Parking positioning method and device, electronic equipment and storage medium
By projecting the parking space points perceived by the vehicle into point classes in the map, the problem of balancing positioning accuracy and computing resources in the automatic parking assistance system is solved, achieving high-precision positioning and saving of computing resources.
Patent Information
- Application Number
- CN202411369562.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-29
- Publication Date
- 2025-01-17
AI Technical Summary
The positioning scheme in the existing automatic parking assist system is difficult to achieve high-precision positioning and save computing resources at the same time, and has high requirements for perception capabilities.
By obtaining the vehicle's parking space points and self-vehicle sensor data at the current moment, the parking space points are projected into point classes on the map, and the operation is repeated until the parking space points at all times are projected into the corresponding point classes, thereby determining the parking spaces in the target area. .
It achieves high-precision parking positioning while saving computing resources. It is suitable for functional scenarios of automatic parking assistance systems. The technical solution is simple and convenient, and has a wider scope of application.
Smart Images
Figure CN119321764A_ABST
Abstract
Description
Technical Field
[0001] The embodiments of the present application relate to the field of intelligent driving technology, and in particular to a parking positioning method, device, electronic device and storage medium. Background Art
[0002] Automated Parking Assist (APA) is an important driving assistance function in modern automotive technology, and positioning technology is the basis of all automatic parking technologies. There are various positioning solutions in existing technologies in APA. Solution 1) Construct a two-dimensional odometer based on the wheel speed and gyroscope data provided by the vehicle body. The odometer implementation of this solution is relatively simple, so the odometer lacks correction means and is easily affected by speed bumps and uneven road conditions, which reduces the accuracy of the odometer. At the same time, this solution has higher requirements for perception capabilities to implement APA. Solution 2) Construct a visual odometer based on the IMU provided by the vehicle body combined with a camera, or construct a laser odometer in combination with a lidar. This solution can achieve high-precision positioning, but the visual odometer calculation method is more complex and has a large amount of calculation, and the cost of the laser odometer is significantly increased. Solution 3) Semantic positioning is achieved based on IMU, wheel speedometer, and semantic information perceived in real time by vision. This solution can achieve high-precision positioning, but it has high requirements for perception capabilities. Summary of the invention
[0003] The present application provides a parking positioning method, device, electronic device and storage medium, which can not only achieve high-precision positioning, but also save computing resources and are more suitable for functional scenarios of automatic parking assistance systems.
[0004] In a first aspect, an embodiment of the present application provides a parking positioning method, the method comprising:
[0005] When the vehicle is traveling in the target area, the parking spots sensed by the vehicle at the current moment and the self-vehicle sensor data of the vehicle at the current moment are obtained; wherein the parking spots include: parking space vertices and parking space edge points;
[0006] Based on the vehicle sensor data of the vehicle at the current moment, project the parking spot sensed by the vehicle at the current moment into a point class in the map; repeat the above operation until the parking spot sensed by the vehicle at each moment is projected into a corresponding point class in the map;
[0007] Each parking space in the target area is determined based on each point class in the map.
[0008] In a second aspect, an embodiment of the present application further provides a parking positioning device, the device comprising: an acquisition module, a projection module and a determination module; wherein:
[0009] The acquisition module is used to acquire the parking space sensed by the vehicle at the current moment and the self-vehicle sensor data of the vehicle at the current moment when the vehicle is traveling in the target area; wherein the parking space includes: parking space vertices and parking space edge points;
[0010] The projection module is used to project the parking spot sensed by the vehicle at the current moment into a point class in the map based on the vehicle sensor data at the current moment; and repeatedly perform the above operation until the parking spot sensed by the vehicle at each moment is projected into the corresponding point class in the map;
[0011] The determination module is used to determine each parking space in the target area based on each point class in the map.
[0012] In a third aspect, an embodiment of the present application provides an electronic device, including:
[0013] one or more processors;
[0014] a memory for storing one or more programs,
[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the parking positioning method described in any embodiment of the present application.
[0016] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the parking positioning method described in any embodiment of the present application is implemented.
