Methods and apparatus for determining vehicle location

Abstract

This application relates to the field of autonomous driving, and provides a method and apparatus for determining the position of a vehicle. The method is applied to a target vehicle and includes: determining the target vehicle's velocity likelihood function, heading likelihood function, first distance likelihood function, and second distance likelihood function; and determining the target vehicle's position likelihood function with or against these functions; and determining the predicted position region S of the target vehicle. p According to the predicted location area S p The predicted location is determined by the location likelihood function, where the predicted location is the solution to the location likelihood function, and the predicted location region is S. p The solution is used to constrain the position likelihood function. The above method can improve the accuracy of vehicle positioning.

Description

Technical Field

[0001] This application relates to the field of autonomous driving, specifically to a method and apparatus for determining the position of a vehicle. Background Technology

[0002] Autonomous driving is a technology that relies on communication, computer vision, and network control to achieve driverless functionality. Safety is the primary goal of autonomous driving. The onboard control system needs to obtain precise vehicle location information to control the vehicle's speed, heading, and other parameters, thus preventing accidents. Current vehicle positioning results still require further improvement. Summary of the Invention

[0003] This application provides a method, apparatus, computer-readable storage medium, and computer program product for determining vehicle location, which can improve the accuracy of vehicle positioning.

[0004] Firstly, a method for determining the position of a vehicle is provided. This method is applied to a target vehicle and includes: determining the velocity likelihood function of the target vehicle. Heading likelihood function First distance likelihood function Second distance likelihood function in, Used to estimate the target vehicle's predicted location. speed, Used to estimate the target vehicle's predicted location. The course, Used to estimate the target vehicle's predicted location. The distance from the roadside unit (RSU) to the target vehicle. Used to estimate the target vehicle's predicted location. The distance from the onboard unit (OBU) to the target vehicle; based on and Determine the position likelihood function of the target vehicle and or Negative correlation; determining the predicted location region S of the target vehicle. p According to the predicted location area S p and position likelihood function Determine the predicted location Among them, the predicted location For position likelihood function The solution predicts the location region S. p Used to constrain the position likelihood function The solution.

[0005] The above method can be performed by the target vehicle. Based on the causal estimation results, the target vehicle first determines the causal factors of the position likelihood function, namely, the velocity likelihood function, the heading likelihood function, the first distance likelihood function, and the second distance likelihood function. Then, based on these causal factors, the position likelihood function is estimated to obtain the predicted position. Since the causal factors for estimating the predicted position include the first and second distance likelihood functions, the positioning data is more comprehensive, and the predicted position obtained in this embodiment is more accurate.

[0006] In a second aspect, an apparatus for determining the location of a vehicle is provided, including a unit for performing any of the methods in the first aspect. The apparatus may be a terminal device or a chip within a terminal device. The apparatus may include an input unit and a processing unit.

[0007] When the device is a terminal device, the processing unit may be a processor, and the input unit may be a communication module such as an antenna; the terminal device may also include a memory for storing computer program code, and when the processor executes the computer program code stored in the memory, the terminal device performs any of the methods in the first aspect.

[0008] When the device is a chip within a terminal device, the processing unit can be an internal processing unit of the chip, and the input unit can be an input / output interface, pin, or circuit, etc.; the chip may also include a memory, which can be an internal memory of the chip (e.g., registers, cache, etc.) or an external memory (e.g., read-only memory, random access memory, etc.); the memory is used to store computer program code, and when the processor executes the computer program code stored in the memory, the chip performs any of the methods in the first aspect.

[0009] Thirdly, a computer-readable storage medium is provided that stores computer program code, which, when executed by a device for determining the location of a vehicle, causes the device to perform any of the methods in the first aspect.

