Method for locating digital key, and related apparatus

Abstract

A method for locating a digital key, and a related apparatus. The method comprises: acquiring positioning information between a digital key of a vehicle and different base stations; determining, by means of a network model and on the basis of the positioning information, a result of a positioning region where the digital key is located, and / or obtaining, by means of calculation on the basis of multiple pieces of positioning information, a result of a positioning region where the digital key is located; and setting a positioning region having a highest repetition rate among results of multiple positioning regions as a region where the digital key is located.

Description

Method for positioning digital key and related device

[0001] This application claims priority to Chinese Patent Application No. 202410695306.7, filed on May 30, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0002] The present disclosure relates to the field of automotive electronics, and in particular, to a method for positioning a digital key and related device. BACKGROUND

[0003] A digital key is a scheme for realizing mutual authentication and data exchange between a vehicle and a smart device using radio technology. Based on the position of the digital key, the vehicle can perform operations such as unlocking, locking, starting, and the like. According to different transmission signals, the vehicle can generally determine the position of the digital key through infrared, ultrasonic, Bluetooth, radio frequency identification, ultra-wideband, and the like. Compared with schemes for positioning (e.g., determining the position of the digital key) based on other signals, the ultra-wideband (UWB) technology has higher positioning accuracy and better reliability and security, and thus is the current trend in the field of positioning. SUMMARY

[0004] Some embodiments of the present disclosure provide a method for positioning a digital key and related device. The positioning area with the highest repetition rate among the multiple results of the area where the digital key is located is taken as the area where the digital key is located, thereby improving the accuracy and reliability of positioning the digital key.

[0005] In a first aspect, some embodiments of the present disclosure provide a method for positioning a digital key, the method comprising:

[0006] obtaining multiple positioning information, wherein the multiple positioning information comprises information between a digital key of a vehicle and different base stations, and the different base stations are located at different positions of the vehicle; determining multiple positioning areas according to the multiple positioning information, respectively, wherein the multiple positioning areas are used to indicate an area where the digital key is located; and taking a positioning area with the highest repetition rate among the multiple positioning areas as the area where the digital key is located.

[0007] In the method, the vehicle obtains positioning information between the digital key and different base stations, determines multiple positioning results of a region where the digital key is located according to the multiple positioning information, and regards a positioning region with the highest repetition rate in the multiple positioning results as the region where the digital key is located. Even in the case that there is a base station that cannot work or collects information incorrectly among the multiple base stations, the vehicle can determine the most accurate result as the region where the digital key is located according to information collected by other base stations, and has the characteristics of high accuracy and strong robustness. Compared with the related art in which the digital key is positioned according to only one base station, the present disclosure selects the positioning result with the highest repetition rate, and can effectively avoid problems caused by a single base station. Compared with the related art in which information between a base station and a digital key is analyzed offline to realize positioning of the digital key, the present disclosure can obtain positioning information between a base station and a digital key in real time, and can process the positioning information in real time. The real-time positioning of the digital key is improved, which is beneficial to the application of the vehicle in an actual scene.

[0008] In an optional implementation of the first aspect, the determining multiple positioning regions according to the multiple positioning information respectively includes:

[0009] inputting the multiple positioning information into corresponding network models respectively to obtain a positioning region corresponding to a classification result in the multiple positioning regions, wherein the classification result is an output result of the network model, and the network model is obtained by training according to information between the digital key and any base station in the different base stations;

[0010] determining a positioning region corresponding to a position coordinate in the multiple positioning regions according to any two positioning information in the multiple positioning information, wherein the positioning region corresponding to the position coordinate is a region where the position coordinate is located.

[0011] In the method, the vehicle can determine a positioning region corresponding to the digital key according to the positioning information by using the network model, and can further determine a positioning region where the digital key is located by calculation according to the multiple positioning regions. The positioning region where the digital key is located is determined by multiple means, and errors caused by a single positioning method are avoided.

[0012] In an optional implementation of the first aspect, in the case that the different base stations include a first base station and a second base station, the positioning information includes first positioning information corresponding to the first base station and second positioning information corresponding to the second base station, the network model includes a first network model corresponding to the first base station and a second network model corresponding to the second base station, and the determining multiple positioning regions according to the multiple positioning information respectively includes:

[0013] determining three positioning regions according to the first positioning information and the second positioning information.

[0014] In the above method, using two base stations can effectively improve the accuracy of positioning the digital key in consideration of cost factors. Among them, compared with using one base station, the two base stations respectively communicate with the data key, and respectively obtain the first positioning information and the second positioning information between the digital key. The vehicle can determine three positioning areas according to the first positioning information and the second positioning information, that is, three positioning results of the area where the digital key is located. If the area where the digital key is located is determined according to the three positioning results, the possibility of positioning error can be greatly reduced.

[0015] For example, if there is only one base station on the vehicle, if the base station does not work, the digital key cannot be positioned. If the data collected by the base station is incorrect, the accuracy of the positioning result will be seriously affected. However, some embodiments of the present disclosure use two base stations. Even if one of the base stations does not work or the information collected by one of the base stations is incorrect, the vehicle can still position the digital key according to the information collected by the other base station. Compared with using three, four or even more base stations, using two base stations can effectively solve the problem of low positioning accuracy of the digital key. Considering the cost factor, using two base stations is the most suitable solution, which meets the actual production and application.

[0016] In an optional implementation of the first aspect, the determining three positioning areas according to the first positioning information and the second positioning information comprises:

[0017] inputting the first positioning information into the first network model to obtain a first positioning area corresponding to a first classification result in the three positioning areas, wherein the first classification result is a result output by the first network model;

[0018] inputting the second positioning information into the second network model to obtain a second positioning area corresponding to a second classification result in the three positioning areas, wherein the second classification result is a result output by the second network model;

[0019] determining a third positioning area corresponding to the position coordinates in the three positioning areas according to the first positioning information and the second positioning information.

[0020] In the method, the first positioning area is determined by the first network model, and the second positioning area is determined by the second network model. Two network models are used to meet the characteristics of the first base station and the second base station respectively. If only one network model is used to determine the positioning area, the first positioning information corresponding to the first base station and the second positioning information corresponding to the second base station need to be input into the network model at the same time. If one of the two base stations cannot collect the positioning information with the digital key, the result cannot be output by the network model according to the positioning information collected by the other base station. Therefore, according to the two network models, the robustness in extreme cases can be dealt with.

[0021] In an optional implementation of the first aspect, the method further includes:

[0022] The positioning area with the highest repetition rate among the first positioning area, the second positioning area, and the third positioning area is determined as the area where the digital key is located.

[0023] In the method, because the first positioning area, the second positioning area, and the third positioning area are all results of the area where the digital key is located determined according to the positioning information, the positioning result with the highest accuracy, that is, the positioning result with the highest repetition rate, among the three positioning results is determined as the area where the digital key is located, thereby ensuring the stability and reliability of positioning.

[0024] In an optional implementation of the first aspect, the positioning information includes one or more of the following: feature information corresponding to channel impulse response information between the digital key and the base station, distance between the digital key and the base station, and phase difference between the digital key and the base station, wherein the phase difference between the digital key and the base station is the phase difference corresponding to the arrival of the signal sent by the digital key to the multiple antennas of the base station.

