DE102020124304A1 - METHOD, DEVICE, DEVICE FOR NAVIGATION AND COMPUTER-READABLE STORAGE MEDIUM - Google Patents

Abstract

Embodiments of the present disclosure relate to a method for navigation, a device for navigation, a device for navigation and a computer-readable storage medium. A method of navigation includes: receiving a first user input relating to a navigation destination from a user; Determining at least one destination address associated with the navigation destination and parking difficulty information in a vicinity of the at least one destination address; and providing the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user. Embodiments of the present disclosure also provide the user with parking difficulty information in the vicinity of the destination address while providing the user with the destination address associated with the navigation destination, thereby helping the user to select an appropriate destination address in advance Make an effective parking decision.

Description

TECHNICAL AREA

Embodiments of the present disclosure relate to the field of navigation, and in particular to a method for navigation, a device for navigation, a device for navigation and a computer-readable storage medium.

BACKGROUND

Existing navigation applications generally only provide destination address information relating to a navigation destination and cannot provide information as to whether parking around the destination address is easy. Although some parking lots can provide real-time parking information in some way to indicate a real-time parking status of the parking lot (such as a total number of parking spaces, the number of parking spaces occupied and / or the number of free parking spaces), such real-time parking information is typically used in navigation applications not provided. Such real-time parking information often only applies to some of the parking lots, and there are a large number of parking lots that cannot provide such real-time parking information. Furthermore, such real-time parking information can only indicate the parking status of a parking lot at the current time, and it cannot predict or indicate the parking status of a parking lot at a future time (e.g. when the user arrives).

In addition, when a navigation application is actually used, a user tends to use a place of interest such as a shopping mall or a restaurant as a destination without using a parking lot as a navigation destination. Conventional solutions do not provide information about the parking difficulty information around the points of interest. Therefore, the user cannot determine whether it is easy to park around a destination to be entered before driving.

SHORT REPRESENTATION

In view of the problems set forth above, example embodiments of the present disclosure provide a solution to navigation. This solution also provides the user with parking difficulty information in the vicinity of the destination address while providing the user with the destination address associated with the navigation destination, thereby helping the user to select an appropriate destination address in advance and an effective one To make parking decision.

In the first aspect of the present disclosure, a method of navigation is provided. The method includes: receiving a first user input relating to a navigation destination from a user; Determining at least one destination address associated with the navigation destination and the parking difficulty information in a vicinity of the at least one destination address; and providing the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user.

In some exemplary embodiments, determining the parking difficulty information includes: determining a time at which the user reaches the at least one destination address; and predicting the parking difficulty information in the vicinity of the at least one destination address at that time.

In some exemplary embodiments it is provided that providing the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address simultaneously includes for the user: presenting the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user.

In some exemplary embodiments, it is provided that the parking difficulty information includes at least one of the following: an index which indicates a parking difficulty; and the number of empty parking spaces.

In some exemplary embodiments, it is provided that the method further comprises: receiving a second user input relating to a selected destination address from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information; Determining a navigation route from a current position of the user to a selected destination address; and providing the navigation route to the user.

In some exemplary embodiments it is provided that the prediction of the parking difficulty information in the vicinity of the at least one destination address at this time comprises: determining a parking area which corresponds to the at least one destination address, the parking area having one or more parking spaces in the vicinity of the at least comprises a destination address; Predicting a parking requirement for the parking area at this time based on historical parking information from at least one parking space to one historical time corresponding to that time; and predicting the parking difficulty information in the vicinity of each of the at least one destination address at that time based on the parking need.

In some exemplary embodiments, it is provided that predicting a parking requirement for the parking area at this time comprises: determining a historical parking requirement of the at least one parking space at the historical time based on the historical parking information; Determining a mapping relationship between the historical parking requirement and a spatial feature of the at least one parking space; and predicting the parking needs of the parking area at that time based on the spatial feature of the parking area and the mapping relationship.

In some exemplary embodiments, it is provided that the spatial feature comprises at least one of the following: a distance to at least one location of interest; and the number of surrounding points of interest.

In some exemplary embodiments, it is provided that the method further comprises: predicting the parking difficulty information for each of the one or more parking spaces at that time based on the parking requirement, the one or more parking spaces including a parking space about which no historical parking information is available can be provided.

In the second aspect of the present disclosure, an apparatus for navigation is provided. The device comprises: a first receiving module which is configured to receive a first user input relating to a navigation destination from a user; a first determination module which is set up to determine at least one destination address associated with the navigation destination and information about parking difficulty in a vicinity of the at least one destination address; and a first provision module which is set up to provide the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user.

In some exemplary embodiments it is provided that the first determination module comprises: a determination unit which is set up to determine a time at which the user reaches the at least one destination address; and a prediction unit configured to predict the parking difficulty information in the vicinity of the at least one destination address at that time.

In some exemplary embodiments it is provided that the first provision module comprises: a presentation unit which is set up to present the at least one destination address and the information about the parking difficulty level in the vicinity of the at least one destination address at the same time for the user.

In some exemplary embodiments, it is provided that the parking difficulty information includes at least one of the following: an index which indicates a parking difficulty; and the number of empty parking spaces.

In some exemplary embodiments, it is provided that the device further comprises: a second receiving module, which is configured to receive a second user input relating to a selected destination address from the user in response to the user choosing one of the at least one destination address based on the parking Difficulty information selects to perform; a second determination module which is configured to determine a navigation route from a current position of the user to a selected destination address; and a second provision module which is configured to provide the navigation route to the user.

