CN115424245A - Parking space identification method, electronic device and storage medium - Google Patents

Parking space identification method, electronic device and storage medium Download PDF

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Publication number
CN115424245A
CN115424245A CN202211243203.4A CN202211243203A CN115424245A CN 115424245 A CN115424245 A CN 115424245A CN 202211243203 A CN202211243203 A CN 202211243203A CN 115424245 A CN115424245 A CN 115424245A
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China
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parking space
obstacle
determining
candidate
condition
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罗圣钦
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Human Horizons Shanghai Autopilot Technology Co Ltd
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Human Horizons Shanghai Autopilot Technology Co Ltd
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Priority to CN202211243203.4A priority Critical patent/CN115424245A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/586—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of parking space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/32—Indexing scheme for image data processing or generation, in general involving image mosaicing

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Multimedia (AREA)
  • Traffic Control Systems (AREA)

Abstract

The present disclosure provides a parking space recognition method, an electronic device, and a storage medium, and relates to the field of automotive technologies, in particular to the field of parking space recognition technologies, and in particular, to a parking space recognition method, an electronic device, and a storage medium for a vehicle. The specific implementation scheme is as follows: determining a drivable area of a vehicle and candidate parking spaces in the drivable area; identifying identification information of the candidate parking spaces; and determining the parking space effectiveness of the candidate parking spaces according to the identified identification information. Through the process, the integrity of the idle parking space can be determined in the driving area, and then the effectiveness of the parking space is judged according to the obstacle information in the parking space. So can promote the accuracy of parking stall discernment, avoid discerning the extravagant phenomenon of parking stall that the mistake leads to.