[0025] In the above method, the feature information corresponding to the channel impulse response information between the digital key and the base station, the distance and the phase difference are comprehensively considered in the present disclosure. Because the feature information corresponding to the channel impulse response information is mainly used to determine whether the digital key is located inside or outside the vehicle, and the phase difference is mainly used to determine the incident angle of the signal received by the base station from the digital key, thereby determining the actual position of the digital key. In addition, when the digital key is inside the vehicle, the phase difference between the digital key and the base station obtained by the vehicle can be inaccurate, and the feature information corresponding to the channel impulse response information and the distance are needed to assist in determining. When the distance between the digital key and the vehicle is small, the feature information corresponding to the channel impulse response information obtained by the vehicle is relatively accurate, and when the distance between the digital key and the vehicle is large, the accuracy of the feature information corresponding to the channel impulse response information obtained by the vehicle is low, and the distance and the phase difference are needed to assist in determining. Therefore, the feature information corresponding to the channel impulse response information between the digital key and the base station, the distance and the phase difference are determined after in-depth research and are parameters suitable for practical application.

[0026] At the same time, the feature information corresponding to the channel impulse response information between the digital key and the base station is used instead of directly using the channel impulse response information between the digital key and the base station, because the signal can be affected by various factors during transmission, thereby causing the channel impulse response information between the digital key and the base station at the same position to be inconsistent at different times, affecting the positioning result of the digital key. However, the feature information of the channel impulse response information between the digital key and the base station at the same position is basically consistent, so the embodiment extracts the feature of the channel impulse response information to obtain the feature information corresponding to the channel impulse response information between the digital key and the base station, which can ensure the accuracy of positioning the digital key. Moreover, the data amount of the feature information corresponding to the channel impulse response information is much smaller than that of the channel impulse response information, which can save computing power and improve positioning speed.

[0027] In a second aspect, some embodiments of the present disclosure provide a training method for positioning a digital key model, the method comprising:

[0028] Obtaining a plurality of positioning information between the digital key of the vehicle and the base station when the digital key is in different areas, wherein the base station is located in the vehicle;

[0029] Taking the area corresponding to each of the plurality of positioning information as the classification result of the digital key, and constructing a training set;

[0030] The network model is trained according to the training set, and a trained network model is obtained, wherein the trained network model is configured to output the classification result according to the positioning information between the digital key and the base station, and the classification result is configured to indicate the area where the digital key is located.

[0031] In the above method, the positioning information between the base station and the digital key is first obtained in different areas, so as to construct a training set corresponding to different areas. The network model trained according to the training set can directly output the corresponding result according to the positioning information. In actual use, the efficiency of positioning the digital key is improved.

[0032] In a third aspect, some embodiments of the present disclosure provide a device for positioning a digital key, the device comprising:

[0033] A communication unit configured to obtain a plurality of positioning information, wherein the plurality of positioning information comprises information between a digital key of a vehicle and different base stations located at different positions of the vehicle;

[0034] A processing unit configured to determine a plurality of positioning areas according to the plurality of positioning information, wherein the positioning area is used to indicate the area where the digital key is located;

[0035] The processing unit is further configured to take the positioning area with the highest repetition rate in the plurality of positioning areas as the area where the digital key is located.

[0036] In an optional scheme of the third aspect, the processing unit, for determining a plurality of positioning areas according to the plurality of positioning information, comprises:

[0037] inputting the plurality of positioning information into corresponding network models respectively to obtain the positioning area corresponding to the classification result in the plurality of positioning areas, wherein the classification result is the result output by the network model, and the network model is trained according to the information between the digital key and the base station;

[0038] determining the positioning area corresponding to the position coordinates in the plurality of positioning areas according to any two positioning information in the plurality of positioning information, wherein the positioning area corresponding to the position coordinates is the area where the position coordinates are located.

[0039] In an optional embodiment of the third aspect, the processing unit is configured to, when the different base stations include a first base station and a second base station, wherein the positioning information includes first positioning information corresponding to the first base station and second positioning information corresponding to the second base station, and the network model includes a first network model corresponding to the first base station and a second network model corresponding to the second base station, and the step of determining multiple positioning areas based on the multiple positioning information includes:

[0040] Three positioning areas are determined based on the first positioning information and the second positioning information.

[0041] In an alternative approach of the third aspect, the processing unit determines three positioning areas based on the first positioning information and the second positioning information, including:

[0042] The first positioning information is input into the first network model to obtain the first positioning region corresponding to the first classification result in the three positioning regions, wherein the first classification result is the result output by the first network model;

[0043] The second positioning information is input into the second network model to obtain the second positioning region corresponding to the second classification result in the three positioning regions, wherein the second classification result is the result output by the second network model;

[0044] Based on the first positioning information and the second positioning information, the third positioning region corresponding to the position coordinates in the three positioning regions is determined.

[0045] In an alternative embodiment of the third aspect, the processing unit selects the location area with the highest repetition rate among the plurality of location areas as the area where the digital key is located, including:

[0046] The location with the highest repetition rate among the first, second, and third location areas is selected as the area where the digital key is located.

[0047] In one optional aspect of the third aspect, the positioning information includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the base station, the distance between the digital key and the base station, and the phase difference between the digital key and the base station, wherein the phase difference between the digital key and the base station is the phase difference corresponding to the signal sent by the digital key to the base station reaching multiple antennas of the base station.

[0048] Fourthly, some embodiments of this disclosure provide a vehicle including a processor and a memory, the processor being coupled to the memory, the memory storing a computer program, the processor being configured to invoke and run the computer program, causing the vehicle to perform the methods described in either the first or second aspect above.

[0049] Fifthly, some embodiments of this disclosure provide a computing device including a processor and a memory; the processor is coupled to the memory, the memory storing a computer program, and the processor is configured to invoke and run the computer program to cause the computing device to perform the methods described in either the first or second aspect above.

[0050] In some embodiments, the computing device further includes a communication interface for receiving and / or sending data, and / or for providing input and / or output to the processor.

[0051] It should be noted that the above embodiments are illustrated using a processor (or general-purpose processor) that executes the method by invoking a computer-specified instruction. In practice, the processor can also be a dedicated processor, in which case the computer instructions are pre-loaded into the processor. Optionally, the processor can include both dedicated and general-purpose processors.

[0052] In some embodiments, the processor and memory may also be integrated into a single device, meaning that the processor and memory can be integrated together.

[0053] Sixthly, some embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when run on a computer or processor, implements the method described in either the first or second aspect above.

[0054] The beneficial effects of the technical solutions provided in the third to sixth aspects of this disclosure can be referred to the beneficial effects of the technical solutions in the first and second aspects, and will not be repeated here. Attached Figure Description

[0055] The accompanying drawings used in the description of some embodiments of this disclosure will be briefly introduced below.