[0010] Fourthly, a computer program product is provided, the computer program product comprising: computer program code, which, when run by a device for determining the location of a vehicle, causes the device to perform any of the methods in the first aspect. Attached Figure Description

[0011] Figure 1 This is a schematic diagram illustrating an application scenario applicable to this application;

[0012] Figure 2 This is a flowchart of a positioning method provided in this application;

[0013] Figure 3 This is a flowchart of a method for solving vehicle positions provided in this application;

[0014] Figure 4 This is a schematic diagram of a factor graph model provided in this application;

[0015] Figure 5 This is a flowchart of a method for constructing and solving a loss function provided in this application;

[0016] Figure 6 This is a flowchart of an update node position provided in this application;

[0017] Figure 7 This is a schematic diagram of the structure of a device for determining the position of a vehicle provided in this application;

[0018] Figure 8 This is a schematic diagram of the structure of an electronic device for determining the location of a vehicle, as provided in this application. Detailed Implementation

[0019] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0020] Figure 1 This is a schematic diagram illustrating an application scenario applicable to this application.

[0021] On-board units (OBUs) 1 to OBUs 4 are electronic devices located in the vehicle. They can communicate with a base station (BS) via wireless communication technology, and multiple OBUs can also communicate with each other. Figure 1 As shown by the bidirectional arrows, the communication method between OBUs can be device-to-device (D2D). Taking OBU1 as an example, OBU1 can receive D2D signals from OBU2, OBU3, and OBU4; subsequently, OBU1 can use the received D2D signals for positioning.

[0022] BS1, BS2, and BS3 are three vehicle-to-everything (V2X) infrastructures with communication and ranging capabilities, used to provide positioning services for vehicles traveling on the road. Therefore, BS1, BS2, and BS3 can be referred to as roadside units (RSUs). These three base stations can be fifth-generation (5G) base stations. thBase stations in 5G (Generation 3) mobile communication networks. 5G mobile communication networks are characterized by low latency and high reliability. They can not only transmit massive amounts of data at high speeds and ensure superior communication quality, but also enable real-time, high-precision fusion positioning of multi-source signals and heterogeneous networks. In addition, 5G signals have higher frequencies and bandwidths, which improves wireless ranging accuracy and enhances the signal's resistance to multipath propagation.

[0023] Because of the advantages mentioned above, OBU1 can receive 5G signals from BS1, BS2, and BS3 and use them for positioning. However, 5G signals and D2D signals are signals from two different communication systems, and finding a way to utilize both signals simultaneously for positioning requires creative effort.

[0024] The following describes the positioning method using D2D signals and 5G signals provided in this application.

[0025] The positioning method provided in this application can be divided into four stages: constructing a cooperative positioning network, cooperative positioning within the cooperative positioning network, node position updating, and network element updating, such as... Figure 2 As shown, these four stages will be introduced below.

[0026] Phase 1: Building a collaborative positioning network.

[0027] The main members of the cooperative positioning network are 5G base stations and vehicle-to-everything (V2X) OBUs, etc. Thanks to the large-scale and dense deployment of 5G base stations, OBUs can be assisted in networking and communication through 5G base stations, saving the cost of building the vehicle network system. 5G base stations can be integrated with the global positioning system (GPS) module, mainly used to provide basic location values ​​and satellite absolute time. By utilizing the low latency and high reliability of 5G, combined with multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies, time synchronization between base station clusters can be achieved, ensuring the accuracy of absolute time and relative time synchronization of each OBU in the cooperative positioning network.

[0028] Node m (e.g., OBU1) can broadcast a request to join the cooperative localization network to surrounding nodes, and join the network based on the responses from surrounding nodes, such as... Figure 1As shown, after receiving response messages from BS1, BS2, and BS3, as well as response messages from OBU2, OBU3, and OBU4, OBU1 joins the cooperative localization network composed of these nodes. This application does not limit the specific method by which node m joins the cooperative localization network.

[0029] Phase Two: Collaborative Positioning Network Cooperative Positioning.