Description

Parking space identification method, electronic device and storage medium
Technical Field
The present disclosure relates to the field of automotive technologies, and in particular, to a parking space recognition method, an electronic device, and a storage medium for a vehicle.
Background
With the increasing quantity of automobile reserves in modern society, the problem of 'difficult parking' in cities brings great trouble to automobile owners. In the existing parking space identification technology, under the conditions that the parking space is not complete enough or a small obstacle exists in the parking space, the parking space is judged to be invalid, and the accuracy of parking space identification is low.
Therefore, how to improve the accuracy of parking space identification becomes a problem to be solved.
Disclosure of Invention
The disclosure provides a parking space identification method, electronic equipment and a storage medium.
According to an aspect of the present disclosure, there is provided a parking space method, which may include the steps of:
determining a drivable area of a vehicle and candidate parking spaces in the drivable area;
identifying identification information of the candidate parking spaces;
and determining the parking space effectiveness of the candidate parking spaces according to the identified identification information.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method according to any one of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform a method in any of the embodiments of the present disclosure.
According to the technical scheme, the parking space identification method can determine the integrity of the idle parking space in the drivable area in the garage, and further judge the effectiveness of the parking space according to the obstacle information in the parking space. So can promote the accuracy of parking stall discernment, avoid discerning the extravagant phenomenon of parking stall that the mistake leads to.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow chart of a method of identifying a parking space according to the present disclosure;
FIG. 2 is a flow chart of a candidate parking space determination method according to the present disclosure;
FIG. 3 is an exemplary diagram of an inscribed rectangle determination method according to this disclosure;
FIG. 4 is a flow chart of a method for confirming parking stall validity based on an obstacle according to the present disclosure;
FIG. 5 is a block diagram of a parking spot identification device according to the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing parking space recognition in an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
As shown in fig. 1, the present disclosure relates to a parking space identification method, which may include the steps of:
s101: determining a driving available area of the vehicle and candidate parking spaces in the driving available area;
s102: identifying identification information of the candidate parking spaces;
s103: and determining the parking space effectiveness of the candidate parking spaces according to the identified identification information.
The embodiment is suitable for a vehicle host computer or a cloud computer in communication connection with a vehicle. This embodiment is applicable to the situation when carrying out parking stall discernment when automatic parking, and the situation when carrying out parking stall discernment when especially being applicable to automatic parking in the garage scene.
Before step S101 is executed, the vehicle host or a cloud computer in communication connection with the vehicle may receive the parking space identification request, and determine a drivable area of the vehicle based on the parking space identification request. The parking space identification request can be triggered based on specific operation of a user or can be triggered after the vehicle is automatically identified based on the environment state.
The method comprises the steps of determining a drivable area of a vehicle and candidate parking spaces in the drivable area, wherein the drivable area in an environment image is determined by utilizing a visual recognition technology, and the candidate parking spaces are determined in the drivable area based on the size of the parking spaces. The size of the parking space can comprise length data and width data of the parking space, and the parking space can be used as a candidate parking space under the condition that the size of the parking space meets the parking requirement. For example, a slot having a length of 4m and a width of 2m, and a length and a width of 3.8m and 1.8m, respectively, may be used as a candidate slot. The size of the parking space and the size information of the vehicle can also take other values, and the information is not exhaustive here.
The size of the parking space can also comprise integrity data of the parking space, for example, the area of a standard parking space is calculated by using the parking space line data, the projected area of a large obstacle in the parking space is calculated, and the integrity of the parking space is calculated based on the area of the standard parking space and the projected area of the large obstacle. And under the condition that the integrity of the parking space is not less than a first preset threshold value, the parking space can be used as a candidate parking space. Wherein the first preset threshold may be 80%,85%,90%, etc., which are not exhaustive here.
And after at least one candidate parking space is determined, identifying the candidate parking space to obtain the identification information of the parking space. Wherein, the candidate parking space can be identified through at least one of ultrasonic radar detection or visual perception technology. For example, the method may be that an ultrasonic radar is used to detect an obstacle, and when the result of the obstacle detection shows that there is no obstacle, a visual perception technology is used to verify the identification information in the candidate parking space. The identification information may be a parking prohibition sign, a parking permission sign lamp, a ground lock, etc., and is not limited herein.
And determining the parking space effectiveness of the candidate parking spaces according to the identified identification information. Specifically, which candidate parking spaces are valid parking spaces may be determined according to the content of the identification information in the parking spaces.
Through above process, can promote the accuracy of parking stall discernment, avoid discerning the extravagant phenomenon of parking stall that the mistake leads to.
In one embodiment, determining a drivable region of a vehicle and candidate slots therein includes: and identifying the drivable area and the candidate parking spaces in the drivable area by utilizing a pre-trained network model from the panoramic mosaic image.
