Parking guiding method and device, computer equipment and readable storage medium

Abstract

The invention relates to a parking guiding method and device, computer equipment, a computer readable storage medium and a computer program product, and can be applied to the technical field of intelligent transportation. The method comprises the following steps: acquiring vehicle attribute information and operation state information of a target vehicle corresponding to a parking area; determining parking demand information of the target vehicle according to the vehicle attribute information and the operation state information in combination with a trajectory analysis result of the target vehicle; under the condition that the parking demand information shows that the parking demand exists, according to the parking demand information and a parking space state model of the parking area, matching processing is carried out on the target vehicle and candidate parking spaces of the parking area, and target parking space information of the target vehicle is obtained; and according to the target parking space information and the current position information of the target vehicle, generating parking guide path information of the target vehicle, and sending the parking guide path information to the target vehicle. By adopting the method, the accuracy of parking guidance can be improved.

Description

Technical Field

[0001] This application relates to the field of intelligent transportation technology, and in particular to a parking guidance method, device, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] In the field of intelligent transportation technology, parking guidance technology is an important means to improve parking management efficiency and user experience. By providing vehicles with parking space information and guidance services, it can effectively alleviate the parking problem.

[0003] Existing parking guidance solutions recommend parking spaces based solely on single information such as vehicle location, making it difficult to accurately determine the actual parking needs of vehicles, resulting in low accuracy in parking guidance. Summary of the Invention

[0004] Therefore, it is necessary to provide a parking guidance method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of parking guidance in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a parking guidance method. The method includes:

[0006] Obtain vehicle attribute information and operational status information of the target vehicle corresponding to the parking area;

[0007] Based on the vehicle attribute information and the operational status information, and combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined;

[0008] When the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle.

[0009] Based on the target parking space information and the current location information of the target vehicle, a parking guidance path information for the target vehicle is generated, and the parking guidance path information is sent to the target vehicle.

[0010] In one embodiment, the trajectory analysis result includes the target vehicle's historical trajectory information and current motion state information;

[0011] The step of determining the parking demand information of the target vehicle based on the vehicle attribute information and the operational status information, combined with the trajectory analysis results of the target vehicle, includes:

[0012] Based on the historical trajectory information and the current motion state information, the target vehicle is subjected to behavior prediction processing to obtain the behavior prediction result of the target vehicle;

[0013] Based on the vehicle attribute information, the operational status information, and the behavior prediction results, the parking demand information of the target vehicle is determined.

[0014] In one embodiment, the step of matching the target vehicle with candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle includes:

[0015] Based on the parking demand information, a set of candidate parking spaces for the parking area is selected from the parking space status model;

[0016] Based on the preset matching elements and the weight values ​​corresponding to each matching element, the target vehicle is matched with each candidate parking space in the candidate parking space set to obtain the matching degree score of each candidate parking space.

[0017] Based on the matching score of each candidate parking space, a target parking space is determined from the set of candidate parking spaces, and the target parking space information is obtained based on the target parking space.

[0018] In one embodiment, the parking space status model includes static attribute information and dynamic status information of each parking space in the parking area;

[0019] The static attribute information includes location information, size information, and type information; the dynamic status information includes occupancy status information and reservation status information.

[0020] In one embodiment, the method further includes:

[0021] Acquire image data of the parking area;

[0022] The image data is processed to detect parking spaces, thereby obtaining the area information of each parking space in the parking area;

[0023] Based on the area information of each parking space, the status recognition process is performed on each parking space to obtain the occupancy status information of each parking space;

[0024] The parking space status model is updated based on the occupancy status information of each parking space.

[0025] In one embodiment, generating parking guidance path information for the target vehicle based on the target parking space information and the current location information of the target vehicle includes:

[0026] Obtain the road condition information corresponding to the parking area;

[0027] Based on the target parking space information, the current location information, and the road condition information, the main route information and alternative route information of the target vehicle are generated;

[0028] The parking guidance route information is generated based on the main route information and the alternative route information.

[0029] Secondly, this application also provides a parking guidance device. The device includes:

[0030] The information acquisition module is used to acquire vehicle attribute information and operational status information of the target vehicles corresponding to the parking area;

[0031] The information determination module is used to determine the parking demand information of the target vehicle based on the vehicle attribute information and the operating status information, combined with the trajectory analysis results of the target vehicle.

[0032] The parking space matching module is used to match the target vehicle with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area when the parking demand information indicates that there is a parking demand, so as to obtain the target parking space information of the target vehicle.

[0033] The information sending module is used to generate parking guidance path information for the target vehicle based on the target parking space information and the current location information of the target vehicle, and send the parking guidance path information to the target vehicle.

[0034] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0035] Obtain vehicle attribute information and operational status information of the target vehicle corresponding to the parking area;

[0036] Based on the vehicle attribute information and the operational status information, and combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined;

[0037] When the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle.

[0038] Based on the target parking space information and the current location information of the target vehicle, a parking guidance path information for the target vehicle is generated, and the parking guidance path information is sent to the target vehicle.

[0039] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0040] Obtain vehicle attribute information and operational status information of the target vehicle corresponding to the parking area;

[0041] Based on the vehicle attribute information and the operational status information, and combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined;

[0042] When the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle.

[0043] Based on the target parking space information and the current location information of the target vehicle, a parking guidance path information for the target vehicle is generated, and the parking guidance path information is sent to the target vehicle.

[0044] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0045] Obtain vehicle attribute information and operational status information of the target vehicle corresponding to the parking area;

[0046] Based on the vehicle attribute information and the operational status information, and combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined;

[0047] When the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle.

[0048] Based on the target parking space information and the current location information of the target vehicle, a parking guidance path information for the target vehicle is generated, and the parking guidance path information is sent to the target vehicle.

