A method, device, electronic device and storage medium for determining location information
Through the extended Kalman filtering method combined with the vehicle state and received signal strength value, the problem of inaccurate positioning in wireless positioning in complex environments is solved, and higher positioning accuracy is achieved.
Patent Information
- Application Number
- CN202011280857.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2020-11-16
- Publication Date
- 2024-12-10
- Estimated Expiration
- 2040-11-16
AI Technical Summary
The wireless positioning method based on the received signal strength is affected by factors such as signal reflection, multipath propagation, antenna gain and obstacles in complex road traffic environments, resulting in inaccurate positioning.
The extended Kalman filtering method is adopted, combining the vehicle's position information, speed information and received signal strength value, and the vehicle's position and speed information at subsequent moments is determined through the state transition equation and prediction update, so as to reduce noise fluctuations and improve positioning accuracy.
In complex environments, the accuracy of vehicle position information and positioning accuracy are improved through pre-filtering and updating methods, and the distance measurement error is reduced.
Smart Images
Figure CN112462326B_ABST
Abstract
Description
Technical Field
[0001] One or more embodiments of the present specification relate to the field of positioning technology, and in particular, to a method, device, electronic device, and storage medium for determining location information. Background Art
[0002] Wireless positioning based on signal feature measurement refers to the use of wireless networks and positioning terminals to provide information such as the location, speed and direction of the node to be measured. At present, there are many types of wireless network positioning methods, among which there are four types of distance-based positioning technologies: based on signal transmission time, based on signal transmission time difference (TDOA), based on signal arrival angle, and based on received signal strength RSSI value.
[0003] Compared with the other three methods, the positioning method based on received signal strength RSSI has the advantages of no need to add additional hardware equipment to the node, low power consumption, and low cost. However, due to the complex and changeable road traffic environment, it will be affected by various factors such as signal reflection, multipath propagation, antenna gain, obstacles, etc. The signal has a strong time-varying property, which makes the positioning inaccurate when the ranging error is large. Summary of the invention
[0004] In view of this, an object of one or more embodiments of the present specification is to provide a method and device for determining position information to solve the problem of inaccurate positioning.
[0005] Based on the above purpose, one or more embodiments of this specification provide a method for determining location information, which is applied to a system including a vehicle-mounted unit and a roadside unit, and is characterized by comprising:
[0006] Obtaining the position information and speed information of the target vehicle at a first moment and the received signal strength value between the target vehicle and the roadside unit, wherein the first moment is used to indicate the current moment;
[0007] The position information and speed information of the target vehicle at a second moment are determined based on the position information, the speed information and the received signal strength value, wherein the second moment is used to indicate a moment after the current moment.
[0008] In some embodiments, the above method further comprises:
[0009] Obtaining historical position information and historical speed information of the target vehicle;
[0010] determining a vehicle state equation based on the historical position information and the historical speed information;
[0011] The position information and speed information of the target vehicle at a second moment are determined based on the vehicle state equation, the position information, the speed information and the received signal strength value.
[0012] In some embodiments, the method for determining the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value in the above method is to use an extended Kalman filter method;
[0013] The extended Kalman filter method includes: an updating part and a predicting part, wherein the updating part is used to update the position information and speed information at the second moment and predict the received signal strength value;
[0014] The prediction part is used to predict the position information and speed information at the second moment.
[0015] In some embodiments, the above method further comprises:
[0016] Determine the state transfer equations of the first moment and the third moment, wherein the third moment is used to indicate a moment before the current moment;
[0017] Predicting the received signal strength value at the first moment based on the received signal strength value at the third moment;
[0018] The received signal strength value of the target vehicle at the second moment is determined based on the actual received signal strength value at the first moment, and the position information and speed information at the second moment are updated based on the received signal strength value at the second moment.
[0019] In some embodiments, the above method also includes: sending the location information and speed information at the second moment to at least one roadside unit.