[0056] Figure 1 is a schematic diagram of a system architecture for locating a digital key according to some embodiments of the present disclosure;

[0057] Figure 2 is a flowchart of a method for locating a digital key according to some embodiments of the present disclosure;

[0058] Figure 3 is a schematic diagram of feature extraction of a CIR signal according to some embodiments of the present disclosure;

[0059] Figure 4 is a schematic diagram of the phase difference between a digital key and a base station according to some embodiments of the present disclosure;

[0060] Figure 5 is a schematic diagram of a positioning area according to some embodiments of the present disclosure;

[0061] Figure 6 is a flowchart of a method for determining the area where a digital key is located according to some embodiments of the present disclosure;

[0062] Figure 7 is a flowchart of a method for training a network model according to some embodiments of the present disclosure;

[0063] Figure 8 is a block diagram of the functional units of a device for locating a digital key according to some embodiments of the present disclosure;

[0064] Figure 9 is a schematic diagram of a computing device according to some embodiments of the present disclosure;

[0065] Figure 10 is a block diagram of a vehicle according to some embodiments of the present disclosure. Detailed Implementation

[0066] The following describes some embodiments of this disclosure in detail with reference to the accompanying drawings.

[0067] The terms "first," "second," "third," and "fourth," etc., in this disclosure, claims, and accompanying drawings are used to distinguish different objects and not to describe a particular order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0068] Typically, the solution for locating digital keys involves deploying UWB (Ultra Wide Band) base stations on the vehicle. The digital key's location is then determined by signal transmission between the digital key and the UWB base station. However, if a UWB base station malfunctions, the vehicle cannot locate the digital key based on that base station, impacting the user experience.

[0069] To facilitate understanding of the location digital key method and related apparatus of some embodiments of this disclosure, the technical problems to be solved by this disclosure are analyzed and proposed below.

[0070] The following are three methods for locating digital keys in related technologies.

[0071] The first approach involves installing six UWB base stations in different locations on the vehicle, such as a "2+4" layout. Four UWB base stations are placed externally: one each at the front left and right headlights, and one each at the rear left and right headlights. Two UWB base stations are placed inside the vehicle: one near the rearview mirror and one on the rear seat roof. The four external base stations are used for distance measurement when the digital key is outside the vehicle. The two internal base stations are used for distance measurement when the digital key is inside the vehicle. Communication is then established between these six UWB base stations and the motion sensor on the digital key to obtain its location data. The vehicle processes this location data to pinpoint the digital key. The drawbacks are that six UWB base stations are costly, and their role in locating the digital key is irreplaceable. If any of the six base stations fails to obtain the digital key's location data, the location result will be affected. Furthermore, the vehicle is located solely based on the location information from the digital key, resulting in a limited range of data processing and making it difficult to guarantee the accuracy of the location results.

[0072] The second method involves acquiring the Channel Impulse Response (CIR) signals between the digital key and the base station at different locations. A CIR database is then built based on the locations corresponding to these CIR signals. After the vehicle acquires the CIR signals between the digital key and the base station, it can match these CIR signals with multiple CIR signals in the database to find a matching CIR signal. The location corresponding to the matched CIR signal is then taken as the location of the digital key. However, in practical applications, comparing the acquired CIR signals with the database is time-consuming. Furthermore, since the database relies on calibration data, the accuracy of results obtained by comparing the acquired CIR signals with the database is low, which is not conducive to locating the digital key.

[0073] The third approach involves extracting multipath and non-multipath features from the CIR signal between the base station and the digital key, and then locating the digital key based on these features. However, this method has a relatively simple application scenario and uses offline CIR datasets for analysis, making real-time digital key location impossible. Therefore, this approach lacks real-time capability and is not suitable for practical applications.

[0074] Here, multipath can refer to a signal reaching the receiver through multiple paths (e.g., a device in a vehicle that receives CIR signals); non-multipath can refer to a signal reaching the receiver through a single path.

[0075] In summary, the following three problems exist in the relevant technologies for locating digital keys:

[0076] (1) To ensure the accuracy of digital key positioning, there are usually requirements on the number of base stations. For example, the number of base stations should be as large as possible to cover all areas around the vehicle. This results in high cost and poor flexibility in the use of digital keys.

[0077] (2) In applications involving the location of digital keys for vehicles, whether the research is conducted inside the vehicle (e.g., for location) or outside the vehicle, most applications fall under non-line-of-sight (NLOS) scenarios. If wireless signals propagate in a non-line-of-sight manner in NLOS scenarios, it can lead to location errors of up to hundreds of meters, becoming the primary source of location errors. Relying solely on the multipath and non-multipath characteristics in CIR signals for digital key location is insufficient to eliminate the impact of NLOS scenarios on digital key location.

[0078] (3) Current positioning schemes that use the CIR signal between the base station and the digital key typically compare the CIR signal between the base station and the digital key with a pre-set CIR database, and use the location corresponding to the CIR signal as the location of the digital key. The above scheme is time-consuming, has poor anti-interference capabilities, and the positioning result depends on the calibration of the CIR database, resulting in low accuracy of the positioning result.

[0079] In view of this, some embodiments of this disclosure provide a method for locating a digital key, which obtains location information between the digital key and different base stations, including feature information extracted from the CIR signals between the digital key and the base stations. Then, the vehicle determines multiple location areas based on the multiple location information. Finally, the vehicle selects the location area with the highest repetition rate among the multiple location areas as the location of the digital key.

[0080] The following describes the system architecture used in some embodiments of this disclosure. It should be noted that the system architecture and business scenarios described in this disclosure are for the purpose of more clearly illustrating the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided by this disclosure. Those skilled in the art will understand that, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided by this disclosure are equally applicable to similar technical problems.

[0081] Figure 1 is a schematic diagram of a system architecture for locating a digital key according to some embodiments of the present disclosure. As shown in Figure 1, the system includes a vehicle 10 and a digital key 11, and the vehicle 10 includes multiple base stations.

[0082] Vehicle 10 can be a vehicle powered by electricity, a vehicle powered by gasoline, or a vehicle powered by a new energy hybrid powertrain. For example, when vehicle 10 is powered by electricity, it can be a new energy vehicle, such as a pure electric vehicle, a range-extended electric vehicle, a hybrid electric vehicle, or a fuel cell electric vehicle. When vehicle 10 is powered by gasoline, it can be a car, an agricultural transport vehicle, a tractor, or a trailer. When vehicle 10 is a car, it can be a sedan, an SUV, a truck, a bus, or a van.

[0083] Vehicle 10 includes multiple base stations. For example, Figure 1 illustrates a vehicle 10 with two base stations (e.g., a first base station 101 and a second base station 102). However, vehicle 10 may not be limited to these two base stations. This disclosure does not limit the number or location of base stations; the number and location can be determined based on actual needs. A base station is a site capable of communicating with the digital key 11 wirelessly. This disclosure does not limit the form of the base station, as long as it can establish communication with the digital key 11. The form of the base station can be determined based on the wireless communication method between the base station and the digital key 11. For example, when the base station communicates with the digital key 11 using ultra-wideband technology, the base station can be a UWB anchor point.

[0084] The digital key 11 can be a radio key (also known as a remote key) or a mobile device. Mobile devices include, but are not limited to, programmable mobile phones (e.g., smartphones), laptops, or wearable devices. Wearable devices can be devices such as smartwatches, smart bracelets, or Near Field Communication (NFC) smart cards. Unlike traditional mechanical keys, the digital key 11 can not only unlock or start the vehicle 10, but also interact with the vehicle 10 to enable functions such as personalized vehicle settings.

[0085] In one possible implementation, if the vehicle 10 has a first base station 101, a second base station 102 and a third base station, then the vehicle 10 obtains the first positioning information between itself and the digital key 11 through the first base station 101, the second positioning information between itself and the digital key 11 through the second base station 102, and the third positioning information between itself and the digital key 11 through the third base station.