The vehicle can acquire the all-round mosaic image of the area where the vehicle is located based on the parking space identification request. Specifically, firstly, a video image acquired by a vehicle is preprocessed to generate a panoramic stitching image of the surrounding environment of the vehicle. The preprocessing mode can be that video images are read according to frames, and after correction and perspective transformation are carried out on each image frame, a ring-view stitched image around the vehicle is formed by stitching, wherein the ring-view stitched image contains depth information of the surrounding environment of the vehicle.
Based on the panoramic mosaic image obtained through preprocessing, the travelable area of the vehicle can be determined through determining the boundary line. The boundary line may include an interface line between the ground and a wall surface, a projection line of an obstacle on the ground, and the like, which are not exhaustive herein.
In one embodiment, the ring-view stitched image may be input to a pre-trained model, and the boundary line of the travelable region may be determined according to an output result of the model.
The area in which the boundary line is closed is determined as a workable area. In addition, a specific display process may be performed on the region outside the boundary line as the no-travel region, where the display process may be a blurring display process or a process of displaying a specific color, for example, black, gray, and the like, and is not limited herein.
The embodiment provides a real-time semantic segmentation technology realized based on an artificial intelligence technology, and the artificial intelligence technology can be realized by utilizing a pre-trained target recognition model to recognize a panoramic mosaic image. The pre-trained target recognition model may be an artificial intelligence network model selected according to needs, such as a convolutional neural network, which is not limited herein. The target recognition model may be trained using annotated video image samples, and in particular, image data samples may be used as input data to the target recognition model. The target recognition model can obtain a predicted value of the target recognition result according to the input data, and the predicted value can be represented in the form of probability. For example, the probability that the target recognition result is "vehicle" is a%, and the probability that the target recognition result is "vehicle line" is b%. And adjusting parameters in the target recognition model by using the error between the marked recognition result (the true value of the recognition result) and the predicted value of the recognition result. The above error can be embodied by a loss function, and the effect of the loss function can be understood as: when a predicted value obtained by forward propagation of a target recognition model to be trained is close to a true value, a smaller value of the loss function is selected; conversely, the value of the loss function increases. The loss function is a function having parameters in the target recognition model as arguments.
And adjusting all parameters in the target recognition model to be trained by utilizing the errors. The error is propagated reversely in each layer of the target recognition model to be trained, and the parameter of each layer of the target recognition model to be trained is adjusted according to the error until the output result of the target recognition model to be trained is converged or the expected effect is achieved.
As shown in fig. 2, in an embodiment, the method for identifying a candidate parking space includes:
s201: determining the contour line of the parking space according to the parking space line and the projection line;
s202: determining the length-width ratio of a rectangle inscribed in the contour line area according to the contour line and the vehicle body size information of the vehicle;
s203: and under the condition that the length-width ratio is not less than a first preset threshold value, taking the parking space as a candidate parking space.
The vehicle position line and the projection line can be determined based on a semantic segmentation technology. Under the condition that the intersection point does not exist between the contour line and the parking space line, the obstacle does not occupy the parking space, and the parking space can be used as a candidate parking space; and under the condition that the contour lines and the parking space lines have intersection points, the contour lines show that the barriers occupy partial parking spaces, and the contour lines of the parking spaces which are not occupied by the barriers can be determined by utilizing the position lines and the projection lines.
The aspect ratio of the inscribed rectangle of the contour line region is determined based on the contour line and the body size information of the vehicle, and may be implemented by first determining the length or width of the inscribed rectangle using the body size information, and then determining the inscribed rectangle of the contour line region using the length or width of the inscribed rectangle. The vehicle body size information comprises vehicle body length and vehicle body width, and the long side and the wide side of the inscribed rectangle are parallel to the long side and the wide side of the parking space line respectively.
And determining the aspect ratio of a rectangle inscribed in the contour line area, and taking the parking space as a candidate parking space under the condition that the aspect ratio is not less than a first preset threshold value. The first preset threshold may be set according to a ratio of the length to the width of the vehicle body, for example, the ratio of the length to the width of the vehicle body is 2.5, and then the first preset threshold may be set to 2.6,2.7, etc., which are not exhaustive here.
For example, as shown in fig. 3, the vehicle body width is 1.8m, the width of the inscribed rectangle may be determined to be 2m in consideration of the data margin required for parking, the width of the inscribed rectangle may be used as a constraint condition, a plurality of candidate inscribed rectangles may be determined, and the candidate inscribed rectangle having the largest area may be used as the outline region inscribed rectangle. And determining the aspect ratio of a rectangle inscribed in the contour line area, and taking the parking space as a candidate parking space under the condition that the aspect ratio is not less than a first preset threshold value.
In one embodiment, determining the parking space validity of the candidate parking space according to the identified identification information includes: and under the condition that the identification information does not belong to the stop prohibition sign, determining the candidate parking space as the effective parking space. The prohibition mark may be a ground lock or a spray mark, which is not limited herein.
As shown in fig. 4, in one embodiment, the method further includes:
s401: detecting obstacles of the candidate parking spaces by using an ultrasonic radar;