[0020] In a second aspect, an embodiment of the present application further provides a device for determining position information, which is applied to a system including a vehicle and a roadside unit, including:
[0021] An acquisition module: used to acquire the position information, speed information and received signal strength value between the target vehicle and the roadside unit at a first moment, wherein the first moment is used to indicate the current moment;
[0022] Determination module: used to determine the position information and speed information of the target vehicle at a second moment based on the position information, the speed information and the received signal strength value, wherein the second moment is used to indicate a moment after the current moment.
[0023] In some embodiments, the acquisition module is further used to:
[0024] Obtaining historical position information and historical speed information of the target vehicle;
[0025] determining a vehicle state equation based on the historical position information and the historical speed information;
[0026] The position information and speed information of the target vehicle at a second moment are determined based on the vehicle state equation, the position information, the speed information and the received signal strength value.
[0027] In some embodiments, the above-mentioned determination module is further used to:
[0028] Determine the state transfer equations of the first moment and the third moment, wherein the third moment is used to indicate a moment before the current moment;
[0029] Predicting the received signal strength value at the first moment based on the received signal strength value at the third moment;
[0030] The position information and speed information of the target vehicle at the second moment are determined based on the actual received signal strength value at the first moment.
[0031] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the location information determination method as described above when executing the program.
[0032] In a fourth aspect, an embodiment of the present application further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the location information determination method as described above.
[0033] From the above, it can be seen that a method for determining location information provided by one or more embodiments of this specification is applied to a system including a vehicle and a roadside unit, including: obtaining the location information, speed information and received signal strength between the target vehicle and the roadside unit at a first moment, wherein the first moment is used to indicate the current moment; predicting the location information and speed information of the target vehicle at a second moment based on the location information, speed information and received signal strength, wherein the second moment is used to indicate the moment after the current moment. This specification combines the vehicle's speed information, location information and received signal strength to accurately determine the vehicle's location. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] In order to more clearly illustrate one or more embodiments of the present specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or more embodiments of the present specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0035] Figure 1 A schematic diagram of one or more embodiments of this specification;
[0036] Figure 2 A schematic diagram of one or more embodiments of this specification;
[0037] Figure 3 A schematic diagram of one or more embodiments of this specification;
[0038] Figure 4 A schematic diagram of one or more embodiments of this specification;
[0039] Figure 5 A schematic diagram of one or more embodiments of this specification;
[0040] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided by this embodiment is shown. DETAILED DESCRIPTION
[0041] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.
[0042] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of this specification should be understood by people with ordinary skills in the field to which this disclosure belongs. The "first", "second" and similar words used in one or more embodiments of this specification do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0043] Figure 1 It is a schematic diagram of one or more embodiments of this specification.
[0044] The scenario of the intelligent transportation vehicle network in the embodiment of the present application includes an on-board unit configured on the vehicle 101, a roadside unit 102 and an edge cloud server 103 configured on the roadside unit. The on-board unit and the roadside unit 102 are communicatively connected, and communication can be performed through Vehicle-To-Infrastructure V2I technology.
[0045] Figure 2 It is a schematic diagram of one or more embodiments of this specification.
[0046] A method for determining location information provided in an embodiment of the present application is combined with Figure 2 , applied to Figure 1 In the scenario, the following three steps are included: S201, S202 and S203:
[0047] S201: Acquire the position information and speed information of the target vehicle at a first moment, and the received signal strength value between the target vehicle and the roadside unit; wherein the first moment is used to indicate the current moment;
[0048] Specifically, when the target vehicle enters the radio frequency area of the roadside unit RSU, the on-board unit OBU will establish a communication connection with the roadside unit RSU, and the on-board unit will send the current vehicle location information and the current speed information to the roadside unit RSU and the edge cloud server configured by the roadside unit RSU; the roadside unit RSU obtains the received signal strength RSSI value.
[0049] S202: Determine the position information and speed information of the target vehicle at a second moment based on the position information, the speed information and the received signal strength value; wherein the second moment is used to indicate a moment after the current moment.
[0050] Specifically, the roadside unit RSU obtains its current position information, speed information and received signal strength RSSI value, and the edge cloud server configured in the roadside unit RSU predicts the position information and speed information of the target vehicle at the next moment.