[0017] The embodiment of the present application proposes a parking positioning method, device, electronic device and storage medium. When a vehicle is traveling in a target area, the parking spot sensed by the vehicle at the current moment and the vehicle's own vehicle sensor data at the current moment are first obtained; wherein the parking spot includes: parking space vertices and parking space edge points; then based on the vehicle's own vehicle sensor data at the current moment, the parking spot sensed by the vehicle at the current moment is projected into a point class in the map; the above operation is repeated until the parking spot sensed by the vehicle at each moment is projected into the corresponding point class in the map; then each parking space in the target area is determined based on each point class in the map. That is to say, in the technical solution of the present application, the vertex and edge information of the sensed parking space can be used to construct information such as vertical parking spaces, horizontal parking spaces, and oblique parking spaces, which can effectively improve the positioning accuracy and improve the performance of the entire automatic parking assistance system. However, in the prior art, it is impossible to meet the requirements of positioning accuracy and computing resources at the same time. Therefore, compared with the prior art, the parking positioning method, device, electronic device and storage medium proposed in the embodiments of the present application can not only achieve high-precision positioning, but also save computing resources, and are more suitable for the functional scenarios of the automatic parking assistance system; and the technical solution of the embodiments of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 A schematic diagram of a parking positioning method provided in accordance with an embodiment of the present application;
[0019] Figure 2 A schematic diagram of the structure of a parking space provided in one embodiment of the present application;
[0020] Figure 3 A schematic diagram of a parking positioning method provided by another embodiment of the present application;
[0021] Figure 4 A flowchart of a parking positioning method provided in yet another embodiment of the present application;
[0022] Figure 5 A schematic diagram of the structure of a parking positioning device provided in one embodiment of the present application;
[0023] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION
[0024] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.
[0025] Figure 1 This is a flow chart of a parking positioning method provided by an embodiment of the present application. The method can be executed by a parking positioning device or electronic device. The device or electronic device can be implemented by software and / or hardware. The device or electronic device can be integrated into any smart device with network communication function. Figure 1 As shown, the parking positioning method may include the following steps:
[0026] S101. When a vehicle is traveling in a target area, a parking spot sensed by the vehicle at a current moment and self-vehicle sensor data of the vehicle at a current moment are obtained; wherein the parking spot includes: a parking spot vertex and a parking spot edge point.
[0027] Figure 2 This is a schematic diagram of the structure of a parking space provided in one embodiment of the present application. Figure 2 As shown in the figure, the four vertices of the parking space are: P0, P1, P2 and P3; the deep learning module can extract the parking space vertices (P0, P3) and parking space edge points (P00, P01, P30, P31) perceived in real time. Because the information perceived by the camera may not be complete, at least P0, P00, P01 or P3, P30, P31 must be perceived.
[0028] S102, based on the vehicle's own vehicle sensor data at the current moment, projecting the parking spot sensed by the vehicle at the current moment into a point class in the map; repeating the above operation until the parking spot sensed by the vehicle at each moment is projected into a corresponding point class in the map.
[0029] In this step, the vehicle's ego state at the current moment can be predicted based on the vehicle's ego sensor data at the current moment and the predetermined vehicle's ego state at the previous moment of the current moment; wherein the vehicle's ego state at the current moment includes: the vehicle's position and posture at the current moment; then, based on the vehicle's ego state at the current moment, the parking spot sensed by the vehicle at the current moment is projected from the camera coordinate system to the map coordinate system to obtain the vehicle's parking spot in the map coordinate system at the current moment; and then the vehicle's parking spot in the map coordinate system at the current moment is projected to a point class in the map.
[0030] S103: Determine each parking space in the target area based on each point class in the map.
[0031] In this step, the centroid of each point class can be calculated; then the centroids of each point class are connected to determine each parking space in the target area.
[0032] The parking positioning method proposed in the embodiment of the present application, when the vehicle is traveling in the target area, first obtains the parking spot sensed by the vehicle at the current moment and the vehicle's own vehicle sensor data at the current moment; wherein the parking spot includes: parking space vertices and parking space edge points; then based on the vehicle's own vehicle sensor data at the current moment, the parking spot sensed by the vehicle at the current moment is projected to a point class in the map; the above operations are repeated until the parking spots sensed by the vehicle at each moment are projected to the corresponding point class in the map; then the parking spaces in the target area are determined based on the various point classes in the map. That is to say, in the technical solution of the present application, the vertex and edge information of the sensed parking spaces can be used to construct information such as vertical parking spaces, horizontal parking spaces, and oblique parking spaces, which can effectively improve the positioning accuracy and improve the performance of the entire automatic parking assistance system. However, in the prior art, it is impossible to meet the requirements of positioning accuracy and computing resources at the same time. Therefore, compared with the prior art, the parking positioning method proposed in the embodiment of the present application can not only achieve high-precision positioning, but also save computing resources, and is more suitable for the functional scenarios of the automatic parking assistance system; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.
