Parking space recommendation method and device, electronic equipment, medium, product and vehicle

Abstract

The invention relates to the technical field of auxiliary driving, in particular to a parking space recommendation method and device, electronic equipment, a medium, a product and a vehicle, and the method comprises the steps: obtaining the map information of a parking lot where the vehicle is located, and the position information of the vehicle, the map information comprising a plurality of parking spaces in the parking lot; according to the map information and the position information, a plurality of parking parameters are determined, the parking parameters comprise a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space and a third parameter related to the size of an aisle, and the aisle is a drivable area between the parking space and the obstacle; and determining a recommendation score of each parking space in the plurality of parking spaces based on the plurality of parking parameters. Therefore, whether each parking space is convenient to park or not is comprehensively evaluated according to multiple dimensions such as the current vehicle position, the aisle size of each parking space and the position parameter of each parking space, and the problem that parking is difficult due to the fact that the parking space closest to the vehicle serves as the recommended parking space is effectively avoided.

Description

Technical Field

[0001] This application relates to the field of driver assistance technology, specifically to a parking space recommendation method, device, electronic equipment, medium, product, and vehicle. Background Technology

[0002] In current driver assistance technology, the nearest parking space to the vehicle is usually recommended. However, in some cases, the nearest parking space may not be suitable for the vehicle to enter, making it difficult for the vehicle to park in the recommended space.

[0003] Therefore, how to obtain accurate recommended parking spaces has become a problem that needs to be solved. Summary of the Invention

[0004] This application provides a parking space recommendation method, apparatus, electronic device, medium, product, and vehicle, the method being used to solve the problem of how to obtain accurate recommended parking spaces.

[0005] In a first aspect, embodiments of this application propose a parking space recommendation method applied to an in-vehicle terminal. The method includes: acquiring map information of the parking lot where the vehicle is located, and the vehicle's location information, wherein the map information includes multiple parking spaces in the parking lot; determining multiple parking parameters based on the map information and location information, wherein the parking parameters include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the passageway, wherein the passageway is a drivable area between the parking space and an obstacle; and determining a recommendation score for each parking space among the multiple parking parameters.

[0006] Understandably, the vehicle terminal can comprehensively evaluate the ease of parking in each space based on multiple dimensions, such as the current vehicle location, the aisle size of each parking space, and the location parameters of each parking space. When two or more parking spaces are identified, the vehicle terminal can assess the accurate recommended order of the parking spaces and provide the user with the optimal recommended parking space, effectively avoiding the problem of difficulty in parking due to recommending the parking space closest to the vehicle.

[0007] In some implementations of the first aspect mentioned above, the parking parameters also include a fourth parameter related to the historical recommended score of the parking space. The historical recommended score is the recommended score of the parking space in the vehicle's historical parking process, and the fourth parameter is positively correlated with the historical recommended score.

[0008] In some embodiments, the fourth parameter can be a negative value of the historical recommendation order determined based on the historical recommendation score. For example, if there are a total of five parking spaces, and the five parking spaces are labeled to obtain parking space numbers [1,2,3,4,5], and the recommendation order obtained based on the historical recommendation score is [3,2,1,4,5], then the negative value of the recommendation order can be used as the fourth parameter. For example, the fourth parameter for the above five parking spaces can be [-3,-2,-1,-4,-5], thereby accurately representing the magnitude of the historical recommendation score.

[0009] In some implementations of the first aspect mentioned above, the third parameter includes a first aisle size parameter and a second aisle size parameter, wherein the first aisle size parameter is the size of the aisle where the vehicle is located along a first direction, the first direction being perpendicular to the entrance line of each parking space; the second aisle size parameter is the size of the aisle along a second direction, the second direction being the extension direction of the entrance line of each parking space.

[0010] Here, the aisle is the space between the obstacle and the parking space that allows vehicles to pass through, so the size of the aisle will affect whether a vehicle can be parked smoothly in the parking space.

[0011] In some implementations of the first aspect mentioned above, the method for determining the first aisle size parameter includes: determining an obstacle grid map based on map information; creating N first empty grid vectors in the obstacle grid map, the N first empty grid vectors being stacked sequentially along a first direction starting from the entrance line of each parking space, where N is a positive integer greater than or equal to 2, the first N-1 first empty grid vectors of the N first empty grid vectors matching the empty areas of the obstacle grid map, and the Nth first empty grid vector corresponding to the obstacle area of ​​the obstacle grid map; and using the dimensions of the N-1 first empty grid vectors along the first direction as the first aisle size parameter.

[0012] It is understandable that in an obstacle grid map, the grid state occupied by an obstacle can be defined as occupied, and the grid state without obstacles can be defined as free.

[0013] Therefore, by defining each grid as an empty first empty grid vector, the vehicle terminal can quickly match the grids defined as empty in the obstacle grid map, thereby efficiently determining the empty area in the first direction.

[0014] In some embodiments, the grid size of the first empty grid vector is the same as the grid size of the obstacle grid map.

[0015] In some embodiments, the vehicle terminal can use the KMP algorithm (Knuth-Morris-Pratt string search algorithm) to quickly match the first empty grid vector with the grid defined as free in the obstacle grid map. As an efficient string matching algorithm, it can avoid invalid character backtracking during text search. It reduces the number of repeated comparisons and improves matching efficiency by adjusting the position of the first empty grid vector using known partial matching information when the match fails.

[0016] In some implementations of the first aspect mentioned above, the first fully empty grid vector among the N first fully empty grid vectors covers the entrance of each parking space, and the (i+1)th first fully empty grid vector covers the ith first fully empty grid vector, where i is a positive integer that takes values ​​sequentially from 1 to N-1.

[0017] For example, the first empty grid vector covers the entrance of the parking space, and the second empty grid vector covers the first empty grid vector, thereby ensuring the length of the first empty grid vector to avoid deviating from the grid position where the obstacle is located.

[0018] In some implementations of the first aspect mentioned above, the length of the (i+1)th first empty raster vector is greater than the length of the ith first empty raster vector.

[0019] For example, the second first empty grid vector is longer than the first first empty grid vector, so that the vehicle terminal can predict sufficient single-sided aisle parking space in this way.

[0020] In some implementations of the first aspect mentioned above, the method for determining the second passageway size parameter includes: determining an obstacle grid map based on map information; creating M second empty grid vectors in the obstacle grid map, the M second empty grid vectors being stacked sequentially along a second direction starting from the entrance of the parking space, where M is a positive integer greater than or equal to 2, the first M-1 second empty grid vectors of the M second empty grid vectors matching the empty areas of the obstacle grid map, and the Mth second empty grid vector corresponding to the obstacle area of ​​the obstacle grid map; and using the dimensions of the M-1 second empty grid vectors along the second direction as the second passageway size parameter.

[0021] It is understandable that the vehicle terminal can quickly match the grids defined as empty in the obstacle grid map by defining each grid as an empty second empty grid vector, thereby efficiently determining the empty area in the second direction.

[0022] In some embodiments, the grid size of the second empty grid vector is the same as the grid size of the obstacle grid map.

[0023] In some embodiments, the vehicle terminal can use the KMP algorithm to quickly match the second empty grid vector with the grid defined as free in the obstacle grid map. As an efficient string matching algorithm, it can avoid invalid character backtracking during text search. By adjusting the position of the second empty grid vector using known partial matching information when the match fails, it reduces the number of repeated comparisons and improves matching efficiency.