In some exemplary embodiments, it is provided that the prediction unit is further configured to carry out the following: determining a parking area which corresponds to the at least one destination address, the parking area comprising one or more parking spaces in the vicinity of the at least one destination address; Predicting a parking requirement for the parking area at this time based on historical parking information from at least one parking space at a historical time corresponding to this time; and predicting the parking difficulty information in the vicinity of each destination address of the at least one destination address at that time based on the parking need.

In some exemplary embodiments, it is provided that the prediction unit is further configured to carry out the following: determining a historical parking requirement of the at least one parking space at the historical time based on the historical parking information; Determining a mapping relationship between the historical parking requirement and a spatial feature of the at least one parking space; and predicting the parking needs of the parking area at that time based on the spatial feature of the parking area and the mapping relationship.

In some exemplary embodiments, it is provided that the spatial feature include at least one of the following: a distance to at least one location of interest; and the number of surrounding points of interest.

In some exemplary embodiments, it is provided that the prediction unit is further configured to perform the following: Predicting the parking difficulty information for each of the one or more parking spaces at that time based on the parking requirement, wherein the one or more parking spaces include a parking space which cannot provide historical parking information.

In the third aspect of the present disclosure, a device for navigation is provided. The device comprises: at least one processing unit; and at least one memory that is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, wherein the instructions cause the device to carry out a method according to the first aspect of the present disclosure when the instructions from the at least one processing unit are executed.

In the fourth aspect of the present disclosure, a computer-readable storage medium is provided on which a computer program is stored, the computer program causing a device to execute a method according to the first aspect of the present disclosure when the computer program is executed by the device.

As can be seen from the above description, the solution for navigation according to the exemplary embodiments of the present disclosure also provides the user with parking difficulty information in the vicinity of the destination address while providing the user with the destination address associated with the navigation destination. Further, this solution is able to predict parking difficulty information in the vicinity of the destination address when the user arrives at the destination address, thereby helping the user to select an appropriate destination address in advance and make an effective parking decision.

It should be understood that the contents described in this disclosure of the invention neither limit the key features or essential features of exemplary embodiments of the present disclosure, nor limit the scope of the present disclosure. Other features of the present disclosure will be readily understood from the following description.

Figure list

• 1 ein Blockdiagramm einer beispielhaften Umgebung gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 2 ein Ablaufdiagramm eines beispielhaften Verfahrens zur Navigation gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 3 ein Blockdiagramm eines beispielhaften Systems zum Bestimmen der Einpark-Schwierigkeitsgrad-Information in der Umgebung von mindestens einer Zieladresse zu einer bestimmten Zeit gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 4 ein Ablaufdiagramm eines beispielhaften Verfahrens zum Bestimmen der Einpark-Schwierigkeitsgrad-Information in der Umgebung von mindestens einer Zieladresse zu einer bestimmten Zeit gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 5 eine schematische Darstellung des Unterteilen vom Parkbereich gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 6 ein Ablaufdiagramm eines beispielhaften Verfahrens zum Vorhersagen eines Parkbedarfs vom Zielparkbereich zu einer bestimmten Zeit gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 7 eine schematische Darstellung des Erzeugens eines Zeitreihenmodells gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung;
• 8 ein Blockdiagramm einer beispielhaften Vorrichtung zur Navigation gemäß einem Ausführungsbeispiel der vorliegenden Offenbarung; und
• 9 ein Blockdiagramm eines Rechengeräts, das die Ausführungsbeispiele der vorliegenden Offenbarung ausführen kann.

The foregoing and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description in conjunction with the drawings. In the drawings, the same or similar reference symbols designate the same or similar elements. It shows:

• 1 FIG. 3 is a block diagram of an exemplary environment according to an embodiment of the present disclosure;
• 2 a flowchart of an exemplary method for navigation according to an exemplary embodiment of the present disclosure;
• 3 FIG. 3 is a block diagram of an exemplary system for determining parking difficulty information in the vicinity of at least one destination address at a particular time in accordance with an embodiment of the present disclosure; FIG.
• 4th a flowchart of an exemplary method for determining the parking difficulty information in the vicinity of at least one destination address at a particular time according to an embodiment of the present disclosure;
• 5 a schematic representation of the subdivision of the parking area according to an embodiment of the present disclosure;
• 6th a flowchart of an exemplary method for predicting a parking requirement from the target parking area at a specific time according to an exemplary embodiment of the present disclosure;
• 7th a schematic representation of the generation of a time series model according to an embodiment of the present disclosure;
• 8th a block diagram of an exemplary device for navigation according to an embodiment of the present disclosure; and
• 9 FIG. 3 is a block diagram of a computing device that can carry out the embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following, exemplary embodiments of the present disclosure are described in more detail with reference to the drawings. Although some exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be incorporated into various Shapes can be implemented and should not be interpreted as being limited to the embodiments described herein. It goes without saying that the drawings and exemplary embodiments of the present disclosure serve only as examples and do not serve to limit the scope of protection of the present disclosure.

In describing the exemplary embodiments of the present disclosure, the term “comprising” and similar terms should be understood as “including, but not limited to. The term "based on" should be understood as based at least in part on. The term “an embodiment” or “the embodiment” should be understood as “at least one embodiment”. The terms “first,” “second,” etc. may refer to different or the same objects. The following may include other explicit and implicit definitions as well.

