CN115408609A

CN115408609A - Parking route recommendation method and device, electronic equipment and computer readable medium

Info

Publication number: CN115408609A
Application number: CN202211040979.6A
Authority: CN
Inventors: 李锋; 骆沛; 倪凯
Original assignee: HoloMatic Technology Beijing Co Ltd
Current assignee: HoloMatic Technology Beijing Co Ltd
Priority date: 2022-08-29
Filing date: 2022-08-29
Publication date: 2022-11-29

Abstract

The embodiment of the disclosure discloses a parking route recommendation method, a parking route recommendation device, electronic equipment and a computer readable medium. One embodiment of the method comprises: acquiring a parking lot image in response to the fact that the target vehicle drives into the preset geographic fence; performing visual place identification on the parking lot image to obtain an identification result, wherein the identification result comprises a parking route set; in response to the fact that the recognition result meets the preset recognition completion condition, determining the route score value of each parking route in the parking route set included in the recognition result to obtain a route score value set; sequencing all parking routes in the parking route set based on the route score set to obtain a parking route sequence; and sending the parking route sequence to a terminal for selection by a user. This embodiment improves the accuracy of the parking route recommendation function.

Description

Parking route recommendation method and device, electronic equipment and computer readable medium

Technical Field

Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a parking route recommendation method and apparatus, an electronic device, and a computer-readable medium.

Background

The parking route recommending function is a technology for recommending an existing route to a user in the driving process and helping the user finish automatic parking. At present, the existing routes are recommended to users in a general way as follows: memorizing the driving route during the first parking; and when the vehicle is parked again, the memory route is sent to the terminal for automatic parking.

However, the inventors have found that when a parking route is recommended in the above manner, there are often technical problems as follows:

firstly, the parking route recommendation function can only recommend a fixed parking route, and when a user does not drive according to the fixed parking route, the parking route recommendation function can not recommend the parking route any more, thereby causing the accuracy of the parking route recommendation function to be insufficient;

secondly, the starting point of the recommended parking route can only be in a fixed range, and when the vehicle of the user is in other positions of the route, the route cannot be adjusted in time, so that the limitation of the parking route recommendation function is increased, and the adaptability of the parking route recommendation function is insufficient.

The above information disclosed in this background section is only for enhancement of understanding of the background of the inventive concept and, therefore, it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art in this country.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Some embodiments of the present disclosure propose parking route recommendation methods, apparatuses, electronic devices, and computer readable media to solve one or more of the technical problems set forth in the background section above.

In a first aspect, some embodiments of the present disclosure provide a method of parking route recommendation, the method comprising: acquiring a parking lot image in response to determining that the target vehicle enters the preset geo-fence; performing visual place identification on the parking lot image to obtain an identification result, wherein the identification result comprises a parking route set; in response to the fact that the recognition result meets the preset recognition completion condition, determining the route score value of each parking route in the parking route set included in the recognition result to obtain a route score value set; sequencing all parking routes in the parking route set based on the route score set to obtain a parking route sequence; and sending the parking route sequence to a terminal for selection by a user.

In a second aspect, some embodiments of the present disclosure provide a parking route recommendation device, including: an acquisition unit configured to acquire a parking lot image in response to determining that the target vehicle enters a preset geo-fence; the recognition unit is configured to perform visual location recognition on the parking lot image to obtain a recognition result, wherein the recognition result comprises a parking route set; the determining unit is configured to respond to the condition that the recognition result meets the preset recognition completion condition, determine the route score value of each parking route in the parking route set included in the recognition result, and obtain a route score value set; the sequencing unit is configured to sequence each parking route in the parking route set based on the route score set to obtain a parking route sequence; a transmitting unit configured to transmit the parking route sequence to the terminal for selection by the user.

In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device, on which one or more programs are stored, which, when executed by one or more processors, cause the one or more processors to implement the method described in any implementation of the first or second aspects.