s402: under the condition that the ultrasonic radar does not detect the obstacle, identifying whether the obstacle exists in the candidate parking space by using a pre-trained visual perception model;
s403: and under the condition that the visual perception model does not identify the obstacle, determining the candidate parking space as the effective parking space.
And under the condition that the ultrasonic radar does not detect the obstacle and the visual perception model does not identify the obstacle, determining the candidate parking space as the effective parking space.
In one embodiment, the method further comprises: determining the attribute of the obstacle under the condition that the obstacle exists in the candidate parking space; the attribute of the obstacle includes at least one of an obstacle type and an obstacle size;
and under the condition that the attribute of the barrier accords with the preset condition, determining the candidate parking space as the effective parking space.
The types of obstacles include ground lock in an open state, ground lock in a closed state, foam, plastic bag, etc., which are not exhaustive here. The visual perception model may be an artificial intelligence model of a pre-training number, which is not described herein.
The determination process that the attribute of the obstacle meets the preset condition may be based on the size of the obstacle, or may be based on both the size and the type of the obstacle, or may be based on only the type of the obstacle.
In one embodiment, the determination that the attribute of the obstacle meets the preset condition includes:
and under the condition that the size of the obstacle is not larger than a second preset threshold value, determining that the attribute of the obstacle meets the preset condition. The size of the obstacle may be the size of the obstacle in a three-dimensional space of length, width, and height, and the second preset threshold may be set as needed, which is not limited herein.
In one embodiment, the determination that the attribute of the obstacle meets the preset condition includes:
determining that the attribute of the obstacle meets a preset condition under the condition that the size of the obstacle is larger than a second preset threshold and the type of the obstacle belongs to a specified type; the designated type is an obstacle type which does not affect the parking space effectiveness judgment. Wherein, the designated type can be low-density substances which do not influence the parking space effectiveness judgment, such as plastic foam, cartons, plastic bags and the like, which is not exhaustive here.
As shown in fig. 5, the present disclosure relates to a parking space recognition device 500, which includes:
a drivable area determining module 501, configured to determine a drivable area of a vehicle and candidate parking spaces therein;
an identification recognition module 502 for recognizing identification information of the candidate parking space;
and a parking space validity determining module 503, configured to determine the parking space validity of the candidate parking space according to the identified identification information.
The present disclosure relates to a parking space recognition apparatus, wherein a driving region determining module 501 is used for recognizing a driving region and candidate parking spaces therein from a panoramic stitching image by using a pre-trained network model
The present disclosure relates to a parking space recognition device, wherein, sign identification module 502 includes:
the contour line determining submodule is used for determining the contour line of the parking space according to the parking space line and the projection line;
the aspect ratio determining submodule is used for determining the aspect ratio of a rectangle inscribed in the contour line area according to the contour line and the size information of the vehicle body;
and the candidate parking space determining submodule is used for taking the parking space as the candidate parking space under the condition that the length-width ratio is not smaller than the first preset threshold value.
The present disclosure relates to a parking space recognition apparatus, wherein the parking space validity determination module 503 is configured to determine a candidate parking space as an effective parking space when the identification information does not belong to a parking prohibition sign.
The utility model relates to a parking stall recognition device still includes:
the first detection submodule is used for detecting obstacles of the candidate parking spaces by using ultrasonic radar;
the second detection submodule is used for identifying whether the obstacle exists in the candidate parking space or not by utilizing a pre-trained visual perception model under the condition that the obstacle is not detected by the ultrasonic radar;
and the parking space effectiveness determining submodule is used for determining the candidate parking space as an effective parking space under the condition that the visual perception model does not identify the obstacle.
The utility model relates to a parking stall recognition device still includes:
the parking space detection method comprises the steps of determining the attribute of an obstacle under the condition that the obstacle exists in a candidate parking space; the attribute of the obstacle includes at least one of an obstacle type and an obstacle size;
and under the condition that the attribute of the barrier accords with the preset condition, determining the candidate parking space as the effective parking space.
The utility model relates to a parking stall recognition device, wherein, the attribute of barrier accords with the certain mode of predetermineeing the condition, includes:
and under the condition that the size of the obstacle is not larger than a second preset threshold value, determining that the attribute of the obstacle meets the preset condition. Determining that the attribute of the obstacle meets a preset condition under the condition that the size of the obstacle is larger than a second preset threshold and the type of the obstacle belongs to a specified type; the designated type is an obstacle type which does not affect the parking space effectiveness judgment.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 6 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, and the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the methods and processes described above, for example, the method of parking space recognition. For example, in some embodiments, the method of spot identification may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of a computer program may be loaded onto and/or installed onto device 600 via ROM602 and/or communications unit 609. When loaded into RAM 603 and executed by the computing unit 601, the computer program may perform one or more of the steps of the method of parking space identification described above. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of parking space identification.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims (10)