1 Figure 3 shows a block diagram of an exemplary environment 100 according to an embodiment of the present disclosure. As in 1 shown, the exemplary environment 100 generally a user 110 and a navigation device 120 contain. The navigation device 120 may include, and is not limited to, a cell phone, computer, personal digital assistant, game console, portable device, vehicle communication device, virtual reality device, or augmented reality device, or the like. The navigation device 120 can be provided with an input device, such as a touch screen and a keyboard, to enable the user 110 it can operate. The navigation device 120 with an output device, such as a display and a loudspeaker, for providing information to the user 110 be provided. The navigation device 120 can also be equipped with a positioning device to determine its location. The navigation device 120 can use a navigation application to provide navigation services to the user 110 be installed.

2 shows a flow chart of a method 200 for navigation according to an exemplary embodiment of the present disclosure. The procedure 200 for example, through the in 1 shown navigation device 120 are executed. It goes without saying that the procedure 200 can also have additional blocks that are not shown, and / or the blocks shown can be omitted. For example, the blocks can 240-250 may be omitted in some embodiments. The scope of the present disclosure is not limited in this regard.

At the block 210 receives the navigation device 120 from the user 110 a user input (also referred to herein as “first user input”) relating to a navigation destination.

In some embodiments, the navigation destination that the user 110 enter the name of a specific place of interest. The “point of interest” mentioned here can include restaurants, shopping centers, schools, parking lots, and so on. For example, this can be done by the user 110 The navigation destination entered may be a "Wangfujing Bookstore", a "Beijing Department Store" or a "Wangfujing Department Store Parking Lot". Alternatively, in other exemplary embodiments, the navigation destination that the user 110 rather than specifically specifying a particular place of interest in the area, such as "Wangfujing,""Zhongguancun," and so on.

At the block 220 determines the navigation device 120 at least one destination address associated with the navigation destination and the parking difficulty information in the vicinity of the at least one destination address.

In some exemplary embodiments it is provided that if this is done by the user 110 entered navigation destination displays the name of a specific place of interest, the navigation device 120 determine the address of the place of interest as the destination address (for example, by querying a map). The navigation device 120 can further determine the parking difficulty information in the vicinity of the place of interest. For example, if the user 110 If the navigation destination entered is “Beijing Department Store”, the navigation device can 120 determine by consulting the map that the address of the Beijing department store is No. 255 Wangfujing Street, and further determine the parking difficulty information in the vicinity of the Beijing department store.

Alternatively, it is provided in other exemplary embodiments that if this is done by the user 110 entered navigation destination displays the name of a specific area, the navigation device 120 can determine the corresponding address for one or more places of interest within the area as the destination address, and wherein the parking difficulty information in the vicinity of each place of interest is determined. For example, if this is from the user 110 entered navigation destination is "Wangfujing", the navigation device can 120 by querying the map for corresponding addresses of one or more places of interest within the area (such as "Wangfujing Bookstore", "Wangfujing Snack Zone", "Wangfujing Department Store Parking Lot" or the like) and further determine the parking difficulty information around these destination addresses.

In some exemplary embodiments, the parking difficulty information in the vicinity of the destination address can be represented as an index which indicates the difficulty of parking around the destination address. For example, a higher index indicates a higher degree of difficulty in parking; conversely, a lower index indicates a lower level of difficulty in parking. The index can also be displayed as a label. For example, the label may indicate “difficult to park” when the index is above a certain threshold, and when the index is below a certain threshold the label may indicate “easy to park”. Additionally or alternatively, the parking difficulty information in the vicinity of the destination address can also indicate the number of remaining free parking spaces in one or more parking spaces in the vicinity of the destination address.

In some exemplary embodiments, the navigation device can 120 determine the parking difficulty information in the vicinity of the at least one destination address at the current time (for example the time or the period of time in which the user 110 enter the navigation destination). Alternatively or additionally, in some exemplary embodiments, the navigation device 120 determine the time (for example the point in time or the period of time) at which the user arrives from his current location at at least one destination address, and predict the parking difficulty information, the parking difficulty information the parking difficulty in the Environment of the at least one destination address when the user reaches the at least one destination address, specifies. The determination of the parking difficulty information is described below in connection with 3-7 described in detail.

At the block 230 represents the navigation device 120 the user 110 at the same time the determined at least one destination address and the parking difficulty information in the vicinity of the at least one destination address are ready.

In some exemplary embodiments, the navigation device can 120 the user 110 at the same time present the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address in a visual manner (for example graphically or textually). In some exemplary embodiments, the parking difficulty information can be presented in association with the at least one destination address as an information link. In other exemplary embodiments, the navigation device 120 the user 110 simultaneously providing the determined at least one destination address and the parking difficulty information in the vicinity of the at least one destination address in an audible manner (for example an information broadcast) or in some other way. The scope of the present disclosure is not limited in this regard.

In some embodiments, the user can 110 select the destination address to which he will travel based on the parking difficulty information in the vicinity of the at least one destination address. For example, the user can 110 Select a destination address at which the vehicle can be parked more easily than the destination to which it will drive from a plurality of destination addresses.

Additionally, in response to the user's choice 110 receives the navigation device 120 at block 240 a user input (referred to herein as a "second user input") related to the selected destination address from the user 110 . In the block 250 determines the navigation device 120 a navigation route from the user's current location 110 to a selected destination address. In the block 260 represents the navigation device 120 the navigation route for the user 110 ready.