[0024] In some implementations of the first aspect mentioned above, the recommended score for each parking space among multiple parking spaces is determined based on multiple parking parameters, including: determining the recommended score for each parking space using the superior-inferiority distance method based on multiple parking parameters.

[0025] It is understandable that the superior-inferiority distance method mainly uses the evaluation index formed by multiple parking parameters of each parking space to accurately reflect the difference in the superiority and inferiority of each parking space for parking schemes, thereby obtaining the recommended score for each parking space.

[0026] In some implementations of the first aspect mentioned above, the superior-inferiority distance method is used to determine the recommended score for each parking space among multiple parking spaces based on multiple parking parameters. This includes: determining the parameter type of each parking parameter based on the influence of multiple parking parameters on the parking difficulty of the vehicle; converting the values ​​of the parking parameters from their original values ​​to standard values ​​based on the parameter type; constructing a maximum value vector, where the j-th element of the maximum value vector is the maximum value among the standard values ​​of the j-th parking parameter of the multiple parking spaces; constructing a minimum value vector, where the k-th element of the minimum value vector is the minimum value among the standard values ​​of the k-th parking parameter of the multiple parking spaces; determining the first distance between the parking parameters of each parking space and the maximum value vector; determining the second distance between the parking parameters of each parking space and the minimum value vector; and determining the recommended score for each parking space based on the first and second distances.

[0027] Among them, the maximum value vector can be a vector composed of the maximum values ​​of each parking parameter, and the minimum value vector can be a vector composed of the minimum values ​​of each parking parameter.

[0028] In some embodiments, the first distance and the second distance can be determined based on Euclidean distance.

[0029] In some implementations of the first aspect mentioned above, the parameter types include extremely large parameters and interval parameters, wherein the first and third parameters are extremely large parameters, and the second parameter is an interval parameter.

[0030] It is understandable that the larger the value of a very large parameter, the better, while the optimal value of an interval parameter is within a certain interval.

[0031] In some embodiments, the value of the fourth parameter, determined based on historical recommendation scores, is also better the larger it is; therefore, the fourth parameter can be an extremely large parameter.

[0032] In some implementations of the first aspect mentioned above, the value of the parking parameter is converted from its original value to a standard value according to the parameter type of the parking parameter, including: if the parking parameter is a very large parameter, the maximum value between the original value and the second preset value of the parking parameter is taken as the standard value of the parking parameter; if the parking parameter is an interval parameter, the standard value of the parking parameter is determined according to the distance between the parking parameter and the first preset interval.

[0033] It's understandable that, to facilitate subsequent normalization of all parking parameters, the values ​​for all parameter types can be converted from their original values ​​to standard values ​​based on the parameter type. Parking parameters under standard values ​​have a consistent impact on parking difficulty. For example, an upper limit (corresponding to a second preset value) can be set for the values ​​of extremely large parameters, and the maximum value between the original and second preset values ​​can be used as the standard value for the parking parameter. Similarly, interval-type parameters can be positively normalized to extremely large parameters based on the distance between the parking parameter and a first preset interval, thus determining the standard value for the parking parameter.

[0034] In some implementations of the first aspect above, determining the standard value of the parking parameter according to the distance between the parking parameter and the first preset interval includes: if the original value of the parking parameter is located within the first preset interval, then the standard value of the parking parameter is determined as a third preset value; if the original value of the parking parameter is less than the starting point value of the first preset interval, then the standard value of the parking parameter is determined as the difference between the third preset value and a first correction amount, the first correction amount being determined based on the difference between the starting point value of the interval and the original value of the parking parameter; if the original value of the parking parameter is greater than the ending point value of the first preset interval, then the standard value of the parking parameter is determined as the difference between the third preset value and a second correction amount, the second correction amount being determined based on the difference between the original value of the parking parameter and the ending point value of the interval.

[0035] The first preset interval is the optimal interval for the interval-type parameters. The closer the parking parameters are to the first preset interval, the larger the standard value of the parking parameters will be.

[0036] It can be understood that the vehicle terminal converts the original value of the parking parameter that is smaller than the starting value of the first preset interval into the difference between the second preset value and the first correction amount, and converts the original value of the parking parameter that is larger than the ending value of the first preset interval into the difference between the second preset value and the second correction amount. This can convert the original value of the parking parameter that deviates more from the first preset interval into a smaller standard value, so as to achieve the positive processing of converting the interval type parameter into an extremely large parameter.

[0037] In some implementations of the first aspect mentioned above, the first parameter includes the first parking space width and the second parking space width, wherein the first parking space width is determined based on the parking space line, and the second parking space width is determined based on the obstacles on both sides of the parking space.

[0038] In some implementations of the first aspect mentioned above, the second parameter includes a first distance parameter and a second distance parameter, wherein the first distance parameter is the distance between the parking space and the vehicle along a first direction, the second distance parameter is the distance between the parking space and the vehicle along a second direction, the first direction is the extension direction of the entrance line of the parking space, and the second direction is perpendicular to the extension direction of the entrance line of the parking space.

[0039] Secondly, this application also provides a parking space recommendation device applied to an in-vehicle terminal. The device comprises a map acquisition module, a parameter acquisition module, and a data processing module. The map acquisition module acquires map information of the parking lot where the vehicle is located, as well as the vehicle's location information. The map information includes multiple parking spaces in the parking lot. The parameter acquisition module determines multiple parking parameters based on the map information and location information. The parking parameters include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the passageway, wherein the passageway is a drivable area between the parking space and an obstacle. The data processing module determines a recommendation score for each parking space based on the multiple parking parameters.

[0040] Thirdly, embodiments of this application also provide an electronic device, including: one or more processors; one or more memories; the one or more memories storing one or more programs, which, when executed by one or more processors, cause the electronic device to perform the parking space recommendation method proposed in the first aspect and various implementations thereof.

[0041] Fourthly, embodiments of this application also provide a computer-readable medium storing instructions that, when executed on a machine, cause the machine to perform the parking space recommendation method proposed in the first aspect and various implementations thereof.

[0042] Fifthly, embodiments of this application also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the parking space recommendation method proposed in the first aspect and various implementations thereof.

[0043] Sixthly, embodiments of this application also provide a vehicle, which includes the electronic equipment proposed in the third aspect above.

[0044] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be referred to the first aspect and the beneficial effects of various implementations of the first aspect, which will not be elaborated here.