In a fourth aspect, some embodiments of the disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first or second aspects.

In a fifth aspect, some embodiments of the present disclosure provide a computer program product comprising a computer program that, when executed by a processor, implements the method described in any of the implementations of the first aspect.

The above embodiments of the present disclosure have the following beneficial effects: by means of the parking route recommendation method of some embodiments of the present disclosure, the accuracy of the parking route recommendation function can be improved. Specifically, the reason why the accuracy of the parking route recommendation function is insufficient is that: the parking route recommendation function can only recommend a fixed parking route, and when the user does not travel along the fixed parking route, the parking route recommendation function cannot recommend the parking route any more, thereby causing the accuracy of the parking route recommendation function to be insufficient. Based on this, the parking route recommendation method of some embodiments of the present disclosure first acquires a parking lot image in response to determining that the target vehicle enters the preset geo-fence. And secondly, performing visual location identification on the parking lot image to obtain an identification result, wherein the identification result comprises a parking route set. Therefore, at least one parking route suitable for the parking lot where the target vehicle is located can be identified and obtained, the parking route can be recommended to a user conveniently in the follow-up process, and the situation that the user needs to drive according to the fixed parking route is avoided. Then, in response to determining that the recognition result meets a preset recognition completion condition, determining a route score value of each parking route in a parking route set included in the recognition result, and obtaining a route score value set. Therefore, the routes can be conveniently sequenced in a mode of meeting the requirements of the user. And then, sequencing the parking routes in the parking route set based on the route score set to obtain a parking route sequence. Therefore, the parking route sequence which meets the requirements of the user can be obtained and provided for the user to select. And finally, transmitting the parking route sequence to a terminal for the user to select. Thus, the user can obtain a recommended parking route. Therefore, some parking route recommendation methods of the present disclosure may still recommend a new parking route according to the location of the target vehicle in the parking lot when the user does not drive according to the fixed parking route, thereby improving the accuracy of the parking route recommendation function.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.

FIG. 1 is a flow diagram of some embodiments of a parking route recommendation method according to the present disclosure;

FIG. 2 is a schematic block diagram of some embodiments of a parking route recommendation device according to the present disclosure;

FIG. 3 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and the embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

FIG. 1 illustrates a flow 100 of some embodiments of a parking route recommendation method according to the present disclosure. The parking route recommendation method comprises the following steps:

step 101, in response to determining that the target vehicle drives into a preset geo-fence, acquiring a parking lot image.

In some embodiments, the subject of execution of the parking lot recommendation method may acquire the parking lot image from the on-board camera of the target vehicle by means of a wired connection or a wireless connection in response to determining that the target vehicle is driven into the preset geo-fence. The target vehicle may be a vehicle that is parking. The geofence may be a virtual boundary around a parking location into which the target vehicle is driven. In practice, the coordinates of the target vehicle may be obtained from a positioning system, and the coordinates of the target vehicle may be determined to be within the geofence of the parking lot, so as to obtain the parking lot image.

As an example, the positioning system may be: GPS (Global Positioning System), satellite network, and local radio frequency identifiers (e.g., wi-Fi nodes or bluetooth beacons).

And 102, performing visual site identification on the parking lot image to obtain an identification result.

In some embodiments, the execution subject may perform visual location recognition on the parking lot image to obtain a recognition result.

In some optional implementations of some embodiments, the performing the subject performing the visual location recognition on the parking lot image to obtain the recognition result may include the following steps:

in a first step, a historic parking lot image data information set is obtained. Each of the historical parking lot image data information sets may include a historical parking lot image group, a camera position and posture matrix set, and a historical parking route. Here, the above-described historical parking lot image group may be a continuous frame parking lot image captured by the vehicle-mounted camera when the user performs manual parking. Each historical parking field image in the historical parking field image group corresponds to one camera position and posture matrix in the camera position and posture matrix set. The camera position and posture matrix can be used for representing the vehicle position and posture of the target vehicle at the moment of shooting the corresponding historical parking lot image. The above-described historical parking route may include a start point, an end point, and a parking path of the parking route.

