Valet parking positioning method and device and vehicle

Abstract

The invention discloses a valet parking positioning method and device and a vehicle, relates to the technical field of valet parking positioning, and discloses a valet parking positioning method comprising the following steps: obtaining WiFi access point information in a scene; obtaining a WiFi database based on the WiFi access point information in the scene; and based on the WiFi database and a first preset algorithm, obtaining a real-time position of the vehicle based on WiFi positioning. As a social common WiFi system generally exists indoors, especially in scenes such as shopping malls, common WiFi can be used for assisting in positioning. When errors of other positioning technologies are large, a plurality of WiFi access points are arranged in the parking lot, information such as signal intensity of the access points is collected, and a WiFi database is established. When a valet parking system needs to be positioned, WiFi signals received in real time can be matched with data in the fingerprint database, so that valet parking positioning is realized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle positioning, and in particular to a valet parking positioning method, device and vehicle. Background Art

[0002] Valet Parking (VP) is an advanced intelligent parking technology that integrates multiple sensors, high-precision maps, advanced autonomous driving algorithms and communication technologies to enable the vehicle to automatically find a parking space, park in the space, and automatically drive out of the parking space and return to the owner as needed without human operation.

[0003] With the continuous advancement of technology and the reduction of costs, automatic valet parking technology will be more widely used in the future. At the same time, with the advancement of smart city construction and the popularization of autonomous driving technology, automatic valet parking technology will be deeply integrated with smart cities, smart transportation and other fields, bringing people a more intelligent and convenient travel experience.

[0004] However, valet parking has a large positioning error problem when facing indoor parking scenarios, which can easily lead to parking failure. Summary of the invention

[0005] The main purpose of the present application is to provide a valet parking positioning method, device and vehicle, aiming to solve the technical problem of large positioning error in valet parking in indoor parking scenarios.

[0006] To achieve the above-mentioned purpose, the present application proposes a valet parking positioning method, the method comprising: obtaining WiFi access point information within a scene; obtaining a WiFi database based on the WiFi access point information within the scene; and obtaining the vehicle's real-time position based on WiFi positioning based on the WiFi database and a first preset algorithm.

[0007] In one embodiment, the step of obtaining the real-time location of the vehicle based on the WiFi database includes: obtaining WiFi data to which the vehicle is currently connected; matching the WiFi data and the WiFi database using the first preset algorithm to obtain a matching result; and obtaining the real-time location of the vehicle based on the matching result.

[0008] In one embodiment, the step of obtaining the real-time position of the vehicle includes: obtaining the position of the vehicle at the last moment; obtaining data information of a vehicle-mounted inertial sensor, the data information including at least acceleration, angular velocity and magnetic field direction; based on the data information, obtaining the stride and direction of the vehicle; based on the position at the last moment, the stride and the direction, obtaining the real-time position of the vehicle.

[0009] In one embodiment, before the step of obtaining the WiFi access point information within the scene, it also includes: obtaining feature point information within the scene; obtaining a SLAM map based on the feature point information; obtaining the real-time position of the vehicle based on the SLAM map and a preset algorithm; detecting whether the real-time position of the vehicle is in an invalid state; when the real-time position is not in an invalid state, outputting the real-time position to the valet parking module, and executing the step of obtaining the real-time position of the vehicle based on the SLAM map and the preset algorithm; when the real-time position is in an invalid state, executing the step of obtaining the WiFi access point information within the scene.

[0010] In one embodiment, after the step of obtaining the real-time position of the vehicle based on the failure point position of the vehicle, the step further includes: performing map matching based on the real-time position of the vehicle and the SLAM map to obtain a matching result; based on the matching result, obtaining the deviation between the real-time position of the vehicle and the SLAM map; and performing position correction on the real-time position of the vehicle based on the deviation to obtain the corrected real-time position of the vehicle.

[0011] In one embodiment, after the step of correcting the real-time position of the vehicle based on the deviation and obtaining the corrected real-time position of the vehicle, the method further includes: obtaining the real-time position of the vehicle based on WiFi based on the WiFi database and a first preset algorithm; filtering the corrected real-time position of the vehicle and the real-time position of the vehicle based on WiFi to obtain a comprehensive real-time position; and outputting the comprehensive real-time position to the valet parking module.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a valet parking positioning device, which includes: a parameter acquisition module, used to obtain WiFi access point information within a scene; a calculation module, used to obtain a WiFi database based on the WiFi access point information within the scene; and also used to obtain the vehicle's real-time position based on WiFi positioning based on the WiFi database and a first preset algorithm.