[0030] Using time of arrival (TOA) as the basis for positioning, ranging models are established for the 5G base station to the OBU and for the OBUs themselves. Gaussian distribution models of vehicle speed and heading are then built using the optimized node states as prior values. Vehicle state prediction is performed based on these Gaussian distribution models. Then, based on the Markov process assumption, a Bayesian principle is used to construct a cooperative positioning node factor graph model and a loss function. The loss function is then optimized to obtain the OBU's location, thus solving the positioning problem. This process is as follows: Figure 3 As shown.

[0031] Before constructing the factor graph model and loss function, it is first necessary to define the state of node m.

[0032] We can define the set of OBUs containing node m at time k (such as OBU1, OBU2, OBU3, and OBU4) as follows: Define the set of BSs (such as BS1, BS2, and BS3) near node m at time k as follows: The position variable of node m at time k can be defined as: The motion variables of node m at time k can be defined as follows: in, and This represents the coordinates of node m at time k. This represents the velocity of node m at time k. This represents the heading angle of node m at time k. It also represents the state of node m at time k. It can be defined as:

[0033]

[0034] When node m performs ranging based on the TOA of the 5G signal, the ranging model can be described as follows:

[0035]

[0036] in, express Let ||·|| represent the distance from a base station bs to a node m, where ||·|| denotes the Euclidean distance. Let b represent the position variable of bs at time k. The expression represents the Gaussian white noise of the 5G signal received by node m from bs at time k, where c is the speed of light. This represents the TOA (Time of Arrival) of the 5G signal sent by bs at time k to node m.

[0037] When node m performs ranging based on the TOA of the D2D signal, the ranging model can be described as follows:

[0038]

[0039] in, express The distance from node n to node m in the given information. This represents the position variable of node n at time k. This represents the Gaussian white noise of the D2D signal received by node m from node n at time k. The TOA of the 5G signal sent by node n at time k is shown to arrive at node m.

[0040] Before solving the localization problem, the following parameters need to be defined. The state can be represented as: The distance measurement set from the 5G base station to the OBU node can be represented as: The set of distance measurements between OBU nodes can be represented as:

[0041] The localization problem can be reduced to determining the state of node m at time k. The process of determining the posterior distribution is to solve the posterior distribution likelihood equation shown in formula (4):

[0042]

[0043] The motion of the OBU in the cooperative localization network can be modeled as a Markov process, and the OBUs are independent of each other, and their motion states do not affect each other, as shown in Equation (5):

[0044]

[0045] Distance measurement between OBUs depends only on the current state and is independent of previous states. Based on the established system positioning network, the distance measurement between 5G base stations and OBUs can be considered independent. Therefore, the distance measurement likelihood equation can be divided into two parts: 5G base station measurement and cooperative measurement between OBU nodes, as shown in equation (6).

[0046]

[0047] Assuming that the noise in the ranging models between OBU nodes and between 5G base stations and OBU nodes is Gaussian white noise, and that the variables are independent, then Satisfy the requirements of formulas (7) to (9):

[0048]

[0049]

[0050]

[0051] Where w is the empirical model coefficient, and C / N0 is the signal-to-noise ratio of the 5G signal to the D2D signal.

[0052] The Bayesian algorithmic localization problem can be reduced to determining the posterior distribution of the state of node m at each time point. Therefore, the state at time k... The posterior distribution likelihood equation can be expressed as:

[0053]

[0054] Where D\m represents In addition to All variables other than those mentioned above.

[0055] Based on formulas (5) to (9), formula (10) can be transformed into:

[0056]

[0057] in, Let k be the ranging likelihood equation (i.e., the ranging likelihood function) between each OBU and the 5G base station at time k. Let k be the ranging likelihood equation between each OBU; The state of node m at time k is predicted based on the vehicle motion model of node m. Let f(x) represent the posterior distribution likelihood of node m at the previous time step (k-1).

[0058] Formula (11) can be solved using a factor graph model. For ease of description, formula (11) can be expressed as:

[0059]

[0060] in, The factor graphical model of the cooperative localization network is taken as input, and thus, the factor graphical model can be represented as follows: Figure 4 The form shown, where, The state of each OBU at time k-1. Let r be the state of each OBU at time k.1,2 Let r be the ranging likelihood equation between OBU1 and OBU2. 2,D Let OBU2 be the distance likelihood equation between OBU2 and OBUD.