[0051] It should be understood that the current moment in the embodiment of the present application may be moment t, and the next moment may be moment t+1, and this does not limit the protection scope of this specification.
[0052] S203: Send the location information and speed information at the second moment to at least one roadside unit.
[0053] In a specific embodiment, after an edge cloud server configured in a roadside unit RSU predicts the location information and speed information of the target vehicle at the next moment, the transmission here may be to other roadside units.
[0054] Figure 3 It is a schematic diagram of one or more embodiments of this specification.
[0055] In some embodiments, in combination Figure 3 The above method further includes three steps S301, S302 and S303:
[0056] S301: Acquire historical position information and historical speed information of the target vehicle.
[0057] In a specific embodiment, the historical position information and historical speed information of the target vehicle may be obtained through the vehicle-mounted unit.
[0058] S302: Determine a vehicle state equation based on the historical position information and the historical speed information.
[0059] In a specific embodiment, based on historical position information and historical speed information, the vehicle state equation is modeled to determine the vehicle state equation, where the vehicle state equation includes at least the vehicle's position information and speed information, and the speed information includes at least: horizontal speed and vertical speed.
[0060] S303: Determine the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value.
[0061] In a specific embodiment, the method for determining the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value is to use an extended Kalman filter method.
[0062] In a specific embodiment, the first moment indicates the current moment, and the second moment indicates the next moment of the current moment. The historical speed information of the vehicle and the received signal strength value when the vehicle communicates with the roadside unit are used as inputs of the vehicle state equation. Since the received signal strength value and the vehicle speed are in a nonlinear relationship, the Kalman filter method cannot be used directly. The extended Kalman filter method is used to approximate the nonlinear problem as a linear problem for processing, and the speed and position of the vehicle at the next moment are optimally estimated to determine the optimal position information and speed information.
[0063] The extended Kalman filter method includes: an updating part and a predicting part, wherein the updating part is used to update the position information and speed information at the second moment and predict the received signal strength value; the predicting part is used to predict the position information and speed information at the second moment.
[0064] The received power of the wireless signal as input is affected by the distance between the transceiver units and the propagation factor, and the value of the propagation factor depends on the environment in which the wireless signal propagates. In complex environments, the received signal strength value is easily affected by noise and building shielding, resulting in large fluctuations and data loss, which makes the input information contain large errors and affects the optimal estimation of the vehicle position information.
[0065] In order to solve the above problems, combined Figure 4 The above method further includes three steps S401, S402 and S403:
[0066] S401: Determine the state transfer equations at the first moment and the third moment.
[0067] In a specific embodiment, the first moment indicates the current moment, and the third moment indicates the moment before the current moment. For example, the current moment may be moment t, and the moment before may be moment t-1.
[0068] S402: Predicting a received signal strength value at the first moment based on the received signal strength value at the third moment.
[0069] In a specific embodiment, based on the state transition equation, the received signal strength value at the previous moment predicts the received signal strength value at the current moment.
[0070] It should be understood that in complex environments, in order to reduce and eliminate noise fluctuations and data mutations in the received signal strength RSSI value, the extended Kalman filter method is used to use the position information of the previous moment to smooth and accurately output the received signal strength RSSI value, make an optimal estimate of the vehicle position information at the current moment, and use the optimal estimate to predict the received signal strength RSSI value at the next moment, forming a closed loop to achieve the purpose of improving positioning accuracy.
[0071] S403: Determine a received signal strength value of the target vehicle at a second moment based on the actual received signal strength value at the first moment, and update the position information and speed information at the second moment based on the received signal strength value at the second moment.
[0072] It should be understood that through the three steps of S401, S402 and S403, for the received signal strength RSSI value, the received signal strength RSSI value is predicted using the updated vehicle state, and the state transfer equation between the received signal strength RSSI value at the previous moment and the current moment is established. The received signal strength RSSI value at the previous moment is used to predict the received signal strength RSSI value at the current moment, and then the optimal estimate is made based on the actual RSSI value received. This process is called pre-filtering, and the received signal strength RSSI value after pre-filtering is used to update the vehicle position information and speed information, and this cycle is repeated to further improve the accuracy of position information determination.