[0013] In addition, to achieve the above objectives, the present application also proposes a vehicle, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the valet parking positioning method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the valet parking positioning method described above are implemented.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the valet parking positioning method described above are implemented.

[0016] One or more technical solutions proposed in this application have at least the following technical effects:

[0017] Since ordinary WiFi systems are widely available indoors, especially in shopping malls, ordinary WiFi can be used for auxiliary positioning. When the errors of other positioning technologies are large, multiple WiFi access points can be arranged in the parking lot, and information such as the signal strength of these access points can be collected to establish a WiFi database. When the valet parking system needs to be positioned, it can match the real-time received WiFi signal with the data in the fingerprint library to achieve valet parking positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A flowchart of the first embodiment of the valet parking positioning method of the present application is provided;

[0021] Figure 2 A flowchart of an implementation of the valet parking positioning method provided in Example 1 of the present application;

[0022] Figure 3 A flowchart of another implementation of the valet parking positioning method provided in Example 1 of the present application;

[0023] Figure 4 A brief flow chart of the second embodiment of the valet parking positioning method provided in the present application;

[0024] Figure 5 This is a schematic diagram of the module structure of the valet parking positioning device according to an embodiment of the present application;

[0025] Figure 6 Schematic diagram of the device structure of the hardware operating environment involved in the valet parking positioning method in the embodiment of the present application.

[0026] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0028] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0029] The main solution of the embodiment of the present application is: obtaining WiFi access point information within the scene; obtaining a WiFi database based on the WiFi access point information within the scene; and obtaining the vehicle's real-time position based on WiFi positioning based on the WiFi database and a first preset algorithm.

[0030] In this embodiment, for the convenience of description, the following description is made with identification of a vehicle as the execution subject.

[0031] Since the existing valet parking function mostly uses SLAM feature recognition technology when positioning in indoor scenes, it does have the limitation of large errors when there are fewer feature points. This limitation is mainly manifested in the following aspects: in indoor or complex parking environments, it is impossible to accurately locate the vehicle position when there are fewer feature points or when the environment changes greatly; traditional positioning systems (such as GPS) may cause reduced positioning accuracy due to factors such as signal blocking and multipath effects; valet parking systems usually rely on sensors and algorithms to perceive the surrounding environment and make decisions. However, in indoor scenes, due to the presence of buildings, vehicles and other obstacles, the perception capabilities of these sensors may be limited, resulting in positioning errors.

[0032] This application provides a solution. Since ordinary WiFi systems are common in indoor places, especially in shopping malls, ordinary WiFi can be used for auxiliary positioning. When the errors of other positioning technologies are large, a WiFi database is established by arranging multiple WiFi access points in the parking lot and collecting information such as the signal strength of these access points. When the valet parking system needs to be positioned, the valet parking positioning can be achieved by comparing the real-time received WiFi signal with the data in the fingerprint library.

[0033] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a vehicle, etc. capable of realizing the above functions. The following takes a vehicle as an example to illustrate this embodiment and the following embodiments.

[0034] Based on this, the embodiment of the present application provides a valet parking positioning method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the valet parking positioning method of the present application. In this embodiment, the valet parking positioning method includes steps S10 to S30:

[0035] Step S10, obtaining WiFi access point information in the scene.

[0036] It should be noted that the WiFi access point information generally includes the network name (SSID), signal strength (RSSI), geographic location information, etc.; the WiFi access point information in the scene refers to the network name (SSID) and signal strength (RSSI), etc. of the WiFi access point in the scene where valet parking is required.

[0037] It can be understood that the vehicle of the present application is installed with a device with a Wi-Fi scanning function, which can scan and collect surrounding WiFi signals. Currently, most vehicle smart cockpits have WiFi connection functions.

[0038] It is understandable that if the indoor scene is large, it can be divided into multiple small areas or reference points (RP). Each area or reference point is given a unique identifier, such as a number or name. Then, at each area or reference point, use a WiFi scanning device to collect RSSI data of surrounding WiFi access points, and ensure that enough data samples are collected at each point to improve the accuracy of the fingerprint library.

[0039] Step S20: acquiring a WiFi database based on the WiFi access point information in the scene.

[0040] It should be noted that the WiFi database may be in the form of a fingerprint database, and may be constructed based on the WiFi access point information in the scene, especially the signal strength (RSSI).