[0061] In the factor graph model, each factor needs to define its corresponding noise or confidence level and its propagation. Therefore, the confidence level can be divided into prediction confidence, 5G measurement confidence, confidence from the cooperating terminal, and confidence transmitted to the cooperating terminal.

[0062] Prediction confidence can be defined as:

[0063]

[0064] Formula (12) represents the confidence of node m at time k based on the vehicle motion model from time k-1 to time k, which can be interpreted as the prediction stage in the localization process.

[0065] Positioning information from a 5G base station depends on ranging accuracy; therefore, 5G measurement confidence can be expressed as:

[0066]

[0067] The confidence from the collaborative mobile terminal can be expressed as:

[0068]

[0069] In this context, "cooperative mobile terminal" refers to the OBU (On-Board Unit) in the cooperative positioning network excluding node m, and "n" represents one OBU within the cooperative mobile terminal.

[0070] The confidence transmitted to the collaborative mobile terminal (i.e., the confidence of node m) can be expressed as:

[0071]

[0072] The following describes methods for constructing and solving loss functions. For example... Figure 5 As shown, the method includes the following.

[0073] S510, Determine the velocity likelihood function of the target vehicle. Heading likelihood function First distance likelihood function Second distance likelihood function in, Used to estimate the target vehicle's predicted location. speed, Used to estimate the target vehicle's predicted location. The course, Used to estimate the target vehicle's predicted location. The distance from the RSU to the target vehicle. Used to estimate the target vehicle's predicted location. The distance from the OBU to the target vehicle.

[0074] The target vehicle is node m. Predicted location. That is, the current location of the target vehicle needs to be determined.

[0075] Determine the velocity likelihood function and heading likelihood function Previously, the target vehicle could obtain its speed during movement using sensors such as wheel speedometers, and its heading angle during movement using an inertial measurement unit (IMU); the target vehicle could also determine the speed measurement standard deviation σ based on the sensor parameters. v and the standard deviation of the heading angle measurement σ a .

[0076] For example, the target vehicle can obtain its position at the previous time (time k-1). according to and Determine the distance d between the previous position and the predicted position, and based on... and Determine the angle α between the previous position and the predicted position. d and α can be expressed as:

[0077]

[0078]

[0079] Subsequently, the target vehicle can be determined based on d and v. (k) and σ v Determine the velocity likelihood function Furthermore, according to a, a (k) and σ a Determine the heading likelihood function

[0080] From formulas (7) to (9), it can be seen that both vehicle speed and heading follow a Gaussian distribution. and It can be represented as:

[0081]

[0082]

[0083] From formulas (7) to (9), it can be seen that the distance measurement noise between the 5G base station and the OBU, as well as between the various OBUs in the cooperative positioning network, is Gaussian noise. Therefore, the first distance likelihood function... Second distance likelihood function It can be represented as:

[0084]

[0085]

[0086] Among them, (X) bs ,Y bs ( ) represents the coordinates of a 5G base station in a cooperative positioning network. Let σ be the distance from the 5G base station to the target vehicle at the current time (time k). bs→m This represents the standard deviation of the ranging measurement for the 5G base station. The coordinates of an OBU other than the target vehicle are determined by the collaborative localization network. σ represents the distance from the OBU to the target vehicle at the current moment. n→m This represents the standard deviation of the distance measurement of the OBU.

[0087] S520, according to and Determine the position likelihood function of the target vehicle and or Negative correlation.

[0088] The position likelihood function of the target vehicle can be expressed as:

[0089]

[0090] S530, determine the predicted location area S of the target vehicle. p .

[0091] Based on the characteristics of the Gaussian distribution, the probability that the data falls within three standard deviations around the mean is 99.73%. Therefore, the range for speed and heading predictions can be determined. and a (k) -3σ a <a<a (k) +3σ a Thus, the feasible region of the optimal location solution, i.e., the predicted location region S, can be calculated using formulas (17) and (18). p .

