Vehicle positioning method and system for parking lot, electronic equipment and storage medium

Abstract

The invention provides a vehicle positioning method and system for a parking lot, electronic equipment and a storage medium, and relates to the technical field of intelligent traffic. The method comprises the following steps: acquiring a parking data packet of a vehicle in a parking lot through automatic driving equipment; matching the parking data packet with the map coordinate information, and determining a matching result; and generating vehicle positioning information based on a matching result. According to the invention, the reconstruction cost is obviously reduced, the construction period is shortened, and the dependence on the existing infrastructure of the parking lot is reduced.

Description

Parking lot vehicle location methods, systems, electronic devices and storage media Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a vehicle positioning method, system, electronic device and storage medium for a parking lot. Background Technology

[0002] With the continuous advancement of urbanization and the sustained growth of motor vehicle ownership, the contradiction between supply and demand of urban parking resources is becoming increasingly prominent. Especially in old business districts, communities, and commercial complexes, the intelligent upgrading of parking facilities has become an important technical field for improving urban traffic management and services.

[0003] Existing traditional parking lot renovation solutions typically rely on large-scale deployment of hardware equipment, including geomagnetic sensors, Wi-Fi or Bluetooth beacons, and high-precision positioning systems, along with the laying of power and communication lines to achieve parking space status monitoring and vehicle location navigation functions. However, the above methods have the following problems: high renovation costs, long construction periods, and high dependence on infrastructure. Summary of the Invention

[0004] This application provides a vehicle positioning method, system, electronic equipment, and storage medium for parking lots, which can significantly reduce renovation costs, shorten construction cycles, and reduce reliance on existing parking lot infrastructure.

[0005] Firstly, this application provides a vehicle positioning method for a parking lot, including:

[0006] Data packets of vehicles parked in the parking lot are obtained through autonomous driving equipment;

[0007] The parked data packets are matched with map coordinate information to determine the matching result;

[0008] Based on the matching results, vehicle location information is generated.

[0009] In one possible implementation, parking data packets are collected in the following manner:

[0010] Based on the parking space occupancy status of the parking lot, the movement path of the autonomous driving equipment is planned to obtain the inspection path;

[0011] Based on the inspection path, autonomous driving equipment is used to collect parking data packets.

[0012] In one possible implementation, based on the inspection path, the autonomous driving equipment collects parking data packets, including:

[0013] Based on the inspection path, the initial parking data packets of the parking lot are collected using autonomous driving equipment.

[0014] Based on the edge computing module, the initial parking data packet is deduplicated and verified to obtain the parking data packet. The edge computing module is deployed in the autonomous driving device.

[0015] In one possible implementation, based on the edge computing module, the initial parking data packet is deduplicated to obtain the parking data packet, including:

[0016] The edge computing module performs the following operations: extracts the timestamp and location coordinates of the parking data in the initial parking data packet; determines whether the parking data is duplicate data based on the timestamp and location coordinates; if the parking data is duplicate data, deletes the parking data and obtains the parking data packet.

[0017] In one possible implementation, determining whether parking data is duplicate data based on timestamps and location coordinates includes:

[0018] Based on license plate numbers, parking data is grouped to obtain target parking data;

[0019] The rate of change of location coordinates is determined based on the location coordinates of the target parking data and the location coordinates of at least one historical target parking data prior to the timestamp;

[0020] Determine whether the timestamp of the target parking data satisfies the continuity condition with the timestamp of at least one historical target parking data point;

[0021] When the rate of change of the location coordinates is less than the preset change threshold, and the timestamp of the target parking data meets the continuity condition, the target parking data is identified as duplicate data.

[0022] In one possible implementation, after collecting the parking data packets, the method further includes:

[0023] When the autonomous driving device is in the charging / discharging position, the device uploads the parking data packet to the cloud server so that the cloud server can obtain the parking data packet.

[0024] In one possible implementation, matching the parking data packet with map coordinate information to determine the matching result includes:

[0025] A coordinate system transformation algorithm is used to convert the location coordinates in the parked data packet into local coordinate coefficient values ​​on the map;

[0026] Retrieve the target map element corresponding to the local coordinate coefficient value from the map coordinate information;

[0027] The matching relationship between vehicles and target map elements is determined as the matching result.