[0086] Then, the first, second, and third positioning information are input into the corresponding network models to obtain the first positioning area corresponding to the first positioning information, the second positioning area corresponding to the second positioning information, and the fourth positioning area corresponding to the third positioning information. The network model is trained based on the positioning information between the digital key 11 and the base station. The vehicle 10 can train the network model based on the positioning information between the digital key 11 and the base station, and the vehicle 10 can also acquire the trained network model.

[0087] Vehicle 10 can also determine a positioning area based on any two of a plurality of positioning information. For example, a third positioning area can be determined based on the first and second positioning information, a fifth positioning area can be determined based on the second and third positioning information, or a sixth positioning area can be determined based on the first and third positioning information.

[0088] Finally, the vehicle 10 uses the location with the highest repetition rate among the above multiple location areas as the location of the digital key 11. For example, the location with the highest repetition rate among the first, second, third, fourth, fifth, and sixth location areas is used as the location of the digital key 11.

[0089] Figure 2 is a flowchart of a method for locating a digital key according to some embodiments of the present disclosure. The method is applied to the system shown in Figure 1. As shown in Figure 2, the method includes, but is not limited to, steps S201 to S203.

[0090] Step S201: Obtain multiple location information.

[0091] For example, before locating a digital key, a vehicle can first obtain location information between the digital key and a base station via a base station. Since the base station is located inside the vehicle, obtaining the location information between the digital key and the base station is equivalent to obtaining the location information between the digital key and the vehicle itself. The vehicle can also obtain location information between the digital key and different base stations. For instance, if there are two base stations located at different locations within the vehicle, the vehicle can obtain location information between itself and the digital key through each of these base stations.

[0092] In one possible implementation, the location information acquired by the vehicle includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the base station, the distance between the digital information and the base station, and the phase difference between the digital key and the base station.

[0093] Channel impulse response information refers to the channel's response to an input impulse signal. For example, in ultra-wideband (UWB) technology, UWB base stations receive impulse signals from UWB tags or reflecting objects. These impulse signals contain information about objects in the environment, such as their position and motion state. By analyzing these impulse signals using UWB technology, vehicles can perform functions such as locating or detecting objects in the environment.

[0094] In some embodiments of this disclosure, after acquiring the channel impulse response information between the digital key and the base station, the vehicle performs feature extraction on this channel impulse response information to obtain feature information corresponding to the channel impulse response information between the digital key and the base station. This is because signals may be affected by various factors during transmission, resulting in inconsistent channel impulse response signals between a digital key and a base station at the same location at different times, affecting the positioning result of the digital key. However, the feature information of the channel impulse response signals between a digital key and a base station at the same location is basically consistent. Therefore, by performing feature extraction on the channel impulse response information in some embodiments of this disclosure to obtain the feature information corresponding to the channel impulse response information between the digital key and the base station, the accuracy of vehicle positioning of the digital key can be guaranteed.

[0095] For example, Figure 3 is a schematic diagram of feature extraction of CIR signals provided by some embodiments of this disclosure. As shown in Figure 3, the vehicle mainly extracts the basic features, window features, and attenuation features of the CIR signal between the digital key and the base station. The feature information of the CIR signal will be described below from the three aspects of basic features, window features, and attenuation features.

[0096] The CIR signal can be expressed as follows: h(t), t=0,…,end

[0097] Where t represents time, and t is greater than or equal to 0. h(t) represents the amplitude of the CIR signal at time t.

[0098] As shown in Figure 3, the vehicle extracts two features from the basic characteristics of the CIR signal: energy and maximum amplitude. For example, energy can be determined according to the following expression: ∫h(t)dt

[0099] Where h(t) represents the amplitude of the CIR signal at time t.

[0100] The maximum amplitude can be determined by the following expression: maxh(t)

[0101] Here, since h(t) represents the amplitude of the CIR signal at time t, the maximum amplitude can be the maximum value of h(t).

[0102] As shown in Figure 3, the vehicle extracts two features from the window features of the CIR signal: the Echo Density Profile (EDP) and the time-frequency domain features. For example, the vehicle uses a Hanning window of length N and 50% coverage to window the CIR signal, and then extracts features from the CIR signal corresponding to each window. For example, the length N of the Hanning window can be 128.

[0103] The echo density profile (EDP) can be determined using the following expression:

[0104] Where τ can take any value from (t-δ) to (t+δ). δ represents the window sampling length for windowing the CIR signal, where 2δ+1 is the sampling length of the window. σ is the standard deviation of the window, which can be determined according to the following expression:

[0105] Furthermore, the 'w' in the echo density profile expression is a sliding window, such as a Hanning window, used to window the CIR signal. The expression for 'w' is as follows: w(n) = 0.54 - 0.46cos(2πn / N), 0 ≤ n ≤ N

[0106] As shown in Figure 3, the vehicle extracts features such as spectral centroid, spectral bandwidth, spectral roll-off point, and spectral flatness from the time-frequency domain characteristics of the CIR signal. These features can be used to represent the frequency distribution of the CIR signal.

[0107] The spectral centroid (SC) can be determined according to the following expression:

[0108] Among them, f i Let |X(f) be the frequency of the windowed CIR signal at point i. i )| represents the amplitude value of the windowed CIR signal at point i after Fourier transform.

[0109] The spectral bandwidth (SBW) can be determined using the following expression:

[0110] Where SC is the centroid of the above spectrum, f i Let |X(f) be the frequency of the windowed CIR signal at point i. i )| represents the amplitude value of the windowed CIR signal at point i after Fourier transform.

[0111] The Spectral Rolloff (SR) is used to indicate the index of frequencies in the windowed CIR signal that fall below a specified percentage of the total spectral energy. For example, the specified percentage could be 90% or 85%. When the specified percentage is 85%, the SR can be determined using the following expression:

[0112] Where g is the spectral roll-off point, f i Let |X(f) be the frequency of the windowed CIR signal at point i. i )| represents the amplitude value of the windowed CIR signal at point i after Fourier transform.

[0113] Spectral flatness (SF) can be determined by the following expression:

[0114] Where p(n) is the power spectrum of the window function, and the window function used in some embodiments of this disclosure is the Hanning window.

[0115] As shown in Figure 3, the vehicle extracts two features from the attenuation characteristics of the CIR signal: attenuation time index and peak attenuation index.

[0116] The calculation process for the Decay Time Index (DTI) is as follows:

[0117] First, the vehicle determines the energy decay (EDC) function of the CIR signal according to the following expression:

[0118] Where temp is the cutoff time of the CIR signal. EDC(t) represents the energy of the CIR signal, that is, the amount of energy remaining in the CIR signal at time t as time progresses.

[0119] Then, the DTI is determined based on the variance of EDC. For example, assuming t∈[0, 1024], a total of 1025 values ​​from EDC(0) to EDC(1024) are determined according to the above expression regarding EDC. The variances of EDC(0) to EDC(1024) are calculated with a certain step size. For example, a step size of 10 is used. The variances var1 from EDC(0) to EDC(9), var2 from EDC(10) to EDC(19), and so on are calculated respectively.