[0092] S540, based on the predicted location area S p and position likelihood function Determine the predicted location Among them, the predicted location For position likelihood function The solution predicts the location region S. p Used to constrain the position likelihood function The solution.

[0093] Based on the causal estimation results, the likelihood function first determines the cause of the position likelihood function for the target vehicle, namely, the velocity likelihood function, the heading likelihood function, the first distance likelihood function, and the second distance likelihood function. Then, based on this cause, the position likelihood function is estimated to obtain the predicted position. Because the cause of the predicted position estimation includes the first and second distance likelihood functions, the positioning data is more comprehensive, and the predicted position obtained in this embodiment is more accurate.

[0094] Phase 3: Node position update.

[0095] Figure 6 The process of updating node positions is illustrated.

[0096] The target vehicle is first initialized with prediction confidence according to formula (13). The predicted confidence is then used as the initial confidence passed to the cooperative localization network, i.e., in equation (16). Subsequently, the target vehicle receives ranging information from the 5G base station and OBU in the cooperative positioning network, and obtains its own speed and heading.

[0097] The target vehicle determines the predicted location area S according to formulas (17) and (18). p .

[0098] The target vehicle receives measurement confidence data transmitted to it by the OBU in the cooperative localization network. The 5G measurement confidence and the location confidence from the OBU in the cooperative positioning network are calculated according to formulas (14) and (15), respectively.

[0099] The loss function is constructed based on formulas (19) to (22). In the predicted location region S p Solving the loss function Obtain the coordinates of the target vehicle at time k. and This determined the location of the target vehicle.

[0100] Subsequently, the target vehicle can... and renew The updated Substituting into formula (16), we perform the update and transmission of the confidence in the cooperative localization network, that is, Subsequently, the updated prediction confidence is used as the initial confidence passed to the cooperative localization network to predict the location at the next time step, and the node position is updated.

[0101] Optionally, the target vehicle can send its own location information to other nodes in the cooperative positioning network. In order to facilitate other nodes in the collaborative positioning network according to Update your location.

[0102] Phase 4: Network element update.

[0103] To reduce algorithm complexity and network communication burden, nodes entering the cooperative localization network need to be screened. For a node to join the network, if the vehicle is within line of sight and the signal strength is greater than the threshold RSS, then... th The vehicle can then be included in the network; the removal of nodes mainly depends on the maximum a posteriori probability calculated from the node's location. Each vehicle can be removed based on its current location information. Determine the maximum a posteriori of its own state According to the maximum a posteriori The decision to remove a node is based on whether a certain threshold is reached and the number of cooperating nodes in the current network.

[0104] For example, a target vehicle can receive a ranging signal from an OBU and calculate the signal strength of that ranging signal. If the signal strength is greater than or equal to a strength threshold, and the OBU is within line-of-sight, it indicates that the OBU is a nearby available cooperative localization node. The target vehicle can then determine a second distance likelihood function based on the OBU's ranging signal. That is, the OBU is incorporated into the cooperative positioning network of the target vehicle.

[0105] For example, the target vehicle can be determined based on its predicted location. Determine the position variable of the target vehicle at the predicted location. in, Subsequently according to and motion variables Determine the state variables of the target vehicle in, T represents the transpose matrix. The speed of the target vehicle at the predicted location. Let the heading angle of the target vehicle at the predicted position be [value]; subsequently, [value] Substitute into formula (16) to determine the positioning confidence of the target vehicle at the current time. Subsequently according to Determine the maximum posterior probability of the target vehicle. when When the probability threshold is less than or equal to the probability threshold, and when the number of nodes in the cooperative positioning network to which the target vehicle is located is greater than the number threshold, the target vehicle is determined to exit the cooperative positioning network.