3 Figure 3 shows a block diagram of an exemplary system 300 for determining the parking difficulty information in the vicinity of at least one destination address at a specific time according to an exemplary embodiment of the present disclosure. The system 300 for example, at the in 1 navigation device shown 120 be implemented to display the parking difficulty information at the in 2 shown block 220 to determine, the parking difficulty information indicating the current parking difficulty in the vicinity of the at least one destination address or the parking difficulty in the vicinity of the at least one destination address when the user reaches the at least one destination address. As in 3 shown, the exemplary device 300 generally a time series analysis unit 310 , a mapping relationship determining unit 320 , a parking demand forecast unit 330 and a spatial interpolation unit 340 include.

In some exemplary embodiments, the time series analysis unit 310 the historical park information 301 from at least one parking space 350 record at historical times. The historical time can correspond to a specific time, which can be a historical point in time or a historical period. For example, if the parking difficulty information is in the vicinity of the If at least one destination address is to be determined during the period from 10:00 to 10:30 a.m. on Monday morning, the historical time on the previous Monday morning can be from 10:00 a.m. to 10:30 a.m. For example, if it is necessary to determine the parking difficulty information in the vicinity of the at least one destination address on Monday morning at 10:00 a.m., the historical time may be on the previous Monday morning at 10:00 a.m. The historical park information 301 can check the corresponding parking data for the parking lot 350 included at each historical point in time in the historical time (for example, if the historical time is a historical period, the historical point in time can be selected every 5 minutes or 10 minutes or the like). For example, the parking data can include the number of occupied parking spaces, the number of available parking spaces, the occupied parking space (for example area) and / or the available parking space of the parking lot at any historical point in time 350 at the historical point in time.

In some exemplary embodiments, the time series analysis unit 310 a time series analysis on the recorded historical park information 301 to obtain a time series model that corresponds to at least one historical point in time in historical time. The time series model can describe a mapping relationship between a parking requirement and a set of factors that lead to a change in the parking requirement over time. The time series analysis unit 310 can use the resulting set of time series models to calculate historical parking needs 302 of the parking lot 350 to determine the historical time. The historical need for parking 302 can be an index that indicates the difficulty level of the parking space 350 indicates historical time (for example, a higher index indicates a higher degree of difficulty in parking; conversely, a lower index indicates a lower degree of difficulty in parking). The historical need for parking 302 may be a mapping relationship determining unit 320 to provide.

In some embodiments, the mapping relationship determining unit 320 a set of spatial features 303 of the parking lot 350 and historical parking needs 302 by the time series analysis unit 310 determined. The spatial feature 303 can be a distance to a parking space 350 to one or more places of interest (e.g. a traffic point, a shopping center or the like) and / or the corresponding number of different places of interest (e.g. schools, office buildings, sights or the like) around the parking lot 350 around (e.g. within a threshold distance). In some embodiments, the mapping relationship determining unit 320 the mapping relationship 304 between the parking requirement of the parking lot and the spatial feature of the parking lot further based on the spatial feature 303 and historical parking needs 302 determine. The mapping relationship 304 can be used to predict the parking needs of a parking area at a particular time, and thus the mapping relationship 304 hereinafter also referred to as the prediction model 304 designated. The predictive model 304 can be the parking demand forecast unit 330 to be provided.

In some embodiments, the parking demand predictor 330 a parking area 360 which corresponds to the at least one destination address and hereinafter also referred to as the “destination parking area 360 "Is designated, determine. For example, the target parking area 360 based on one by the user 110 entered navigation destination can be determined, the target parking area 360 may include one or more parking spaces in the vicinity of the at least one destination address associated with the navigation destination. As further, for example, below in connection with 5 may be the target parking area 360 can also be determined based on the subdivision of a larger geographic area. In some embodiments, the parking demand predictor 330 also a set of spatial features 305 of the target parking area 360 determine. The spatial feature 305 can be the spatial feature 303 correspond. For example, the spatial feature 305 a distance of the target parking area 360 to one or more places of interest (eg traffic points, shopping centers or the like) and / or the corresponding number of different places of interest (eg schools, office buildings, sights or the like) within the target parking area 360 include. The parking requirement forecast unit 330 can use the predictive model 304 use the parking needs 306 of the target parking area 360 at a specific time (e.g. the current time at which the user 110 a navigation destination, or the time at which the user arrives at at least one destination address) based on the spatial feature 305 to predict. According to the historical parking needs 302 can the need for parking 306 be an index of the parking difficulty level of the target parking space 360 indicating at a certain time. According to the historical parking needs 302 can the need for parking 306 be an index of the parking difficulty level of the target parking space 360 at a certain time (for example, a higher index indicates a higher level of difficulty in parking; conversely, a lower index indicates a lower level of difficulty in parking). The need for parking 306 can use the spatial interpolation unit 340 to be provided.