[0120] Finally, the vehicle determines the maximum value of the aforementioned variance as the DTI. The index position corresponding to the maximum value of the variance is the index position where the CIR signal attenuation is most severe.

[0121] The calculation process for the Peak Deacy Exponent (PDE) is as follows:

[0122] First, the vehicle determines the attenuation exponential function CEF(t) after the first peak of the CIR signal according to the following expression: CEF(t) = A p e (-γt)

[0123] Then, the amplitude A of the CIR signal h(t) after the first peak occurs. p Substituting this into the decay exponential function CEF(t) above, we can obtain the unknown γ.

[0124] Finally, the vehicle uses the value obtained by fitting the unknown γ using the least squares method as the value of PDE.

[0125] It is understandable that the aforementioned extraction of CIR signal features facilitates the calculation and statistical analysis of characteristics such as attenuation, peak value, frequency distribution, and amplitude distribution of the CIR signal. Compared to directly comparing the acquired CIR signal between the base station and the digital key with a pre-determined CIR signal database to locate the digital key, some embodiments of this disclosure calculate and statistically analyze the attenuation, peak value, frequency distribution, and amplitude distribution of the CIR signal, and then determine the area where the digital key is located based on these features. This solves the problem of mismatch between the CIR signal between the base station and the digital key and the pre-determined CIR signal database, which makes it difficult to determine the area where the digital key is located. Furthermore, the amount of feature information is less than the amount of CIR signal data, which is beneficial for improving the training efficiency of the subsequent network model.

[0126] In one possible implementation, the distance d between the digital key and base station i is... i This can be determined using the two-way time-of-flight method. For example, the digital key first sends a data packet to base station i and records the packet transmission time t. ia After receiving the data packet, base station i records the packet reception time t. ib Then, base station i returns the data packet to the digital key and records the time t when the data packet is returned. ib After receiving the aforementioned returned data packet, the digital key records the time t when the returned data packet was received. ia Then, the distance between the digital key and base station i is determined according to the following expression: d i =c*[(t ia ′-t ia )-(t ib -t ib ′)]

[0127] Where c is the speed of light.

[0128] In one possible implementation, the phase difference between the digital key and the base station, i.e., the phase-difference-of-arriva (PDOA), is the phase difference that occurs when the signal sent by the digital key to the base station reaches multiple antennas of the base station.

[0129] For example, Figure 4 is a schematic diagram of the phase difference between a digital key and a base station according to some embodiments of this disclosure. As shown in Figure 4, taking a base station comprising two antennas as an example, the distance between antenna element 1 and antenna element 2 is d. The distances at which the signal sent by the digital key to the base station reaches antenna element 1 and antenna element 2 are different. The vehicle can determine the phase difference between the digital key and the base station by calculating the difference between the signal received by antenna element 1 and the signal received by antenna element 2. The angle of the signal received by antenna element 1 is θ. The signal sent by the digital key to the base station is expressed as follows:

[0130] The signal received by antenna element 1 is given by expression (1): x0=S0(t) (1)

[0131] The signal received by antenna element 2 is given by expression (2):

[0132] According to expressions (1) and (2), it can be seen that the signal received by antenna element 1 differs from the signal received by antenna element 2. Therefore, the phase difference between the digital key and the base station is the phase difference between the signals received by antenna element 1 and antenna element 2 of the base station, which is the value in expression (2). In one possible implementation, the vehicle acquires PDOA provided by the UWB base station.

[0133] Step S202: Determine multiple positioning areas based on multiple positioning information.

[0134] A location area is a pre-defined region. The vehicle can locate the digital key by determining its location within that area. Since multiple base stations are located at different locations on the vehicle, the vehicle can obtain location information between itself and the digital key through these base stations. Based on this location information, multiple location areas are determined, and these areas indicate the location of the digital key.

[0135] For example, Figure 5 is a schematic diagram of a positioning area according to some embodiments of this disclosure. As shown in Figure 5, the area for locating the digital key is divided into four positioning areas as needed: an interior area, an unlocking area, a welcoming area, and an invalid area. These four positioning areas are independent of each other, and the vehicle can perform corresponding operations based on the area where the digital key is located. For example, when the digital key is in the interior area (which refers to all areas inside the vehicle), the vehicle can start the engine, facilitating driving or operation by the user. When the digital key is in the unlocking area, the vehicle can unlock the doors. When the digital key is in the welcoming area, the vehicle determines that a user is approaching and can alert the user by honking the horn or illuminating the lights. When the digital key is in the invalid area, the user carrying the digital key is too far from the vehicle, and the vehicle does not respond.

[0136] In one possible implementation, the vehicle inputs multiple positioning information into the corresponding network model to obtain the positioning regions corresponding to the classification results in multiple positioning regions. The vehicle can also determine the positioning region corresponding to the position coordinates in multiple positioning regions based on any two positioning information from the multiple positioning information.

[0137] The classification result is the output of the network model, which is trained based on the information between the digital key and the base station. The location area corresponding to the location coordinates is the region where the location coordinates are located. Since the network model is trained based on the information between the digital key and the base station, the vehicle can use the classification result corresponding to the location information output by the network model. Then, the location area where the digital key is located is determined based on the classification result. Alternatively, the vehicle can also determine the location coordinates of the digital key based on the two location information corresponding to any two base stations, and then determine the location area where the digital key is located based on the location coordinates.

[0138] In one possible implementation, taking two base stations as an example, where the base stations include a first base station and a second base station, the positioning information includes first positioning information corresponding to the first base station and second positioning information corresponding to the second base station. The network model includes a first network model corresponding to the first base station and a second network model corresponding to the second base station. The vehicle can determine the positioning results of the three digital keys, i.e., three positioning areas, based on the first and second positioning information. For example, the vehicle can determine the area where the digital key is located as the first positioning area based on the first positioning information. The vehicle can determine the area where the digital key is located as the second positioning area based on the second positioning information, and the vehicle can determine the area where the digital key is located as the third positioning area based on the first and second positioning areas.

[0139] In one possible implementation, the vehicle inputs first positioning information into a first network model to obtain a first positioning region corresponding to a first classification result. The vehicle inputs second positioning information into a second network model to obtain a second positioning region corresponding to a second classification result. The vehicle determines a third positioning region corresponding to the location coordinates of the digital key based on the first and second positioning information. The first classification result is the output of the first network model, and the second classification result is the output of the second network model.

[0140] Because the vehicle has two base stations, a first base station and a second base station, located at different positions, the vehicle can obtain location information between itself and the digital key through both base stations. For example, the vehicle obtains first location information between itself and the digital key using the first base station, and second location information using the second base station. Then, the vehicle uses a first network model corresponding to the first base station and a second network model corresponding to the second base station to derive a classification result. For example, the vehicle determines a first classification result based on the first network model, and determines a first location area where the digital key is located based on the first classification result. The vehicle then determines a second classification result based on the second network model, and determines a second location area where the digital key is located based on the second classification result.

[0141] In one possible implementation, the vehicle determines the third positioning area based on the position coordinates determined by the first positioning information and the second positioning information.