[0045] The technical solution provided in this application has at least the following beneficial effects:

[0046] The vehicle terminal can comprehensively evaluate the ease of parking in each space based on multiple dimensions, such as the current vehicle location, the aisle size of each parking space, and the location parameters of each parking space. When two or more parking spaces are identified, the vehicle terminal can evaluate the accurate recommended order of the parking spaces and provide the user with the optimal recommended parking space, effectively avoiding the problem of difficulty in parking due to recommending the parking space closest to the vehicle. Attached Figure Description

[0047] Figure 1 A schematic diagram of a recommended parking space scenario is shown;

[0048] Figure 2 A schematic diagram illustrating the implementation process of a parking space recommendation method according to some embodiments of this application is shown;

[0049] Figure 3 A schematic diagram of a first parking space width according to some embodiments of this application is shown;

[0050] Figure 4 A schematic diagram of a second parking space width according to some embodiments of this application is shown;

[0051] Figure 5 A schematic diagram of a first distance parameter proposed according to some embodiments of this application is shown;

[0052] Figure 6 A schematic diagram of a second distance parameter proposed according to some embodiments of this application is shown;

[0053] Figure 7 A schematic diagram of another second distance parameter proposed according to some embodiments of this application is shown;

[0054] Figure 8 A schematic diagram of a first aisle dimension parameter according to some embodiments of this application is shown;

[0055] Figure 9 A schematic diagram of a second aisle dimension parameter according to some embodiments of this application is shown;

[0056] Figure 10 A schematic diagram of a coordinate system according to some embodiments of this application is shown;

[0057] Figure 11A schematic diagram of a fully empty raster vector according to some embodiments of this application is shown;

[0058] Figure 12 A schematic diagram illustrating another method for obtaining the first aisle dimension parameters according to some embodiments of this application is shown;

[0059] Figure 13 A schematic diagram illustrating yet another method for obtaining second aisle dimension parameters according to some embodiments of this application is shown;

[0060] Figure 14 A schematic diagram of the frame structure of a parking space recommendation device according to some embodiments of this application is shown;

[0061] Figure 15 This diagram illustrates the structure of an electronic device according to some embodiments of the present application. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0063] Figure 1 A schematic diagram of a recommended parking space scenario is shown.

[0064] refer to Figure 1 As vehicle 01 travels along trajectory G1, it can identify the first pillar Z1, the first parking space P1, the second parking space P2, the third parking space P3, the second pillar Z2, the third pillar Z3, the fourth parking space P4, the fifth parking space P5, and the fourth pillar Z4 in the surrounding environment based on perception information.

[0065] Understandably, vehicle 01 will usually select the nearest parking space, such as the fifth parking space P5, as the best recommended parking space.

[0066] However, parking spaces P4 and P5 are relatively narrow for vehicle 01, making parking difficult. Therefore, recommending the fifth parking space P5, which is closest to the vehicle, as the best parking space is not accurate.

[0067] Therefore, to address the aforementioned problem of how to obtain accurate recommended parking spaces, this application proposes a parking space recommendation method. The method includes: obtaining map information of the parking lot where the vehicle is located, and the vehicle's location information; the map information includes multiple parking spaces in the parking lot; determining multiple parking parameters based on the map information and location information; the parking parameters include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the distance between the parking space and obstacles; and determining a recommendation score for each parking space based on the multiple parking parameters.

[0068] The technical solution provided in this application has at least the following beneficial effects:

[0069] The system comprehensively evaluates the ease of parking for each space based on multiple dimensions, including the current vehicle location, the aisle dimensions of each parking space, and the location parameters of each parking space. When two or more parking spaces are identified, the onboard terminal can determine the accurate recommended order of the parking spaces and provide the user with the optimal recommended parking space. This effectively avoids the problem of difficulty in parking due to recommending the parking space closest to the vehicle.

[0070] The following describes in detail, with reference to the accompanying drawings, a parking space recommendation method proposed in this application.

[0071] Figure 2 The illustration shows a schematic diagram of the implementation process of a parking space recommendation method according to some embodiments of this application.

[0072] refer to Figure 2 The entity responsible for implementing this process can be the vehicle-mounted terminal, which will not be elaborated here for the sake of simplicity.

[0073] For example, the implementation process includes the following steps:

[0074] S201, obtain map information of the parking lot where the vehicle is located, as well as the vehicle's location information. The map information includes multiple parking spaces in the parking lot.

[0075] For example, the vehicle terminal can construct a map of the parking lot based on the sensor parameters fed back by the sensors on the vehicle, and then obtain the map information of the parking lot and the vehicle's location information. The map information may include multiple parking spaces in the parking lot and can represent the location and outline information of each parking space.

[0076] It is understood that the sensors used by vehicles to acquire map information may include visual sensors (such as cameras), radar sensors (such as millimeter-wave radar, lidar, etc.), ultrasonic sensors, infrared sensors, inertial measurement units (IMUs), global positioning system (GPS) / global navigation satellite system (GNSS) receivers, and wheel speed sensors.

[0077] Among them, the visual sensor can capture multi-view images of the parking lot around the vehicle, the radar sensor (which can provide three-dimensional point cloud data) and the ultrasonic sensor can obtain the distance between the vehicle and targets such as obstacles in the parking lot around the vehicle, the infrared sensor can detect the distance between the vehicle and the heat-generating target, and the wheel speed sensor can detect the rotation speed of the vehicle's wheels to infer the vehicle's speed and mileage.

[0078] Based on the information acquired by the aforementioned sensors, the vehicle-mounted terminal can construct a vehicle coordinate system with the vehicle (e.g., the center point of the rear axle) as the origin. Within this coordinate system, it can mark the positions of all surrounding obstacles based on the acquired sensor parameters, thereby constructing a map of the parking area where the vehicle is located. For example, referring to the example above... Figure 1 The vehicle-mounted terminal can use vehicle 01 as the origin of the coordinate system to mark the locations of different parking spaces and different pillars in the surrounding area.

[0079] In some embodiments, the vehicle terminal may construct a grid map to quickly determine multiple parking parameters using map tiles divided into cells (corresponding to grids). These parameters may include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the aisle, where the aisle is the drivable area between the parking space and an obstacle. The cell size may be a predefined length and width, such that the size of each grid corresponds to that cell. Therefore, the corresponding parameters (such as the aforementioned first, second, or third parameter) can be determined based on the number of grids.

[0080] As is understandable, the specific implementation process of quickly determining parking parameters using grid maps will be explained in detail below with relevant accompanying figures. For the sake of brevity, it will not be elaborated here.

[0081] S202. Based on map information and location information, determine multiple parking parameters, including a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the passageway, wherein the passageway is the drivable area between the parking space and the obstacle.

[0082] For example, the first parameter related to the size of the parking space includes a first parking space width and a second parking space width, wherein the first parking space width is determined based on the parking space lines and the second parking space width is determined based on the obstacles on both sides of the parking space.

[0083] Figure 3 A schematic diagram of a first parking space width according to some embodiments of this application is shown.

[0084] refer to Figure 3 The parking space is defined by the entrance line A1 and the parking space outline A2. The width W1 of the parking space outline A2 along the extension direction of the entrance line A1 is the first width of the parking space.

[0085] Figure 4 A schematic diagram of a second parking space width according to some embodiments of this application is shown.

[0086] refer to Figure 4 ,exist Figure 3 Based on the example parking space, a first obstacle B1 and a second obstacle B2 are set on both sides of the parking space. The distance (e.g., the minimum distance) between the first obstacle B1 and the second obstacle B2 along the extension direction of the entrance line A1 of the parking space can be the width of the parking space.

[0087] For example, the second parameter related to the distance between the vehicle and the parking space includes a first distance parameter and a second distance parameter. The first distance parameter is the distance between the vehicle and the parking space along a first direction, and the second distance parameter is the distance between the vehicle and the parking space along a second direction. The first direction is perpendicular to the extension direction of the entrance line A1 of the parking space, and the second direction is the extension direction of the entrance line A1 of the parking space.