[0028] Secondly, this application provides a vehicle positioning device for a parking lot, comprising:

[0029] The acquisition module is used to acquire parking data packets of vehicles in the parking lot through autonomous driving equipment;

[0030] The determination module is used to match the parked data packets with map coordinate information and determine the matching result;

[0031] The generation module is used to generate vehicle location information based on the matching results.

[0032] In one possible implementation, parking data packets are collected in the following manner:

[0033] Based on the parking space occupancy status of the parking lot, the movement path of the autonomous driving equipment is planned to obtain the inspection path;

[0034] Based on the inspection path, autonomous driving equipment is used to collect parking data packets.

[0035] In one possible implementation, the vehicle positioning device in the parking lot further includes a processing module, which is specifically used for:

[0036] Based on the inspection path, the initial parking data packets of the parking lot are collected using autonomous driving equipment.

[0037] Based on the edge computing module, the initial parking data packet is deduplicated and verified to obtain the parking data packet. The edge computing module is deployed in the autonomous driving device.

[0038] In one possible implementation, the processing module is specifically used for:

[0039] The edge computing module performs the following operations: extracts the timestamp and location coordinates of the parking data in the initial parking data packet; determines whether the parking data is duplicate data based on the timestamp and location coordinates; if the parking data is duplicate data, deletes the parking data and obtains the parking data packet.

[0040] In one possible implementation, the determining module is specifically used for:

[0041] Based on license plate numbers, parking data is grouped to obtain target parking data;

[0042] The rate of change of location coordinates is determined based on the location coordinates of the target parking data and the location coordinates of at least one historical target parking data prior to the timestamp;

[0043] Determine whether the timestamp of the target parking data satisfies the continuity condition with the timestamp of at least one historical target parking data point;

[0044] When the rate of change of the location coordinates is less than the preset change threshold, and the timestamp of the target parking data meets the continuity condition, the target parking data is identified as duplicate data.

[0045] In one possible implementation, the processing module is further configured to:

[0046] When the autonomous driving device is in the charging / discharging position, the device uploads the parking data packet to the cloud server so that the cloud server can obtain the parking data packet.

[0047] In one possible implementation, the determining module is specifically used for:

[0048] A coordinate system transformation algorithm is used to convert the location coordinates in the parked data packet into local coordinate coefficient values ​​on the map;

[0049] Retrieve the target map element corresponding to the local coordinate coefficient value from the map coordinate information;

[0050] The matching relationship between vehicles and target map elements is determined as the matching result.

[0051] Thirdly, this application provides a vehicle positioning system for a parking lot, including: an autonomous driving device and a cloud server; wherein,

[0052] Autonomous driving equipment used to collect data on vehicle parking in parking lots;

[0053] A cloud server for performing the first aspect and / or various possible implementations of the first aspect as described above.

[0054] Fourthly, this application provides an electronic device, including: a memory and a processor;

[0055] The memory stores instructions that the computer executes;

[0056] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0057] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the first aspect and / or various possible embodiments of the first aspect.

[0058] In a sixth aspect, this application provides a computer program product, including a computer program that, when executed, implements the first aspect and / or various possible implementations of the first aspect.

[0059] This application provides a vehicle positioning method, system, electronic device, and storage medium for parking lots, relating to the field of intelligent transportation technology. The method includes: acquiring parking data packets of vehicles in the parking lot using an autonomous driving device; matching the parking data packets with map coordinate information to determine the matching result; and generating vehicle positioning information based on the matching result. This application utilizes autonomous driving equipment instead of fixed hardware to acquire parking data packets of vehicles in the parking lot. The mobility of the autonomous driving equipment allows this application to quickly acquire parking data packets without altering the existing infrastructure of the parking lot, thereby reducing the cost of parking lot renovation and shortening the construction period. The parking data packets are matched with map coordinate information to determine the matching result, and vehicle positioning information is generated based on the matching result. The entire process does not rely on fixed sensors or base station deployments; through dynamic data acquisition by the autonomous driving device and data matching of the dynamically acquired data, intelligent positioning of the parking lot is ultimately achieved. Attached Figure Description

[0060] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0061] Figure 1 is a schematic flowchart of the vehicle positioning method for parking lots provided in an embodiment of this application;

[0062] Figure 2 is an architecture diagram of the vehicle positioning method for parking lots provided in the embodiments of this application;