[0049] The aforementioned parking guidance method, device, computer equipment, computer-readable storage medium, and computer program product acquire vehicle attribute information and operational status information of a target vehicle corresponding to a parking area; based on the vehicle attribute information and operational status information, and combined with the trajectory analysis results of the target vehicle, determine the parking demand information of the target vehicle; when the parking demand information indicates the existence of parking demand, match the target vehicle with candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle; based on the target parking space information and the current location information of the target vehicle, generate parking guidance path information for the target vehicle, and send the parking guidance path information to the target vehicle. This solution obtains vehicle attribute and operational status information of the target vehicle and combines it with trajectory analysis results to determine the target vehicle's parking demand information. This helps to accurately determine whether the target vehicle has parking needs and the specific content of those needs, thus avoiding ineffective guidance for vehicles without parking needs. By matching the target vehicle with candidate parking spaces based on parking demand information and parking space status models, it helps to match the target vehicle with the optimal parking space that meets its parking needs, thereby improving the accuracy of vehicle-parking space matching. By generating parking guidance path information based on the target parking space information and the target vehicle's current location information, it helps to provide the target vehicle with precise route navigation, thus improving the accuracy of parking guidance. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating a parking guidance method in one embodiment;

[0052] Figure 2 This is a flowchart illustrating the steps for determining parking demand information in one embodiment;

[0053] Figure 3 This is a flowchart illustrating the steps for determining target parking space information in one embodiment;

[0054] Figure 4 This is a structural block diagram of a parking guidance device in one embodiment;

[0055] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0058] In one exemplary embodiment, such as Figure 1 As shown, a parking guidance method is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc.; the server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0059] Step S101: Obtain the vehicle attribute information and operational status information of the target vehicle corresponding to the parking area;

[0060] Step S102: Based on vehicle attribute information and operational status information, and combined with the trajectory analysis results of the target vehicle, determine the parking demand information of the target vehicle.

[0061] Step S103: If the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle.

[0062] Step S104: Based on the target parking space information and the current location information of the target vehicle, generate parking guidance path information for the target vehicle and send the parking guidance path information to the target vehicle.

[0063] The parking area can refer to the area where roadside parking spaces are located, or the parking management area within the coverage of the system monitoring.

[0064] The target vehicle can be a driverless taxi or other type of vehicle that enters the communication range of the parking area.

[0065] Among them, vehicle attribute information can be information that can characterize the basic characteristics of the target vehicle, such as vehicle type, vehicle size, and license plate number.

[0066] The operational status information can be the order status information of the target vehicle obtained through interaction with the dispatch platform, including whether the target vehicle is idle or whether the order is about to be completed.

[0067] The trajectory analysis results can be obtained by analyzing the target vehicle's driving intention based on its historical trajectory, current speed, turn signal status, and other characteristics.

[0068] Among them, parking demand information can be information that indicates whether the target vehicle has parking needs and the specific details of the parking needs, including the expected parking duration and parking preferences.

[0069] The parking space status model can be a three-layer model consisting of a physical layer, a state layer, and a prediction layer. The physical layer records the static attributes of the parking space, the state layer records the dynamic information of the parking space, and the prediction layer predicts the future availability of the parking space based on historical data.

[0070] Among them, the candidate parking space can be one or more parking spaces that are currently available in the parking area and meet the parking needs of the target vehicle.

[0071] The matching process can be a process that comprehensively considers multiple factors such as distance, parking space size matching degree, expected parking time matching, price, and vehicle preferences to perform matching calculations between the target vehicle and candidate parking spaces.

[0072] The target parking space information can be the relevant information of the parking space that is most suitable for the target vehicle after matching and processing, including the parking space location, parking space number, etc.

[0073] The current location information can be the real-time positioning information of the target vehicle, with positioning accuracy reaching the centimeter or meter level.

[0074] The parking guidance route information can be the driving route information from the current location of the target vehicle to the target parking space. The parking guidance route information includes detailed turning instructions and navigation information such as reference signs.

[0075] Optionally, the terminal obtains vehicle attribute information and operational status information of the target vehicle corresponding to the parking area. The terminal obtains vehicle attribute information such as vehicle type, license plate information, and driving direction through visual recognition, and operational status information such as order status of the target vehicle through interaction with the dispatch platform. Based on the vehicle attribute information and operational status information, and combined with the trajectory analysis results of the target vehicle, the terminal determines the parking demand information of the target vehicle. The trajectory analysis results are obtained by predicting behavior based on features such as the target vehicle's historical trajectory, current speed, and turn signal status. If the parking demand information indicates the existence of parking demand, the terminal matches the target vehicle with candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle. The matching process comprehensively considers factors such as distance, parking space size matching degree, expected parking duration matching, price, and vehicle preference. Based on the target parking space information and the current location information of the target vehicle, the terminal generates parking guidance path information for the target vehicle and sends the parking guidance path information to the target vehicle.

[0076] For example, the terminal identifies target vehicles entering the communication range of the parking area through multimodal fusion, obtains vehicle type and license plate information through visual recognition, and obtains vehicle identification and operational status information through vehicle-road cooperative communication. The terminal establishes a vehicle behavior prediction model, predicting whether the target vehicle needs to park based on its historical trajectory, current speed, turn signal status, and other features, and reserves resources in advance. When it is determined that the target vehicle has a parking need, the terminal queries the information of each layer in the parking space status model, including the parking space size and type recorded in the physical layer, the occupancy status and reservation status recorded in the status layer, and the future availability prediction recorded in the prediction layer. It then uses a weighted matching algorithm to calculate the matching score between the target vehicle and each candidate parking space, and selects the parking space with the highest score as the target parking space. Based on the target parking space information and the current location information of the target vehicle, the terminal plans a parking guidance path in conjunction with real-time traffic conditions, generates parking guidance path information containing detailed turning instructions and reference signs, and sends the parking guidance path information to the target vehicle through vehicle-road cooperative communication. At the same time, the target parking space is set to a locked state to prevent other vehicles from occupying it.

[0077] In the above parking guidance method, the vehicle attribute information and operational status information of the target vehicle corresponding to the parking area are obtained; based on the vehicle attribute information and operational status information, and combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined; if the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area according to the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle; based on the target parking space information and the current location information of the target vehicle, the parking guidance path information of the target vehicle is generated and sent to the target vehicle. This solution obtains vehicle attribute and operational status information of the target vehicle and combines it with trajectory analysis results to determine the target vehicle's parking demand information. This helps to accurately determine whether the target vehicle has parking needs and the specific content of those needs, thus avoiding ineffective guidance for vehicles without parking needs. By matching the target vehicle with candidate parking spaces based on parking demand information and parking space status models, it helps to match the target vehicle with the optimal parking space that meets its parking needs, thereby improving the accuracy of vehicle-parking space matching. By generating parking guidance path information based on the target parking space information and the target vehicle's current location information, it helps to provide the target vehicle with precise route navigation, thus improving the accuracy of parking guidance.