In some exemplary embodiments, the spatial interpolation unit 340 the need for parking 307 for each partial parking area in the target parking area 360 at a given time using a space interpolation technique based on parking needs 306 of the target parking area 360 predict. In some embodiments, each partial parking area can correspond to a destination address. That is, the space interpolation unit 340 can parking needs 307 in the vicinity of each of the at least one destination address at the specific time based on the parking requirement 306 of the target parking area 360 predict. For example, the need for parking 306 as an index of the parking difficulty in the middle of the target parking area 360 to be viewed as. The need for parking 307 in the vicinity of each destination address at that particular time can be based on the need for parking 306 and the distance between the destination address and the center of the destination parking area 360 to be determined. Further, the parking difficulty information in the vicinity of each destination address can be generated based on the parking requirement in the vicinity of the destination address. In some exemplary embodiments, each partial parking area can have a parking space in the target parking area 360 correspond to (e.g. a parking lot that can provide historical parking information, or a parking lot that cannot provide historical parking information that is different from the parking lot 350 can distinguish). That is, the space interpolation unit 340 can determine the parking needs of each parking space in the target parking area 360 at that particular time based on parking needs 306 of the target parking area 360 predict. For example, the parking requirement for each parking space at the specific time can be based on the parking requirement 306 and the distance between the parking lot and the center of the target parking area 360 to be determined.

4th shows a flow diagram of an exemplary method 400 for determining the parking difficulty information in the vicinity of at least one destination address at a specific time according to an exemplary embodiment of the present disclosure. For example, the procedure 400 as an exemplary implementation of the in 2 shown blocks 220 to be viewed as. The procedure 400 is used in conjunction with below 3 described in detail.

At the block 410 becomes a target parking area 360 which corresponds to at least one destination address. In some embodiments, the target parking area 360 based on one by the user 110 entered navigation destination can be determined, the target parking area 360 for example, one or more parking spaces in the vicinity of the at least one destination address associated with the navigation destination. Alternatively, in other exemplary embodiments, a geographic area (e.g. the city in which the user 110 located), which includes all parking spaces that the user 110 can be divided into a set of parking areas, and wherein a parking area associated with at least one destination address in the set of parking areas as a destination parking area 360 is selected.

5 FIG. 12 shows a schematic diagram of determining a target parking area according to an embodiment of the present disclosure. For example shows 5 a geographic area 500 which includes multiple parking spaces (as indicated by the circular symbol in 5 shown). In some exemplary embodiments, a predetermined proportion (for example 20%) of parking spaces can be randomly selected from the geographic area 500 can be selected as the parking spaces 520-1 , 520-2 , ..., 520-6 (collectively or individually as a “parking lot 520 "Labeled), as in 5 shown. In some embodiments, the geographic area 500 based on the selected parking space 520 can be divided into a set of parking areas, so that each parking space 520 is included in a corresponding parking area and the number of parking spaces in each parking area 510 is lower than a predetermined threshold. In particular, in some exemplary embodiments, a circular area can be arranged so that the position of each selected parking space 520 is set as the center of the circle of the circular area, gradually expanding each circular area to include more parking spaces until the two circular areas are tangent or the number of parking spaces contained in the circular area reaches a predetermined threshold. In this way the geographic area 500 into a set of parking areas 510-1 , 510-2 , ..., 510-6 (collectively as the "parking area 510 "Or" circular area 510 “), Whereby the parking spaces in the same parking area have spatial similarities. For example, a parking area can be in the parking areas 510-1 , 510-2 , ..., 510-6 associated with the at least one destination address as the destination parking area 560 to be selected.

Additionally or alternatively, to obtain full coverage of the geographic area 500 In some exemplary embodiments, one or more space division points (in 5 shown by a square symbol) can also be placed in blank areas other than the circular area 510 are included. By connecting these space division points, the geographical area 500 can be divided into a variety of irregularly shaped parking areas. For example, a parking area in these irregularly shaped parking areas, which is associated with at least one destination address, can be used as the destination parking area 360 to be selected.

Back to 4th , at the block 420 becomes the need for parking 306 of the target parking area 360 at a certain time corresponding to the historical time based on the historical park information 301 of the parking lot 350 predicted at the historical time.

6th shows a flow diagram of an exemplary method 600 for predicting a parking requirement from the target parking area at a specific time according to an exemplary embodiment of the present disclosure. For example, the procedure 600 as an exemplary implementation of the in 4th shown blocks 420 to be viewed as.

At the block 610 is based on the historical park information 301 historical parking needs 302 of the parking lot 350 determined at the historical time.

In some exemplary embodiments, a time series analysis can be performed on the acquired historical parking information 301 can be carried out in order to get a time series model that corresponds to at least one historical point in time in historical time. The time series model can describe a mapping relationship between a parking requirement and a set of factors that lead to a change in the parking requirement over time. For example, it is assumed here that at least one historical point in time, which is included in the historical time, as {t _i | i ∈ [1, M]}, where M represents the number of historical points in time and M ≥ 1.

