CN115147804A - Parking space identification method, system, equipment, medium and program product - Google Patents
Parking space identification method, system, equipment, medium and program product Download PDFInfo
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Abstract
The application provides a parking space identification method, the method utilizes ultrasonic waves to detect a parking space, candidate parking spaces are obtained, whether the candidate parking spaces are real parking spaces or not is determined according to obstacle information learned by multi-path perspective images, when the candidate parking spaces are real parking spaces, the position information of the real parking spaces is output, the influence of environment, precision and obstacles on parking space identification can be avoided, the position information of the real parking spaces is obtained, accurate and stable input information is provided for automatic parking, the completion of an automatic parking function is assisted, the use experience of a user is enhanced, and the automatic parking success rate of the parking spaces is improved.
Description
Technical Field
The present application relates to the field of automotive technologies, and in particular, to a method, a system, a device, a computer-readable storage medium, and a computer program product for identifying a parking space.
Background
The unmanned technology is a technology which can automatically plan a driving route and control a vehicle to drive by sensing a road environment through a vehicle-mounted sensor system. In recent years, research and development of an automatic parking function in an unmanned technology are always hot topics in academic circles and industrial circles, and how to accurately identify parking spaces in automatic parking is a primary challenge in a plurality of difficulties faced by researchers and engineers at present.
In general, an ultrasonic radar mounted on a vehicle detects an obstacle near a parking space, and receives an echo returned by the ultrasonic radar to determine whether or not there is an obstacle around the parking space, thereby enabling parking as a parking space. However, the obstacle information returned by the ultrasonic waves is single, and only whether an obstacle exists can be judged, but specific obstacle information cannot be determined, and specific types and types of the obstacles cannot be determined, that is, a gap cannot be determined to be a legal parking space or an illegal parking space, for example, the gap may be a parking space between two vehicles or a gap between landscape trees on two sides of a road.
Therefore, an accurate parking space recognition method is needed in the art.
Disclosure of Invention
The application provides a parking space identification method. The method can accurately identify the parking space by utilizing the ultrasonic waves and the multi-path panoramic image, and improves the parking space identification precision.
In a first aspect, the present application provides a parking space identification method, including:
carrying out parking space detection by utilizing ultrasonic waves to obtain candidate parking spaces in the area, wherein the candidate parking spaces are represented by parking space reflection points;
judging whether the candidate parking space is a real parking space or not according to the barrier information in the area learned by the multi-path all-around images;
and when the candidate parking space is the real parking space, outputting the position information of the real parking space.
In some possible implementation manners, determining whether the candidate parking space is a real parking space according to the obstacle information in the area learned by the multi-path panoramic image includes:
and when the obstacles in the area learned by the multi-path all-around images are vehicles in a static state, determining that the candidate parking space is the real parking space.
In some possible implementations, the method further includes:
determining the obstacles in the area as vehicles through deep learning according to the multi-path all-round images;
according to the motion parameters of the vehicle, carrying out dead reckoning through a vehicle motion model to obtain a position sequence of the vehicle;
and judging whether the vehicle is in a static state or not according to the position sequence of the vehicle.
In some possible implementations, determining obstacles in the area as vehicles through deep learning from the multiple surround view images includes:
determining the specific position of the vehicle in the area through deep learning according to the multi-path all-round images;
and when the specific position of the vehicle is consistent with the parking space reflection point, determining that the obstacle in the area is the vehicle.
In some possible implementations, the multiple surround view images include multiple frames of multiple surround view images taken at different locations.
In some possible implementations, the multiple look-around images are obtained by a vehicle-mounted look-around camera.
In a second aspect, the present application provides a parking space recognition device, the device includes:
the candidate parking space determining module is used for detecting parking spaces by utilizing ultrasonic waves to obtain candidate parking spaces in the area, and the candidate parking spaces are represented by parking space reflection points;
the real parking space judging module is used for judging whether the candidate parking space is a real parking space according to the barrier information in the area learned by the multi-path panoramic image;
and the parking space information output module is used for outputting the position information of the real parking space when the candidate parking space is the real parking space.
In some possible implementation manners, the real parking space determination module is specifically configured to:
and when the obstacles in the area learned by the multi-path all-around images are vehicles in a static state, determining that the candidate parking space is the real parking space.