[0063] Figure 3 is a schematic diagram of the vehicle-side service layer architecture provided in an embodiment of this application;

[0064] Figure 4 is a schematic diagram of the user presentation layer provided in an embodiment of this application;

[0065] Figure 5 is a schematic diagram of the vehicle positioning device for a parking lot provided in an embodiment of this application;

[0066] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0067] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0068] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0069] With the acceleration of urbanization and the continuous growth of motor vehicle ownership, the contradiction between supply and demand for urban parking resources is becoming increasingly acute. Especially in older commercial districts, communities, and commercial complexes, parking lots built in the early stages generally suffer from aging infrastructure, low levels of intelligence, and poor utilization of parking spaces. For example, many older parking lots lack modern equipment such as Wi-Fi, Bluetooth, or geomagnetic sensors, making it impossible to achieve real-time monitoring of parking space status, vehicle location, and navigation functions. Furthermore, users often rely on manual recording or vague memory to find their vehicles after parking, frequently encountering difficulties in locating their cars, leading to numerous complaints and increasing the burden on customer service.

[0070] There are two main technical approaches to the intelligent transformation of existing parking lots, each with its own characteristics but also facing corresponding challenges:

[0071] One approach is the traditional sensor deployment solution, which involves installing geomagnetic sensors or cameras in parking spaces and uploading the data to the cloud via Wi-Fi / 4G networks to monitor parking space status and locate vehicles. However, this solution has high requirements for power and network coverage, especially in underground parking lots or areas with signal shielding, often requiring additional fiber optic cables or repeater equipment, leading to complex construction and significantly increased costs.

[0072] The second option is a high-precision positioning system, which uses technologies such as Ultra-Wideband (UWB), Bluetooth beacons, or LiDAR to achieve centimeter-level accurate positioning by deploying fixed base stations or reflectors. However, this type of solution relies on dense hardware deployment (such as base station installation and reflector maintenance) and dedicated algorithm support. Not only is the transformation cycle long (usually requiring 2-4 weeks), but the equipment maintenance cost is also high, making it difficult to adapt to the needs of dynamic adjustments to parking lot layouts.

[0073] In summary, existing technologies still have significant shortcomings in terms of cost control, deployment efficiency, and environmental adaptability, making it difficult to meet the urgent need for low-cost and rapid renovation of old parking lots.

[0074] Against this backdrop, intelligent positioning technology based on autonomous driving equipment has emerged as an innovative solution. It does not require large-scale infrastructure upgrades and can achieve dynamic perception and positioning services for parking lots through sensors and algorithms carried by autonomous driving equipment, providing a low-cost and highly flexible alternative for the intelligent upgrading of old parking lots.

[0075] To address this, this application provides a vehicle positioning method for parking lots, which involves acquiring parking data packets of vehicles in the parking lot through an autonomous driving device; matching the parking data packets with map coordinate information to determine the matching result; and generating vehicle positioning information based on the matching result.

[0076] This application applies to the renovation of underground parking lots in commercial complexes with a building area of ​​≥50,000㎡ and ≥1,000 parking spaces. This application also applies to the renovation of underground parking lots in older shopping malls, communities, and commercial complexes.

[0077] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0078] The vehicle positioning method for parking lots provided in this application can be executed by a computing device such as a server or server cluster. The server can be a mobile phone, computer, tablet, or other similar device. This application does not impose any particular restrictions on the implementation method of the executing entity.

[0079] Figure 1 is a flowchart illustrating a vehicle positioning method for a parking lot provided in an embodiment of this application. As shown in Figure 1, the method includes:

[0080] S101. Obtain parking data packets of vehicles in the parking lot through the autonomous driving equipment.

[0081] Autonomous driving equipment refers to machines or devices that integrate software and hardware systems such as perception, decision-making, and control, and can autonomously perform tasks, move, or operate in specific or completely open environments without continuous and direct human intervention.

[0082] Optionally, the autonomous driving equipment can be a swarm of unmanned vehicles, which is a collaborative system composed of multiple unmanned vehicles with autonomous navigation capabilities. Each unmanned vehicle has sensor data acquisition and edge computing capabilities, such as an inspection system consisting of five unmanned vehicles deployed in a parking lot of a commercial complex. This is only an example, and the embodiments of this application do not limit the selection of autonomous driving equipment.