And secondly, performing image feature extraction on each historical parking lot image in the historical parking lot image group to generate a first image feature vector, so as to obtain a first image feature vector set. The first image feature vector can be used for representing the overall attribute of the image. The overall properties of the image may include, but are not limited to, at least one of: a color feature, a texture feature, or a shape feature. The first image feature vector can be extracted from each historical parking lot image in the historical parking lot image group through a preset extraction method, and a first image feature vector set is obtained.

As an example, the preset extraction method may be a netvlad (net Vector of Local Aggregated Descriptors, neural network Local aggregation descriptor Vector) algorithm. Here, the netvlad algorithm is an improvement of the vlad (Vector of Local Aggregated descriptor) algorithm.

In practice, the local aggregation descriptor vector algorithm can perform global feature extraction by the following method:

firstly, local feature extraction is carried out on an image to generate a local feature vector, and a local feature vector set is obtained.

By way of example, the above-mentioned method for extracting local features from an image includes, but is not limited to, a SIFT (Scale-invariant feature transform) algorithm, ORB (organized FAST and Rotated BRIEF) algorithm, super neural network algorithm, hfnet neural network algorithm, and the like.

And secondly, clustering the local characteristic vectors in the local characteristic vector set to generate a clustering center to obtain a clustering center set. Here, the above-mentioned cluster center may be a local feature vector specified in advance.

As an example, the above method of clustering the local feature vectors in the local feature vector set may be a K-means (K-means) clustering algorithm.

And thirdly, determining a global feature map based on the clustering center set. Wherein, the global feature map can be represented by a matrix. The above-mentioned pair of global feature maps may be determined by the above-mentioned local aggregation descriptor vector algorithm.

And fourthly, reducing the dimension of the global feature map to obtain a global feature vector. Here, the reducing the dimension of the global feature includes: and performing normalization processing on the local feature vector of each clustering center in the global feature map to obtain the normalized global features. And then, carrying out normalization processing on the whole normalized global feature map to obtain a global feature vector.

The neural network local aggregation descriptor vector algorithm can perform global feature extraction in the following way:

firstly, local feature extraction is carried out on an image to generate a local feature vector, and a local feature vector set is obtained. Here, the local feature extraction may be performed on the image by the above-described local feature extraction method to generate a local feature vector.

And secondly, clustering the local feature vectors in the local feature vector set to generate a clustering center to obtain a clustering center set. Here, the above-described cluster center may be a local feature vector designated in advance. As an example, the above method of clustering local feature vectors in the local feature vector set may be a neural network-based method.

And thirdly, determining a global feature map based on the clustering center set. Here, the global feature map may be represented by a matrix. The above-mentioned pair of global feature maps may be determined by the above-mentioned neural network local aggregation descriptor vector algorithm.

And fourthly, reducing the dimension of the global feature map to obtain a global feature vector. Here, the reducing the dimension of the global feature includes: and performing normalization processing on the local feature vector of each cluster center in the global feature map to obtain a normalized global feature map. And then, carrying out normalization processing on the whole normalized global feature map to obtain a global feature vector.

And thirdly, extracting a second image characteristic vector from the parking lot image to obtain a second image characteristic vector. Here, the second image feature vector may be extracted from the parking lot image by the neural network local aggregation descriptor vector algorithm, so as to obtain the second image feature vector.

And fourthly, determining the historical parking lot image corresponding to the first image feature vector matched with the second image feature vector in the first image feature vector set as the target parking lot image based on the first image feature vector set and the second image feature vector.

In some optional implementations of some embodiments, the executing body may determine, as the target parking lot image, an image of the historic parking lot corresponding to the first image feature vector matching the second image feature vector in the first image feature vector set based on the first image feature vector set and the second image feature vector, and may include:

the first substep is to determine a distance value between each first image feature vector in the first image feature vector set and the second feature vector to obtain a distance value set.