In some possible implementation manners, the real parking space judging module is further used for:
determining the obstacles in the area as vehicles through deep learning according to the multi-path all-round images;
according to the motion parameters of the vehicle, carrying out dead reckoning through a vehicle motion model to obtain a position sequence of the vehicle;
and judging whether the vehicle is in a static state or not according to the position sequence of the vehicle.
In some possible implementation manners, the real parking space determination module is specifically configured to:
determining a specific position of the vehicle when the obstacle in the area learned by the multi-way surround view image is the vehicle in a stationary state;
and when the specific position of the vehicle is consistent with the parking space reflection point, determining the candidate parking space as the real parking space.
In some possible implementations, the multiple surround view images include multiple frames of multiple surround view images taken at different locations.
In some possible implementations, the multiple look-around images are obtained by a vehicle-mounted look-around camera.
In a third aspect, the present application provides an apparatus comprising a processor and a memory. The processor and the memory communicate with each other. The processor is configured to execute instructions stored in the memory, so as to cause the device to perform the method for identifying a parking space as in the first aspect or any one of the implementations of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and the instructions instruct a device to execute the parking space identification method according to the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the present application provides a computer program product containing instructions that, when run on a device, cause the device to perform the method for identifying a parking space according to the first aspect or any implementation manner of the first aspect.
The present application can further combine to provide more implementations on the basis of the implementations provided by the above aspects.
According to the technical scheme, the embodiment of the application has the following advantages:
the embodiment of the application provides a parking space identification method, which comprises the steps of detecting a parking space by utilizing ultrasonic waves to obtain a candidate parking space, determining whether the candidate parking space is a real parking space or not according to barrier information learned by a multi-path perspective image, and outputting position information of the real parking space when the candidate parking space is the real parking space. The parking space recognition can be prevented from being influenced by environment, precision and obstacles on the parking space recognition, the position information of the real parking space is obtained, accurate and stable input information is provided for automatic parking, the completion of the automatic parking function is assisted, the use experience of a user is enhanced, and the automatic parking success rate of the parking space is improved.
Drawings
In order to more clearly illustrate the technical method of the embodiments of the present application, the drawings required in the embodiments will be briefly described below, and obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flow chart of a parking space identification method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a parking space identification method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a parking space recognition device according to an embodiment of the present application.
Detailed Description
The scheme in the embodiments provided in the present application will be described below with reference to the drawings in the present application.
The terms "first", "second" in the embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or as implying any indication of the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.
Some technical terms referred to in the embodiments of the present application will be first described.
The ultrasonic wave refers to a sound wave with frequency higher than 20000 Hertz (Hz), and has the characteristics of good directivity, strong adaptability, strong penetrating power and the like. Ultrasonic radar measures a measured distance by using ultrasonic transmission through reflection of a measured object and time difference after reception of an echo, and is often used for detecting surrounding obstacles on an unmanned automobile.
Generally, an ultrasonic radar detects a parking space by detecting obstacle information in a specific area. Ideally, the ultrasonic radar determines the empty space between two vehicles by using the returned ultrasonic information, and then determines whether the empty space is a parking space. However, the information returned by the ultrasonic radar is generally single, and the specific information of the obstacles cannot be determined, namely the gap between the obstacles cannot be a legal parking space or an illegal parking space. Therefore, parking based on the information may fail, which may affect user experience and development of unmanned technology.
In view of this, the present application provides a method for identifying a parking space, which may be executed by a processing device. The processing device refers to a device having data processing capability, and may be an electronic control unit on a vehicle, for example. An electronic control unit is a control device composed of an integrated circuit for implementing a series of functions such as analysis processing and transmission of data, and generally includes a plurality of components such as an input circuit, an a/D (analog/digital) converter, a microcomputer, and an output circuit.
Specifically, the processing device detects the parking spaces by using ultrasonic waves to obtain candidate parking spaces in the region, wherein the candidate parking spaces are represented by parking space reflection points, whether the candidate parking spaces are real parking spaces or not is judged according to obstacle information in the region learned by the multi-path panoramic image, and when the candidate parking spaces are the real parking spaces, position information of the real parking spaces is output, so that the position information of the real parking spaces is obtained.