[0078] In one exemplary embodiment, reference is made to Figure 2 The trajectory analysis results include the target vehicle's historical trajectory information and current motion status information; based on vehicle attribute information and operational status information, combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined, including:

[0079] Step S201: Based on historical trajectory information and current motion state information, perform behavior prediction processing on the target vehicle to obtain the behavior prediction result of the target vehicle;

[0080] Step S202: Determine the parking demand information of the target vehicle based on vehicle attribute information, operational status information, and behavior prediction results.

[0081] The historical trajectory information can be a record of the target vehicle's driving path before entering the parking area, including the sequence of location points the target vehicle passed through and the corresponding time information.

[0082] The current motion status information can be the real-time driving status data of the target vehicle, including current speed, acceleration, turn signal status, driving direction, and other information.

[0083] Among them, behavior prediction processing can be a process that analyzes the historical trajectory information and current motion state information of the target vehicle based on the vehicle behavior prediction model to predict whether the target vehicle needs to stop and the estimated stopping time.

[0084] Among them, the behavior prediction result can be the result of predicting the parking intention of the target vehicle. The behavior prediction result represents whether the target vehicle has the intention to park and the expected parking time.

[0085] Optionally, the terminal acquires the target vehicle's historical trajectory information and current motion status information. The historical trajectory information includes the sequence of recent travel locations of the target vehicle, and the current motion status information includes the target vehicle's current speed, turn signal status, and driving direction. Based on the historical trajectory information and current motion status information, the terminal performs behavior prediction processing on the target vehicle. By analyzing the target vehicle's driving characteristics through a vehicle behavior prediction model, the terminal predicts whether the target vehicle intends to park, obtaining the target vehicle's behavior prediction result. Based on the vehicle attribute information, operational status information, and behavior prediction result, the terminal comprehensively judges the target vehicle's parking needs and determines the target vehicle's parking demand information, which includes whether there is a parking demand, the expected parking duration, and parking preferences.

[0086] The technical solution provided in this embodiment obtains behavior prediction results by performing behavior prediction processing on the target vehicle based on historical trajectory information and current motion status information. This helps to predict the parking intention of the target vehicle in advance. By combining vehicle attribute information, operating status information and behavior prediction results to determine parking demand information, it helps to improve the accuracy of parking demand judgment.

[0087] In one exemplary embodiment, reference is made to Figure 3 Based on parking demand information and the parking space status model of the parking area, the target vehicle is matched with candidate parking spaces in the parking area to obtain the target parking space information of the target vehicle, including:

[0088] Step S301: Based on the parking demand information, select a set of candidate parking spaces for the parking area from the parking space status model;

[0089] Step S302: Based on the preset matching elements and the weight values ​​corresponding to each matching element, perform matching processing on the target vehicle and each candidate parking space in the candidate parking space set to obtain the matching degree score of each candidate parking space.

[0090] Step S303: Based on the matching score of each candidate parking space, determine the target parking space from the candidate parking space set, and obtain the target parking space information based on the target parking space.

[0091] The candidate parking space set can be a set of multiple parking spaces selected from the parking space status model that meet the parking needs of the target vehicle.

[0092] The matching elements can be evaluation factors used to assess the degree of matching between the target vehicle and the candidate parking space. These matching elements include factors such as distance, parking space size matching degree, expected parking duration matching, price, and vehicle preference.

[0093] The weight value can be the proportion of each matching element in the matching process. Different matching elements correspond to different weight values, and the weight value can be dynamically adjusted according to factors such as real-time traffic conditions, weather conditions, and time periods.

[0094] The matching score can be a comprehensive score calculated based on each matching element and its corresponding weight value, which is used to characterize the degree of matching between the target vehicle and each candidate parking space.

[0095] Optionally, the terminal selects candidate parking spaces that meet the parking needs from the parking space status model based on the parking demand information of the target vehicle, and obtains a set of candidate parking spaces for the parking area. The terminal obtains preset matching elements and the weight values ​​corresponding to each matching element. The matching elements include distance, parking space size matching degree, expected parking time matching, price, and vehicle preference. Based on each matching element and the corresponding weight value, the terminal performs a weighted matching calculation on the target vehicle and each candidate parking space in the candidate parking space set to obtain the matching degree score of each candidate parking space. Based on the matching degree scores of each candidate parking space, the terminal selects the parking space with the highest matching degree score from the candidate parking space set as the target parking space, and generates target parking space information based on the location information and number information of the target parking space.

[0096] The technical solution provided in this embodiment helps to narrow down the matching range by filtering a set of candidate parking spaces from the parking space status model based on parking demand information. By matching each candidate parking space according to preset matching elements and weight values ​​to obtain a matching score, it is beneficial to comprehensively consider multiple factors for parking space matching, thereby improving the accuracy of vehicle-parking space matching.

[0097] In an exemplary embodiment, the parking space status model includes static attribute information and dynamic status information of each parking space in the parking area; wherein, the static attribute information includes location information, size information and type information; and the dynamic status information includes occupancy status information and reservation status information.

[0098] Among them, static attribute information can be information that records the fixed physical attributes of parking spaces. Static attribute information is stored and maintained in the physical layer of the parking space state model.

[0099] The location information can be the specific coordinates or relative location of the parking space within the parking area.

[0100] The size information can be physical dimensions such as the length and width of the parking space.

[0101] The type information can be a category identifier for parking spaces, such as regular parking spaces, charging parking spaces, and accessible parking spaces.

[0102] Among them, dynamic status information can be the dynamic status data of parking spaces, which is stored and updated in the status layer of the parking space status model.

[0103] The occupancy status information can be a status indicator indicating whether the parking space is currently occupied by a vehicle.

[0104] The reservation status information can include whether the parking space has been reserved and the reservation time period.

[0105] The technical solution provided in this embodiment records the static attribute information and dynamic status information of each parking space through a parking space status model, which is conducive to a comprehensive understanding of the physical characteristics and real-time status of each parking space, thereby providing an accurate data foundation for parking space matching processing.

[0106] In an exemplary embodiment, the method further includes: acquiring image data of the parking area; performing parking space detection processing on the image data to obtain area information of each parking space in the parking area; performing state recognition processing on each parking space based on the area information of each parking space to obtain occupancy status information of each parking space; and updating the parking space status model based on the occupancy status information of each parking space.