[0073] The present application is further explained below in conjunction with specific vehicle state equations, state transfer equations, and extended Kalman filtering methods.
[0074] In a vehicle network, the network includes an onboard unit OBU, a roadside unit RSU, and an edge cloud server configured by the roadside unit RSU. The onboard unit and the roadside unit communicate through V2I technology. The location of RSUi is represented by (x i ,y i ), the position of the vehicle at time t is represented by (x(t), y(t)), and the speed of the vehicle in the horizontal and vertical directions is represented by (v x (t),v y (t)).
[0075] In order to predict the vehicle's position information at the next moment, an n-dimensional vehicle state equation is modeled. The vehicle state equation can be expressed as:
[0076] X(t+1)=F(t)X(t)+G(t)u(t)+w(t)
[0077] Z(t)=H(t)X(t)+v(t)
[0078] Where X(t) is the state vector of the vehicle at time t, including the position (x(t), y(t)) and the horizontal and vertical velocities represented by (v x (t),v y (t)); F(t) is the state transfer matrix of the vehicle at time t; G(t) is the input vector of the vehicle at time t; u(t) is the acceleration of the vehicle at time t; Z(t) is the RSSI value received by the RSU at time t; H(t) is the observation matrix of the vehicle at time t; w(t) represents the system error; v(t) represents the observation error, all of which are white noise with Gaussian distribution, obeying N(0,Q) and N(0,R) distribution respectively.
[0079] The vehicle state X(t) is predicted by the extended Kalman filter, including the position (x(t), y(t)) and the horizontal and vertical velocities (v x (t),v y (t)), wherein the extended Kalman filter method includes: an updating part and a predicting part, the updating part is used to update the position information and speed information at the first moment, and the predicting part is used to predict the position information and speed information at the second moment.
[0080] At time t it is known is the vehicle state, P(t) is the error covariance matrix, and the position information is determined as follows:
[0081] Then, first, predict the vehicle status information
[0082]
[0083] in, is the predicted value of the vehicle state X(t+1) at time t+1, i.e., the prior estimate; It is the updated value of the vehicle state X(t) at time t, that is, the posterior estimate.
[0084] Next, calculate the covariance matrix:
[0085]
[0086] in, is the prior error covariance at time t+1, represents the covariance of the posterior error at time t; Q represents the covariance matrix of v(t).
[0087] Next, the Kalman gain is updated:
[0088]
[0089]
[0090] in, is the Kalman gain of the vehicle at time t+1, and its change is affected by Influence. R represents the covariance matrix of v(t).
[0091] Update vehicle state information and error covariance matrix;
[0092]
[0093] P(t+1)=(IK(t+1)H(t+1))P(t+1)
[0094] in, It is the optimal estimate of the vehicle status information at time t+1.
[0095] Use the updated vehicle status to receive the signal strength RSSI value;
[0096] D(t+1)=H(t+1)F(t+1)H(t+1) -1
[0097]
[0098]
[0099] in, is the prior estimate of the received signal strength RSSI value at time t+1; D(t+1) is the state transition matrix of the RSSI signal value at time t+1.
[0100] Compute the covariance matrix of the observations and their Kalman gain:
[0101]
[0102]
[0103] in, is the prior error covariance at time t+1, represents the covariance of the posterior error at time t; Q Z Indicates v Z The covariance matrix of (t).
[0104] Update the observations and their error covariance matrix:
[0105]
[0106] P Z (t+1)=(IK Z (t+1))P Z (t+1)
[0107] in, is the optimal estimate of the received signal strength RSSI value at time t+1, and the updated observation value Bring in
[0108] P(t+1)=(IK(t+1)H(t+1))P(t+1)
[0109] Used to update vehicle status information in this cycle.
[0110] In the traditional positioning based on the received signal strength RSSI value, the received signal strength value is severely attenuated in a complex environment and is interfered by noise to cause oscillation, which makes the received signal strength RSSI value less stable and reduces the positioning accuracy.