In order to facilitate understanding of the technical scheme of the application, the parking space identification method provided by the application is introduced below by combining with the accompanying drawings.
Referring to a flow chart of a parking space identification method shown in fig. 1, the specific steps of the method are as follows.
S102: the processing equipment utilizes ultrasonic waves to detect the parking spaces, and candidate parking spaces in the area are obtained.
Wherein, the candidate parking stall passes through the parking stall reflection point and characterizes.
In some possible implementations, the ultrasonic waves may be ultrasonic waves emitted by a vehicle-mounted ultrasonic radar. A vehicle-mounted ultrasonic radar is a common sensor, and is generally used as a reversing radar.
Generally, the method for determining the parking space by using ultrasonic waves can only determine that an obstacle exists according to a reflection point, but cannot further determine specific information of the obstacle, so that misjudgment of the parking space between the obstacles is caused. For example, ultrasound may identify a gap between landscape trees on both sides of a road as a parking spot.
In this embodiment, the processing device obtains the candidate parking spaces in the area according to the parking space reflection point information.
S104: and the processing equipment judges whether the candidate parking space is a real parking space or not according to the barrier information in the area learned by the multi-path panoramic image.
The multi-channel all-round looking images are acquired through the vehicle-mounted all-round looking camera and comprise multi-frame multi-channel all-round looking images acquired by the vehicle at different positions.
If only one frame of multi-channel all-round-looking image of the vehicle at a certain position is obtained, the problems that important information in the obtained image is blocked, the picture identification is inaccurate and the like exist, and the identification precision is influenced. In addition, the specific state information of the vehicle cannot be obtained by only obtaining the single-frame multi-path all-around image, for example, the obstacle may be determined as the vehicle, but the specific state of the vehicle is not determined, and there may be a case where the vehicle is stopping or the vehicle stops, and the vehicle is in a moving state, which may cause misjudgment and cause an unexpected safety accident.
Specifically, the vehicle-mounted all-round camera acquires a spliced image of the multi-path all-round images of the surrounding environment of the vehicle. And when the obstacles in the area learned by the multi-path all-around images are vehicles in a static state, determining that the candidate parking space is the real parking space.
The processing device splices the acquired multi-path all-around images into a bird's-eye view, inputs the bird's-eye view into a pre-trained Deep Learning (DL) model, and the deep learning model outputs the obstacles in the multi-path all-around images as vehicles.
The deep learning model is constructed through a deep learning method, the problem that the traditional machine learning visual algorithm and the traditional image algorithm fail in image identification under the conditions of strong light, shadow and partial shielding can be solved through the deep learning method, and the detection rate of a single-frame image and the robustness of multiple scenes can be improved.
In this embodiment, utilize degree of deep learning model to detect the vehicle in the region through multichannel look around the image, can avoid receiving the influence of barriers such as trees, taper barrel and flower bed, cause the problem of information acquisition not accurate enough in the region.
The processing equipment performs dead reckoning on the obstacle vehicle through a vehicle motion model according to vehicle motion parameters such as wheel pulse and steering wheel angle of the vehicle, and obtains a position sequence of the vehicle.
The track estimation is a method of obtaining a track and information around a vehicle without using an external navigation object, such as a vehicle corner and a vehicle wheel speed.
And the processing equipment judges whether the vehicle is in a static state or not according to the position sequence of the vehicle, and when the vehicle is in the static state, the candidate parking space corresponding to the vehicle is probably the real parking space.
In the embodiment, by combining dead reckoning and a single-frame detection algorithm, the vehicle states detected for multiple times in the time sequence information can be tracked and updated, an accurate position sequence of the vehicle is obtained, the defect that the vehicle information is inaccurate due to shielding and single-frame missing detection is overcome, the detection rate of the vehicle is improved, and the detection precision is improved through multiple frames of images.
The processing equipment can acquire the real motion state of the vehicle by tracking the target of the vehicle in the area, so as to determine whether the vehicle is in a static state or not, and avoid the occurrence of unexpected safety accidents caused by the fact that the vehicle is in the motion state and stops continuously.