1. A parking space identification method comprises the following steps:
determining a drivable area of a vehicle and candidate parking spaces in the drivable area;
identifying the identification information of the candidate parking spaces;
and determining the parking space effectiveness of the candidate parking spaces according to the identified identification information.
2. The method of claim 1, wherein the determining the drivable region of the vehicle and the candidate slots therein comprises:
and identifying the drivable area and the candidate parking spaces in the drivable area by utilizing a pre-trained network model from the panoramic mosaic image.
3. The method of claim 2, wherein the identification of the candidate slots comprises:
determining the contour line of the parking space according to the parking space line and the barrier projection line;
determining the length-width ratio of a rectangle inscribed in the contour line region according to the contour line and the body size information of the vehicle;
and taking the parking space as the candidate parking space under the condition that the length-width ratio is not less than a first preset threshold value.
4. The method of claim 3, wherein the determining the space availability of the candidate space according to the identified identification information comprises:
and under the condition that the identification information does not belong to a stop prohibition sign, determining the candidate parking space as an effective parking space.
5. The method of claim 1, further comprising:
detecting obstacles of the candidate parking spaces by using an ultrasonic radar;
under the condition that the ultrasonic radar does not detect the obstacle, identifying whether the obstacle exists in the candidate parking space by using a pre-trained visual perception model;
and under the condition that the visual perception model does not identify the obstacle, determining the candidate parking space as an effective parking space.
6. The method of claim 5, further comprising:
determining the attribute of an obstacle under the condition that the obstacle exists in the candidate parking space; the attribute of the obstacle comprises at least one of an obstacle type and an obstacle size;
and under the condition that the attribute of the obstacle accords with a preset condition, determining the candidate parking space as an effective parking space.
7. The method according to claim 6, wherein the determination that the attribute of the obstacle meets the preset condition comprises:
and determining that the attribute of the obstacle meets a preset condition under the condition that the size of the obstacle is not larger than a second preset threshold value.
8. The method according to claim 6, wherein the determination that the attribute of the obstacle meets the preset condition comprises:
when the size of the obstacle is larger than a second preset threshold value and the type of the obstacle belongs to a specified type, determining that the attribute of the obstacle meets a preset condition; the specified type is an obstacle type which does not influence the parking space effectiveness judgment.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.
CN202211243203.4A 2022-10-11 2022-10-11 Parking space identification method, electronic device and storage medium Pending CN115424245A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115762234A (en) * 2023-01-10 2023-03-07 广东广宇科技发展有限公司 Intelligent community parking management method
CN116206483A (en) * 2023-04-21 2023-06-02 深圳市速腾聚创科技有限公司 Parking position determining method, electronic device and computer readable storage medium

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115762234A (en) * 2023-01-10 2023-03-07 广东广宇科技发展有限公司 Intelligent community parking management method
CN116206483A (en) * 2023-04-21 2023-06-02 深圳市速腾聚创科技有限公司 Parking position determining method, electronic device and computer readable storage medium
CN116206483B (en) * 2023-04-21 2023-08-04 深圳市速腾聚创科技有限公司 Parking position determining method, electronic device and computer readable storage medium

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