wobei PD_i den Parkbedarf des Parkplatzes zum historischen Zeitpunkt ti darstellt; PLD, PLSR_i, PLCR_i und PLC_i einen Satz von Faktoren darstellen, die eine zeitliche Veränderung des Parkbedarfs verursachen; und w1_i, w2_i, w3_i und w4_i die jeweiligen Gewichte dieser Faktoren darstellen. Insbesondere kann PLD einen Dichtheitsgrad der Parkplätze um den Parkplatz herum angeben, der beispielsweise wie folgt berechnet werden kann: $$PLD=\frac{Cn{t}_m}{Cn{t}_n}$$

wobei Cnt_m die Anzahl von Parkplätzen in einem relativ kleinen Bereich (z.B. einem kreisförmigen, quadratischen oder einem beliebig geformten Bereich) angibt, der auf den Parkplatz zentriert ist, und Cnt_n die Anzahl von Parkplätzen in einem relativ großen Bereich angibt, der auf den Parkplatz zentriert ist. PLSR_i kann das Verhältnis (hierin auch als „erstes Verhältnis“ bezeichnet) des verfügbaren Raums des Parkplatzes zum gesamten verfügbaren Raum der umgebenden Parkplätze zum historischen Zeitpunkt ti darstellen, das zum Beispiel wie folgt berechnet werden kann: $${\text{PLSR}}_i=\frac{S_i}{{\displaystyle \sum {}_1^{n}S{r}_i}}$$

wobei S_i einen verfügbaren Raum des Parkplatzes zum Zeitpunkt ti darstellt, Sr_i(r∈ [1, n]) einen verfügbaren Raum des r-ten Parkplatzes um den Parkplatz herum zum Zeitpunkt ti darstellt und n die Anzahl der Parkplätze um den Parkplatz herum darstellt. PLCR_i kann das Verhältnis (hierin auch als „zweites Verhältnis“ bezeichnet) der Anzahl der verfügbaren Parkzellen des Parkplatzes zu der Anzahl der gesamten verfügbaren Parkzellen der umgebenden Parkplätze zum historischen Zeitpunkt ti darstellen, das zum Beispiel wie folgt berechnet werden kann: $${\text{PLCR}}_i=\frac{C_i}{{\displaystyle \sum {}_1^{n}C{r}_i}}$$

wobei C_i die Anzahl der verfügbaren Parkzellen des Parkplatzes zum Zeitpunkt ti darstellt, Cr_i(r∈ [1, n]) die Anzahl der verfügbaren Parkzellen des r-ten Parkplatzes um den Parkplatz herum zum Zeitpunkt ti darstellt, wobei n die Anzahl der Parkplätze um den Parkplatz herum darstellt. PLC_i kann die räumliche Ausnutzung des Parkbereichs zum historischen Zeitpunkt ti darstellen, die beispielsweise wie folgt berechnet werden kann: $${\text{PLC}}_i=\frac{C_i}{S_i}$$

Es versteht sich, dass die in Formel (1) ausgewählten Faktoren lediglich beispielhaft sind und den Umfang der vorliegenden Offenbarung nicht einschränken sollen. In anderen Ausführungsbeispielen kann das Zeitreihenmodell auch basierend auf anderen Faktoren erstellt werden und ist nicht nur auf die vorstehend gezeigten beschränkt.In some exemplary embodiments, the created time series model for each historical point in time ti can be expressed as: $${\text{PD}}_i=\text{w}{1}_i\ast \text{PLD}+\text{w}{2}_i\ast {\text{PLSR}}_i+\text{w}{3}_i\ast {\text{PLCR}}_i+\text{w}{\mathrm{4th}}_i\ast {\text{PLC}}_i$$

where PD _i represents the parking requirement of the parking lot at the historical point in time ti; PLD, PLSR _i , PLCR _i and PLC _{i represent} a set of factors that cause the parking demand to vary over time; and w1 _i , w2 _i , w3 _i and w4 _{i represent} the respective weights of these factors. In particular, PLD can indicate a degree of tightness of the parking spaces around the parking lot, which can be calculated as follows, for example: $$P\mathrm{L.}\mathrm{D.}=\frac{\mathrm{C.}n{t}_m}{\mathrm{C.}n{t}_n}$$

where Cnt _{m is} the number of parking spaces in a relatively small area (e.g. a circular, square or any shaped area) that is centered on the parking lot, and Cnt _{n is} the number of parking spaces in a relatively large area that is on the Parking is centered. PLSR _i can represent the ratio (also referred to herein as the "first ratio") of the available space in the parking lot to the total available space in the surrounding parking spaces at the historical point in time ti, which can be calculated, for example, as follows: $${\text{PLSR}}_i=\frac{{\mathrm{S.}}_i}{{\displaystyle \sum {}_1^n\mathrm{S.}{r}_i}}$$

where S _{i represents} an available space of the parking lot at time ti, Sr _i (r∈ [1, n]) represents an available space of the r-th parking lot around the parking lot at time ti, and n is the number of parking spaces around the parking lot represents. PLCR _i can represent the ratio (also referred to herein as the "second ratio") of the number of available parking spaces in the parking lot to the total number of available parking spaces in the surrounding parking spaces at the historical point in time ti, which can be calculated, for example, as follows: $${\text{PLCR}}_i=\frac{{\mathrm{C.}}_i}{{\displaystyle \sum {}_1^n\mathrm{C.}{r}_i}}$$

where C _{i represents} the number of available parking spaces in the parking lot at time ti, Cr _i (r∈ [1, n]) represents the number of available parking spaces in the r-th parking space around the parking space at time ti, where n is the number of Represents parking spaces around the parking lot. PLC _i can display the spatial utilization of the parking area at the historical point in time ti, which can be calculated, for example, as follows: $${\text{PLC}}_i=\frac{{\mathrm{C.}}_i}{{\mathrm{S.}}_i}$$

It should be understood that the factors selected in formula (1) are exemplary only and are not intended to limit the scope of the present disclosure. In other exemplary embodiments, the time series model can also be created based on other factors and is not limited only to those shown above.