[0088] Figure 5 A schematic diagram of a first distance parameter proposed according to some embodiments of this application is shown.

[0089] refer to Figure 5 The first distance parameter can be the distance Y1 between the rear axle center point 011 of vehicle 01 and the entrance line A1 of the parking space along the Y direction. The extension direction of the entrance line A1 of the parking space is taken as the X direction (as an example of the second direction), and the Y direction (as an example of the first direction) is perpendicular to the extension direction of the entrance line A1 of the parking space.

[0090] Understandable. Figure 5The example parking space is a perpendicular parking space. The first distance parameter is determined based on the entrance line A1 of the parking space and the extension direction of the entrance line A1. This can avoid the influence of the different shapes of parking space contours under different parking space types on the first distance parameter. For example, the entrance line A1 of the parking space can be determined relatively fixed for angled parking spaces, horizontal parking spaces, or perpendicular parking spaces, and the extension direction of the entrance line A1 of the parking space can be easily determined without being disturbed by the extension direction and shape of the parking space contour A2 (for example, the parking space contour in the width direction of angled parking spaces is usually a diagonal line). This can effectively improve the reliability of the first distance parameter.

[0091] Figure 6 A schematic diagram of a second distance parameter proposed according to some embodiments of this application is shown.

[0092] refer to Figure 6 The X direction is taken as the extension direction of the entrance line A1 of the parking space (as an example of the second direction), and the Y direction (as an example of the first direction) is perpendicular to the extension direction of the entrance line of the parking space. The second distance parameter can be the distance X1 along the X direction between the rear axle center point 011 of vehicle 01 and the midpoint of the entrance line A1 of the parking space.

[0093] In other embodiments, the second distance parameter can be predicted and determined during vehicle movement.

[0094] Figure 7 A schematic diagram of another second distance parameter proposed according to some embodiments of this application is shown.

[0095] refer to Figure 7 If vehicle 01 is in motion, the second distance parameter can be predicted based on the sum of the ideal distance value L_s and the driving compensation value L_e.

[0096] Among them, the ideal distance value L_s can be preset based on parameters such as the model of vehicle 01, and the driving compensation value L_e can be determined based on the driving speed value of vehicle 01. For example, it can be the square of the driving speed value. Braking compensation is added between the midpoint of vehicle 01 and the entrance line A1 of the parking space to ensure the stability of the recommended parking space order after the vehicle stops.

[0097] For example, the third parameter related to the aisle size may include a first aisle size parameter and a second aisle size parameter, wherein the first aisle size parameter is the size of the aisle along a first direction, which is perpendicular to the entrance line of each parking space; and the second aisle size parameter is the size of the aisle along a second direction, which is the extension direction of the entrance line of each parking space.

[0098] Here, the aisle is the drivable area between the parking space and the obstacle, so the size of the aisle will affect whether the vehicle can be parked smoothly in the parking space.

[0099] Figure 8 A schematic diagram of a first aisle dimension parameter according to some embodiments of this application is shown.

[0100] refer to Figure 8 Taking the extension direction of the parking space entrance line A1 as the X direction (as an example of the second direction), and the Y direction (as an example of the first direction) perpendicular to the extension direction of the parking space entrance line A1, the third obstacle B3 is located in the Y direction of the parking space. The first aisle dimension parameter is the perpendicular distance W3 between the third obstacle B3 and the entrance line A1 along the Y direction.

[0101] Figure 9 A schematic diagram of a second passageway dimension parameter proposed according to some embodiments of this application is shown.

[0102] refer to Figure 9 Taking the extension direction of the parking space entrance line A1 as the X direction (as an example of the second direction), and the Y direction (as an example of the first direction) perpendicular to the extension direction of the parking space entrance line A1, the fourth obstacle B4 is located on one side of the parking space. The second aisle dimension parameter is the distance L1 between the fourth obstacle B4 and the entrance line A1 along the X direction.

[0103] In some embodiments, the parking parameters further include a fourth parameter related to the historical recommended score of the parking space, which is positively correlated with the historical recommended score of the parking space, where the historical recommended score is the recommended score of the parking space in the vehicle's historical parking process.

[0104] In some embodiments, the fourth parameter can be a negative value of the historical recommendation order determined based on the historical recommendation score. For example, if there are a total of five parking spaces, and the five parking spaces are labeled to obtain parking space numbers [1,2,3,4,5], and the recommendation order obtained based on the historical recommendation score is [3,2,1,4,5], then the negative value of the recommendation order can be used as the fourth parameter. For example, the fourth parameter for the above five parking spaces can be [-3,-2,-1,-4,-5], thereby accurately representing the magnitude of the historical recommendation score.

[0105] It is understandable that accurately and quickly obtaining the first and second aisle dimension parameters is quite difficult in parking scenarios. Therefore, this application proposes a specific implementation method for quickly obtaining the first and second aisle dimension parameters.

[0106] The following detailed description, in conjunction with the accompanying drawings, details the specific implementation method for quickly obtaining the first and second aisle dimension parameters.

[0107] Figure 10A schematic diagram of a coordinate system proposed according to some embodiments of this application is shown.

[0108] Figure 11 A schematic diagram of a fully empty raster vector according to some embodiments of this application is shown.

[0109] Figure 12 A schematic diagram illustrating another method for obtaining the first aisle dimension parameters according to some embodiments of this application is shown.

[0110] Figure 13 A schematic diagram is shown, illustrating yet another method for obtaining second aisle dimension parameters according to some embodiments of this application.

[0111] refer to Figure 10 For ease of description, a coordinate system is constructed with one corner of the parking space as the origin. The extension direction of the parking space entrance line A1 is taken as the X direction (as an example of the second direction), and the Y direction (as an example of the first direction) is perpendicular to the extension direction of the parking space entrance line A1.

[0112] In some embodiments of this application, in order to quickly determine the size parameters of the first aisle and the second aisle, an obstacle grid map corresponding to the parking lot can be determined based on map information, and an empty area can be matched in the obstacle grid map using an empty grid vector. The empty area is an area without obstacles. Thus, the size parameters of the first aisle can be quickly determined based on the superposition size of the empty grid vector in the first direction, and the size parameters of the second aisle can be quickly determined based on the superposition size of the empty grid vector in the second direction.

[0113] refer to Figure 11 A fully empty grid can be a one-dimensional vector composed of multiple fully empty attribute grids. The width of each grid is a preset width, and the length of each grid is a preset length. The length and width of each grid can be freely set according to the requirements.

[0114] For example, the method for determining the size parameter of the first passageway includes: determining the obstacle grid map corresponding to the parking lot based on map information; creating N first empty grid vectors in the obstacle grid map, the N first empty grid vectors being stacked sequentially along a first direction starting from the entrance line of each parking space, where N is a positive integer greater than or equal to 2, the first N-1 first empty grid vectors of the N first empty grid vectors matching the empty area of ​​the obstacle grid map, and the Nth first empty grid vector corresponding to the obstacle area of ​​the obstacle grid map; and using the size of the N-1 first empty grid vectors along the first direction as the size parameter of the first passageway.

[0115] It is understandable that in an obstacle grid map, the grid state occupied by an obstacle can be defined as occupied, and the grid state without obstacles can be defined as free.

