，设置为根据历史数据帧的状态信息预测在目标数据帧对应时刻的曲线坐标系下的车辆位置；
    向量确定模块，设置为根据所述车辆位置确定波束成形向量；
    波束收发模块，设置为基于所述波束成形向量发送波束并接收回波；
    状态估计模块，设置为基于扩展卡尔曼滤波算法和所述回波的测量参数确定所述目标数据帧的状态信息，所述目标数据帧的状态信息包括修正后的所述车辆位置。
  9. 一种通信单元，包括：存储器，以及一个或多个处理器；
    所述存储器，配置为存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-7中任一项所述的车辆跟踪方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1-7中任一所述的车辆跟踪方法。
PCT/CN2023/099778 2022-06-23 2023-06-13 车辆跟踪方法、装置、通信单元及存储介质 WO2023246551A1 (zh)

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