In order to create a time series model as shown in formula (1), the weights w1 _j , w2 _i , w3 _i and w4 _i must be determined for each factor. In some exemplary embodiments, it is provided that these weights can be set as default values in an initial situation, for example w1 _i is 0.1, w2 _i is 0.1, w3 _i is 0.1, w4 _i is 0.4. The initial curve can be based on the number of parking cells Pdi in the parking lot 350 to be drawn, which are actually documented at each historical point in time ti (i ∈ [1, 6]) within the historical period, for example by the initial curve 710 in 7th shown. The curve 710 can be smoothed by adjusting the weights w1 _i , w2 _i , w3 _i and w4 _i , creating a smooth curve 720 results. In this way the weights w1 _i , w2 _i , w3 _i and w4 _i can be determined for each factor. It should be understood that the manner previously described for determining the weights of each factor is merely exemplary. In other exemplary embodiments, any known method (eg random forest algorithm) or a method to be developed in the future can also be used to determine the weights for each factor. In this way, a mapping relationship between a parking requirement and a set of factors can be determined which lead to a change in the parking requirement over time.

After that in 1 The time series model shown has been created and the weights for each factor have been determined, formula (1) can be used to calculate the historical parking needs 302 of the parking lot 350 to determine the historical time. For example, for each historical point in time ti, the values for a set of factors PLD, PLSR _i , PLCR _i and PLC _i can be determined which determine the parking requirement of the parking lot 350 influence. Then formula (1) can be used to calculate the parking requirement PD _{i of} the parking space 350 to be determined at the historical point in time ti, such as by the smoothed curve 720 in 7th shown. In some embodiments, the historical parking needs 302 of the parking lot 350 at the historical period can be determined by averaging the parking requirements at the various historical points in time.

At the block 620 becomes a mapping relationship 304 between the historic parking needs 302 and the spatial feature 303 of the parking lot 350 certainly.

In some embodiments, the spatial feature 303 of the parking lot 350 a distance of a parking lot 350 to one or more places of interest (e.g. a traffic point, a shopping center or the like) and / or the corresponding number of different places of interest (e.g. schools, office buildings, sights or the like) around the parking lot 350 around (e.g. within a threshold distance). In some embodiments, the captured spatial feature can 303 of the parking lot 350 preferably a distance from the parking lot 350 to a subway station, the number of schools around the parking lot 350 around, the number of sights around the parking lot 350 around and a distance from the parking lot 350 to include a mall.

wobei PD den Parkbedarf darstellt, Dsub die Entfernung zur U-Bahnstation darstellt, Dsch die Entfernung zur Schule darstellt, Dm die Entfernung zum Einkaufszentrum darstellt und Na die Anzahl von Sehenswürdigkeiten darstellt. Es versteht sich, dass die in Formel (6) ausgewählten räumlichen Merkmale lediglich beispielhaft sind und den Umfang der vorliegenden Offenbarung nicht einschränken sollen. In anderen Ausführungsbeispielen kann das Vorhersagemodell 304 auch basierend auf anderer räumlicher Merkmale erstellt werden und ist nicht nur auf die vorstehend gezeigten beschränkt.In some embodiments, the mapping relationship 304 between the historic parking needs 302 and the spatial feature 303 of the parking lot 350 (i.e. the predictive model 304 ) can be represented as: $$P\mathrm{D.}=w1\ast \mathrm{D.}sub+w2\ast \mathrm{D.}scH+w3\ast \mathrm{D.}m+w\mathrm{4th}\ast Na$$

where PD represents the need for parking, Dsub represents the distance to the subway station, Dsch represents the distance to the school, Dm represents the distance to the shopping center and Na represents the number of attractions. It goes without saying that the spatial features selected in formula (6) are merely exemplary and are not intended to limit the scope of the present disclosure. In other embodiments, the predictive model 304 can also be created based on other spatial features and is not limited to only those shown above.

To the predictive model shown in formula (6) 304 To create the weights w1, w2, w3 and w4 must be determined for the individual spatial features. In some exemplary embodiments, the weights w1, w2, w3 and w4 for the individual spatial features can be based on the historical parking requirement 302 and the spatial features 303 can be determined using a linear regression algorithm. Alternatively, in some embodiments, the weights w1, w2, w3, and w4 for the individual spatial features can be determined using any known method (eg, random forest algorithm) or a method that may be developed in the future.

In this way, the mapping relationship between the parking requirement and the spatial features of the parking area can be determined, the mapping relationship representing both a correlation between the parking requirement and the spatial features and also taking into account a correlation between the parking requirement and a set of factors that lead to a lead to a temporal change in parking requirements.

At the block 630 becomes the need for parking 306 of the target parking area 360 at a certain time based on the mapping relationship 304 and the spatial feature 305 of the target parking area 360 predicted.

The spatial feature 305 may, in some embodiments, the spatial feature 303 correspond. For example, this can be spatial feature 305 a distance of the target parking area 360 to one or more places of interest (eg traffic points, shopping centers or the like) and / or the corresponding number of different places of interest (eg schools, office buildings, sights or the like) within the target parking area 360 include. In some embodiments, the captured spatial feature can 305 of the target parking area 360 preferably a distance from the target parking area 360 to a subway station, the number of schools in the target parking area 360 , the number of attractions in the target parking area 360 and a distance of the target parking area 360 to include a mall. In some exemplary embodiments it is provided that the parking requirement 306 of the target parking area 360 at a certain time by the onset of spatial features 305 into the mapping relationship 304 as shown in the formula (6) can be derived.