[0107] The image data can be video frames or image frames of the parking area collected by a visual perception device.

[0108] Among them, the parking space detection and processing can be a process of locating and extracting the boundaries of parking space areas in image data by combining deep learning with traditional image processing.

[0109] Among them, the area information can be the location range information of each parking space in the image determined after parking space detection processing.

[0110] The status recognition process can be a process of determining whether a parking space is occupied by a vehicle based on the area information of each parking space. The status recognition process adopts an anti-shake mechanism, and the status change is confirmed only when the detection results of multiple consecutive frames are consistent.

[0111] The update process can be an event-driven mechanism that updates the dynamic status information of the corresponding parking space in the parking space status model in real time when a change in the parking space status is detected.

[0112] Optionally, the terminal acquires image data of the parking area. The image data is collected in real time by a visual perception device. The terminal performs parking space detection processing on the image data, quickly locates the parking space area through a deep learning model, and uses image processing algorithms to perform accurate boundary extraction to obtain the area information of each parking space in the parking area. Based on the area information of each parking space, the terminal performs state recognition processing on each parking space to determine whether each parking space is occupied by a vehicle. The terminal sets an anti-shake mechanism, and only confirms the state change when the detection results of multiple consecutive frames are consistent, thus obtaining the occupancy status information of each parking space. Based on the occupancy status information of each parking space, the terminal uses an event-driven mechanism to update the parking space status model and maintain the dynamic status information of each parking space in real time.

[0113] The technical solution provided in this embodiment, by acquiring image data of the parking area and performing parking space detection and status recognition processing, is conducive to real-time perception of the occupancy status of each parking space. By updating the parking space status model based on the occupancy status information, it is beneficial to maintain the data accuracy and timeliness of the parking space status model, thereby improving the accuracy of subsequent parking space matching.

[0114] In an exemplary embodiment, parking guidance path information for the target vehicle is generated based on the target parking space information and the current location information of the target vehicle, including: obtaining road condition information corresponding to the parking area; generating main path information and alternative path information for the target vehicle based on the target parking space information, the current location information, and the road condition information; and generating parking guidance path information based on the main path information and the alternative path information.

[0115] Among them, road condition information can be real-time traffic data of the road corresponding to the parking area, including information such as road congestion level, traffic flow and traffic speed.

[0116] The main path information can be a preferred driving route planned based on the target parking space information, current location information, and road condition information. The main path information is used to guide the target vehicle from its current location to the target parking space.

[0117] The alternative route information can be an alternative driving route that can be switched when the main route is abnormal. The alternative route information and the main route information together constitute the complete parking guidance route information.

[0118] Optionally, the terminal obtains road condition information corresponding to the parking area, including real-time traffic conditions and traffic speed. Based on the target parking space information, the current location information of the target vehicle, and the road condition information, the terminal performs route planning calculations to generate the main route information of the target vehicle. At the same time, the terminal reserves alternative driving routes and generates alternative route information. When the main route is congested or abnormal, it can automatically switch to the alternative route. Based on the main route information and the alternative route information, the terminal generates parking guidance route information containing detailed turning instructions and reference signs, and sends the parking guidance route information to the target vehicle.

[0119] The technical solution provided in this embodiment obtains the road condition information corresponding to the parking area and generates main path information and alternative path information based on the target parking space information, current location information and road condition information. This is beneficial for planning driving routes for target vehicles to avoid congested road sections. By providing parking guidance path information that combines the main path and alternative paths, the flexibility of parking guidance is improved, thereby improving the success rate of parking guidance.

[0120] The following is an example illustrating the parking guidance method provided in this application. This example demonstrates the application of this method to a terminal.

[0121] This embodiment relates to the field of intelligent transportation system technology, specifically to an intelligent roadside parking guidance system based on edge computing, which is particularly suitable for providing real-time parking space detection and intelligent guidance services for driverless taxis. It belongs to the cross-application field of vehicle-road cooperation, artificial intelligence, edge computing and fifth-generation mobile communication technology.

[0122] With the rapid development of autonomous driving technology and the accelerated commercialization process, the driverless taxi industry is rapidly emerging. Several major cities have already launched pilot commercial operations of driverless taxis.

[0123] Industry pain point analysis:

[0124] According to industry research data:

[0125] On average, driverless taxis spend 30 to 35% of their time waiting for passengers each day; finding a suitable parking space takes an average of 5 to 10 minutes, accounting for 15% of non-operational time; invalid cruising mileage caused by searching for parking spaces accounts for 8 to 12% of the total mileage; the average utilization rate of roadside parking spaces in the city is only 65%, with some areas exceeding 95% during peak hours, indicating uneven distribution.

[0126] Current status of related technology development:

[0127] In terms of vehicle-road cooperative technology development, the technical standards for vehicle-to-everything (V2X) connectivity have matured, and cellular vehicle-to-everything (V2X) has become the mainstream technology route; the commercial deployment of 5G mobile communication networks has accelerated, with the number of 5G mobile communication base stations exceeding 4 million; the deployment scale of roadside units has expanded, with coverage exceeding 60% in key cities.

[0128] In terms of edge computing technology maturity, the computing power of edge computing devices has been greatly improved, with a single device reaching 20 to 50 trillion operations per second; the artificial intelligence inference framework has been optimized and matured, with inference speed increased by more than 10 times; and the edge cloud collaborative architecture is becoming increasingly perfect, supporting elastic expansion.

[0129] In the field of computer vision technology, the accuracy of target detection algorithms has exceeded 98%; real-time processing capability has reached more than 60 frames per second; and adaptability to low light and severe weather has been significantly improved.

[0130] Regarding the current state of the smart parking market, the market size exceeds 20 billion yuan, with an annual growth rate of 25%; existing solutions mainly serve manned vehicles.

[0131] Current technologies mainly include:

[0132] The autonomous parking system is characterized by autonomous parking based on onboard sensors. It supports automatic parking space finding within parking lots and is mainly used in closed parking lots. Its technical limitations include reliance on high-precision maps, lack of roadside parking space management capabilities, and lack of active guidance.

[0133] The smart parking solution is characterized by the use of cameras and artificial intelligence recognition to provide parking space management and billing functions. Its application scenarios are commercial parking lots and park parking management. Its technical limitations are that the centralized architecture has large latency and does not support direct communication between vehicles and the Internet of Things. It is mainly aimed at human-driven vehicles.