And a second substep of determining the historical parking lot image corresponding to the first image feature vector corresponding to the minimum distance value in the distance value set as a target parking lot image.

And fifthly, extracting local features of the target parking lot image to obtain a first local feature point set. The local feature extraction may be performed on the target parking lot image by the local feature extraction method.

And sixthly, extracting a description vector for each local feature point in the first local feature point set to generate a first description vector, so as to obtain a first description vector set. In this case, the description vector extraction may be performed on each local feature point in the first local feature point set by using the local feature extraction method described above to generate a first description vector.

And seventhly, extracting local features of the parking lot image to obtain a second local feature point set. The parking lot image may be subjected to local feature extraction by the local feature extraction method.

And eighthly, extracting the description vector of each local characteristic point in the second local characteristic point set to generate a second description vector, so as to obtain a second description vector set. The description vector extraction may be performed on each local feature point in the second local feature point set by the local feature extraction method to generate a second description vector.

And ninthly, determining a target point set based on the first description vector set and the second description vector set.

In some optional implementations of some embodiments, the determining, by the execution subject, a set of target points based on the first set of description vectors and the second set of description vectors may include:

a first sub-step, determining a vector distance value between each first description vector in the first description vector set and each second description vector in the second description vector set to generate a vector distance value set, resulting in a vector distance value set.

And a second substep of determining the minimum vector distance value in each vector distance value set in the vector distance value set as a minimum vector distance value to obtain a minimum vector distance value set.

And a third substep, determining a first local feature point corresponding to the minimum vector distance smaller than a preset distance threshold in the minimum vector distance value set as a target point, and obtaining a target point set.

And step ten, generating a target camera position and posture matrix based on the target point set. The target camera position and posture matrix can be generated through a multi-view geometric method.

And step eleven, generating a parking route based on the position and posture matrix of the target camera.

In some optional implementations of some embodiments, the executing body generating the parking route based on the target camera position and posture matrix may include:

and adjusting the historical parking route based on the camera position and posture matrix which is matched with the target camera position and posture matrix in the camera position and posture matrix set to obtain the parking route. The adjustment of the historical parking route may be performed by determining a camera position corresponding to the camera position and posture matrix as a starting point of the parking route, and determining a parking path from the starting point to an end point of the historical parking route as the parking route.

In practice, the above-mentioned generation of the relevant content of the parking route is taken as an invention point of the embodiment of the present disclosure, and solves the technical problem mentioned in the background art that "the starting point of the recommended parking route can only be within a fixed range, and when the user vehicle is located at other positions of the route, the route cannot be adjusted in time, so that the limitation of the parking route recommendation function is increased, and thus, the adaptability of the parking route recommendation function is insufficient". Among these factors, the factors that cause the inadaptability of the parking route recommendation function are often as follows: the starting point of the recommended parking route can only be in a fixed range, and when the vehicle of the user is located at other positions of the route, the route cannot be adjusted in time, so that the limitation of the parking route recommendation function is increased. If the factors are solved, the effect that the starting point of the recommended parking route is not limited within a fixed range can be achieved, when the vehicle of the user is in other positions of the route, the parking route recommending function adjusts the parking route in time, the limitation of the parking route recommending function is reduced, and therefore the adaptability of the parking route recommending function is improved. In order to achieve the effect, the historical parking lot image which is most similar to the parking lot where the user vehicle is located in the historical parking lot image group can be obtained through a global feature matching method and is determined as the target parking lot image. Therefore, the historical parking routes corresponding to the historical parking lot image group comprising the target parking lot image can be obtained. Then, a target point set is determined through a local feature matching method. Then, based on the target point set, a target camera position and posture matrix is generated. Thereby, the position of the camera that captures the above-described image of the target parking field in the parking route can be determined. And finally, generating a parking route based on the position and posture matrix of the target camera. Thus, the target camera position corresponding to the target camera position and posture matrix can be determined as the starting point of the parking route, and the recommended parking route can be obtained. Thus, the adaptability of the parking route recommendation function is improved.