[0142] For example, the vehicle obtains the first phase difference between the digital key and the first base station in the first location information, and obtains the second phase difference between the digital key and the second base station in the second location information. For example, see the embodiment section regarding the phase difference between the digital key and the base station in step S201. That is, the vehicle obtains the phase difference corresponding to expression (2) in step S201. Value. For example, the first phase difference between the digital key and the first base station. Second phase difference between digital key and second base station

[0143] Then, the vehicle determines the first phase difference according to the following expression. The angle of arrival of the corresponding signal can be seen in Figure 4, which is the angle θ, i.e., the first phase difference. The corresponding incident angle θ1 when the electromagnetic wave reaches the antenna of the first base station:

[0144] Where λ is the first phase difference The wavelength of the corresponding signal, d is the distance between the two antennas in the first base station.

[0145] The vehicle determines the second phase difference according to the following expression. The corresponding signal's angle of arrival, i.e., the second phase difference The corresponding incident angle θ2 when the electromagnetic wave reaches the antenna of the second base station:

[0146] Where λ is the second phase difference The wavelength of the corresponding signal, d is the distance between the two antennas in the second base station.

[0147] The vehicle calculates the position coordinates of the digital key based on the aforementioned angle of arrival, using the following expression:

[0148] Where θ1 is the angle of arrival of the signal corresponding to the first phase difference, and θ2 is the angle of arrival of the signal corresponding to the second phase difference. (x1, y1) are the location coordinates of the first base station, and (x2, y2) are the location coordinates of the second base station. t y t ) represents the location coordinates of the digital key.

[0149] Finally, the vehicle can solve the above expression for the digital key's position coordinates using the least squares method to determine the digital key's position coordinates (x...). t y t Therefore, the third positioning area is determined based on the area corresponding to the location coordinates of the digital key.

[0150] In one possible implementation, if the vehicle cannot obtain the corresponding location information through either the first or second base station, then the vehicle can input the location information obtained from the other base station into the corresponding network model. Based on the classification results output by the network model, the location area is determined, and then the obtained location area is directly used as the area where the digital key is located.

[0151] Step S203: The location area with the highest repetition rate among multiple location areas is selected as the area where the digital key is located.

[0152] For example, since multiple location areas are determined based on at least one of different location information or different network models, and all are used to indicate the area corresponding to the digital key, to ensure the accuracy of digital key location, the location area with the highest repetition rate among the multiple location areas can be identified as the area where the digital key is located. Even if there are erroneous location areas among the multiple location areas, the area with the highest repetition rate can be determined as the area where the digital key is located. In this way, erroneous results from the multiple location areas can be effectively eliminated, improving the accuracy and reliability of location.

[0153] In one possible implementation, an example is given where a first base station and a second base station are located at different positions on the vehicle. The vehicle obtains a first positioning area, a second positioning area, and a third positioning area in step S202. Then, the vehicle votes on these three positioning areas, selecting the area with the highest repetition rate as the region where the digital key is located. For example, if the first positioning area is the unlocking area, the second positioning area is the welcoming area, and the third positioning area is the unlocking area, it can be seen that the unlocking area has the highest repetition rate. Therefore, the vehicle determines the region corresponding to the digital key as the unlocking area. The vehicle's voting on the results of these three positioning areas can resolve the problem of incorrectly determined positioning areas due to calculation errors or errors in base station information collection, thus improving the stability and accuracy of positioning.

[0154] For example, Figure 6 is a flowchart of a method for determining the area where a digital key is located, provided in some embodiments of this disclosure. As shown in Figure 6, taking the presence of a first base station and a second base station located at different positions on a vehicle as an example, the method for determining the area where the digital key is located includes, but is not limited to, one or more of S601-S606.

[0155] S601, Obtain first positioning information.

[0156] The vehicle obtains the first positioning information between the digital key and the first base station in real time through the first base station. The first positioning information includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the first base station, the distance between the digital key and the first base station, and the phase difference between the digital key and the first base station.

[0157] S602, Obtain second positioning information.

[0158] The vehicle obtains second positioning information between the digital key and the second base station in real time via the second base station. The second positioning information includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the second base station, the distance between the digital key and the second base station, and the phase difference between the digital key and the second base station.

[0159] S603, input the first positioning information into the first network model.

[0160] The vehicle inputs the first positioning information obtained by S601 into the first network model to obtain the first positioning area corresponding to the first classification result.

[0161] S604, input the second positioning information into the second network model.

[0162] The vehicle inputs the second positioning information obtained by S602 into the second network model to obtain the second positioning area corresponding to the second classification result.

[0163] S605, Determine the third positioning area.

[0164] The vehicle determines the location coordinates of the digital key based on the first and second location information of the digital key, and then determines the third location area where the digital key is located based on the location coordinates.

[0165] S606, Determine the area where the digital key is located.

[0166] The vehicle determines the location of the digital key by voting among the first, second, and third location areas, and identifying the location area with the highest repetition rate among these three location areas.

[0167] Next, we will introduce the training method for the location digital key model.

[0168] In one possible implementation, the vehicle acquires location information between itself and a base station when the digital key is in different areas, and then uses the areas corresponding to the location information as classification results to construct a training set. The vehicle then trains the network model using the training set to obtain the trained network model.

[0169] The trained network model is used to output classification results based on the location information between the digital key and the base station. The classification results are used to indicate the area where the digital key is located.

[0170] For example, the construction of the training set can be carried out while the digital key moves at a constant speed in the positioning area, and the vehicle obtains the positioning information between the digital key and the base station through the base station. The positioning information includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the base station, the distance between the digital key and the base station, and the phase difference between the digital key and the base station. The process of obtaining the positioning information can be referred to in step S201, and will not be repeated here.

[0171] For example, if the positioning area is divided into four regions: the in-vehicle area, the unlocking area, the welcome area, and the invalid area, when the digital key moves at a constant speed within the unlocking area, the vehicle obtains multiple positioning information between itself and the digital key via a base station. These multiple positioning information are then labeled with the unlocking area, constructing a training set whose classification result is the unlocking area.

[0172] For example, the network model could be a Light Gradient Boosting Machine (LightGBM). LightGBM is used because it requires less memory and has a faster training speed.

[0173] In one possible implementation, an example is given where a first base station and a second base station are located at different locations on the vehicle. The vehicle obtains multiple location information points for the digital key in different areas via the first base station, and constructs a first training set using the areas corresponding to these multiple location information points as classification results for the digital key. Then, the network model is trained using the first training set to obtain a trained first network model. The first network model is used to output a classification result indicating the area where the digital key is located based on the location information between the digital key and the first base station. Similarly, the vehicle obtains multiple location information points for the digital key in different areas via the second base station, and constructs a second training set using the areas corresponding to these multiple location information points as classification results for the digital key. Then, the network model is trained using the second training set to obtain a trained second network model. The second network model is used to output a classification result indicating the area where the digital key is located based on the location information between the digital key and the second base station.

[0174] In some embodiments of this disclosure, the first network model and the second network model are trained separately so that the vehicle can determine two positioning results using the first positioning information obtained from the first base station and the second positioning information obtained from the second base station. Even if the information collected by either the first or second base station is incorrect or cannot be collected, the positioning result can still be obtained through the network model based on the positioning information collected by the other base station, which can enhance the robustness of the vehicle positioning process in the face of extreme situations.

[0175] For example, Figure 7 is a schematic flowchart of a network model training process according to some embodiments of this disclosure. As shown in Figure 7, the process is illustrated using an example where a first base station and a second base station are located at different positions on a vehicle. The network model training process includes, but is not limited to, one or more of S701-S706.