[0134] The video parking guidance system is characterized by parking space detection based on video stream analysis and guidance via LED screens. Its application scenario is indoor parking lots. Its technical limitations are that it is not suitable for roadside scenarios and cannot be integrated with autonomous driving systems.

[0135] Traditional geomagnetic induction parking systems rely on geomagnetic sensors to detect parking space occupancy. They are used for roadside parking space management and are limited by high installation costs (500 to 800 yuan per sensor), difficult maintenance, and lack of communication capabilities.

[0136] Foreign autonomous driving solutions rely on the vehicle's own ability to find parking spaces, but are limited by a lack of infrastructure support and low efficiency.

[0137] The existing technology has the following main technical problems:

[0138] The system architecture suffers from insufficient real-time performance. Traditional cloud computing architectures have end-to-end latency of 200 to 500 milliseconds, which cannot meet the requirements of autonomous vehicles for latency within 100 milliseconds. Network congestion during peak periods leads to a decline in service quality.

[0139] The system architecture suffers from limited coverage. The existing system mainly covers closed parking lots, with roadside coverage of less than 15%. The monitoring range of single-point devices is limited, usually less than 30 meters, with many blind spots and poor continuity.

[0140] The functional defects include a lack of proactive service capabilities, passive waiting for inquiries, inability to proactively push information, inability to identify vehicle demand status, and a lack of prediction and reservation mechanisms.

[0141] The functional defects include low intelligence, simple presence or absence detection, inability to perform parking space adaptability analysis, and lack of global optimization and scheduling capabilities.

[0142] The technical implementation issues include low system integration, separate deployment of cameras, servers, and communication equipment, inconsistent interfaces, difficulty in integration, numerous points of failure, and poor reliability.

[0143] The technical implementation suffers from poor environmental adaptability, with nighttime recognition accuracy dropping by more than 30%, and it is almost impossible to work in rainy or snowy weather. Strong light and shadow also cause serious interference.

[0144] The lack of a unified standard leads to incompatibility between different vendors' systems, inconsistent data formats, and an inability to achieve cross-platform interoperability.

[0145] Detailed explanation of the technical solution:

[0146] System overall architecture design:

[0147] Hardware system configuration parameters:

[0148] Edge computing performance parameters include: AI computing power greater than or equal to 100 trillion operations per second, using 8-bit integer precision; greater than or equal to 25 trillion operations per second, using 16-bit floating-point precision; CPU performance is an 8-core architecture with a clock speed greater than or equal to 2.2 GHz; memory configuration is 32 gigabytes with a bandwidth greater than or equal to 68 gigabytes per second; storage performance is 1 terabyte of non-volatile memory with a read speed greater than or equal to 3500 megabytes per second, and 128 gigabytes of backup storage; power consumption ranges from 50 to 80 watts during normal operation, with a peak power of less than or equal to 120 watts.

[0149] The visual perception parameters include: image resolution of 3840 x 2160 pixels, frame rate of 60 frames per second; light sensitivity of 0.001 lux at minimum illumination, dynamic range of ≥120 dB; field of view coverage of 120 degrees horizontally and 75 degrees vertically, effective recognition distance of 80 meters; and night vision capability of 850 nm infrared illumination, illumination distance of ≥100 meters.

[0150] The communication capability parameters include: 5G mobile communication performance is downlink speed greater than or equal to 1 gigabits per second, uplink speed greater than or equal to 200 megabits per second, and air interface latency less than or equal to 4 milliseconds; cellular vehicle-to-everything (V2X) performance is communication distance greater than or equal to 500 meters, latency less than or equal to 20 milliseconds, and concurrent connection count greater than or equal to 200; positioning accuracy is real-time dynamic differential positioning accuracy less than or equal to 2 centimeters, and ordinary positioning accuracy less than or equal to 1 meter.

[0151] Environmental adaptability parameters include: operating temperature from -40 degrees Celsius to 70 degrees Celsius; protection level of dustproof and waterproof; shock resistance level of being able to withstand vibration of 10 times the force of gravity; mean time between failures (MTBF) greater than or equal to 50,000 hours.

[0152] Software architecture design principles:

[0153] The system adopts a microservice architecture, with each functional module running independently and loosely coupled. The core includes four major microservice groups: perception service, decision service, communication service, and data service.

[0154] The perception service is responsible for raw data acquisition and preliminary processing, including image denoising, enhancement, distortion correction and other preprocessing operations. It adopts a multi-threaded parallel processing mechanism to ensure real-time processing of multiple video streams.

[0155] The decision service uses intelligent analysis based on perceived data, including parking space status recognition, vehicle behavior prediction, and optimal matching calculation. It adopts a combination of rule engine and machine learning model to ensure the interpretability of decisions and has adaptive learning capabilities.

[0156] The communication service manages all external interactions, including data exchange with vehicles, cloud platforms, and dispatch systems, and implements functions such as protocol conversion, message routing, and quality of service assurance. It supports multiple communication protocols, including Representational State Transition Protocol, Network Sockets Protocol, and Message Queuing Telemetry Transport Protocol.

[0157] The data service is responsible for local data storage and management. It uses a time-series database to store sensor data, a relational database to store business data, and a cache database to cache hot data, thereby achieving data lifecycle management and automatically cleaning up expired data.

[0158] Core functionality implementation approach:

[0159] Real-time monitoring and management of parking spaces:

[0160] The parking space detection method combines deep learning with traditional image processing. First, the deep learning model is used to quickly locate the parking space area, and then traditional algorithms are used for accurate boundary extraction and state judgment.

[0161] The system maintains a three-layer parking space status model: the physical layer records the static attributes of the parking spaces, including location, size, and type; the status layer records dynamic information, including occupancy status, reservation status, and historical records; and the prediction layer predicts future availability based on historical data.

[0162] The parking space status update adopts an event-driven mechanism. When a status change is detected, the update process is triggered immediately. An anti-jitter mechanism is set up, and the status change is confirmed only when the detection results are consistent for 3 consecutive frames to avoid false judgments.

[0163] Establish a parking space profiling system to record the usage characteristics of each parking space, including average parking duration, high-frequency usage periods, and vehicle type preferences, in order to optimize the matching algorithm.