[0083] S102. Match the parked data packet with the map coordinate information to determine the matching result.

[0084] The parking data packets collected by the autonomous driving equipment are matched with map coordinate information to determine the matching result. The map coordinate information can be obtained from a map database that records detailed road network geometry information and precisely located 3D coordinate points within the parking lot.

[0085] For example, matching the parking data packet with map coordinate information to determine the matching result includes: using a coordinate system transformation algorithm to convert the location coordinates in the parking data packet into local coordinate coefficient values ​​of the map; retrieving the target map element corresponding to the local coordinate coefficient value in the map coordinate information; and determining the matching relationship between the vehicle and the target map element as the matching result.

[0086] Directly comparing values ​​from different coordinate systems inevitably leads to significant spatial location discrepancies, causing subsequent matching and retrieval to fail completely. Therefore, this embodiment of the application, through the preliminary step of coordinate system transformation, effectively projects or transforms the vehicle's position coordinates onto the same basis as the map data, thereby eliminating the inherent differences between different coordinate systems and laying a crucial foundation for subsequent high-precision spatial location retrieval and matching.

[0087] After the coordinate system is unified, the target map element corresponding to the local coordinate coefficient value after transformation is retrieved from the map coordinate information. The target map element can be a parking space element with geometric boundary information, a linear element representing the edge of a road, or a point element representing a specific point of interest, such as a charging pile, fire hydrant, or ground lock.

[0088] Optionally, the process of retrieving the target map element corresponding to the local coordinate coefficient value can be specifically as follows: performing a spatial nearest neighbor search to find one or more target map elements that are closest to the local coordinate coefficient value; or performing a spatial inclusion judgment to determine whether the local coordinate coefficient value falls within the geometric boundary of a certain map element.

[0089] By matching different types of map elements, it can adapt to various complex parking scenarios such as roadside parking and parking lot parking. Since the local coordinate coefficients of the vehicle's position are already consistent with the coordinate system of the map element, the retrieval operation can directly utilize efficient algorithms such as spatial indexing to quickly and accurately locate the nearest geographical entity to which the coordinate point falls within the map database. This geographical entity is the target map element. Finally, the matching relationship between the vehicle and the retrieved target map element is determined as the final matching result.

[0090] Furthermore, determining the matching relationship between the vehicle and the target map element as the matching result can be specifically done by generating a data pair containing a unique identifier for the vehicle and a unique identifier for the target map element.

[0091] The above operations not only fundamentally solve the technical problem of incomparable location information due to inconsistent coordinate systems, but more importantly, they can accurately associate the parking location of a vehicle from an abstract coordinate point without business meaning to a map element with clear physical and business attributes. This makes the matching results have practical application value, greatly improves the accuracy and usability of the matching results, and provides reliable and accurate data support for subsequent applications such as parking time and billing, in-park navigation, parking status monitoring, or automatic parking.

[0092] S103. Based on the matching results, generate vehicle location information.

[0093] This step is a technical step that transforms abstract data into intuitive and usable information, and it is a key link connecting backend data processing and frontend user experience. Specifically, the matching relationship determined in the above steps is converted into an image, and the vehicle location information is obtained based on the converted image.

[0094] Optionally, a map engine or graphics drawing tool can be invoked to render a visual image based on existing data containing information such as parking lot layout, parking spaces, pillars, passages, and entrances / exits, and in combination with the matching results obtained in the preceding steps.

[0095] In summary, this step transforms the matching relationships identified in the previous steps into human-readable and intuitive visual location guidance on the front-end user interface. This step significantly improves the practicality of the technology and the user experience, enabling accurate matching results to serve end users in the most efficient way (e.g., a car owner finding their vehicle in a large shopping mall using a mobile app). This step solves the last-mile problem between data matching and user vehicle location.

[0096] This application utilizes autonomous driving equipment instead of fixed hardware to acquire parking data packets of vehicles in a parking lot. The mobility of the autonomous driving equipment allows for rapid acquisition of parking data packets without altering the existing infrastructure of the parking lot, thereby reducing the cost of parking lot renovation and shortening the construction period. The parking data packets are matched with map coordinate information to determine the matching result, and vehicle location information is generated based on the matching result. The entire process does not rely on fixed sensors or base station deployments; through dynamic data acquisition by the autonomous driving equipment and data matching of the dynamically acquired data, intelligent positioning of the parking lot is ultimately achieved.