[0176] S701, obtain the first positioning information and the second positioning information.

[0177] For example, the vehicle obtains first positioning information between the first base station and the digital key via a first base station. This first positioning information includes channel impulse response information between the digital key and the first base station, the distance between the digital key and the first base station, and the phase difference between the digital key and the first base station. The vehicle then obtains second positioning information between the second base station and the digital key via a second base station. This second positioning information also includes channel impulse response information between the digital key and the second base station, the distance between the digital key and the second base station, and the phase difference between the digital key and the second base station.

[0178] S702 performs feature extraction on the channel impulse response information.

[0179] The vehicle extracts features from the channel impulse response information between the digital key and the first base station to obtain first feature information. The vehicle then extracts features from the channel impulse response information between the digital key and the second base station to obtain second feature information.

[0180] S703, construct the first training set.

[0181] The vehicle labels the first feature information, the distance between the digital key and the first base station, and the phase difference between the digital key and the first base station with the corresponding positioning area, and constructs the first training set corresponding to the positioning area.

[0182] S704, construct the second training set.

[0183] The vehicle labels the second feature information, the distance between the digital key and the second base station, and the phase difference between the digital key and the second base station with the corresponding positioning area, and constructs the second training set corresponding to the positioning area.

[0184] S705, training the first network model.

[0185] The vehicle trains the first network model based on the first training set, resulting in the trained first network model.

[0186] S706, train the second network model.

[0187] The vehicle trains a second network model based on the second training set, resulting in a well-trained second network model.

[0188] The methods of some embodiments of this disclosure have been described in detail above. The apparatus of some embodiments of this disclosure is provided below.

[0189] Figure 8 is a block diagram of the functional units of a device for locating a digital key according to some embodiments of this disclosure. The device 80 for locating a digital key may include a communication unit 801 and a processing unit 802. The device 80 for locating a digital key is configured to implement the aforementioned method for locating a digital key, such as the method for locating a digital key shown in Figure 2.

[0190] It should be noted that the above division of multiple units is merely a logical division based on function and does not constitute a limitation on the structure of the device 80 for locating the digital key. In other possible implementations, some functional modules may be further subdivided into more smaller functional modules, and some functional modules may be combined into a single functional module.

[0191] In one possible implementation, the communication unit 801 is used to acquire multiple location information, including information between the vehicle's digital key and different base stations located at different locations of the vehicle.

[0192] Processing unit 802 is used to determine multiple positioning areas based on multiple positioning information, and the positioning area is used to indicate the area where the digital key is located;

[0193] The processing unit 802 is also used to identify the location area with the highest repetition rate among multiple location areas as the area where the digital key is located.

[0194] In another possible implementation, the processing unit 802 determines multiple positioning areas based on multiple positioning information, including:

[0195] Multiple location information are input into the corresponding network model to obtain the location area corresponding to the classification result in multiple location areas. The classification result is the result output by the network model, which is trained based on the information between the digital key and the base station.

[0196] Based on any two of the multiple location information, determine the location area corresponding to the location coordinates in multiple location areas. The location area corresponding to the location coordinates is the area where the location coordinates are located.

[0197] In another possible implementation, where the base station includes a first base station and a second base station, the positioning information includes first positioning information corresponding to the first base station and second positioning information corresponding to the second base station, and the network model includes a first network model corresponding to the first base station and a second network model corresponding to the second base station. The processing unit 802 determines multiple positioning areas based on the multiple positioning information, including:

[0198] Three positioning areas are determined based on the first and second positioning information.

[0199] In another possible implementation, the processing unit 802 determines three positioning areas based on the first positioning information and the second positioning information, including:

[0200] The first positioning information is input into the first network model to obtain the first positioning region corresponding to the first classification result in the three positioning regions. The first classification result is the result output by the first network model.

[0201] The second positioning information is input into the second network model to obtain the second positioning region corresponding to the second classification result in the three positioning regions. The second classification result is the output of the second network model.

[0202] Based on the first and second positioning information, determine the third positioning area corresponding to the position coordinates in the three positioning areas.

[0203] In another possible implementation, the processing unit 802 identifies the location area with the highest repetition rate among multiple location areas as the area where the digital key is located, including:

[0204] The location with the highest repetition rate among the first, second, and third location areas is selected as the area where the digital key is located.

[0205] In another possible implementation, the positioning information includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the base station, the distance between the digital key and the base station, and the phase difference between the digital key and the base station. The phase difference between the digital key and the base station is the phase difference corresponding to the signal sent by the digital key to the base station reaching multiple antennas of the base station.

[0206] It should be noted that, in some embodiments of this disclosure, the implementation and technical effects of each unit can also be described in accordance with the corresponding description of the method embodiment shown in FIG2.

[0207] Please refer to Figure 9, which is a schematic diagram of a computing device provided in some embodiments of this disclosure. As shown in Figure 9, the computing device 90 may include one or more processors 901, one or more memories 902, and one or more communication interfaces 903. These components can be connected via a bus 904 or other means; Figure 9 illustrates a connection via a bus 904.

[0208] The communication interface 903 can be used by the computing device 90 to communicate with other communication devices, such as other computing devices. For example, the communication interface 903 can be a wired interface.

[0209] In some embodiments, the communication interface satisfies at least one of the following: the communication interface is used to receive data, or the communication interface is used to send data, or the communication interface is used to both receive and send data; or,

[0210] The communication interface is used to provide input to the processor, or the communication interface is used to provide output to the processor, or the communication interface is used to provide both input and output to the processor.

[0211] The memory 902 can be coupled to the processor 901 via a bus 904 or an input / output port, or the memory 902 can be integrated with the processor 901. The memory 902 is used to store various software programs and / or multiple sets of instructions or data. For example, the memory 902 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or it can be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0212] Memory 902 may include high-speed random access memory and may also include non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Memory 902 may store an operating system (hereinafter referred to as the system), such as uCOS, VxWorks, RTLinux, or other embedded operating systems. Memory 902 may also store network communication programs that can be used to communicate with one or more additional devices, one or more user devices, or one or more terminals. Memory 902 may exist independently and be connected to processor 901 via bus 904. Memory 902 may also be integrated with processor 901.

[0213] The memory 902 stores the application code for executing the above scheme, and its execution is controlled by the processor 901. The processor 901 executes the application code stored in the memory 902.

[0214] Processor 901 may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Processor 901 may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 901 may also be a combination that implements a specific function, such as including one or more microprocessor combinations, digital signal processors and microprocessors, etc.

[0215] In some embodiments of this disclosure, processor 901 may be used to read and execute computer-readable instructions. For example, processor 901 may be used to invoke a program stored in memory 902 to perform the following operations:

[0216] Multiple location information is obtained, including information between the vehicle's digital key and different base stations located at different locations of the vehicle.

[0217] Multiple location areas are determined based on multiple location information, where the location area is used to indicate the area where the digital key is located;

[0218] The location with the highest repetition rate among multiple location areas is selected as the location of the digital key.

[0219] In one possible implementation, the processor 901 is configured to:

[0220] Multiple location information are input into the corresponding network model to obtain the location area corresponding to the classification result in multiple location areas. The classification result is the result output by the network model, which is trained based on the information between the digital key and the base station.