[0164] Vehicle recognition and requirements analysis:

[0165] By using multimodal fusion to identify approaching vehicles, visual recognition of vehicle type, license plate, and direction of travel, vehicle identification and operational status are obtained through vehicle-to-everything (IoT) communication, and parking intentions are determined through trajectory analysis.

[0166] Establish a vehicle behavior prediction model that predicts whether a vehicle needs to stop based on features such as historical trajectory, current speed, and turn signal status, and reserves resources 2 to 3 minutes in advance.

[0167] This allows for the creation of vehicle profiles, recording vehicle parking preferences such as proximity to intersections or under trees, parking duration distribution, and payment methods, thus providing personalized services.

[0168] Deeply integrated with the dispatch platform, it obtains vehicle order status in real time. For vehicles with orders about to be completed, it pushes parking space information near the destination in advance. For vehicles without orders for a long time, it recommends parking spaces with lower fees.

[0169] Intelligent matching and route planning:

[0170] The matching algorithm takes into account multiple factors: distance (30%), parking space size matching degree (20%), expected parking duration matching degree (20%), price (15%), and vehicle preference (15%).

[0171] Implement a dynamic weighting adjustment strategy, dynamically adjusting the weights of each factor based on real-time traffic conditions, weather conditions, time of day, etc. For example, increase the weight of distance on rainy days to reduce vehicle waiting time.

[0172] The route planning takes into account real-time traffic conditions, avoids congested sections, reserves multiple alternative routes, and automatically switches when the main route is abnormal. The route information includes detailed turning instructions and reference signs.

[0173] To implement a reservation protection mechanism, once a vehicle confirms its reservation, the parking space is locked. The locking duration is dynamically adjusted based on the vehicle's distance, typically the estimated arrival time plus a 2-minute buffer.

[0174] Multi-source data fusion processing:

[0175] A multi-source heterogeneous data fusion framework is established to integrate multiple data sources such as vision, radar, and environmental sensors. A weighted voting mechanism is adopted to resolve data conflicts and improve system robustness.

[0176] Spatiotemporal data alignment uses a unified timestamp mechanism, with all data marked with a GPS timestamp, achieving millisecond-level accuracy. Spatial alignment is achieved through coordinate system transformation, mapping all sensor data to a unified world coordinate system.

[0177] An incremental learning mechanism is implemented, which continuously collects new data and updates model parameters regularly during system operation. A federated learning architecture is adopted, with multiple edge devices working together to train the model and improve its generalization ability.

[0178] The data quality monitoring system assesses data reliability in real time. When a sensor's data is abnormal, its weight is automatically reduced or it is removed. A data completion mechanism is established to fill in missing values ​​using historical data and data from adjacent devices.

[0179] System operation process:

[0180] Initialization and self-test process:

[0181] After the system is powered on, it performs a complete self-test process, including hardware status check, network connectivity test, algorithm model loading, historical data recovery, etc. If the self-test fails, it will automatically attempt to repair, and if it cannot repair, it will report the fault.

[0182] The system executes a scene calibration process to automatically identify fixed parking spaces within the monitoring area, establishes a basic information database of parking spaces, and verifies the accuracy of the identification by comparing it with a high-precision map.

[0183] Establish connections with surrounding systems, including adjacent edge devices, cloud platforms, and dispatch centers, and perform clock synchronization to ensure system time accuracy is within 100 milliseconds.

[0184] Load the operation strategy configuration, including parking space management rules, pricing strategies, priority settings, etc., and select the corresponding operation mode based on factors such as the current time and weather.

[0185] Normal operation monitoring process:

[0186] The system executes a perception-decision-execution loop with a period of 100 milliseconds. In the perception phase, it collects data from all sensors, performs data preprocessing and feature extraction, updates the parking space status, processes vehicle requests, generates control commands, and sends guidance information, updates the display content, and records operation logs.

[0187] Implement a tiered processing strategy, classifying tasks into real-time tasks, near-real-time tasks, and non-real-time tasks. Real-time tasks have a latency of less than 100 milliseconds, including safety-related alarms and priority passage for emergency vehicles. Near-real-time tasks have a latency of less than 1 second, including parking space status updates and regular vehicle guidance. Non-real-time tasks have a latency of more than 1 second, including data statistics and report generation.

[0188] Establish an event-driven mechanism. When a specific event is detected, such as a vehicle entering the monitoring area or a change in the status of a parking space, the corresponding processing procedure is triggered. The event priority is divided into three levels: urgent, important, and general.

[0189] The performance monitoring system tracks metrics such as CPU usage, memory consumption, and network traffic in real time. When resource usage exceeds a threshold, it automatically triggers optimization strategies, such as reducing video processing frame rate or simplifying algorithm models.

[0190] Vehicle interaction service process:

[0191] Once a vehicle enters the communication range, the system proactively sends a handshake message to establish a communication connection and verifies the vehicle's identity through a digital certificate to ensure communication security.

[0192] Differentiated services are provided based on vehicle type. For driverless taxis, their operating status is automatically queried, and parking space information is proactively pushed to available vehicles. For private cars, services are only provided when a request is received. For special vehicles, such as ambulances and fire trucks, their passage needs are given priority.

[0193] The information push adopts a hierarchical strategy: the first layer pushes summary information, including the number of available parking spaces and the distance to the nearest parking space; the second layer pushes a detailed list, including specific information on multiple parking spaces; and the third layer pushes navigation guidance, including the detailed route to the selected parking space.

[0194] Implement a session persistence mechanism to maintain the connection while the vehicle is moving within the monitored area, avoiding duplicate authentication, and gracefully disconnect when the vehicle leaves, releasing related resources.

[0195] Exception handling and fault tolerance mechanisms:

[0196] A multi-level fault-tolerant system is established, with redundant design at the hardware level and key components having primary / backup switching capabilities. At the software level, process monitoring and automatic restart mechanisms are adopted, and at the business level, a service degradation strategy is implemented.

[0197] Regarding parking space conflict handling, when multiple vehicles compete for the same parking space, priority is determined based on factors such as reservation time, vehicle distance, and urgency. Alternative options are automatically recommended for vehicles that are not selected, and appropriate compensation, such as coupons, is provided.

[0198] In terms of communication failure handling, when the fifth-generation mobile communication network is interrupted, it automatically switches to the cellular vehicle-to-everything (V2X) direct connection mode. When the cellular V2X fails, it enables the wireless network hotspot. When all communication is interrupted, it provides basic information services through traditional methods such as LED displays.