And 103, in response to the fact that the recognition result meets the preset recognition completion condition, determining the route score value of each parking route in the parking route set included in the recognition result to obtain a route score value set.

In some embodiments, the executing entity may determine, in response to determining that the recognition result satisfies a preset recognition completion condition, a route score value of each parking route in a parking route set included in the recognition result, and obtain a route score value set. The recognition result may be a result of performing visual location recognition on the parking lot image. The preset recognition completion condition may be that the parking route set in the recognition result includes at least one parking route.

In some optional implementation manners of some embodiments, the determining, by the executing body and in response to determining that the recognition result satisfies a preset recognition completion condition, a route score value of each parking route in a parking route set included in the recognition result, and obtaining a route score value set may include:

firstly, determining the distance value of each parking route in the parking route set to obtain a distance value set.

And secondly, determining a route score value of the parking route based on the route value set. The executing body may determine the route score of the parking route based on the route value set, wherein the route score may be higher as the route score is smaller.

As an example, when the distance value is 500 meters, the route score value is 80, and when the distance value is 600 meters, the route score value is 70.

And step 104, sequencing all parking routes in the parking route set based on the route score set to obtain a parking route sequence.

In some embodiments, the executing body may sort the parking routes in the parking route set based on the route score set, so as to obtain a parking route sequence. The parking routes in the parking route set are sorted based on the route score set, and the parking routes in the parking route set can be sorted in the sequence from high to low according to the route score value.

Step 105, the parking route sequence is sent to the terminal for selection by the user.

In some embodiments, the execution body may transmit the parking route sequence to a terminal for selection by a user. The selection of the user comprises selecting a default route, selecting a specified route and exiting from a parking route recommendation function. The default route may be a first-ranked route in the parking route sequence. The terminal comprises a vehicle-mounted terminal or a user mobile phone terminal.

Optionally, the executing body may further perform the following steps:

and step one, in response to the fact that the recognition result does not meet the preset recognition completion condition, obtaining the coordinates of the target vehicle.

And secondly, matching the target vehicle coordinates with the geo-fence to obtain a matching result. And the matching result is successful matching or unsuccessful matching. The target vehicle may be a vehicle in which the user is driving. The method for the execution subject to perform the matching processing on the coordinates of the target vehicle and the geo-fence may be to acquire the coordinates of the target vehicle from a positioning system, and determine whether the coordinates of the target vehicle are within the geo-fence of the parking lot.

And thirdly, in response to the fact that the matching result is determined to be successful, the parking route recommending operation is executed again. For a specific implementation manner and technical effects of the operation of re-executing the parking route recommendation, reference may be made to steps 102 to 105 in the above embodiment, which are not described herein again.

And fourthly, responding to the received selection operation of the user on the parking route in the parking route sequence, and sending the selected parking route to the control terminal of the target vehicle for parking.

The above embodiments of the present disclosure have the following advantages: by the parking route recommendation method of some embodiments of the present disclosure, the accuracy of the parking route recommendation function can be improved. Specifically, the reason why the accuracy of the parking route recommendation function is insufficient is that: the parking route recommendation function can only recommend a fixed parking route, and when the user does not travel along the fixed parking route, the parking route recommendation function cannot recommend the parking route any more, thereby resulting in insufficient accuracy of the parking route recommendation function. Based on this, the parking route recommendation method of some embodiments of the present disclosure first acquires a parking lot image in response to determining that the target vehicle enters the preset geo-fence. And secondly, performing visual location identification on the parking lot image to obtain an identification result, wherein the identification result comprises a parking route set. Therefore, at least one parking route suitable for the parking lot where the target vehicle is located can be identified and obtained, the parking route can be conveniently recommended to a user subsequently, and the condition that the user must drive according to the fixed parking route is avoided. Then, in response to determining that the recognition result meets a preset recognition completion condition, determining a route score value of each parking route in a parking route set included in the recognition result, and obtaining a route score value set. Therefore, the routes can be conveniently sequenced in a mode of more meeting the requirements of the user. And then, sequencing the parking routes in the parking route set based on the route score set to obtain a parking route sequence. Therefore, the parking route sequence which meets the requirements of the user can be obtained and provided for the user to select. And finally, sending the parking route sequence to a terminal for selection by a user. Thus, the user can obtain a recommended parking route. Therefore, some parking route recommendation methods of the present disclosure may still recommend a new parking route according to the location of the target vehicle in the parking lot when the user is not driving according to the fixed parking route, thereby improving the accuracy of the parking route recommendation function.