[0097] Based on the above embodiments, in some examples, the parking data packets described in step S101 are collected in the following way: The movement path of the autonomous driving device is planned according to the parking space occupancy status of the parking lot to obtain an inspection path; based on the inspection path, the parking data packets are collected using the autonomous driving device. Here, parking space occupancy status refers to real-time data reflecting whether each parking space in the parking lot is occupied by a vehicle.

[0098] In this example, the parking space occupancy status data of the parking lot is read, and combined with the physical layout of the parking lot, the inspection priority of each area is calculated to obtain the inspection path. For example, when the parking space occupancy rate of a certain area of ​​the parking lot exceeds 80%, the autonomous driving equipment will be prioritized for high-frequency inspection of that area, while the inspection frequency of low-occupancy areas will be reduced. The planning result of this movement path serves as the movement command for the autonomous driving equipment, ensuring that the vehicle parking data it collects covers all high-value areas.

[0099] This application's embodiments improve the comprehensiveness and timeliness of data collection by adjusting the inspection path in real time, enabling autonomous driving equipment to prioritize coverage of areas with high parking space occupancy rates. For example, during peak hours, the system can dynamically increase the inspection frequency of parking spaces near entrances / exits to ensure that the parking status of vehicles in these high-turnover areas is collected in a timely manner. This optimization significantly improves the targeting and efficiency of data collection, providing higher-quality raw data for subsequent edge computing processing.

[0100] Furthermore, the above example describes the collection of parking data packets based on the inspection path using an autonomous driving device, which includes: collecting initial parking data packets of the parking lot using the autonomous driving device based on the inspection path; and performing deduplication verification on the initial parking data packets based on the edge computing module to obtain parking data packets, wherein the edge computing module is deployed in the autonomous driving device.

[0101] It is understandable that after determining the inspection path, the autonomous driving equipment is allowed to carry out inspections according to the inspection path, and the mobile sensing nodes of the autonomous driving equipment are used to collect vehicle parking data. The mobile sensing nodes refer to the hardware modules of the autonomous driving equipment that integrate multimodal sensors (such as cameras, global positioning systems, and inertial measurement units) to dynamically collect initial parking data (for example, capturing license plate images and recording position coordinates through cameras).

[0102] After collecting initial parking data, it is uploaded to the edge computing module of the autonomous driving device. The edge computing module then performs deduplication and verification on the initial parking data to obtain the final parking data packet. This significantly reduces the storage and computing load on the cloud server while ensuring the high confidence level of the uploaded parking data packet.

[0103] Furthermore, in some examples, the initial parking data packet is deduplicated based on the edge computing module to obtain the parking data packet. This includes: using the edge computing module to perform the following operations: extracting the timestamp and location coordinates of the parking data in the initial parking data packet; determining whether the parking data is duplicate data based on the timestamp and location coordinates; if the parking data is duplicate data, deleting the parking data to obtain the parking data packet.

[0104] In these examples, the deduplication verification of the initial parking data requires first extracting the timestamps and location coordinates of the parking data in the initial parking data packet. The timestamp is a unique identifier recording the moment the parking data was collected. Subsequently, data analysis is performed on the timestamps and location coordinates to determine if the parking data is duplicated. If the parking data is determined to be duplicated, it must be deleted from the initial parking data packet; the remaining parking data constitutes the final parking data packet.

[0105] This application embodiment effectively distinguishes between legitimate movement and repeatedly collected data by combining timestamp and location difference analysis, significantly improving the accuracy of data processing, avoiding redundant data storage on cloud servers, and ensuring the data reliability of subsequent location services.

[0106] Furthermore, in some embodiments, determining whether parking data is duplicate data based on timestamps and location coordinates includes: grouping parking data based on license plate numbers to obtain target parking data; determining the rate of change of location coordinates based on the location coordinates of the target parking data and the location coordinates of at least one historical target parking data point before the timestamp; determining whether the timestamp of the target parking data and the timestamp of at least one historical target parking data point meet the continuity condition; and determining the target parking data as duplicate data when the rate of change of location coordinates is less than a preset change threshold and the timestamp of the target parking data meets the continuity condition.