[0033] Figure 3 A flow chart of a parking positioning method provided by another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 3 As shown, the parking positioning method may include the following steps:
[0034] S301. When a vehicle is traveling in a target area, a parking spot sensed by the vehicle at the current moment and self-vehicle sensor data of the vehicle at the current moment are obtained; wherein the parking spot includes: a parking spot vertex and a parking spot edge point.
[0035] The vehicle sensor data in the embodiment of the present application refers to the angle data measured by the gyroscope and the speed data measured by the wheel speed sensor; wherein the angle data measured by the gyroscope at time t can be expressed as w; and the speed data measured by the wheel speed sensor can be expressed as v.
[0036] S302. Predict the vehicle's self-state at the current moment based on the vehicle's self-vehicle sensor data at the current moment and the vehicle's self-vehicle state at the previous moment before the current moment, wherein the vehicle's self-vehicle state at the current moment includes: the vehicle's position and posture at the current moment.
[0037] In a specific embodiment of the present application, the vehicle state at time t can be expressed as X t (P t , R t), when the vehicle sensor data at time t+1 is obtained, the vehicle state X at time t+1 can be predicted t+1 (P t+1 , R t+1 ), where P t+1 =P t +R t (v×dt); R t+1 =R t ×exp(T i2b ×w×dt); where T i2b is the predetermined external parameter from the inertial sensor to the vehicle coordinate system, w is the angle data measured by the gyroscope at time t, v is the speed data measured by the wheel speed sensor, and dt is the difference between two adjacent moments.
[0038] S303. According to the vehicle's current state, the parking spot sensed by the vehicle at the current moment is projected from the camera coordinate system to the map coordinate system to obtain the parking spot of the vehicle in the map coordinate system at the current moment.
[0039] In this step, the parking spot perceived by the vehicle at the current moment can be firstly converted to the external parameter T of the vehicle coordinate system through a predetermined camera system. c2b From the camera coordinate system to the vehicle coordinate system, and then through the X t From the vehicle coordinate system to the map coordinate system. m =f(X t , T c2b , p c ), where c represents the camera system, m represents the map system, and f represents the model for projecting the point and line elements of the camera system onto the map system.
[0040] S304, projecting the parking spot of the vehicle in the map coordinate system at the current moment into a point class in the map; repeating the above operation until the parking spots sensed by the vehicle at each moment are projected into corresponding point classes in the map.
[0041] In this step, the parking spot of the vehicle in the map coordinate system at the current moment can be matched with the instantiated parking spot corner point in the map to obtain the matching result of the parking spot of the vehicle in the map coordinate system at the current moment and the instantiated parking spot corner point in the map; then, according to the matching result of the parking spot of the vehicle in the map coordinate system at the current moment and the instantiated parking spot corner point in the map, the vehicle state at the current moment is optimized to obtain the optimized vehicle state of the vehicle at the current moment; then, according to the optimized vehicle state of the vehicle at the current moment, a point class in the map corresponding to the parking spot in the map coordinate system is determined. For example, at time t: use the predicted Xt to match the map at time t-1, optimize to obtain a more accurate Xt', then use Xt' to project the point line at time t into the map, and cluster to process the parking spot. At time t+1: use Xt' to predict Xt+1, match it with the map at time t, optimize to get a more accurate Xt+1', then use Xt+1′ to project the points and lines at time t+1 into the map, and cluster them to get the parking spaces.
[0042] S305: Determine each parking space in the target area based on each point class in the map.
[0043] The parking positioning method proposed in the embodiment of the present application, when the vehicle is traveling in the target area, first obtains the parking spot sensed by the vehicle at the current moment and the vehicle's own vehicle sensor data at the current moment; wherein the parking spot includes: parking space vertices and parking space edge points; then based on the vehicle's own vehicle sensor data at the current moment, the parking spot sensed by the vehicle at the current moment is projected to a point class in the map; the above operations are repeated until the parking spots sensed by the vehicle at each moment are projected to the corresponding point class in the map; then the parking spaces in the target area are determined based on the various point classes in the map. That is to say, in the technical solution of the present application, the vertex and edge information of the sensed parking spaces can be used to construct information such as vertical parking spaces, horizontal parking spaces, and oblique parking spaces, which can effectively improve the positioning accuracy and improve the performance of the entire automatic parking assistance system. However, in the prior art, it is impossible to meet the requirements of positioning accuracy and computing resources at the same time. Therefore, compared with the prior art, the parking positioning method proposed in the embodiment of the present application can not only achieve high-precision positioning, but also save computing resources, and is more suitable for the functional scenarios of the automatic parking assistance system; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.