When the obstacle is a vehicle and is in a stationary state, a specific position of the vehicle is acquired. The specific position of the vehicle can be obtained through the deep learning model, or the specific position of the vehicle can be obtained according to the position sequence of the vehicle, or the specific position of the vehicle can be obtained in other manners.
And when the specific position of the vehicle is consistent with the parking space reflection point of the ultrasonic wave, determining the candidate parking space as the real parking space.
Specifically, the specific position of the vehicle may be converted into coordinates in a world coordinate system, the position reflection point in the ultrasonic wave may also be converted into coordinates in the world coordinate system, and the two coordinates may be compared to determine whether the position reflection point in the ultrasonic wave is the position of the stationary vehicle. The specific position of the vehicle and the reflection point of the parking space in the ultrasonic wave can be converted into coordinates in a world coordinate system, and can also be converted into other coordinates, so that the specific position of the vehicle and the position of the reflection point of the parking space in the ultrasonic wave are accurately compared.
In some possible implementations, the obstacle in the area is greater than 1, and the obstacle is a plurality of obstacles. The parking space reflection points are a plurality of reflection points, and the vehicles are a plurality of vehicles.
When the specific position of the vehicle is consistent with the parking space reflection point, the candidate parking space can be determined as the real parking space. Specifically, when the specific positions of all the vehicles are consistent with the reflection points of all the parking spaces, the candidate parking space can be determined as a real parking space, and when any one of the positions is inconsistent, the candidate parking space is an invalid parking space.
S106: and when the candidate parking space is the real parking space, the processing equipment outputs the position information of the real parking space.
So, can avoid the parking stall discernment to receive environment, precision and barrier to the influence of parking stall discernment, acquire the positional information of true parking stall, provide accurate stable input information for automatic parking, supplementary automatic completion of parking function, reinforcing user's use is experienced, improves the automatic success rate of berthing in parking stall.
In summary, the application provides a parking space identification method, which includes the steps of detecting a parking space by using ultrasonic waves to obtain a candidate parking space, determining whether the candidate parking space is a real parking space according to barrier information learned by a multi-path looking-around image, and outputting position information of the real parking space when the candidate parking space is the real parking space. Therefore, the detection of the surrounding environment, the tracking of the obstacle information and the matching of the ultrasonic parking space reflection points can be comprehensively realized, the problems that the recognized parking space is an illegal parking space and the like due to the fact that the obstacle is recognized as a non-vehicle and the vehicle is in a non-static state are avoided, and the accuracy of parking space recognition is improved.
The application provides another embodiment of a parking space identification method, which is shown in fig. 2.
Specifically, parking space recognition is started, on one hand, a visual deep learning vehicle detection module is started to acquire multi-path all-round-looking images, detect vehicles around the environment, track and update the positions of the vehicles, and convert static vehicle information into world coordinate system coordinates.
And on the other hand, the ultrasonic parking space detection module is started to detect the parking spaces around the environment, and the detected parking space reflection point information is converted into the world coordinate system coordinates. The processing equipment judges whether the parking space reflection point is a static vehicle or not according to the world coordinate system coordinate of the vehicle and the world coordinate system coordinate of the parking space reflection point, namely whether the candidate parking space is a real parking space or not, and when the parking space reflection point is a static vehicle, the processing equipment outputs the position information of the real parking space.
Therefore, the parking space is accurately identified, accurate and stable input information is provided for automatic parking, the completion of the automatic parking function is assisted, the use experience of a user is enhanced, and the automatic parking success rate of the parking space is improved.
Corresponding to the above method embodiment, the present application further provides a parking space recognition device, referring to fig. 3, where the device 300 includes: a candidate parking space determining module 302, a real parking space judging module 304 and a parking space information output module 306.
The candidate parking space determining module 302 is configured to perform parking space detection by using ultrasonic waves to obtain candidate parking spaces in an area, where the candidate parking spaces are represented by parking space reflection points;
a real parking space judging module 304, configured to judge whether the candidate parking space is a real parking space according to the obstacle information in the area learned from the multi-path panoramic image;
and the parking space information output module 306 is configured to output the position information of the real parking space when the candidate parking space is the real parking space.