[0116] Therefore, by defining each grid as an empty first empty grid vector, the vehicle terminal can quickly match the grids defined as empty in the obstacle grid map, thereby efficiently determining the empty area in the first direction.

[0117] In some embodiments, the grid size of the first empty grid vector is the same as the grid size of the obstacle grid map.

[0118] In some embodiments, the vehicle terminal can use the KMP algorithm (Knuth-Morris-Pratt string search algorithm) to quickly match the first empty grid vector with the grid defined as free in the obstacle grid map. As an efficient string matching algorithm, it can avoid invalid character backtracking during text search. It reduces the number of repeated comparisons and improves matching efficiency by adjusting the position of the first empty grid vector using known partial matching information when the match fails.

[0119] refer to Figure 12 N first empty grid vectors are stacked sequentially along the Y direction (an example of the first direction) starting from the entrance line A1 of each parking space. By iteratively matching the first empty grid vectors along the Y direction, the empty area between the obstacle and the entrance line A1 can be determined. The size parameters of the first passage can be determined based on the length of the N-1 first empty grid vectors along the Y direction.

[0120] To avoid deviating from the grid position of the obstacle, a preset number of grids can be added to both ends of the first empty grid vector as flexible space (e.g., Figure 12 (The example dashed box corresponds to the elastic space).

[0121] In some embodiments, the first empty grid vector of the N first empty grid vectors covers the entrance of each parking space, and the (i+1)th first empty grid vector covers the ith first empty grid vector, where i is a positive integer that takes values ​​sequentially from 1 to N-1.

[0122] Continue to refer to Figure 12 The first empty grid vector covers the entrance of the parking space, and the second empty grid vector covers the first empty grid vector, thereby ensuring the length of the first empty grid vector to avoid deviating from the grid position where the obstacle is located.

[0123] In other embodiments, the length of the (i+1)th first empty raster vector is greater than the length of the ith first empty raster vector.

[0124] For example, refer to Figure 12 The second first empty grid vector is longer than the first first empty grid vector, so the vehicle terminal can predict sufficient single-sided aisle parking space in this way.

[0125] In some embodiments, the determination of the second aisle size parameter includes: determining an obstacle grid map corresponding to the parking space based on map information; creating M second empty grid vectors in the obstacle grid map, wherein the M second empty grid vectors are stacked sequentially along a second direction starting from the entrance of the parking space, where M is a positive integer greater than or equal to 2, the first M-1 second empty grid vectors of the M second empty grid vectors match the empty areas of the obstacle grid map, and the Mth second empty grid vector corresponds to the obstacle area of ​​the obstacle grid map; and using the dimensions of the M-1 second empty grid vectors along the second direction as the second aisle size parameter.

[0126] Similarly, by defining each grid as an empty second empty grid vector, the vehicle terminal can quickly match the grids defined as empty in the obstacle grid map, thereby efficiently determining the empty area in the second direction.

[0127] In some embodiments, the grid size of the second empty grid vector is the same as the grid size of the obstacle grid map.

[0128] refer to Figure 13 M second empty grid vectors are stacked sequentially along the X direction (an example of the second direction) starting from the entrance of each parking space. By iteratively matching the second empty grid vectors along the X direction, the empty area between the obstacle and the parking space outline A2 can be determined. The second aisle size parameters can be determined based on the length of M-1 second empty grid vectors along the Y direction.

[0129] In some embodiments, the vehicle terminal can use the KMP algorithm to quickly match the second empty grid vector with the grid defined as free in the obstacle grid map. As an efficient string matching algorithm, it can avoid invalid character backtracking during text search. By adjusting the position of the second empty grid vector using known partial matching information when the match fails, it reduces the number of repeated comparisons and improves matching efficiency.

[0130] To avoid deviating from the grid position of the obstacle, a preset number of grids can be added to both ends of the second empty grid vector as flexible space (e.g., Figure 13 (The example dashed box corresponds to the elastic space).

[0131] Furthermore, based on the multiple parking parameters determined by the above method, the vehicle terminal can evaluate the ease of parking in each parking space from multiple dimensions, and can arrange the parking spaces in a recommended order for users to choose from.

[0132] S203 determines the recommended score for each parking space among multiple parking spaces based on multiple parking parameters.

[0133] For example, the vehicle terminal can use multiple parking parameters as evaluation indicators to determine the recommended score for each parking space among multiple parking spaces.

[0134] In some embodiments, the vehicle terminal can determine the recommended score for each parking space based on multiple parking parameters using the technique for order preference by similarity to ideal solution (TOPSIS).

[0135] The superior-inferiority distance method mainly uses the evaluation index formed by multiple parking parameters of each parking space to accurately reflect the difference in the superiority and inferiority of each parking space for parking schemes, thereby obtaining the recommended score for each parking space.

[0136] For example, the superior-inferiority distance method is used to determine the recommended score for each parking space among multiple parking spaces based on multiple parking parameters. This includes: determining the parameter type of each parking parameter based on its impact on the parking difficulty; converting the values ​​of the parking parameters from their original values ​​to standard values ​​based on their parameter types; constructing a maximum value vector, where the j-th element of the maximum value vector is the maximum value among the standard values ​​of the j-th parking parameter for multiple parking spaces; constructing a minimum value vector, where the k-th element of the minimum value vector is the minimum value among the standard values ​​of the k-th parking parameter for multiple parking spaces; determining the first distance between the parking parameters of each parking space and the maximum value vector; determining the second distance between the parking parameters of each parking space and the minimum value vector; and determining the recommended score for each parking space based on the first and second distances.

[0137] It's understandable that parking parameters have varying impacts on parking difficulty. For example, higher values ​​for some parameters result in easier parking, while other parameters within a certain range minimize parking difficulty, and still others have a specific value that minimizes parking difficulty. Therefore, the onboard terminal can categorize parking parameters based on their impact on parking difficulty. Then, it can standardize the parking parameters under different parameter types (e.g., by forwarding) to facilitate the subsequent unified normalization of all parking parameters and eliminate the influence of the dimensions of each parking parameter on the rating of each parking space.

[0138] For example, the parameter type may include extremely large parameters and interval parameters, wherein the first parameter related to the size of the parking space and the third parameter related to the distance between the parking space and the obstacle are extremely large parameters, and the second parameter related to the distance between the vehicle and the parking space is an interval parameter.

[0139] It is understandable that the larger the value of a very large parameter, the better, while the optimal value of an interval parameter is within a certain interval.

[0140] In some embodiments, the value of the fourth parameter, determined based on historical recommendation scores, is also better the larger it is; therefore, the fourth parameter can be an extremely large parameter.

[0141] To facilitate subsequent normalization of all parking parameters, the values ​​for all parameter types can be converted from their original values ​​to standard values ​​based on the parameter type. Parking parameters under standard values ​​have a consistent impact on parking difficulty. For example, an upper limit (corresponding to a second preset value) can be set for the values ​​of extremely large parameters, and the maximum value between the original and second preset values ​​can be used as the standard value for the parking parameter. Similarly, interval-type parameters can be positively normalized to extremely large parameters based on the distance between the parking parameter and a first preset interval, thus determining the standard value for the parking parameter.