[0044] Figure 4 A flowchart of a parking positioning method provided in another embodiment of the present application is shown below. The above technical solution is further optimized and expanded, and can be combined with the above optional implementations. Figure 4 As shown, the parking positioning method may include the following steps:
[0045] S401. When a vehicle is traveling in a target area, a parking spot sensed by the vehicle at a current moment and self-vehicle sensor data of the vehicle at a current moment are obtained; wherein the parking spot includes: a parking spot vertex and a parking spot edge point.
[0046] S402. Predict the vehicle's self-state at the current moment based on the vehicle's self-vehicle sensor data at the current moment and the vehicle's self-vehicle state at the previous moment before the current moment, wherein the vehicle's self-vehicle state at the current moment includes: the vehicle's position and posture at the current moment.
[0047] S403. According to the vehicle state at the current moment, the parking spot sensed by the vehicle at the current moment is projected from the camera coordinate system to the map coordinate system to obtain the parking spot of the vehicle in the map coordinate system at the current moment.
[0048] S404: Match the parking spot of the vehicle in the map coordinate system at the current moment with the parking spot corner point instantiated in the map to obtain a matching result between the parking spot of the vehicle in the map coordinate system at the current moment and the parking spot corner point instantiated in the map.
[0049] In this step, the distance between the parking spot of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map can be calculated first; if the distance between the parking spot of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map is less than a preset distance threshold, it can be determined that the parking spot of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map are successfully matched; if the distance between the parking spot of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map is not less than the preset distance threshold, it can be determined that the parking spot of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map are not successfully matched.
[0050] S405. Optimize the vehicle state at the current moment according to the matching result between the parking spot of the vehicle in the map coordinate system at the current moment and the instantiated parking spot corner point in the map, and obtain the optimized vehicle state at the current moment.
[0051] In this step, the vehicle's current state, the parking spot perceived by the vehicle at the current moment, and the parking spot corresponding to the parking spot perceived by the vehicle at the current moment in the map can be input into a pre-constructed error function, and the vehicle's current state can be updated through the error function.
[0052] S406. Determine a point class in the map corresponding to the parking spot in the map coordinate system according to the optimized vehicle state at the current moment; repeat the above operation until the parking spot perceived by the vehicle at each moment is projected to the corresponding point class in the map.
[0053] In this step, the parking spots in the map can be clustered according to the matching results between the parking spots of the vehicle in the map coordinate system at the current moment and the instantiated parking corner points in the map, so as to obtain one or more point classes in the map; if the parking spot of the vehicle in the map coordinate system at the current moment belongs to any point class in the map, the parking spot of the vehicle in the map coordinate system at the current moment is added to the any point class.
[0054] S407: Determine each parking space in the target area based on each point class in the map.
[0055] In a specific embodiment of the present application, the vehicle's self-state at the current moment, the parking space perceived by the vehicle at the current moment, and the parking space corresponding to the parking space perceived by the vehicle at the current moment in the map can also be input into a pre-constructed error function, and the vehicle's self-state at the current moment is updated through the error function. Further, the error function may include: a first error function, a second error function, and a third error function; wherein the input of the first error function is: the vehicle's self-state at the current moment, the parking space vertex perceived by the vehicle at the current moment, and the parking space vertex corresponding to the parking space vertex in the map; the output of the first error function is: the distance between the parking space vertex perceived by the vehicle at the current moment and the parking space vertex corresponding to the parking space vertex in the map; the input of the second error function is: the vehicle's self-state at the current moment, the parking space edge point on the short side of the parking space perceived by the vehicle at the current moment, and the parking space edge point in the map. The output of the second error function is: the distance from the parking space edge point on the short side of the parking space perceived by the vehicle at the current moment to the short side of the parking space corresponding to the parking space edge point in the map; the input of the third error function is: the vehicle's own vehicle state at the current moment, the parking space edge point on the long side of the parking space perceived by the vehicle at the current moment, and the two parking space vertices on the long side of the parking space corresponding to the parking space edge point in the map; the output of the third error function is: the distance from the parking space edge point on the long side of the parking space perceived by the vehicle at the current moment to the long side of the parking space corresponding to the parking space edge point in the map. Specifically, the first error function can be expressed as: e0=h0(X t , p c , P); the second error function can be expressed as: e1 = h1 (X t , p00, P0, P3); the third error function can be expressed as: e2 = h2 (X t, p01, P0, P1); where, in the first error function, X t is the vehicle’s state at the current moment, p c is the parking space vertex perceived by the vehicle at the current moment, and P is the parking space vertex corresponding to the parking space vertex in the map; in the second error function, X t is the vehicle's current state, p00 is the parking space edge point on the short side of the parking space perceived by the vehicle at the current moment, P0 and P3 are the two parking space vertices on the short side of the parking space corresponding to the parking space edge point in the map; in the third error function, X t is the vehicle’s state at the current moment, p01 is the parking space edge point on the long side of the parking space perceived by the vehicle at the current moment, and P0 and P1 are the two parking space vertices on the long side of the parking space corresponding to the parking space edge point in the map.