[0106] When the probability threshold is less than or equal to the target vehicle's, it indicates that the target vehicle's positioning accuracy does not meet the requirements. If the number of nodes in the cooperative positioning network is large at this time, the target vehicle can exit the cooperative positioning network to reduce the impact on the positioning accuracy of other vehicles in the cooperative positioning network.

[0107] The foregoing has detailed examples of the methods for determining vehicle location provided in this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0108] This application can divide the device for determining vehicle position into functional units based on the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0109] Figure 7 This is a schematic diagram of a device for determining the position of a vehicle according to this application. The device 700 includes a processing unit 710 for performing the following steps:

[0110] Determine the velocity likelihood function of the target vehicle. Heading likelihood function First distance likelihood function Second distance likelihood function Among them, the Used to estimate the target vehicle at the predicted location The speed, the Used to estimate the target vehicle at the predicted location The course, the Used to estimate the target vehicle at the predicted location The distance from the roadside unit (RSU) to the target vehicle, Used to estimate the target vehicle at the predicted location The distance from the on-board unit (OBU) to the target vehicle;

[0111] According to the above The The and stated Determine the position likelihood function of the target vehicle. The With the The The or the aforementioned Negative correlation;

[0112] Determine the predicted location area S of the target vehicle. p ,

[0113] According to the predicted location region S p and the position likelihood function Determine the predicted location Wherein, the predicted location The position likelihood function The solution, the predicted location region S p Used to constrain the position likelihood function The solution.

[0114] Optionally, the processing unit 710 is specifically used for:

[0115] Obtain the position of the target vehicle at the previous moment.

[0116] According to the above and stated Determine the distance d between the previous position and the predicted position;

[0117] Obtain the speed v of the target vehicle at the current moment. (k) and the speed measurement standard deviation σ of the target vehicle v ;

[0118] According to the d and the v (k) and the σ v Determine the in,

[0119] Optionally, the processing unit 710 is specifically used for:

[0120] Obtain the position of the target vehicle at the previous moment.

[0121] According to the above and stated Determine the angle α between the previous position and the predicted position;

[0122] Obtain the heading angle α of the target vehicle at the current moment. (k) and the standard deviation σ of the heading angle measurement of the target vehicle a ;

[0123] According to a, a (k) and the σ a Determine the in,

[0124] Optionally, the processing unit 710 is specifically used for:

[0125] Obtain the position (X) of the RSU bs ,Y bs The distance from the RSU to the target vehicle at the current moment. and the distance standard deviation σ of the RSU bs→m ;

[0126] According to the (X) bs ,Y bs ), the and the σ bs→m Determine the in,

[0127] Optionally, the processing unit 710 is specifically used for:

[0128] Obtain the location of the OBU The distance from the OBU to the target vehicle at the current moment and the ranging standard deviation σ of the OBU n→m ;

[0129] According to the above The and the σ n→m Determine the in,

[0130] Optionally, the processing unit 710 is specifically used for:

[0131] Obtain the position of the target vehicle at the previous moment.

[0132] According to the above and stated Determine the distance d between the previous position and the predicted position;

[0133] According to the above and stated Determine the angle α between the previous position and the predicted position;

[0134] The S is determined based on d and a. p .

[0135] Optionally, the processing unit 710 is also used for:

[0136] According to the predicted location Determine the position variable of the target vehicle at the predicted location. in,

[0137] According to the above and motion variables Determine the state variables of the target vehicle in, T represents the transpose matrix, the The speed of the target vehicle at the predicted location, the The heading angle of the target vehicle at the predicted position;

[0138] According to the above Determine the location confidence of the target vehicle at the current moment.

[0139] According to the above Determine the maximum posterior probability of the target vehicle.

[0140] When the If the probability threshold is less than or equal to the target vehicle's probability threshold, and if the number of nodes in the positioning network where the target vehicle is located is greater than the number threshold, then the vehicle is determined to exit the positioning network.

[0141] Optionally, the processing unit 710 is specifically used for:

[0142] Determine the measurement confidence level of the RSU.

[0143] Determine the measurement confidence level of the OBU.