With further reference to fig. 2, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a parking route recommendation device, which correspond to those shown in fig. 1, and which may be particularly applied in various electronic devices.

As shown in fig. 2, a parking route recommendation device 200 of some embodiments includes: an acquisition unit 201, a recognition unit 202, a determination unit 203, a sorting unit 204, and a transmission unit 205. Wherein the obtaining unit 201 is configured to obtain the parking lot image in response to determining that the target vehicle enters the preset geo-fence; a recognition unit 202 configured to perform visual location recognition on the parking lot image to obtain a recognition result, wherein the recognition result includes a parking route set; a determining unit 203, configured to determine a route score value of each parking route in a parking route set included in the recognition result in response to determining that the recognition result satisfies a preset recognition completion condition, so as to obtain a route score value set; a sorting unit 204 configured to sort each parking route in the parking route set based on the route score set to obtain a parking route sequence; a transmitting unit 205 configured to transmit the parking route sequence to a terminal for selection by a user.

It will be understood that the units described in the apparatus 200 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations, features and advantages described above with respect to the method are also applicable to the apparatus 200 and the units included therein, and are not described again

Referring now to FIG. 3, a block diagram of an electronic device 300 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device in some embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The terminal device shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 3, the electronic device 300 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage means 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data necessary for the operation of the electronic apparatus 300 are also stored. The processing device 301, the ROM 302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.

Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, or the like; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device 300 to communicate wirelessly or by wire with other devices to exchange data. While fig. 3 illustrates an electronic device 300 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 3 may represent one device or may represent multiple devices, as desired.

In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. The computer program, when executed by the processing apparatus 301, performs the above-described functions defined in the methods of some embodiments of the present disclosure.

It should be noted that the computer readable medium described in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a parking lot image in response to determining that the target vehicle enters the preset geo-fence; performing visual place identification on the parking lot image to obtain an identification result, wherein the identification result comprises a parking route set; in response to the fact that the recognition result meets the preset recognition completion condition, determining the route score value of each parking route in the parking route set included in the recognition result to obtain a route score value set; sequencing all parking routes in the parking route set based on the route score set to obtain a parking route sequence; and sending the parking route sequence to a terminal for selection by a user.

Computer program code for carrying out operations for some embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. 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 the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, which may be described as: a processor includes an acquisition unit, a recognition unit, a determination unit, a sorting unit, and a transmission unit. The names of these units do not in some cases constitute a limitation on the unit itself, and for example, the acquisition unit may also be described as a "unit that acquires images of a parking lot".

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.

Claims

1. A parking route recommendation method comprising:

acquiring a parking lot image in response to determining that the target vehicle enters the preset geo-fence;

performing visual location identification on the parking lot image to obtain an identification result, wherein the identification result comprises a parking route set;

in response to the fact that the recognition result meets a preset recognition completion condition, determining a route score value of each parking route in a parking route set included in the recognition result to obtain a route score value set;

sequencing all parking routes in the parking route set based on the route score set to obtain a parking route sequence;

and sending the parking route sequence to a terminal for selection of a user.