[0056] The parking positioning method proposed in the embodiment of the present application, when the vehicle is traveling in the target area, first obtains the parking spot sensed by the vehicle at the current moment and the vehicle's own vehicle sensor data at the current moment; wherein the parking spot includes: parking space vertices and parking space edge points; then based on the vehicle's own vehicle sensor data at the current moment, the parking spot sensed by the vehicle at the current moment is projected to a point class in the map; the above operations are repeated until the parking spots sensed by the vehicle at each moment are projected to the corresponding point class in the map; then the parking spaces in the target area are determined based on the various point classes in the map. That is to say, in the technical solution of the present application, the vertex and edge information of the sensed parking spaces can be used to construct information such as vertical parking spaces, horizontal parking spaces, and oblique parking spaces, which can effectively improve the positioning accuracy and improve the performance of the entire automatic parking assistance system. However, in the prior art, it is impossible to meet the requirements of positioning accuracy and computing resources at the same time. Therefore, compared with the prior art, the parking positioning method proposed in the embodiment of the present application can not only achieve high-precision positioning, but also save computing resources, and is more suitable for the functional scenarios of the automatic parking assistance system; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.
[0057] Figure 5 This is a schematic diagram of the structure of a parking positioning device provided by an embodiment of the present application. Figure 5 As shown, the parking positioning device includes: an acquisition module 501, a projection module 502 and a determination module 503; wherein,
[0058] The acquisition module 501 is used to acquire the parking space sensed by the vehicle at the current moment and the vehicle sensor data of the vehicle at the current moment when the vehicle is traveling in the target area; wherein the parking space includes: parking space vertices and parking space edge points;
[0059] The projection module 502 is used to project the parking spot sensed by the vehicle at the current moment into a point class in the map based on the vehicle sensor data at the current moment; the above operation is repeatedly performed until the parking spot sensed by the vehicle at each moment is projected into the corresponding point class in the map;
[0060] The determination module 503 is used to determine each parking space in the target area based on each point class in the map.
[0061] The above parking positioning device can execute the method provided by any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in this embodiment, please refer to the parking positioning method provided by any embodiment of the present application.
[0062] Figure 6 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 6 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 6 The electronic device 12 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0063] like Figure 6 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).
[0064] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
[0065] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0066] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.
[0067] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.
[0068] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0069] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the parking positioning method provided in the embodiment of the present application.
[0070] The embodiment of the present application also provides a computer storage medium.
[0071] The computer-readable storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, - but not limited to - a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive lists) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.
[0072] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0073] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
[0074] Computer program code for performing the operation of the present application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can 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 can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).
[0075] The embodiment of the present application also provides a computer program product.
[0076] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0077] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.
Claims
1. A parking positioning method, characterized in that: The method comprises: When the vehicle is traveling in the target area, the parking spots sensed by the vehicle at the current moment and the self-vehicle sensor data of the vehicle at the current moment are obtained; wherein the parking spots include: parking space vertices and parking space edge points; Based on the vehicle sensor data of the vehicle at the current moment, project the parking spot sensed by the vehicle at the current moment into a point class in the map; repeat the above operation until the parking spot sensed by the vehicle at each moment is projected into a corresponding point class in the map; Each parking space in the target area is determined based on each point class in the map.