[0111] Figure 5 It is a schematic diagram of one or more embodiments of this specification.
[0112] In a second aspect, an embodiment of the present application provides a device for determining position information, which is applied to a system including a vehicle and a roadside unit, and is combined with Figure 5 ,include:
[0113] Acquisition module 501: is used to acquire the position information, speed information and received signal strength value between the target vehicle and the roadside unit at a first moment, wherein the first moment is used to indicate the current moment.
[0114] Specifically, when the target vehicle enters the radio frequency area of the roadside unit RSU, the on-board unit OBU will establish a communication connection with the roadside unit RSU, and the on-board unit will send the current vehicle location information and the current speed information to the roadside unit RSU and the edge cloud server configured by the roadside unit RSU; the roadside unit RSU obtains the received signal strength RSSI value.
[0115] Determination module 502: used to determine the position information and speed information of the target vehicle at a second moment based on the position information, the speed information and the received signal strength value, wherein the second moment is used to indicate a moment after the current moment.
[0116] Specifically, the roadside unit RSU obtains its current position information, speed information and received signal strength RSSI value, and the edge cloud server configured in the roadside unit RSU determines the position information and speed information of the target vehicle at the next moment.
[0117] It should be understood that the current moment in the embodiment of the present application may be moment t, and the next moment may be moment t+1, and this does not limit the protection scope of this specification.
[0118] The sending module 503 is used to send the location information and speed information at the second moment to at least one roadside unit.
[0119] In a specific embodiment, after an edge cloud server configured in a roadside unit RSU predicts the location information and speed information of the target vehicle at the next moment, the transmission here may be to other roadside units.
[0120] In some embodiments, the acquisition module is further used to:
[0121] Obtaining historical position information and historical speed information of the target vehicle;
[0122] determining a vehicle state equation based on the historical position information and the historical speed information;
[0123] The position information and speed information of the target vehicle at a second moment are determined based on the vehicle state equation, the position information, the speed information and the received signal strength value.
[0124] In a specific embodiment, the method for determining the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value is to use an extended Kalman filter method.
[0125] In a specific embodiment, the first moment indicates the current moment, and the second moment indicates the next moment of the current moment. The historical speed information of the vehicle and the received signal strength value when the vehicle communicates with the roadside unit are used as inputs of the vehicle state equation. Since the received signal strength value and the vehicle speed are in a nonlinear relationship, the Kalman filter method cannot be used directly. The extended Kalman filter method is used to approximate the nonlinear problem as a linear problem for processing, and the speed and position of the vehicle at the next moment are optimally estimated to determine the optimal position information and speed information.
[0126] The extended Kalman filter method includes: an updating part and a predicting part, wherein the updating part is used to update the position information and speed information at the second moment and predict the received signal strength value; the predicting part is used to predict the position information and speed information at the second moment.
[0127] The received power of the wireless signal as input is affected by the distance between the transceiver units and the propagation factor, and the value of the propagation factor depends on the environment in which the wireless signal propagates. In complex environments, the received signal strength value is easily affected by noise and building shielding, resulting in large fluctuations and data loss, which makes the input information contain large errors and affects the optimal estimation of the vehicle position information.
[0128] In some embodiments, the determining module is further configured to:
[0129] Determine the state transfer equations of the first moment and the third moment, wherein the third moment is used to indicate a moment before the current moment;
[0130] Predicting the received signal strength value at the first moment based on the received signal strength value at the third moment;
[0131] The position information and speed information of the target vehicle at the second moment are determined based on the actual received signal strength value at the first moment.
[0132] In a third aspect, the present specification also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the location information determination method as described above when executing the program.
[0133] In a fourth aspect, the present specification also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the location information determination method as described above.
[0134] It should be noted that the method of one or more embodiments of this specification can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of this specification, and the multiple devices will interact with each other to complete the described method.
[0135] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0136] For the convenience of description, the above devices are described in terms of functions divided into various modules. Of course, when implementing one or more embodiments of this specification, the functions of each module can be implemented in the same or multiple software and / or hardware.