[0221] Based on any two of the multiple location information, determine the location area corresponding to the location coordinates in multiple location areas. The location area corresponding to the location coordinates is the area where the location coordinates are located.

[0222] In one possible implementation, the processor 901 is configured to:

[0223] Three positioning areas are determined based on the first and second positioning information.

[0224] In one possible implementation, the processor 901 is configured to:

[0225] The first positioning information is input into the first network model to obtain the first positioning region corresponding to the first classification result in the three positioning regions. The first classification result is the result output by the first network model.

[0226] The second positioning information is input into the second network model to obtain the second positioning region corresponding to the second classification result in the three positioning regions. The second classification result is the output of the second network model.

[0227] Based on the first and second positioning information, determine the third positioning area corresponding to the position coordinates in the three positioning areas.

[0228] In one possible implementation, the processor 901 is configured to:

[0229] The location with the highest repetition rate among the first, second, and third location areas is selected as the area where the digital key is located.

[0230] In one possible implementation, the positioning information includes one or more of the following: feature information corresponding to the channel impulse response information between the digital key and the base station, the distance between the digital key and the base station, and the phase difference between the digital key and the base station. The phase difference between the digital key and the base station is the phase difference corresponding to the signal sent by the digital key to the base station reaching multiple antennas of the base station.

[0231] It should be noted that, in some embodiments of this disclosure, the implementation and technical effects of each unit can also be described in accordance with the corresponding description of the method embodiment shown in FIG2.

[0232] As shown in Figure 10, some embodiments of this disclosure also provide a vehicle 1000, including a processor 901 and a memory 902. The processor 901 is coupled to the memory 902, and the memory 902 stores a computer program. The processor 901 is used to call and run the computer program to implement the aforementioned method for locating a digital key, such as the method in Figure 2.

[0233] Some embodiments of this disclosure also provide a computer-readable storage medium storing instructions that, when executed on at least one processor, implement the aforementioned method for locating a digital key, such as the method in FIG2.

[0234] Some embodiments of this disclosure also provide a computer program product including computer instructions that, when executed by a computing device, implement the aforementioned method for locating a digital key, such as the method in FIG2.

[0235] In some embodiments of this disclosure, the terms "for example" or "for instance" are used to indicate that something is an example, illustration, or illustration. Any embodiment or design described as "for example" or "for instance" in this disclosure should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of terms such as "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0236] In the embodiments of this disclosure, "at least one" refers to one or more, and "more than one" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, (a and b), (a and c), (b and c), or (a and b and c), where a, b, and c can be single or multiple. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0237] Furthermore, unless otherwise stated, in some embodiments of this disclosure, the use of ordinal numbers such as "first" and "second" is for distinguishing multiple objects and is not for limiting the order, sequence, priority, or importance of the multiple objects. For example, "first device" and "second device" are used only for ease of description and do not indicate differences in the structure, importance, etc. of the first device and the second device. In some embodiments, the first device and the second device may also be the same device.

[0238] In the above embodiments, the term "when..." can be interpreted, depending on the context, as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". The above are merely optional embodiments of this disclosure and are not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the concept and principles of this disclosure should be included within the scope of protection of this disclosure.

[0239] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0240] The above are merely embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this disclosure, and such modifications or substitutions should all be covered within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for positioning a digital key, comprising: obtaining a plurality of positioning information, wherein the plurality of positioning information comprises information between a digital key of a vehicle and different base stations located at different positions of the vehicle; determining a plurality of positioning areas according to the plurality of positioning information respectively, wherein the plurality of positioning areas are used to indicate areas where the digital key is located; taking a positioning area with the highest repetition rate in the plurality of positioning areas as an area where the digital key is located.

2. The method of claim 1, wherein, The determining a plurality of positioning areas according to the plurality of positioning information respectively comprises: inputting the plurality of positioning information into corresponding network models respectively to obtain a positioning area corresponding to a classification result in the plurality of positioning areas, wherein the classification result is a result output by the network model, and the network model is trained according to information between the digital key and any base station in the different base stations; determining a positioning area corresponding to a position coordinate in the plurality of positioning areas according to any two positioning information in the plurality of positioning information, wherein the positioning area corresponding to the position coordinate is an area where the position coordinate is located.

3. The method of claim 2, wherein, In a case where the different base stations comprise a first base station and a second base station, the positioning information comprises first positioning information corresponding to the first base station and second positioning information corresponding to the second base station, the network model comprises a first network model corresponding to the first base station and a second network model corresponding to the second base station, and the determining a plurality of positioning areas according to the plurality of positioning information respectively comprises: determining three positioning areas according to the first positioning information and the second positioning information.

4. The method of claim 3, wherein, The determining three positioning areas according to the first positioning information and the second positioning information comprises: inputting the first positioning information into the first network model to obtain a first positioning area corresponding to a first classification result in the three positioning areas, wherein the first classification result is a result output by the first network model; inputting the second positioning information into the second network model to obtain a second positioning area corresponding to a second classification result in the three positioning areas, wherein the second classification result is a result output by the second network model; determining a third positioning area corresponding to the position coordinate in the three positioning areas according to the first positioning information and the second positioning information.

5. The method of claim 4, wherein, The taking a positioning area with the highest repetition rate in the plurality of positioning areas as an area where the digital key is located comprises: taking a positioning area with the highest repetition rate in the first positioning area, the second positioning area and the third positioning area as an area where the digital key is located.

6. A training method for a digital key positioning model, comprising: obtaining a plurality of positioning information between a digital key of a vehicle and a base station when the digital key is in different areas, wherein the base station is located in the vehicle; taking areas corresponding to the plurality of positioning information respectively as classification results of the digital key to construct a training set; The network model is trained according to the training set to obtain a trained network model, wherein the trained network model is used to output the classification result according to positioning information between the digital key and the base station, and the classification result is used to indicate an area where the digital key is located.

7. The method of any one of claims 1-6, wherein, The positioning information includes one or more of the following: feature information corresponding to channel impulse response information between the digital key and the base station, a distance between the digital key and the base station, and a phase difference between the digital key and the base station, wherein the phase difference between the digital key and the base station is a phase difference corresponding to signals sent by the digital key to the base station when arriving at multiple antennas of the base station.

8. An apparatus for positioning a digital key, comprising: a communication unit configured to obtain a plurality of positioning information, wherein the plurality of positioning information includes information between a digital key of a vehicle and different base stations located at different positions of the vehicle; and a processing unit configured to determine a plurality of positioning areas according to the plurality of positioning information, respectively, wherein the plurality of positioning areas are used to indicate an area where the digital key is located; wherein the processing unit is further configured to take a positioning area with the highest repetition rate in the plurality of positioning areas as the area where the digital key is located.

9. A vehicle comprising a processor and a memory, the processor being coupled to the memory, the memory storing a computer program, and the processor being configured to invoke and run the computer program, so that the vehicle performs the method according to any one of claims 1-7.

10. A computing device comprising a processor and a memory, the processor being coupled to the memory, the memory storing a computer program, and the processor being configured to invoke and run the computer program, so that the computing device performs the method according to any one of claims 1-7.

11. A computer-readable storage medium storing a computer program, the computer program comprising instructions for performing the method according to any one of claims 1-7.

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