[0142] For example, according to the parameter type of the parking parameter, the value of the parking parameter is converted from the original value to the standard value, including: if the parking parameter is a very large parameter, then the maximum value between the original value of the parking parameter and the first preset value is taken as the standard value of the parking parameter; if the parking parameter is an interval parameter, then the standard value of the parking parameter is determined according to the distance between the parking parameter and the first preset interval, and can be expressed by the following formula (1):

[0143] Formula (1);

[0144] Among them, parking parameters For extremely large parameters, The first preset value, This is the converted standard value. It can be understood that this first preset value... That is, extremely large parameters The upper limit will be greater than the first preset value. raw value All values ​​are replaced with this first preset value to convert the values ​​of all parking parameters of the extremely large parameter type to standard values. .

[0145] The first preset interval is the optimal interval for interval parameters. The closer the parking parameters are to the first preset interval, the larger the standard value of the parking parameters will be.

[0146] In some embodiments of this application, determining the standard value of the parking parameter according to the distance between the parking parameter and the first preset interval includes: if the original value of the parking parameter is located within the first preset interval, then the standard value of the parking parameter is determined as a second preset value; if the original value of the parking parameter is less than the starting point value of the first preset interval, then the standard value of the parking parameter is determined as the difference between the second preset value and a first correction amount, wherein the first correction amount is determined based on the difference between the starting point value of the interval and the original value of the parking parameter; if the original value of the parking parameter is greater than the ending point value of the first preset interval, then the standard value of the parking parameter is determined as the difference between the second preset value and a second correction amount, wherein the second correction amount is determined based on the difference between the original value of the parking parameter and the ending point value of the interval.

[0147] It can be understood that the first correction amount and the second correction amount can characterize the deviation between the original value of the parking parameter and the first preset range. The greater the deviation, the smaller the standard value obtained after conversion.

[0148] In some embodiments, the vehicle terminal can determine a first difference between the maximum original value among all interval parameters and the endpoint value of the first preset interval, and determine a second difference between the minimum original value among all interval parameters and the starting value of the first preset interval, and take the larger of the first difference and the second difference as the maximum deviation.

[0149] It can be understood that the maximum deviation is the farthest deviation of all interval-type parameters among all parking parameters from the first preset interval, which can be used to improve the accuracy of the first correction amount and the second correction amount.

[0150] For example, the vehicle terminal can use the ratio between the difference between the starting value of the interval and the original value of the parking parameters and the maximum deviation as the first correction amount.

[0151] Similarly, the vehicle terminal can use the ratio between the difference between the original value of the parking parameters and the end value of the interval, and the maximum deviation, as the second correction amount.

[0152] Therefore, we can obtain the following formulas (2) and (3):

[0153] Formula (2);

[0154] Formula (3);

[0155] in, The starting value of the interval. The endpoint value of the interval. This represents the minimum value of the current parking parameters. The maximum value of the current parameter. The maximum deviation, These are the original values ​​for the parking parameters. The first difference, The second difference is used; the first difference is taken. Second difference The larger value is taken as the maximum deviation, and the first correction amount can be obtained. and the second correction amount , These are the standard values ​​for the parking parameters obtained after conversion.

[0156] Therefore, by converting the original value of the parking parameter that is smaller than the starting point value of the first preset interval into the difference between the second preset value and the first correction amount, and converting the original value of the parking parameter that is larger than the ending value of the first preset interval into the difference between the second preset value and the second correction amount, the vehicle terminal can convert the original value of the parking parameter that deviates more from the first preset interval into a smaller standard value, so as to achieve the positive processing of converting the interval type parameter into an extremely large parameter.

[0157] In some embodiments, if the starting value and the ending value of the first preset interval are the same value (corresponding to the median value), then the interval parameter can be called the median parameter. Based on formulas (2) and (3) for the interval parameter, the following formulas (4) and (5) can be derived to convert the median parameter into a standard value:

[0158] Formula (4);

[0159] Formula (5);

[0160] in, This is the minimum value among the parking parameters. This is the maximum value among the parking parameters. If the median value is taken, then the minimum value can be taken. With the median value The difference and the maximum value With the median value The larger value among the differences is taken as the maximum deviation. , These are the original values ​​for the parking parameters. The standard value after converting parking parameters can be adjusted if it deviates too much from the median value. The original values ​​of parking parameters are converted into smaller standard values ​​to achieve a positive transformation of intermediate parameters into extremely large parameters.

[0161] Furthermore, the vehicle-mounted terminal can construct a maximum value vector, which can be used to characterize the ideal result among each parking parameter; and construct a minimum value vector, which can be used to characterize the negative ideal result among each parking parameter. Then, the vehicle-mounted terminal can determine a first distance between the parking parameters of each parking space and the maximum value vector, used to characterize the degree of difference between the parking parameters of each parking space and the ideal result, and determine a second distance between the parking parameters of each parking space and the minimum value vector, used to characterize the degree of difference between the parking parameters of each parking space and the negative ideal result, thereby determining the recommended score for each parking space based on the first and second distances.

[0162] In other embodiments, before constructing the maximum and minimum value vectors, the vehicle terminal can normalize the parking parameters that have been converted to standard values ​​to remove the influence of dimensions, thereby reducing the amount of data processing and improving data processing efficiency.

[0163] In some embodiments, the vehicle terminal can list all parking parameters in matrix form for normalization, for example, using the following formula (6):

[0164] Formula (6);

[0165] Where x is the standard value of the converted parking parameters, n is the number of parking spaces, and m is the number of parking parameters.

[0166] The matrix in formula (6) is normalized using the following formula (7):

[0167] Formula (7);

[0168] Based on formula (7), normalization formula (6) can be used to obtain the normalized formula (8):

[0169] Formula (8);

[0170] Then the maximum value vector It can be a vector composed of the maximum values ​​in each row of parking parameters, as shown in the following formula (9):

[0171] Formula (9);

[0172] Minimum value vector It can be a vector formed by the minimum value of each parking parameter, as shown in the following formula (10):

[0173] Formula (10);

[0174] In some embodiments, the first distance and the second distance can be determined based on Euclidean distance.

[0175] It is understandable that the vehicle terminal can calculate the maximum distance for each parking space using the following formula (11) and the minimum distance using the following formula (12):

[0176] Formula (11);

[0177] Formula (12);

[0178] in, Let the distance be the first distance to the p-th parking space. The second distance is the distance to the p-th parking space. Let q be the parking parameters corresponding to the p-th parking space. This refers to the q-th parking parameter out of a total of m parking parameters in the maximum value vector. Let p be the q-th parking parameter out of a total of m parking parameters in the maximum value vector, where p is a positive integer less than or equal to n and q is a positive integer less than or equal to m.

[0179] In some embodiments of this application, determining the recommended score for each parking space based on the first distance and the second distance includes: using the ratio of the second distance to the sum of the first distance and the second distance as the recommended score for each parking space.

[0180] For example, the recommended score for each parking space can be obtained using the following formula (13):

[0181] Formula (13);

[0182] in, Let p be the recommended score for the p-th parking space. Let the distance be the first distance to the p-th parking space. Let be the second distance to the p-th parking space, where p is a positive integer less than or equal to n.