[0137] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.
[0138] Figure 6 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.
[0139] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.
[0140] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.
[0141] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.
[0142] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).
[0143] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).
[0144] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.
[0145] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.
[0146] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. In line with the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present specification as described above, which are not provided in detail for the sake of simplicity.
[0147] In addition, to simplify the description and discussion, and in order not to obscure one or more embodiments of the present specification, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, the device may be shown in the form of a block diagram so as to avoid obscuring one or more embodiments of the present specification, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present specification will be implemented (i.e., these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is apparent to those skilled in the art that one or more embodiments of the present specification may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.
[0148] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
[0149] One or more embodiments of this specification are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification should be included in the scope of protection of this disclosure.
Claims
1. A method for determining location information, applied to a system including an on-board unit and a roadside unit, characterized in that: The method comprises: Obtaining the position information and speed information of the target vehicle at a first moment and the received signal strength value between the target vehicle and the roadside unit, wherein the first moment is used to indicate the current moment; Determine the position information and speed information of the target vehicle at a second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value, wherein the second moment is used to indicate a moment after the current moment; Wherein, the vehicle state equation is expressed as: X(t+1)=F(t)X(t)+G(t)u(t)+w(t) Z(t)=H(t)X(t)+v(t) Among them, X(t) is the state vector of the vehicle at time t, including the position (x(t), y(t)) and the horizontal and vertical speeds (v x (t),v y (t)); F(t) is the state transfer matrix of the vehicle at time t; G(t) is the input vector of the vehicle at time t; u(t) is the acceleration of the vehicle at time t; Z(t) is the RSSI value received by the RSU at time t; H(t) is the observation matrix of the vehicle at time t; w(t) represents the system error; v(t) represents the observation error, which are all white noises with Gaussian distribution, respectively obeying N(0,Q) and N(0,R) distributions; The method for determining the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value is to use an extended Kalman filter method, and the extended Kalman filter method comprises the following steps: Predicted vehicle status information: in, is the predicted value of the vehicle state X(t+1) at time t+1, i.e., the prior estimate; It is the updated value of the vehicle state X(t) at time t, i.e., the posterior estimate; Compute the covariance matrix: in, is the prior error covariance at time t+1, represents the covariance of the posterior error at time t; Q represents the covariance matrix of v(t); Update the Kalman gain: in, is the Kalman gain of the vehicle at time t+1, R represents the covariance matrix of v(t); Update vehicle state information and error covariance matrix; P(t+1)=(IK(t+1)H(t+1))P(t+1) in, is the optimal estimate of the vehicle status information at time t+1; Use the updated vehicle status to receive the signal strength RSSI value; D(t+1)=H(t+1)F(t+1)H(t+1) -1 , in, is the a priori estimate of the received signal strength RSSI value at time t+1; D(t+1) is the state transition matrix of the RSSI signal value at time t+1; Compute the covariance matrix of the observations and their Kalman gain: in, is the prior error covariance at time t+1, represents the covariance of the posterior error at time t; Q Z Indicates v Z The covariance matrix of (t); Update the observations and their error covariance matrix: P Z (t+1)=(I-K Z (t+1))P Z (t+1) in, is the optimal estimate of the received signal strength RSSI value at time t+1, and the updated observation value Bring in P(t+1)=(IK(t+1)H(t+1))P(t+1) Update vehicle status information.
2. The method for determining location information according to claim 1, characterized in that: The method further comprises: Obtaining historical position information and historical speed information of the target vehicle; determining the vehicle state equation based on the historical position information and the historical speed information; The position information and speed information of the target vehicle at a second moment are determined based on the vehicle state equation, the position information, the speed information and the received signal strength value.
3. The method for determining position information according to claim 2, characterized in that: The method for determining the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value is to use an extended Kalman filter method; The extended Kalman filter method includes: an updating part and a predicting part, wherein the updating part is used to update the position information and speed information at the second moment and predict the received signal strength value; The prediction part is used to predict the position information and speed information at the second moment.