[0107] In the above embodiments, it can be understood that to determine whether parking data is duplicate data, the parking data in the initial parking data packet first needs to be grouped according to the license plate number to obtain the target parking data. Next, based on the target parking data and at least one historical target parking data point before a timestamp, the rate of change of the position coordinates is determined, and it is judged whether the timestamp of the target parking data and the timestamp of at least one historical target parking data point satisfy the continuity condition. Finally, if the rate of change of the position coordinates is less than a preset change threshold, and the timestamp of the target parking data satisfies the continuity condition, the target parking data is determined to be duplicate data. For example, if the rate of change of the position coordinates is less than 0.3, and the timestamp of the target parking data satisfies the continuity condition, the target parking data is determined to be duplicate data; if the rate of change of the position coordinates is greater than or equal to the preset change threshold, and the timestamp of the target parking data does not satisfy the continuity condition, the target parking data is determined to be non-duplicate data.

[0108] This application embodiment significantly improves the accuracy of data processing by combining timestamp and location difference analysis, avoids storing redundant data on cloud servers, and ensures the data reliability of subsequent location services.

[0109] In some embodiments, after collecting the parking data packets, the method further includes: when the autonomous driving device is in a charging / discharging position, using the autonomous driving device to upload the parking data packets to a cloud server, so that the cloud server can obtain the parking data packets. In this embodiment, it can be understood that after collecting the parking data packets, the autonomous driving device will upload the collected parking data packets to the cloud server via the HTTPS protocol, and the autonomous driving device needs to be in a charging / discharging position to upload the parking data. Specifically, when the autonomous driving device is in a charging / discharging position, the autonomous driving device first encrypts the parking data packets and uploads them to a designated interface of the cloud server via a 5G / 4G network. This transmission process ensures that the data is not tampered with or stolen during transmission.

[0110] In this embodiment of the application, when the autonomous driving device is in the charging / discharging position, the autonomous driving device uploads parking data packets to the cloud server so that the cloud server can obtain the parking data packets. This allows the autonomous driving device to be unrestricted by the basement wireless network and to periodically upload parking data packets.

[0111] Next, taking an autonomous driving system as an example of an unmanned vehicle cluster, we will explain how to utilize the vehicle positioning method for parking lots provided in this application embodiment. Figure 2 is an architecture diagram corresponding to the vehicle positioning method for parking lots provided in this application embodiment. As shown in Figure 2, implementing this method requires a hardware layer, an autonomous driving layer, a vehicle-side business layer, and a cloud-based business layer. Among them, the hardware layer requires unmanned vehicles that can adapt to most autonomous driving algorithms on the market, while the license plate recognition camera can be loaded separately or reused from the camera of the unmanned vehicle.

[0112] The autonomous driving layer includes autonomous driving algorithms and high-precision maps. There are no mandatory requirements for the autonomous driving algorithms; solutions with high-precision maps are allowed, as are solutions without them. If autonomous driving cameras are used for license plate recognition, the corresponding camera channels need to be opened based on the Robot Operating System (ROS). A custom, built-in license plate recognition message channel is preferred.

[0113] Figure 3 is a schematic diagram of the vehicle-side business layer architecture provided in this embodiment of the application. As shown in Figure 3, the vehicle-side business layer includes a data processing module and a module for interfacing with the autonomous vehicle cloud business platform. The data processing module interfaces with the autonomous driving algorithm to acquire sensor data, such as vehicle parking data captured by cameras using Python / Java / C++, including license plate number data and vehicle location data. Locally, the vehicle parking data can undergo certain data duplication checks to avoid duplicate reporting to the cloud server. The processed vehicle parking data is also watermarked locally on the vehicle to reduce computational load on the cloud server. The module for interfacing with the autonomous vehicle cloud business platform sends the processed vehicle parking data to the cloud server via JSON format using HTTPS / MQTT / MQ.

[0114] Cloud-based business layer: Receives event data reported by the cloud server and stores it in a MySQL database; provides map query interface and license plate location query interface to users' mobile devices.

[0115] User Presentation Layer: Based on the query interface provided by the cloud business layer, the license plate location and map returned by the interface are rendered on the user interface. Figure 4 is a schematic diagram of the user presentation layer provided in this embodiment. As shown in Figure 4, the vehicle location query page allows users to enter the license plate number and click OK to obtain the vehicle's location information. The query results will include the license plate number, parking location, related photos, and entry time.