[0199] In terms of adapting to severe weather, the system automatically adjusts image processing parameters in rainy or snowy weather, activates special weather recognition models, increases safety redundancy when visibility is low, extends parking space reservation time, and enters a safety mode in extreme weather, providing only basic services.

[0200] In terms of equipment failure handling, when a hardware failure is detected, the system automatically notifies the maintenance personnel and requests adjacent devices to expand the monitoring range for supplementary coverage, and records the failure site data for subsequent analysis.

[0201] Data security and privacy protection:

[0202] Data security architecture:

[0203] A zero-trust security architecture is adopted, requiring all access requests to be verified and authorized, and the principle of least privilege is implemented, with each module only able to access the necessary data and functions.

[0204] Data transmission is encrypted using transport layer security protocols, with a key length of no less than 256 bits. Critical data is encrypted and stored using advanced encryption standards, and a key rotation mechanism is implemented, automatically updating the key every 30 days.

[0205] Establish a security audit system to record all data access and operation behaviors. An abnormal behavior detection system uses machine learning to identify potential threats, such as abnormal access patterns and data leakage attempts.

[0206] Implement a data anonymization strategy, obfuscating sensitive information such as license plate numbers and vehicle trajectories during storage and transmission, and only providing the original data through a dedicated interface when necessary, such as for law enforcement requirements.

[0207] Privacy protection:

[0208] Establish data classification and grading standards to clarify the protection requirements for different types of data.

[0209] Image data is processed locally without uploading the original images to the cloud. Only structured parking space status information and statistical data are transmitted. When it is necessary to save the image, privacy information such as faces and license plates are automatically blurred.

[0210] Implement the principle of data minimization, collect only the data necessary for system operation, and set data retention periods: parking records are retained for 30 days, statistical data is retained for 1 year, and expired data is automatically deleted.

[0211] It provides a user privacy control interface, allowing car owners to query, correct, and delete their personal data. It also supports anonymous mode, allowing vehicles to choose to use the service anonymously.

[0212] System performance optimization:

[0213] Edge computing optimization:

[0214] Model quantization compression converts a 32-bit floating-point precision model into an 8-bit integer precision model, reducing the model size by 75%, increasing inference speed by 3 to 4 times, and keeping the precision loss within 2%.

[0215] Operator fusion optimization combines multiple computational operators to reduce memory access frequency, batch processing optimization unifies the scheduling of multiple video streams, and improves the utilization of the graphics processor.

[0216] Dynamic resource scheduling adjusts the CPU and GPU frequencies based on real-time load to balance performance and power consumption, and task priority scheduling ensures that critical tasks are executed first.

[0217] Cache optimization strategies include caching frequently accessed data in high-speed storage and preloading mechanisms to load potentially needed models and data in advance.

[0218] Communication performance optimization:

[0219] Message compression transmission uses an efficient serialization protocol, reducing message size by more than 50%. The batch transmission mechanism merges multiple small messages for transmission, reducing communication overhead.

[0220] An adaptive transmission strategy dynamically adjusts the transmission frequency and data volume based on network quality. When the network is congested, video quality is automatically reduced, and control command transmission is prioritized.

[0221] Establish a message priority queue, with urgent messages, such as collision warnings, having the highest priority, and regular update messages having the lowest priority. Implement flow control to prevent network storms.

[0222] Connection pool management: pre-establish connection pools to avoid the overhead of frequent connection establishment and disconnection; long connection keep-alive mechanism: periodically send heartbeat packets to maintain connection status.

[0223] Improved algorithm accuracy:

[0224] The multi-model integration strategy combines the prediction results of multiple models to improve the recognition accuracy and establishes a model confidence evaluation mechanism, triggering manual review for low-confidence results.

[0225] The model is updated online, marginal and error cases are collected, and the model is retrained regularly. An active learning strategy is adopted to prioritize the labeling of the most valuable samples.

[0226] The scene adaptive mechanism automatically switches to a dedicated model based on different time periods, weather, and lighting conditions, and establishes a scene recognition module to automatically determine the characteristics of the current environment.

[0227] Automatic error correction: Errors are automatically detected and corrected through methods such as timing consistency checks and spatial logic verification. A feedback learning mechanism is established to learn and improve from errors.

[0228] Technical effects:

[0229] Performance improvement effect:

[0230] After the system was deployed, the time to find a parking space was reduced from the traditional 5 to 10 minutes to 30 to 60 seconds, improving efficiency by more than 85%. The invalid cruising mileage of driverless taxis was reduced by 70%, and each vehicle saved about 5 kilowatt-hours of electricity per day.

[0231] The parking space utilization rate increased from 65% to 92%, which is equivalent to an increase of 40% in parking resources. The parking space turnover rate during peak hours increased by 50%, effectively alleviating the parking problem.

[0232] The system maintains a high level of recognition accuracy in various environments: 98.5% in sunny weather, 97% in cloudy weather, 96% at night, 94% in light rain, and 90% in heavy rain. The false alarm rate is controlled below 1%, and the missed alarm rate is less than 2%.

[0233] It exhibits excellent end-to-end latency performance, with local processing latency of less than 50 milliseconds, fifth-generation mobile communication latency of less than 20 milliseconds, and overall response time of less than 100 milliseconds, fully meeting real-time requirements.

[0234] Benefit analysis:

[0235] Construction costs have been significantly reduced. Compared to the traditional geomagnetic solution, the cost of modifying each parking space has dropped from 2,000 yuan to 500 yuan, a reduction of 75%. The equipment adopts a modular design, which reduces maintenance costs by 60%.

[0236] It will drive the development of related industries and promote the development of industrial chains such as fifth-generation mobile communication, edge computing, and artificial intelligence.

[0237] Benefit assessment:

[0238] Traffic efficiency has been significantly improved, reducing traffic congestion caused by searching for parking spaces. Road capacity has increased by 15%, illegal parking has been reduced, and road order has improved markedly.

[0239] It has significant environmental benefits, reducing carbon emissions from inefficient vehicle operation by approximately 1,000 tons of carbon dioxide emissions per thousand vehicles per year, reducing vehicle idling time, and improving air quality.

[0240] Improving urban management provides crucial infrastructure for smart city construction, and the accumulated data can be used for decision support in areas such as urban planning and traffic optimization.