[0183] It is understood that through the above steps S201 to S203, the embodiments of this application can comprehensively evaluate whether each parking space is convenient to park based on multiple dimensions such as the current vehicle position, the position of the surrounding obstacles of each parking space, the position parameters of each parking space, and the historical recommended order of parking spaces. When two or more parking spaces are identified, the vehicle terminal can evaluate the accurate recommended order of parking spaces, provide the user with the optimal recommended parking space, and provide the recommended order of parking spaces.

[0184] Figure 14A schematic diagram of the frame structure of a parking space recommendation device according to some embodiments of this application is shown.

[0185] refer to Figure 14 The parking space recommendation device 1400 can be applied to an in-vehicle terminal. The parking space recommendation device 1400 includes a map acquisition module 1401, a parameter acquisition module 1402, and a data processing module 1403.

[0186] The map acquisition module 1401 is used to acquire map information of the parking lot where the vehicle is located, as well as the vehicle's location information. The map information includes multiple parking spaces in the parking lot.

[0187] The parameter acquisition module 1402 is used to determine multiple parking parameters based on map information and location information. The parking parameters include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the passageway. The passageway is the drivable area between the parking space and the obstacle.

[0188] The data processing module 1403 is used to determine the recommended score for each parking space among multiple parking spaces based on multiple parking parameters.

[0189] It is understandable that the specific implementation process of the map acquisition module 1401 can refer to the specific implementation process exemplified in step S201 above, and will not be repeated here.

[0190] It is understood that the specific implementation process of the parameter acquisition module 1402 can be referred to the specific implementation process exemplified in step S202 above, and will not be repeated here.

[0191] It is understood that the specific implementation process of the data processing module 1403 can be referred to the specific implementation process exemplified in step S203 above, and will not be repeated here.

[0192] According to the parking space recommendation method provided in the embodiments of this application, this application also provides an electronic device, which includes: one or more processors; one or more memories; the one or more memories storing one or more programs, which, when executed by one or more processors, cause the electronic device to execute the parking space recommendation method in any of the above embodiments.

[0193] In some embodiments, the vehicle terminal exemplified above may be this electronic device.

[0194] According to the parking space recommendation method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to implement the steps executed by the electronic device in any of the above embodiments.

[0195] According to the parking space recommendation method provided in the embodiments of this application, this application also provides a computer-readable medium storing program code, which, when run on a computer, causes the computer to perform the steps executed by the electronic device in any of the above embodiments.

[0196] In some embodiments, the above-described electronic device may be electronic device 1500 as exemplified below.

[0197] The specific structure of the electronic device 1500 will be described in detail below with reference to the accompanying drawings.

[0198] Figure 15 This diagram illustrates the structure of an electronic device 1500 according to some embodiments of the present application.

[0199] like Figure 15 As shown, the electronic device 1500 includes one or more processors 1501, system memory 1502, non-volatile memory (NVM) 1503, communication interface 1504, input / output (I / O) device 1505, and system control logic 1506 for coupling the processor 1501, system memory 1502, non-volatile memory 1503, communication interface 1504, and input / output (I / O) device 1505. Wherein:

[0200] Processor 1501 may include one or more processing units, such as data processing units or processing circuits that may include a central processing unit (CPU), graphics processing unit (GPU), digital signal processor (DSP), micro-programmed control unit (MCU), artificial intelligence (AI) processor, field programmable gate array (FPGA), neural network processing unit (NPU), etc., and may include one or more single-core or multi-core processors. In some embodiments, processor 1501 may be used to execute instructions to implement the above-described parking space recommendation method.

[0201] System memory 1502 is volatile memory, such as random-access memory (RAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc. System memory 1502 is used for temporary storage of data and / or instructions. For example, in some embodiments, system memory 1502 can be used to store instructions, or it can be used to store original data objects and modified data objects.

[0202] The non-volatile memory 1503 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, the non-volatile memory 1503 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as a hard disk drive (HDD), compact disc (CD), digital versatile disc (DVD), solid-state drive (SSD), etc. In some embodiments, the non-volatile memory 1503 may also be a removable storage medium, such as a secure digital (SD) memory card. In other embodiments, the non-volatile memory 1503 may be used to store instructions, or to store original data objects and modified data objects.

[0203] In some embodiments, system memory 1502 and non-volatile memory 1503 may each include a temporary copy and a permanent copy of instruction 1507. Instruction 1507 may include, when executed by at least one of processors 1501, causing electronic device 1500 to implement the parking space recommendation method provided in the embodiments of this application.

[0204] Communication interface 1504 may include a transceiver for providing a wired or wireless communication interface for electronic device 1500, thereby enabling communication with any other suitable device via one or more networks. In some embodiments, communication interface 1504 may be integrated into other components of electronic device 1500, for example, communication interface 1504 may be integrated into processor 1501. In some embodiments, electronic device 1500 may communicate with other devices through communication interface 1504. For example, electronic device 1500 may establish a communication connection with other devices through communication interface 1504 to send data change requests, obtain original data objects, and send changed data objects to other devices through the communication connection.

[0205] Input / output (I / O) device 1505 may include input devices such as keyboards and mice, and output devices such as monitors. Users can interact with electronic devices 1500 through input / output (I / O) device 1505. For example, business personnel can input / select the content to be changed through input / output (I / O) device 1505.

[0206] System control logic 1506 may include any suitable interface controller to provide any suitable interface to other modules of electronic device 1500. For example, in some embodiments, system control logic 1506 may include one or more memory controllers to provide an interface to system memory 1502 and non-volatile memory 1503.

[0207] In some embodiments, at least one of the processors 1501 may be packaged together with the logic of one or more controllers for the system control logic unit 1506 to form a system in package (SiP). In other embodiments, at least one of the processors 1501 may also be integrated on the same chip with the logic of one or more controllers for the system control logic unit 1506 to form a system-on-chip (SoC).

[0208] Understandable. Figure 15 The structure of the electronic device 1500 shown is merely an example. In other embodiments, the electronic device 1500 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0209] Various embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or a combination of these implementation methods. Embodiments of this application can be implemented as computer modules or module code executing on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0210] Module code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0211] Module code can be implemented using a high-level modular language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used to implement module code when needed. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0212] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, CD-ROMs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic cards or optical cards, flash memory, or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other forms of propagated signals. Therefore, machine-readable media include any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0213] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0214] Various embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or combinations of these implementation methods. Embodiments of this application can be implemented as computer programs or program code executable on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0215] Program code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0216] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0217] It should be noted that all units / modules mentioned in the device embodiments of this application are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above-described device embodiments of this application have not introduced units / modules that are not closely related to solving the technical problems proposed in this application. This does not mean that the above-described device embodiments do not contain other units / modules.

[0218] It should be noted that in the examples and description of this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0219] In this specification, references to "some embodiments" or "embodiments" mean that a specific feature, structure, or characteristic described in connection with an embodiment is included in at least one exemplary implementation or technology disclosed according to embodiments of this application. The phrase "in some embodiments" appearing in various places in the specification does not necessarily refer to the same embodiment.

[0220] Furthermore, the language used in this specification has been primarily chosen for readability and instructional purposes and may not have been chosen to depict or limit the disclosed subject matter. Therefore, the embodiments disclosed herein are intended to illustrate, and not limit, the scope of the concepts discussed herein.