[0116] In summary, compared with traditional infrastructure renovation, the method provided in this application has the following advantages: the deployment time of the unmanned vehicle in this application embodiment is only 1-3 days, which will greatly reduce the renovation time; this application embodiment only requires the purchase of unmanned vehicles and cloud servers, which will greatly reduce costs; the unmanned vehicles deployed in this application embodiment can switch parking lots, which is conducive to reducing usage costs.

[0117] This application's embodiments are applicable to the renovation of underground parking lots in commercial complexes. It employs a three-tiered architecture of "vehicle-cloud-user": the vehicle-side deploys a cluster of unmanned vehicles equipped with multimodal sensors (one vehicle per 200 parking spaces), communicating with the cloud server via 5G / 4G networks; the cloud server includes a MySQL database and RESTful API interfaces, providing user-end apps with services such as parking space status queries and vehicle location tracking. This solution is specifically designed for commercial needs requiring a renovation cycle of 72 hours and a renovation cost of less than 200,000 yuan per parking lot, supporting dynamic scheduling and deployment of unmanned vehicle clusters across multiple parking lots.

[0118] Furthermore, this application embodiment also provides a vehicle positioning system for a parking lot, the system comprising: an autonomous driving device and a cloud server; wherein, the autonomous driving device is used to collect parking data packets of vehicles in the parking lot; and the cloud server is used to execute the method described in the preceding embodiments.

[0119] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0120] Figure 5 is a schematic diagram of the vehicle positioning device for a parking lot provided in this embodiment. As shown in Figure 5, the vehicle positioning device for a parking lot provided in this embodiment includes:

[0121] The acquisition module 501 is used to acquire parking data packets of vehicles in the parking lot through the autonomous driving device;

[0122] The determination module 502 is used to match the parked data packet with the map coordinate information and determine the matching result;

[0123] The generation module 503 is used to generate vehicle location information based on the matching results.

[0124] In one possible implementation, parking data packets are collected in the following manner:

[0125] Based on the parking space occupancy status of the parking lot, the movement path of the autonomous driving equipment is planned to obtain the inspection path;

[0126] Based on the inspection path, autonomous driving equipment is used to collect parking data packets.

[0127] In one possible implementation, the vehicle positioning device in the parking lot further includes a processing module (not shown), which is specifically used for:

[0128] Based on the inspection path, the initial parking data packets of the parking lot are collected using autonomous driving equipment.

[0129] Based on the edge computing module, the initial parking data packet is deduplicated and verified to obtain the parking data packet. The edge computing module is deployed in the autonomous driving device.

[0130] In one possible implementation, the processing module is specifically used for:

[0131] The edge computing module performs the following operations: extracts the timestamp and location coordinates of the parking data in the initial parking data packet; determines whether the parking data is duplicate data based on the timestamp and location coordinates; if the parking data is duplicate data, deletes the parking data and obtains the parking data packet.

[0132] In one possible implementation, the determining module 502 is specifically used for:

[0133] Based on license plate numbers, parking data is grouped to obtain target parking data;

[0134] The rate of change of location coordinates is determined based on the location coordinates of the target parking data and the location coordinates of at least one historical target parking data prior to the timestamp;

[0135] Determine whether the timestamp of the target parking data satisfies the continuity condition with the timestamp of at least one historical target parking data point;

[0136] When the rate of change of the location coordinates is less than the preset change threshold, and the timestamp of the target parking data meets the continuity condition, the target parking data is identified as duplicate data.

[0137] In one possible implementation, the processing module is further configured to:

[0138] When the autonomous driving device is in the charging / discharging position, the device uploads the parking data packet to the cloud server so that the cloud server can obtain the parking data packet.

[0139] In one possible implementation, the determining module 502 is specifically used for:

[0140] A coordinate system transformation algorithm is used to convert the location coordinates in the parked data packet into local coordinate coefficient values ​​on the map;

[0141] Retrieve the target map element corresponding to the local coordinate coefficient value from the map coordinate information;

[0142] The matching relationship between vehicles and target map elements is determined as the matching result.