[0241] It improves the travel experience, reduces anxiety about finding parking spaces, and increases travel efficiency.

[0242] It should be understood that although the steps in the flowcharts of the embodiments described above 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 flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0243] Based on the same inventive concept, this application also provides a parking guidance device for implementing the parking guidance method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more parking guidance device embodiments provided below can be found in the limitations of the parking guidance method described above, and will not be repeated here.

[0244] In one exemplary embodiment, such as Figure 4 As shown, a parking guidance device 400 is provided, which may include:

[0245] The information acquisition module 401 is used to acquire vehicle attribute information and operational status information of the target vehicle corresponding to the parking area;

[0246] The information determination module 402 is used to determine the parking demand information of the target vehicle based on the vehicle attribute information and operating status information, combined with the trajectory analysis results of the target vehicle.

[0247] The parking space matching module 403 is used to match the target vehicle with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area when the parking demand information indicates that there is parking demand, so as to obtain the target parking space information of the target vehicle.

[0248] The information sending module 404 is used to generate parking guidance path information for the target vehicle based on the target parking space information and the current location information of the target vehicle, and send the parking guidance path information to the target vehicle.

[0249] In an exemplary embodiment, the trajectory analysis results include the target vehicle's historical trajectory information and current motion status information; the information determination module 402 is further configured to perform behavior prediction processing on the target vehicle based on the historical trajectory information and current motion status information to obtain the target vehicle's behavior prediction results; and determine the target vehicle's parking demand information based on the vehicle attribute information, operating status information, and behavior prediction results.

[0250] In an exemplary embodiment, the parking space matching module 403 is further configured to: filter a set of candidate parking spaces for a parking area from the parking space status model based on parking demand information; perform matching processing on the target vehicle and each candidate parking space in the candidate parking space set according to preset matching elements and the weight values ​​corresponding to each matching element, and obtain a matching degree score for each candidate parking space; determine the target parking space from the candidate parking space set based on the matching degree score of each candidate parking space, and obtain the target parking space information based on the target parking space.

[0251] In an exemplary embodiment, the parking space status model includes static attribute information and dynamic status information of each parking space in the parking area; wherein, the static attribute information includes location information, size information and type information; and the dynamic status information includes occupancy status information and reservation status information.

[0252] In an exemplary embodiment, the device 400 further includes: a model update module, configured to acquire image data of the parking area; perform parking space detection processing on the image data to obtain area information of each parking space in the parking area; perform state recognition processing on each parking space according to the area information of each parking space to obtain occupancy status information of each parking space; and update the parking space status model according to the occupancy status information of each parking space.

[0253] In an exemplary embodiment, the information sending module 404 is further configured to obtain road condition information corresponding to the parking area; generate main path information and alternative path information for the target vehicle based on the target parking space information, current location information and road condition information; and generate parking guidance path information based on the main path information and alternative path information.

[0254] Each module in the aforementioned parking guidance device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0255] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a parking guidance method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0256] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0257] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0258] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above-described method embodiments.

[0259] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0260] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0261] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0262] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A parking guidance method, characterized in that, The method includes: Obtain vehicle attribute information and operational status information of the target vehicle corresponding to the parking area; Based on the vehicle attribute information and the operational status information, and combined with the trajectory analysis results of the target vehicle, the parking demand information of the target vehicle is determined; When the parking demand information indicates that there is parking demand, the target vehicle is matched with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle. Based on the target parking space information and the current location information of the target vehicle, a parking guidance path information for the target vehicle is generated, and the parking guidance path information is sent to the target vehicle.

2. The method according to claim 1, characterized in that, The trajectory analysis results include the target vehicle's historical trajectory information and current motion status information; The step of determining the parking demand information of the target vehicle based on the vehicle attribute information and the operational status information, combined with the trajectory analysis results of the target vehicle, includes: Based on the historical trajectory information and the current motion state information, the target vehicle is subjected to behavior prediction processing to obtain the behavior prediction result of the target vehicle; Based on the vehicle attribute information, the operational status information, and the behavior prediction results, the parking demand information of the target vehicle is determined.

3. The method according to claim 1, characterized in that, The step of matching the target vehicle with candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area to obtain the target parking space information of the target vehicle includes: Based on the parking demand information, a set of candidate parking spaces for the parking area is selected from the parking space status model; Based on the preset matching elements and the weight values ​​corresponding to each matching element, the target vehicle is matched with each candidate parking space in the candidate parking space set to obtain the matching degree score of each candidate parking space. Based on the matching score of each candidate parking space, a target parking space is determined from the set of candidate parking spaces, and the target parking space information is obtained based on the target parking space.

4. The method according to claim 1, characterized in that, The parking space status model includes the static attribute information and dynamic status information of each parking space in the parking area; The static attribute information includes location information, size information, and type information; the dynamic status information includes occupancy status information and reservation status information.

5. The method according to claim 4, characterized in that, The method further includes: Acquire image data of the parking area; The image data is processed to detect parking spaces, thereby obtaining the area information of each parking space in the parking area; Based on the area information of each parking space, the status recognition process is performed on each parking space to obtain the occupancy status information of each parking space; The parking space status model is updated based on the occupancy status information of each parking space.

6. The method according to any one of claims 1 to 5, characterized in that, The step of generating parking guidance path information for the target vehicle based on the target parking space information and the current location information of the target vehicle includes: Obtain the road condition information corresponding to the parking area; Based on the target parking space information, the current location information, and the road condition information, the main route information and alternative route information of the target vehicle are generated; The parking guidance route information is generated based on the main route information and the alternative route information.

7. A parking guidance device, characterized in that, The device includes: The information acquisition module is used to acquire vehicle attribute information and operational status information of the target vehicles corresponding to the parking area; The information determination module is used to determine the parking demand information of the target vehicle based on the vehicle attribute information and the operating status information, combined with the trajectory analysis results of the target vehicle. The parking space matching module is used to match the target vehicle with the candidate parking spaces in the parking area based on the parking demand information and the parking space status model of the parking area when the parking demand information indicates that there is a parking demand, so as to obtain the target parking space information of the target vehicle. The information sending module is used to generate parking guidance path information for the target vehicle based on the target parking space information and the current location information of the target vehicle, and send the parking guidance path information to the target vehicle.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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