[0041] It is understandable that building a WiFi database is a process involving collecting, processing and analyzing WiFi access point signal strength RSSI data. After obtaining the access point information as described above, the scanned WiFi access point information (including SSID, RSSI, MAC address, etc.) can be recorded together with the corresponding area or reference point identification. A spreadsheet, database or dedicated WiFi fingerprint library software can be used to store this data.

[0042] It should be noted that the stored data needs to be cleaned to remove outliers or obviously erroneous data points, and the RSSI data needs to be smoothed to reduce noise and fluctuations. Useful features such as average RSSI and RSSI variance can be extracted from the WiFi data of each area or reference point.

[0043] These features will be used to construct WiFi fingerprints. Finally, the WiFi features of each area or reference point are stored in the fingerprint library together with its identity, ensuring that the structure of the fingerprint library is clear and convenient for subsequent query and matching.

[0044] Step S30: Based on the WiFi database and the first preset algorithm, the real-time position of the vehicle based on WiFi positioning is obtained.

[0045] It should be noted that the first preset algorithm is required for matching the WiFi data received by the vehicle at the current location with the WiFi database. Specifically, the first preset algorithm can select or develop a positioning algorithm, such as a fingerprint positioning algorithm based on RSSI, a triangulation positioning method or a machine learning algorithm, etc., which is used to calculate the vehicle position based on the WiFi signal strength data, and needs to be able to match the WiFi data scanned in real time with the fingerprint information in the database, so as to estimate the real-time position of the vehicle based on WiFi positioning.

[0046] In one possible implementation, refer to Figure 2 , step S30 may include steps A10 to A30:

[0047] Step A10, obtaining the WiFi data to which the vehicle is currently connected.

[0048] It should be noted that the WiFi scanning device on the vehicle as described above collects RSSI data of surrounding WiFi access points in real time; the real-time collected RSSI data is preprocessed, such as denoising, smoothing, etc. The preprocessed RSSI data is converted into the same format as in the fingerprint library, namely, the RSSI feature vector:

[0049]

[0050] Among them, P0 is set to the RSS reference value received at the reference distance D0, and the path loss coefficient is set to N (calibrable).

[0051] Step A20: performing matching based on the WiFi data and the WiFi database by using the first preset algorithm to obtain a matching result.

[0052] It should be noted that, in this embodiment, the first preset algorithm selects the cosine similarity algorithm. First, the cosine similarity calculation is performed on the RSSI feature vector collected in real time and each fingerprint in the fingerprint library.

[0053] Step A30: Based on the matching result, obtain the real-time position of the vehicle based on WiFi positioning.

[0054] It can be understood that the value of cosine similarity is between -1 and 1, and the closer the value is to 1, the more similar the two vectors are. Finally, the fingerprint with the highest cosine similarity to the real-time RSSI feature vector is found, and the location information corresponding to the fingerprint is the real-time location based on WiFi positioning.

[0055] The specific calculation process can be achieved through the following mathematical equation:

[0056] HX=B

[0057] in,

[0058]

[0059] (x j, ,y j, ) is the coordinates of the WiFi router with the strongest signal at the location of the vehicle, (x 0, ,y 0, ) is the coordinate of the vehicle, d j is the distance from the WiFi base station router to the vehicle, (x 1, ,y 1, ) are the coordinates of other WiFi base stations except the base station with the strongest signal.

[0060] Finally, the real-time coordinate X of the vehicle is obtained:

[0061] X=(H T H) -1 H T B

[0062] In addition, the estimated location is updated to the valet parking system, and the real-time location of the vehicle can be displayed through a map interface or other visualization tools.

[0063] In this implementation, a method for fusing and processing data from multiple WiFi access points and a cosine similarity calculation method are used to further improve positioning accuracy.

[0064] In another possible implementation, referring to Figure 3 , step A30 may include steps B10 to B40:

[0065] Step B10, obtaining the WiFi-based positioning position of the vehicle at the last moment.

[0066] It should be noted that the WiFi-based positioning position of the vehicle at the last moment may be the real-time coordinates (x0, y0) at the last moment calculated by the above method, or may be obtained by other positioning methods.

[0067] Step B20, obtaining data information of the vehicle-mounted inertial sensor, wherein the data information at least includes acceleration, angular velocity and magnetic field direction.

[0068] Step B30, based on the data information, obtaining the step length and direction of the vehicle.