4. The method for determining position information according to claim 3, characterized in that: The method further comprises: Determine the state transfer equations of the first moment and the third moment, wherein the third moment is used to indicate a moment before the current moment; Predicting the received signal strength value at the first moment based on the received signal strength value at the third moment; The received signal strength value of the target vehicle at the second moment is determined based on the actual received signal strength value at the first moment, and the position information and speed information at the second moment are updated based on the received signal strength value at the second moment.
5. The method according to any one of claims 1 to 4, characterized in that: The method further includes: sending the position information and speed information at the second moment to at least one roadside unit.
6. A device for determining position information, applied to a system including a vehicle and a roadside unit, characterized in that: include: An acquisition module: used to acquire the position information, speed information and received signal strength value between the target vehicle and the roadside unit at a first moment, wherein the first moment is used to indicate the current moment; A determination module: used to determine the position information and speed information of the target vehicle at a second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value, wherein the second moment is used to indicate a moment after the current moment; Wherein, the vehicle state equation is expressed as: X(t+1)=F(t)X(t)+G(t)u(t)+w(t) Z(t)=H(t)X(t)+v(t) where X(t) is the state vector of the vehicle at time t, including position (x(t), y(t)) and horizontal and vertical velocities (v x (t),v y (t)); F(t) is the state transfer matrix of the vehicle at time t; G(t) is the input vector of the vehicle at time t; u(t) is the acceleration of the vehicle at time t; Z(t) is the RSSI value received by the RSU at time t; H(t) is the observation matrix of the vehicle at time t; w(t) represents the system error; v(t) represents the observation error, which are all white noises with Gaussian distribution, respectively obeying N(0,Q) and N(0,R) distributions; The method for determining the position information and speed information of the target vehicle at the second moment based on the vehicle state equation, the position information, the speed information and the received signal strength value is to use an extended Kalman filter method, and the extended Kalman filter method comprises the following steps: Predicted vehicle status information: in, is the predicted value of the vehicle state X(t+1) at time t+1, i.e., the prior estimate; It is the updated value of the vehicle state X(t) at time t, i.e., the posterior estimate; Compute the covariance matrix: in, is the prior error covariance at time t+1, represents the covariance of the posterior error at time t; Q represents the covariance matrix of v(t); Update the Kalman gain: in, is the Kalman gain of the vehicle at time t+1, R represents the covariance matrix of v(t); Update vehicle state information and error covariance matrix; P(t+1)=(IK(t+1)H(t+1))P(t+1) in, is the optimal estimate of the vehicle status information at time t+1; Use the updated vehicle status to receive the signal strength RSSI value; D(t+1)=H(t+1)F(t+1)H(t+1) -1 , in, is the a priori estimate of the received signal strength RSSI value at time t+1; D(t+1) is the state transition matrix of the RSSI signal value at time t+1; Compute the covariance matrix of the observations and their Kalman gain: in, is the prior error covariance at time t+1, represents the covariance of the posterior error at time t; Q Z Indicates v Z The covariance matrix of (t); Update the observations and their error covariance matrix: P Z (t+1)=(I-K Z (t+1))P Z (t+1) in, is the optimal estimate of the received signal strength RSSI value at time t+1, and the updated observation value Bring in P(t+1)=(IK(t+1)H(t+1))P(t+1) Update vehicle status information.
7. The position information determining device according to claim 6, characterized in that: The acquisition module is also used for: Obtaining historical position information and historical speed information of the target vehicle; determining a vehicle state equation based on the historical position information and the historical speed information; The position information and speed information of the target vehicle at a second moment are determined based on the vehicle state equation, the position information, the speed information and the received signal strength value.
8. The position information determining device according to claim 6, characterized in that: The determining module is also used for: Determine the state transfer equations of the first moment and the third moment, wherein the third moment is used to indicate a moment before the current moment; Predicting the received signal strength value at the first moment based on the received signal strength value at the third moment; The position information and speed information of the target vehicle at the second moment are determined based on the actual received signal strength value at the first moment.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method for determining position information as described in any one of claims 1 to 5 is implemented.
10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the location information determination method according to any one of claims 1 to 5.