In some possible implementations, the real parking space determination module 304 is specifically configured to:
and when the obstacles in the area learned by the multi-path panoramic image are the vehicles in the static state, determining that the candidate parking space is the real parking space.
In some possible implementations, the real parking space determination module 304 is further configured to:
determining the obstacles in the area as vehicles through deep learning according to the multi-path all-round images;
according to the motion parameters of the vehicle, carrying out dead reckoning through a vehicle motion model to obtain a position sequence of the vehicle;
and judging whether the vehicle is in a static state or not according to the position sequence of the vehicle.
In some possible implementations, the real parking space determination module 304 is specifically configured to:
determining a specific position of the vehicle when the obstacle in the area learned by the multi-way surround view image is the vehicle in a stationary state;
and when the specific position of the vehicle is consistent with the parking space reflection point, determining the candidate parking space as the real parking space.
In some possible implementations, the multiple surround view images include multiple frames of multiple surround view images taken at different locations.
In some possible implementations, the multiple around view images are obtained by a vehicle-mounted around view camera.
The application provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the instructions are run on equipment, the equipment is enabled to execute the parking space identification method.
The application provides a computer program product containing instructions which, when run on a device, cause the device to perform the above-mentioned stall identification method.
It should be noted that the above-described embodiments of the apparatus are merely schematic, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiments of the apparatus provided in the present application, the connection relationship between the modules indicates that there is a communication connection therebetween, which may be specifically implemented as one or more communication buses or signal lines.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, an exercise device, or a network device) to execute the method according to the embodiments of the present application.
In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, training device, or data center to another website site, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that a computer can store or a data storage device, such as a training device, data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
Claims (10)
1. A parking space identification method is characterized by comprising the following steps:
carrying out parking space detection by utilizing ultrasonic waves to obtain candidate parking spaces in an area, wherein the candidate parking spaces are represented by parking space reflection points;
judging whether the candidate parking space is a real parking space or not according to the barrier information in the area learned by the multi-path all-around images;
and when the candidate parking space is a real parking space, outputting the position information of the real parking space.
2. The method of claim 1, wherein the determining whether the candidate space is a real space according to the obstacle information in the area learned from the multiple surround view images comprises:
and when the obstacles in the area learned by the multi-path all-around images are vehicles in a static state, determining that the candidate parking space is a real parking space.
3. The method of claim 2, further comprising:
determining the obstacles in the area as vehicles through deep learning according to the multipath panoramic images;
according to the motion parameters of the vehicle, carrying out dead reckoning through a vehicle motion model to obtain a position sequence of the vehicle;
and judging whether the vehicle is in a static state or not according to the position sequence of the vehicle.
4. The method of claim 2, wherein determining the candidate space as the real space when the obstacle in the area learned from the multiple panoramic images is a vehicle in a stationary state comprises:
determining a specific position of the vehicle when the obstacle in the area learned from the multiple surround-view images is the vehicle in a stationary state;
and when the specific position of the vehicle is consistent with the parking space reflection point, determining that the candidate parking space is a real parking space.
5. The method of claim 1, wherein the multiple surround view images comprise multiple frames of multiple surround view images taken at different locations.
6. The method of claim 1, wherein the multiple surround view images are obtained by an onboard surround view camera.
7. The utility model provides a parking stall recognition device which characterized in that, the device includes:
the candidate parking space determining module is used for detecting parking spaces by utilizing ultrasonic waves to obtain candidate parking spaces in an area, and the candidate parking spaces are represented by parking space reflection points;
the real parking space judging module is used for judging whether the candidate parking space is a real parking space according to the barrier information in the region learned by the multi-path panoramic image;
and the parking space information output module is used for outputting the position information of the real parking space when the candidate parking space is the real parking space.
8. An apparatus, comprising a processor and a memory;
the processor is to execute instructions stored in the memory to cause the device to perform the method of any of claims 1 to 6.
9. A computer-readable storage medium comprising instructions that direct a device to perform the method of any of claims 1-6.
10. A computer program product, characterized in that it causes a computer to carry out the method according to any one of claims 1 to 6, when said computer program product is run on a computer.
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| CN202110350000.4A CN115147804A (en) | 2021-03-31 | 2021-03-31 | Parking space identification method, system, equipment, medium and program product |
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