[0069] It should be noted that the vehicle-mounted inertial sensors ensure that the vehicle is equipped with inertial sensors such as accelerometers, gyroscopes and magnetometers; these sensors should be able to provide data such as the vehicle's acceleration, angular velocity and magnetic field direction in real time. Based on the acceleration data, the kinematic model is used to estimate the vehicle's step length, that is, the distance the vehicle moves in one time step. Based on the gyroscope and magnetometer data, the vehicle's direction, that is, the vehicle's heading, is calculated.

[0070] Step B40, based on the position at the last moment, the step length and the direction, obtain the real-time position of the vehicle based on WiFi positioning.

[0071] Specifically, in this implementation, the subsequent position of the vehicle can be calculated through the vehicle-mounted inertial system and the real-time coordinates at the previous moment.

[0072]

[0073] Among them, θ i d i, (i=1, ..., k), i is the last signal period, and θ is the azimuth angle.

[0074] This implementation simplifies the calculation process through inertial calculation, further reduces the positioning time, and improves the applicability of WiFi positioning.

[0075] This embodiment provides a valet parking positioning method, by arranging multiple WiFi access points in the parking lot and collecting information such as the signal strength of these access points to establish a WiFi database. When the valet parking system needs to be positioned, it can match the WiFi signal received in real time with the data in the fingerprint library to achieve valet parking positioning.

[0076] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 4Before step S10, the valet parking positioning method further includes steps S01 to S04:

[0077] Step S01, acquiring feature point information in a scene, and acquiring a SLAM map based on the feature point information.

[0078] It should be noted that, first of all, the feature point information in the scene can be collected by using sensors such as vehicle-mounted cameras and lidar, such as corner points, edges, textures, etc. Secondly, it is necessary to ensure that the collected feature point information has sufficient quantity and quality for the subsequent construction of the SLAM map.

[0079] It should be noted that SLAM map construction needs to use the collected feature point information, combined with SLAM algorithms (such as ORB-SLAM, LSD-SLAM, etc.) to build a three-dimensional map of the scene. The map should usually contain the three-dimensional coordinates of the feature points, associated image frame information, and possible semantic labels.

[0080] Step S02: Based on the SLAM map and the second preset algorithm, the real-time position of the vehicle based on SLAM positioning is obtained.

[0081] It is understandable that during the driving process of the vehicle, sensor data is collected in real time and matched with the SLAM map to obtain the real-time position of the vehicle based on SLAM positioning. The second preset algorithm generally includes a positioning algorithm based on methods such as feature point matching, optical flow tracking or direct method.

[0082] Step S03, detecting whether the real-time position of the vehicle based on SLAM positioning is in an invalid state.

[0083] Step S04: when the real-time position based on SLAM positioning is in an invalid state, the step of acquiring the WiFi access point information in the scene is performed.

[0084] It should be noted that monitoring key indicators in the SLAM positioning process, such as the number of feature point matches, positioning accuracy, tracking stability, etc., can effectively determine whether the real-time position based on SLAM positioning is in an invalid state. When these indicators are higher than the preset threshold, the calculated real-time position is updated to the vehicle tracking system for subsequent processing and analysis. When these indicators are lower than the preset threshold, it is determined that SLAM positioning is in an invalid state. At this point, the failure handling mechanism should be triggered immediately, such as switching to the real-time position based on WiFi positioning described in this application to continue to realize the positioning function.

[0085] In addition, in a feasible implementation manner, after the step of obtaining the real-time position of the vehicle based on WiFi positioning based on the WiFi database and the first preset algorithm, it also includes: performing map matching based on the real-time position of the vehicle based on WiFi positioning and the SLAM map to obtain a matching result; based on the matching result, obtaining the deviation between the real-time position of the vehicle based on WiFi positioning and the SLAM map; performing position correction on the real-time position of the vehicle based on WiFi positioning based on the deviation to obtain the corrected real-time position of the vehicle based on WiFi positioning.

[0086] It is understandable that, based on the current position of the vehicle, this step is repeated subsequently to estimate the position of the vehicle at all times, but the calculation results are often accompanied by the problem of cumulative errors.

[0087] Therefore, in order to address this limitation, after obtaining the vehicle location information, the current location is compared with the indoor map through map matching technology to ensure that the vehicle trajectory can be correctly mapped to the indoor learned route.

[0088] This application uses the observation probability matrix and the state transition matrix to solve this problem. The observation probability matrix is ​​mainly used to measure the probability of observing a specific location data given the real path point. In this way, the algorithm can quantify the degree of match between the observed data and the indoor path points, and then identify possible deviations.