[0144] According to the above and stated Determine the

[0145] Optionally, the processing unit 710 is also used for:

[0146] Send the to the nodes in the positioning network

[0147] Optionally, the processing unit 710 is also used for:

[0148] Receive the ranging signal from the OBU;

[0149] The signal strength of the ranging signal is determined based on the ranging signal.

[0150] When the signal strength is greater than or equal to the strength threshold, and the OBU is within line-of-sight range, the determination is made.

[0151] The specific manner in which the device 700 performs the method for determining the vehicle's location and the beneficial effects thereof can be found in the relevant description in the method embodiments.

[0152] Figure 8 A schematic diagram of the structure of an electronic device for determining the location of a vehicle, as provided in this application, is shown. Figure 8 The dashed lines in the diagram indicate that the unit or module is optional. Device 800 can be used to implement the methods described in the above method embodiments. Device 800 can be an on-board unit (OBU).

[0153] The device 800 includes one or more processors 801 that can support the implementation of the methods in the method embodiments. The processor 801 can be a general-purpose processor or a special-purpose processor. For example, the processor 801 can be a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices such as discrete gates, transistor logic devices, or discrete hardware components.

[0154] The processor 801 can be used to control the device 800, execute software programs, and process data from the software programs. The device 800 may also include a communication unit 805 for inputting (receiving) and outputting (transmitting) signals.

[0155] For example, device 800 may be a chip, communication unit 805 may be the input and / or output circuit of the chip, or communication unit 805 may be the communication interface of the chip, and the chip may be a component of terminal equipment or other electronic equipment.

[0156] For example, device 800 can be a terminal device, communication unit 805 can be the transceiver of the terminal device, or communication unit 805 can be the transceiver circuit of the terminal device.

[0157] The device 800 may include one or more memories 802, which store a program 804. The program 804 can be executed by a processor 801 to generate instructions 803, causing the processor 801 to execute the method described in the above method embodiments according to the instructions 803. Optionally, the memory 802 may also store data. Optionally, the processor 801 may also read data stored in the memory 802 (e.g., the position of the target vehicle at the previous moment). This data may be stored at the same storage address as the program 804, or it may be stored at a different storage address than the program 804.

[0158] The processor 801 and memory 802 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0159] The device 800 may also include an antenna 806. The communication unit 805 is used to realize the transmission and reception functions of the device 800 through the antenna 806.

[0160] This application also provides a computer program product that, when executed by processor 801, implements the methods described in any of the method embodiments of this application.

[0161] The computer program product can be stored in memory 802, for example, program 804. Program 804 is finally converted into an executable object file that can be executed by processor 801 after processing such as preprocessing, compilation, assembly and linking.

[0162] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, implements the methods described in any of the method embodiments of this application. The computer program may be a high-level language program or an executable object program.

[0163] The computer-readable storage medium is, for example, memory 802. Memory 802 can be volatile memory or non-volatile memory, or memory 802 can include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0164] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process and technical effects of the above-described apparatus and equipment can be referred to the corresponding processes and technical effects in the foregoing method embodiments, and will not be repeated here.

[0165] In the several embodiments provided in this application, the systems, apparatuses, and methods disclosed can be implemented in other ways. For example, some features of the method embodiments described above can be omitted or not performed. The apparatus embodiments described above are merely illustrative; the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components can be combined or integrated into another system. Furthermore, the coupling between units or components can be direct coupling or indirect coupling, including electrical, mechanical, or other forms of connection.

[0166] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0167] Furthermore, the terms "system" and "network" are often used interchangeably in this paper. The term "and / or" in this paper merely describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " in this paper generally indicates that the preceding and following related objects have an "or" relationship.