2. The method of claim 1, wherein the method further comprises:

in response to determining that the recognition result does not meet the preset recognition completion condition, acquiring a target vehicle coordinate;

matching the target vehicle coordinates and the geo-fence to obtain a matching result, wherein the matching result is successful matching or unsuccessful matching;

in response to determining that the matching result is a successful match, re-executing parking route recommendation operation;

in response to receiving a selection operation of the user on a parking route in the parking route sequence, sending the selected parking route to a control terminal of the target vehicle for parking.

3. The method of claim 1, wherein the determining a route score value for each parked route in a set of parked routes included in the recognition result in response to determining that the recognition result satisfies a preset recognition completion condition comprises:

determining a distance value of each parking route in the parking route set to obtain a distance value set;

determining a route score for the parking route based on the set of route values.

4. The method of claim 1, wherein the performing visual location identification on the parking lot image to obtain an identification result comprises:

acquiring a historical parking lot image data information set, wherein each piece of historical parking lot image data information in the historical parking lot image data information set comprises a historical parking lot image group, a camera position and posture matrix set and a historical parking route;

performing image feature extraction on each historical parking lot image in the historical parking lot image group to generate a first image feature vector, so as to obtain a first image feature vector set;

extracting a second image characteristic vector from the parking lot image to obtain a second image characteristic vector;

determining the historical parking lot images corresponding to the first image feature vectors matched with the second image feature vectors in the first image feature vector set as target parking lot images on the basis of the first image feature vector set and the second image feature vectors;

performing local feature extraction on the target parking lot image to obtain a first local feature point set;

extracting a description vector of each local feature point in the first local feature point set to generate a first description vector, so as to obtain a first description vector set;

performing local feature extraction on the parking lot image to obtain a second local feature point set;

extracting a description vector of each local feature point in the second local feature point set to generate a second description vector, so as to obtain a second description vector set;

determining a set of target points based on the first set of description vectors and the second set of description vectors;

generating a target camera position and posture matrix based on the target point set;

and generating the parking route based on the target camera position and posture matrix.

5. The method of claim 4, wherein determining, based on the first set of image feature vectors and the second set of image feature vectors, a historic parking lot image corresponding to a first image feature vector in the first set of image feature vectors that matches the second set of image feature vectors as a target parking lot image comprises:

determining a distance value between each first image feature vector in the first image feature vector set and the second feature vector to obtain a distance value set;

and determining the historical parking lot image corresponding to the first image feature vector corresponding to the minimum distance value in the distance value set as a target parking lot image.

6. The method of claim 4, the determining a set of target points based on the first set of description vectors and the second set of description vectors, comprising:

determining a vector distance value between each first description vector in the first description vector set and each second description vector in the second description vector set to generate a vector distance value set, so as to obtain a vector distance value set;

determining the minimum vector distance value in each vector distance value group in the vector distance value group set as the minimum vector distance value to obtain a minimum vector distance value set;

and determining a first local feature point corresponding to the minimum vector distance smaller than a preset distance threshold in the minimum vector distance value set as a target point to obtain a target point set.

7. The method of claim 4, the generating the parking route based on the target camera position and pose matrix, comprising:

and adjusting the historical parking route based on the camera position and posture matrix which is matched with the target camera position and posture matrix in the camera position and posture matrix set to obtain the parking route.

8. A parking route recommendation device comprising:

an acquisition unit configured to acquire a parking lot image in response to a determination that the target vehicle enters a preset geo-fence;

the recognition unit is configured to perform visual location recognition on the parking lot image to obtain a recognition result, wherein the recognition result comprises a parking route set;

a determining unit configured to determine a route score value of each parking route in a parking route set included in the recognition result in response to determining that the recognition result satisfies a preset recognition completion condition, resulting in a route score value set;

the sorting unit is configured to sort each parking route in the parking route set based on the route score set to obtain a parking route sequence;

a transmitting unit configured to transmit the parking route sequence to a terminal for selection by a user.

9. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-7.

10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.