[0089] Observation probability matrix B:

[0090]

[0091] In this embodiment, the vehicle estimated position observation point p i The measurement error can be described by a Gaussian distribution with a mean of 0 and a standard deviation of 2 meters. The matching degree is calculated by the formula Vehicle estimated position p i Learning route target points The closer the distance is, the more likely it is that the vehicle's estimated position is the vehicle's actual learning route target point. In this way, a quantitative evaluation is performed to deduce the actual driving path.

[0092] State transfer matrix A:

[0093] a ij =P(q t+1 =r j |q t =r i )=F t (ri →r j )·V(r i →r j )

[0094] In this embodiment, the design space analysis function F t (r i →r j ) represents the indoor memory line topology. The present invention uses the vehicle position p confirmed in the previous step i-1, p i, and its reference point (target point) The vehicle speed information is used to infer the path (p i-1, to p i, ) is the best fit path ( arrive ) possibility to avoid vehicle deviation.

[0095]

[0096] Among them, setting p i, and p i-1, The spatial distance between t→t+1 =d il→i = dist(p i, , p i-1, ) to indicate. Setting and The shortest length between t→t+1 =w (il，t)→(i，s) is the target driving route.

[0097] In this embodiment, V(r i →r j ) function parameters are used to judge the rationality of the path.

[0098]

[0099] In this embodiment, the velocity between adjacent points is expressed by a function V(r i →r j ) indicates that from arrive The target point is the most ′ 1, e ′ 2, …, e ′ k ], each segment ′ u The maximum speed limit is e ′ u ·v r At this time, the speed is brought into the vehicle system to calculate the current position. If it exceeds a reasonable threshold, the present invention does not need to correct it.

[0100] This implementation method uses an algorithm to correct real-time positioning, which not only considers the time and speed limitations in the motion trajectory, but also combines the spatial characteristics of the indoor geometric map, so that the position can be accurately mapped to the indoor map, achieving accurate position correction.

[0101] Furthermore, in engineering applications, the acquired real-time position needs to be filtered. Therefore, the step of correcting the real-time position of the vehicle based on WiFi positioning based on the deviation and obtaining the corrected real-time position of the vehicle based on WiFi positioning also includes: obtaining the real-time position of the vehicle based on WiFi positioning based on the WiFi database and the first preset algorithm; filtering the corrected real-time position of the vehicle based on WiFi positioning and the real-time position of the vehicle based on WiFi positioning to obtain a comprehensive real-time position; and outputting the comprehensive real-time position to the valet parking module.

[0102] Specifically, the state equation of the filter used in the present invention is:

[0103] In the filter module equation, F = I 3×3 ,W(t)=[w x w y w z ] T is the environmental noise reduction parameter, E[W(t)]=0.

[0104] The system state variable of the filter in the present invention is: x = [Δx Δy Δz] T , its output value is: Z(t)=H(t)X(t)+V(t).

[0105] Where H = I 3×3 ,V(t)=[v x v y v z ] T is the environmental noise reduction parameter, [V(t)]=0.

[0106] The output trajectory correction value is:

[0107] Therefore, the output result of the present invention is Z(t), and filtering correction is performed on the vehicle.

[0108] This embodiment introduces the acquisition of real-time position based on SLAM positioning, provides an opportunity to use the acquisition of real-time position based on WiFi positioning, and combines and filters the inertial calculation results after SLAM map matching and the WiFi fingerprint matching results, providing a more accurate indoor fusion positioning result.

[0109] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the valet parking positioning method of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0110] This application also provides a valet parking positioning device, please refer to Figure 5 The valet parking positioning device includes: a parameter acquisition module 10 for acquiring WiFi access point information within a scene; a calculation module 20 for acquiring a WiFi database based on the WiFi access point information within the scene; and for acquiring a real-time WiFi positioning-based position of a vehicle based on the WiFi database and a first preset algorithm.

[0111] The valet parking positioning device provided by the present application adopts the valet parking positioning method in the above embodiment, which can solve the technical problem of large positioning error of valet parking in indoor parking scenes. Compared with the prior art, the beneficial effects of the valet parking positioning device provided by the present application are the same as the beneficial effects of the valet parking positioning method provided by the above embodiment, and other technical features in the valet parking positioning device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0112] The present application provides a vehicle, comprising: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the valet parking positioning method in the above-mentioned embodiment 1.