Claims

1. A parking space recommendation method, characterized in that, Applied to vehicle-mounted terminals, the method includes: Obtain map information of the parking lot where the vehicle is located, as well as the location information of the vehicle. The map information includes multiple parking spaces in the parking lot. Based on the map information and the location information, multiple parking parameters are determined. The parking parameters include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the passageway. The passageway is the drivable area between the parking space and the obstacle. Based on the multiple parking parameters, a recommended score is determined for each of the multiple parking spaces.

2. The parking space recommendation method according to claim 1, characterized in that, The parking parameters also include a fourth parameter related to the historical recommended score of the parking space, wherein the historical recommended score is the recommended score of the parking space in the vehicle's historical parking process, and the fourth parameter is positively correlated with the historical recommended score.

3. The parking space recommendation method according to claim 1, characterized in that, The third parameter includes the first aisle dimension parameter and the second aisle dimension parameter, wherein, The first aisle dimension parameter is the dimension of the aisle where the vehicle is located along a first direction, which is perpendicular to the entrance line of each parking space; The second aisle dimension parameter is the dimension of the aisle along a second direction, which is the extension direction of the entrance line of each parking space.

4. The parking space recommendation method according to claim 3, characterized in that, The methods for determining the dimensions of the first passageway include: An obstacle grid map is determined based on the map information; N first empty grid vectors are created in the obstacle grid map. The N first empty grid vectors are stacked sequentially along the first direction starting from the entrance line of each parking space, where N is a positive integer greater than or equal to 2. The first N-1 first empty grid vectors of the N first empty grid vectors are matched with the empty area of ​​the obstacle grid map, and the Nth first empty grid vector corresponds to the obstacle area of ​​the obstacle grid map. The dimensions of the N-1 first empty grid vectors along the first direction are used as the first aisle size parameters.

5. The parking space recommendation method according to claim 4, characterized in that, The first of the N first empty grid vectors covers the entrance of each parking space, and the (i+1)th first empty grid vector covers the ith first empty grid vector, where i is a positive integer that takes values ​​sequentially from 1 to N-1.

6. The parking space recommendation method according to claim 5, characterized in that, The length of the (i+1)th first empty raster vector is greater than the length of the ith first empty raster vector.

7. The parking space recommendation method according to claim 3, characterized in that, The methods for determining the dimensions of the second passageway include: An obstacle grid map is determined based on the map information; M second empty grid vectors are created in the obstacle grid map. The M second empty grid vectors are stacked sequentially along the second direction starting from the entrance of the parking space, where M is a positive integer greater than or equal to 2. The first M-1 second empty grid vectors of the M second empty grid vectors match the empty area of ​​the obstacle grid map, and the Mth second empty grid vector corresponds to the obstacle area of ​​the obstacle grid map. The dimensions of the M-1 second empty grid vectors along the second direction are used as the second aisle dimension parameters.

8. The parking space recommendation method according to claim 1 or 2, characterized in that, The step of determining the recommended score for each of the multiple parking spaces based on the multiple parking parameters includes: Based on the aforementioned parking parameters, the recommended score for each parking space is determined using the superior-inferiority distance method.

9. The parking space recommendation method according to claim 8, characterized in that, The method of using superior-inferiority distance to determine the recommended score for each of the multiple parking spaces based on the multiple parking parameters includes: Based on the impact of the multiple parking parameters on the parking difficulty of the vehicle, the parameter type of each parking parameter is determined; Based on the parameter type of the parking parameter, the value of the parking parameter is converted from the original value to the standard value; Construct a maximum value vector, wherein the j-th element of the maximum value vector is the maximum value among the standard values ​​of the j-th parking parameter of the plurality of parking spaces; Construct a minimum value vector, where the k-th element of the minimum value vector is the minimum value among the standard values ​​of the k-th parking parameter of the plurality of parking spaces; Determine the first distance between the parking parameters of each parking space and the maximum value vector; Determine the second distance between the parking parameters of each parking space and the minimum value vector; The recommended score for each parking space is determined based on the first distance and the second distance.

10. The parking space recommendation method according to claim 9, characterized in that, The parameter types include extremely large parameters and interval parameters, wherein the first parameter and the third parameter are extremely large parameters, and the second parameter is an interval parameter.

11. The parking space recommendation method according to claim 10, characterized in that, The step of converting the values ​​of the parking parameters from their original values ​​to standard values ​​based on the parameter type of the parking parameters includes: If the parking parameter is the extremely large parameter, then the maximum value between the original value and the second preset value of the parking parameter shall be taken as the standard value of the parking parameter; If the parking parameter is an interval-type parameter, then the standard value of the parking parameter is determined according to the distance between the parking parameter and the first preset interval.

12. The parking space recommendation method according to claim 11, characterized in that, Determining the standard value of the parking parameters based on the distance between the parking parameters and the first preset interval includes: If the original value of the parking parameter is within the first preset range, then the standard value of the parking parameter is determined as the third preset value; If the original value of the parking parameter is less than the starting point value of the first preset interval, then the standard value of the parking parameter is determined as the difference between the third preset value and the first correction amount, wherein the first correction amount is determined based on the difference between the starting point value of the interval and the original value of the parking parameter. If the original value of the parking parameter is greater than the end value of the first preset interval, then the standard value of the parking parameter is determined as the difference between the third preset value and the second correction amount, which is determined based on the difference between the original value of the parking parameter and the end value of the interval.

13. The parking space recommendation method according to claim 1, characterized in that, The first parameter includes the first parking space width and the second parking space width, wherein, The first parking space width is determined based on the parking space lines, and the second parking space width is determined based on the obstacles on both sides of the parking space.

14. The parking space recommendation method according to claim 1, characterized in that, The second parameter includes a first distance parameter and a second distance parameter, wherein, The first distance parameter is the distance between the parking space and the vehicle along a first direction, and the second distance parameter is the distance between the parking space and the vehicle along a second direction. The first direction is the extension direction of the entrance line of the parking space, and the second direction is perpendicular to the extension direction of the entrance line of the parking space.

15. A parking space recommendation device, applied to an in-vehicle terminal, characterized in that, The device includes a map acquisition module, a parameter acquisition module, and a data processing module, wherein... The map acquisition module is used to acquire map information of the parking lot where the vehicle is located, as well as the location information of the vehicle. The map information includes multiple parking spaces in the parking lot. The parameter acquisition module is used to determine multiple parking parameters based on the map information and the location information. The parking parameters include a first parameter related to the size of the parking space, a second parameter related to the distance between the vehicle and the parking space, and a third parameter related to the size of the passageway. The passageway is the drivable area between the parking space and the obstacle. The data processing module is used to determine the recommended score for each of the multiple parking spaces based on the multiple parking parameters.

16. An electronic device, characterized in that, include: One or more processors; One or more memories; the one or more memories storing one or more programs that, when executed by the one or more processors, cause the electronic device to perform the parking space recommendation method according to any one of claims 1-14.

17. A computer-readable medium, characterized in that, The computer-readable medium stores instructions that, when executed on a machine, cause the machine to perform the parking space recommendation method according to any one of claims 1-14.

18. A computer program product, characterized in that, Includes a computer program / instruction that, when executed by a processor, implements the parking space recommendation method according to any one of claims 1-14.

19. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 16.

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