[0143] The vehicle positioning device for parking lots provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0144] It should be noted that the division of the various modules in the above device is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can be implemented entirely in software via processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, a processing module can be a separate processing element, or it can be integrated into an integrated circuit within the above device. Alternatively, it can be stored as program code in the device's memory, and its functions can be called and executed by a processing element. The implementation of other modules is similar. Moreover, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0145] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented by calling program code through a processing element, that processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to implement a System-On-a-Chip (SOC).

[0146] Figure 6 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 6, the electronic device 600 provided in this embodiment may include: a processor 601, and a memory 602 communicatively connected to the processor, wherein:

[0147] The memory stores instructions that the computer executes;

[0148] The processor executes computer execution instructions stored in memory to implement the method described in the foregoing method embodiments.

[0149] It should be understood that processor 601 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor. Memory 602 may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0150] Optionally, the electronic device 600 may also include a communication interface 603. In specific implementations, if the communication interface 603, memory 602, and processor 601 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0151] Optionally, in a specific implementation, if the communication interface 603, memory 602, and processor 601 are integrated on a single integrated circuit, then the communication interface 603, memory 602, and processor 601 can communicate through an internal interface.

[0152] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the methods described in any of the foregoing embodiments.

[0153] It is understood that the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0154] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an ASIC. Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic device.

[0155] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a computer-readable storage medium, include several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application.

[0156] This application also provides a computer program product, including a computer program that, when executed, implements the method described in any of the foregoing embodiments.

[0157] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0158] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0159] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as these combinations of technical features do not contradict each other, they should be considered within the scope of this specification.

[0160] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0161] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A vehicle positioning method for a parking lot, characterized in that, include: The system acquires parking data packets of vehicles in the parking lot using an autonomous driving device; it then matches the parking data packets with map coordinate information to determine the matching result. Based on the matching results, vehicle location information is generated.

2. The method according to claim 1, characterized in that, The parking data packets are collected in the following way: based on the parking space occupancy status of the parking lot, the movement path of the autonomous driving device is planned to obtain the inspection path; Based on the inspection path, the parking data packet is collected using the autonomous driving device.

3. The method according to claim 2, characterized in that, The step of collecting the parking data packet using the autonomous driving device based on the inspection path includes: collecting the initial parking data packet of the parking lot using the autonomous driving device based on the inspection path; and performing deduplication verification on the initial parking data packet based on the edge computing module to obtain the parking data packet, wherein the edge computing module is deployed in the autonomous driving device.

4. The method according to claim 3, characterized in that, The edge computing module performs deduplication verification on the initial parking data packet to obtain the parking data packet, including: using the edge computing module to perform the following operations: extracting the timestamp and location coordinates of the parking data in the initial parking data packet; determining whether the parking data is duplicate data based on the timestamp and the location coordinates; if the parking data is duplicate data, deleting the parking data to obtain the parking data packet.

5. The method according to claim 4, characterized in that, The step of determining whether the parking data is duplicate data based on the timestamp and the location coordinates includes: grouping the parking data based on the license plate number to obtain target parking data; determining the rate of change of location coordinates based on the location coordinates of the target parking data and the location coordinates of at least one historical target parking data point before the timestamp; determining whether the timestamp of the target parking data and the timestamps of the at least one historical target parking data point meet the continuity condition; and determining the target parking data as duplicate data when the rate of change of location coordinates is less than a preset change threshold and the timestamp of the target parking data meets the continuity condition.

6. The method according to any one of claims 2 to 5, characterized in that, After collecting the parking data packet, the method further includes: when the autonomous driving device is in a charging / discharging position, using the autonomous driving device to upload the parking data packet to a cloud server, so that the cloud server can obtain the parking data packet.

7. The method according to any one of claims 1 to 5, characterized in that, The step of matching the parking data packet with map coordinate information to determine the matching result includes: using a coordinate system transformation algorithm to convert the position coordinates in the parking data packet into local coordinate coefficient values ​​of the map; retrieving the target map element corresponding to the local coordinate coefficient value in the map coordinate information; and determining the matching relationship between the vehicle and the target map element as the matching result.

8. A vehicle positioning system for a parking lot, characterized in that, include: Autonomous driving equipment and cloud server; wherein, the autonomous driving equipment is used to collect parking data packets of vehicles in the parking lot; The cloud server is used to perform the method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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