[0113] Reference below Figure 6 , which shows a schematic diagram of a structure of a vehicle suitable for implementing the embodiment of the present application. The vehicle in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The vehicle shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0114] like Figure 6As shown, the vehicle may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for vehicle operation are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the vehicle to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a vehicle with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0115] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0116] The vehicle provided by the present application adopts the valet parking positioning method in the above embodiment, which can solve the technical problem of large positioning error of valet parking in indoor parking scenes. Compared with the prior art, the beneficial effects of the vehicle provided by the present application are the same as the beneficial effects of the valet parking positioning method provided by the above embodiment, and other technical features in the vehicle are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0117] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0118] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0119] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the valet parking positioning method in the above-mentioned embodiment.

[0120] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0121] The computer-readable storage medium may be included in the vehicle, or may exist independently without being installed in the vehicle.

[0122] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the vehicle, the vehicle: obtains WiFi access point information within the scene; obtains a WiFi database based on the WiFi access point information within the scene; and obtains the vehicle's real-time position based on WiFi positioning based on the WiFi database and a first preset algorithm.

[0123] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0126] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned valet parking positioning method, and can solve the technical problem that valet parking has large positioning errors in indoor parking scenarios. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the valet parking positioning method provided in the above-mentioned embodiment, and will not be repeated here.

[0127] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned valet parking positioning method when executed by a processor.

[0128] The computer program product provided in this application can solve the technical problem of large positioning error in valet parking in indoor parking scenarios. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the valet parking positioning method provided in the above embodiment, which will not be repeated here.

[0129] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A valet parking positioning method, characterized in that: The method comprises: Get the WiFi access point information in the scene; Based on the WiFi access point information in the scene, obtaining a WiFi database; Based on the WiFi database and the first preset algorithm, the real-time position of the vehicle based on WiFi positioning is obtained.

2. The method according to claim 1, characterized in that The step of obtaining the real-time position of the vehicle based on WiFi positioning based on the WiFi database and the first preset algorithm includes: Get the WiFi data currently connected to the vehicle; Based on the WiFi data and the WiFi database, matching is performed by using the first preset algorithm to obtain a matching result; Based on the matching result, the real-time position of the vehicle based on WiFi positioning is obtained.

3. The method according to claim 2, characterized in that The steps of obtaining the real-time position of the vehicle based on WiFi positioning include: Obtain the WiFi-based positioning position of the vehicle at the last moment; Acquiring data information of a vehicle-mounted inertial sensor, wherein the data information includes at least acceleration, angular velocity, and magnetic field direction; Based on the data information, obtaining the step length and direction of the vehicle; Based on the position at the last moment, the step length and the direction, the real-time position of the vehicle based on WiFi positioning is obtained.

4. The method according to claim 1, characterized in that Before the step of obtaining the WiFi access point information in the scene, the method further includes: Acquire feature point information in the scene, and acquire a SLAM map based on the feature point information; Based on the SLAM map and the second preset algorithm, obtaining the real-time position of the vehicle based on SLAM positioning; Detecting whether the real-time position of the vehicle based on SLAM positioning is in an invalid state; When the real-time position based on SLAM positioning is in an invalid state, the step of obtaining the WiFi access point information in the scene is performed.

5. The method according to claim 4, characterized in that After the step of obtaining the real-time position of the vehicle based on WiFi positioning based on the WiFi database and the first preset algorithm, the method further includes: Performing map matching based on the real-time position of the vehicle based on WiFi positioning and the SLAM map to obtain a matching result; Based on the matching result, obtaining a deviation between the real-time position of the vehicle based on WiFi positioning and the SLAM map; Based on the deviation, the real-time position of the vehicle based on WiFi positioning is corrected to obtain the corrected real-time position of the vehicle based on WiFi positioning.

6. The method according to claim 5, characterized in that After the step of correcting the real-time position of the vehicle based on WiFi positioning based on the deviation and obtaining the corrected real-time position of the vehicle based on WiFi positioning, the method further includes: Based on the WiFi database and the first preset algorithm, obtain the real-time position of the vehicle based on WiFi positioning; The corrected real-time position of the vehicle based on WiFi positioning and the real-time position of the vehicle based on WiFi positioning are filtered to obtain a comprehensive real-time position; The integrated real-time position is output to a valet parking module.

7. A valet parking positioning device, characterized in that: The device comprises: Parameter acquisition module, used to obtain WiFi access point information in the scene; The calculation module is used to obtain a WiFi database based on the WiFi access point information in the scene; and is also used to obtain a real-time position of the vehicle based on WiFi positioning based on the WiFi database and a first preset algorithm.

8. A vehicle, characterized in that: The vehicle comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the valet parking positioning method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the valet parking positioning method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the valet parking positioning method according to any one of claims 1 to 6 are implemented.

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