2. The method according to claim 1, characterized in that Based on the vehicle sensor data of the vehicle at the current moment, projecting the parking spot sensed by the vehicle at the current moment into a point class in the map, including: Predicting the vehicle's state at the current moment based on the vehicle's sensor data at the current moment and the vehicle's state at the previous moment determined in advance; wherein the vehicle's state at the current moment includes: the vehicle's position and posture at the current moment; According to the vehicle state at the current moment, the parking position perceived by the vehicle at the current moment is projected from the camera coordinate system to the map coordinate system to obtain the parking position of the vehicle at the current moment in the map coordinate system; The parking location of the vehicle in the map coordinate system at the current moment is projected to a point class in the map.
3. The method according to claim 2, characterized in that Projecting the parking spot of the vehicle in the map coordinate system at the current moment to a point class in the map includes: Matching the parking spot of the vehicle in the map coordinate system at the current moment with the parking spot corner point instantiated in the map, to obtain a matching result between the parking spot of the vehicle in the map coordinate system at the current moment and the parking spot corner point instantiated in the map; According to the matching result of the parking position of the vehicle in the map coordinate system at the current moment and the parking position corner point instantiated in the map, the vehicle state at the current moment is optimized to obtain the optimized vehicle state at the current moment; According to the optimized vehicle state of the vehicle at the current moment, a point class in the map corresponding to the parking position in the map coordinate system is determined.
4. The method according to claim 3, characterized in that Matching the parking spot of the vehicle in the map coordinate system at the current moment with the parking spot corner point instantiated in the map, to obtain a matching result of the parking spot of the vehicle in the map coordinate system at the current moment with the parking spot corner point instantiated in the map, including: Calculating the distance between the parking spot of the vehicle in the map coordinate system at the current moment and the corner point of the parking spot instantiated in the map; If the distance between the parking position of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map is less than a preset distance threshold, it is determined that the parking position of the vehicle in the map coordinate system at the current moment and the parking corner point instantiated in the map are successfully matched.
5. The method according to claim 3, characterized in that: Determining a point class in the map corresponding to the parking spot in the map coordinate system according to the optimized vehicle state at the current moment, including: Clustering the parking spots in the map according to the optimized vehicle state at the current moment to obtain one or more point classes in the map; If the parking spot of the vehicle in the map coordinate system at the current moment belongs to any point class in the map, the parking spot of the vehicle in the map coordinate system at the current moment is added to the any point class.
6. The method according to claim 3, characterized in that: Optimizing the vehicle state at the current moment includes: The vehicle's current state, the parking spot sensed by the vehicle at the current moment, and the parking spot corresponding to the parking spot sensed by the vehicle at the current moment in the map are input into a pre-constructed error function, and the vehicle's current state is updated by the error function.
7. The method according to claim 6, characterized in that The error function includes: a first error function, a second error function and a third error function; wherein, the input of the first error function is: the vehicle state at the current moment, the parking space vertex perceived by the vehicle at the current moment and the parking space vertex corresponding to the parking space vertex in the map; the output of the first error function is: the distance between the parking space vertex perceived by the vehicle at the current moment and the parking space vertex corresponding to the parking space vertex in the map; the input of the second error function is: the vehicle state at the current moment, the parking space edge point on the short side of the parking space perceived by the vehicle at the current moment and the distance between the parking space edge point in the map The output of the second error function is: the distance from the parking space edge point on the short side of the parking space perceived by the vehicle at the current moment to the short side of the parking space corresponding to the parking space edge point in the map; the input of the third error function is: the vehicle state at the current moment, the parking space edge point on the long side of the parking space perceived by the vehicle at the current moment, and the two parking space vertices on the long side of the parking space corresponding to the parking space edge point in the map; the output of the third error function is: the distance from the parking space edge point on the long side of the parking space perceived by the vehicle at the current moment to the long side of the parking space corresponding to the parking space edge point in the map.
8. A parking positioning device, characterized in that: The device comprises: an acquisition module, a projection module and a determination module; wherein, The acquisition module is used to acquire the parking space sensed by the vehicle at the current moment and the self-vehicle sensor data of the vehicle at the current moment when the vehicle is traveling in the target area; wherein the parking space includes: parking space vertices and parking space edge points; The projection module is used to project the parking spot sensed by the vehicle at the current moment into a point class in the map based on the vehicle sensor data at the current moment; and repeatedly perform the above operation until the parking spot sensed by the vehicle at each moment is projected into the corresponding point class in the map; The determination module is used to determine each parking space in the target area based on each point class in the map.
9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the parking positioning method according to any one of claims 1 to 7.
10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the parking positioning method according to any one of claims 1 to 7 is implemented.