[0168] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for determining the location of a vehicle, characterized in that, Applied to a target vehicle, the method includes: Determine the velocity likelihood function of the target vehicle. Heading likelihood function First distance likelihood function Second distance likelihood function Among them, the Used to estimate the target vehicle at the predicted location The speed, the Used to estimate the target vehicle at the predicted location The course, the Used to estimate the target vehicle at the predicted location The distance from the roadside unit (RSU) to the target vehicle, Used to estimate the target vehicle at the predicted location The distance from the on-board unit (OBU) to the target vehicle; According to the above The The and stated Determine the position likelihood function of the target vehicle. The With the The The or the aforementioned Negative correlation; Determine the predicted location area S of the target vehicle p , According to the predicted location region S p and the position likelihood function Determine the predicted location Wherein, the predicted location The position likelihood function The solution, the predicted location region S p Used to constrain the position likelihood function The solution.

2. The method according to claim 1, characterized in that, The determination of the target vehicle and include: Obtain the position of the target vehicle at the previous moment. According to the above and stated Determine the distance d between the previous position and the predicted position; Obtain the speed v of the target vehicle at the current moment. (k) and the speed measurement standard deviation σ of the target vehicle v ; According to the d and the v (k) and the σ v Determine the in, 3. The method according to claim 1, characterized in that, The determination of the target vehicle and include: Obtain the position of the target vehicle at the previous moment. According to the above and stated Determine the angle α between the previous position and the predicted position; Obtain the heading angle α of the target vehicle at the current moment. (k) and the standard deviation σ of the heading angle measurement of the target vehicle a ; According to a, a (k) and the σ a Determine the in, 4. The method according to claim 1, characterized in that, The determination of the target vehicle and include: Obtain the position (X) of the RSU bs ,Y bs The distance from the RSU to the target vehicle at the current moment. and the distance standard deviation σ of the RSU bs→m ; According to the (X) bs ,Y bs ), the and the σ bs→m Determine the in, 5. The method according to claim 1, characterized in that, The determination of the target vehicle and include: Obtain the location of the OBU Distance from the OBU to the target vehicle at the current moment and the ranging standard deviation σ of the OBU n→m ; According to the above The and the σ n→m Determine the in, 6. The method according to any one of claims 1 to 5, characterized in that, The predicted location area S of the target vehicle is determined. p ,include: Obtain the position of the target vehicle at the previous moment. According to the above and stated Determine the distance d between the previous position and the predicted position; According to the above and stated Determine the angle α between the previous position and the predicted position; The S is determined based on d and a. p .

7. The method according to any one of claims 1 to 5, characterized in that, Also includes: According to the predicted location Determine the position variable of the target vehicle at the predicted location. in, Based on motion variables and stated Determine the state variables of the target vehicle in, T represents the transpose matrix, the The speed of the target vehicle at the predicted location, the The heading angle of the target vehicle at the predicted position; According to the above Determine the location confidence of the target vehicle at the current moment. According to the above Determine the maximum posterior probability of the target vehicle. When the If the probability threshold is less than or equal to the target vehicle's probability threshold, and if the number of nodes in the positioning network where the target vehicle is located is greater than the number threshold, then the vehicle is determined to exit the positioning network.

8. The method according to claim 7, characterized in that, According to the Determine the location confidence of the target vehicle at the current moment. include: Determine the measurement confidence level of the RSU. Determine the measurement confidence level of the OBU. According to the above and stated Determine the 9. The method according to claim 7, characterized in that, Also includes: Send the to the nodes in the positioning network 10. The method according to any one of claims 1 to 5, characterized in that, In determining the speed likelihood function of the target vehicle Heading likelihood function First distance likelihood function Second distance likelihood function Previously, the method also included: Receive the ranging signal from the OBU; The signal strength of the ranging signal is determined based on the ranging signal. When the signal strength is greater than or equal to the strength threshold, and the OBU is within line-of-sight range, the determination is made.

11. A device for determining the position of a vehicle, characterized in that, The device includes a processor and a memory coupled together, the memory being used to store a computer program that, when executed by the processor, causes the device to perform the method of any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the method of any one of claims 1 to 10.

13. A computer program product, characterized in that, The computer program product includes computer program code that, when executed by a processor, causes the processor to perform the method of any one of claims 1 to 10.

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