Back to 4th , will be at the block 430 the parking difficulty information in the vicinity of each of the at least one destination address at that particular time using spatial interpolation based on the parking requirement 306 of the target parking area 360 predicted.

In some embodiments, the need for parking 307 in the vicinity of each of the at least one destination address at the specific time based on the parking requirement 306 of the target parking area 360 be predicted. For example, the need for parking 306 as an index of the parking difficulty in the middle of the target parking area 360 to be viewed as. The need for parking 307 in the vicinity of each destination address can be based on the parking needs 306 and the distance of the destination address to the center of the destination parking area 360 to be determined. Further, the parking difficulty information in the vicinity of each destination address can be generated based on the need for parking in the vicinity of the destination address. In some exemplary embodiments, the parking difficulty information associated with the destination address can be generated based on the parking requirements in the vicinity of each destination address in order to indicate the difficulty of parking in the vicinity of the destination address. Alternatively, an association can be established between the parking requirement and the number of parking spaces, as a result of which the parking requirement in the vicinity of each destination address is converted into the number of available parking spaces in the vicinity of the destination address. That is, the parking difficulty information in the vicinity of each destination address may indicate an index indicating the difficulty of parking in the vicinity of the destination address and / or the number of empty parking spaces in the vicinity of the destination address.

Additionally or alternatively, in some exemplary embodiments, the parking requirement for each parking space (for example a parking space that can provide historical parking information or a parking space that cannot provide historical parking information) in the target parking area 360 at that particular time based on parking needs 306 of the target parking area 360 be predicted. For example, the parking requirement for each parking space at the specific time can be based on the parking requirement 306 and the distance of the parking lot to the center of the target parking area 360 to be determined. Similarly, the parking difficulty information of each parking lot can be generated based on the parking requirement for the parking lot at the specified time to indicate the difficulty of parking in the parking lot. The parking difficulty information of each parking lot can indicate an index of the parking difficulty of the parking lot and / or the number of empty parking spaces in the parking lot.

As can be seen from the above description, various problems of the conventional solutions can be solved by the solution for navigation according to the embodiments of the present disclosure. This solution also provides the user with parking difficulty information in the vicinity of the destination address while providing the user with the destination address associated with the navigation destination, thereby helping the user to select an appropriate destination in advance and an effective one To make parking decision.

8th Figure 3 shows a block diagram of an exemplary device 800 for navigation according to an exemplary embodiment of the present disclosure. The device 800 can be used, for example, to implement an in 1 navigation device shown 120 be used. As in 8th shown, the device 800 a first receiving module 810 which is configured to receive a first user input relating to a navigation destination from a user; a first determination module 820 which is configured to determine at least one destination address associated with the navigation destination and the parking difficulty information in the vicinity of the at least one destination address; and a first provision module 830 which is set up to provide the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user.

In some embodiments, the first determination module comprises 820 : a determining unit which is configured to determine perform a time at which the user reaches the at least one destination address; and a prediction unit configured to predict the parking difficulty information in the vicinity of the at least one destination address at that time.

In some exemplary embodiments, the prediction unit is further configured to carry out the following: determining a parking area which corresponds to the at least one destination address, the parking area comprising one or more parking spaces in the vicinity of the at least one destination address; Predicting a parking requirement for the parking area at this time based on historical parking information from at least one parking space at a historical time corresponding to this time; and predicting the parking difficulty information in the vicinity of each of the at least one destination address at that time based on the parking need.

In some exemplary embodiments, the prediction unit is further configured to carry out the following: determining a historical parking requirement of the at least one parking space at the historical time based on the historical parking information; Determining a mapping relationship between the historical parking requirement and a spatial feature of the at least one parking space; and predicting the parking needs of the parking area at that time based on the spatial feature of the parking area and the mapping relationship.

In some embodiments, the spatial feature includes at least one of the following: a distance to at least one place of interest; and the number of surrounding points of interest.

In some exemplary embodiments, the prediction unit is further configured to perform the following: Predicting the parking difficulty information for each of the one or more parking spaces at that time based on the parking requirement, the one or more parking spaces including a parking space beyond which none historical parking information can be provided.

In some exemplary embodiments, the first provision module comprises 830 a presentation unit which is set up to present the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user.

In some embodiments, the parking difficulty information includes at least one of the following: an index indicating a parking difficulty; and the number of empty parking spaces.

In some embodiments, the device comprises 800 further: a second receiving module configured to receive a second user input relating to a selected destination address from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information; a second determination module which is configured to determine a navigation route from the current position of the user to a selected destination address; and a second provision module which is configured to provide the navigation route to the user.

The one in the device 800 contained modules and / or units can be implemented in various ways including software, hardware, firmware, or any combination thereof. In some embodiments, one or more devices can be implemented using software and / or firmware, such as machine executable instructions, stored on a storage medium. In addition to, or as an alternative to, machine-executable instructions, some or all of the units in the device 800 implemented at least in part by one or more hardware logic components. Exemplary types of hardware logic components that can be used include a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System-on-Chip (SOC), a Complex Programming Logic Device (CPLD) etc.

In the 8th modules and / or units shown can be implemented partially or completely as hardware modules, software modules, firmware modules or any combination thereof. In particular, in some embodiments, the flow, method, or process described above may be implemented by a storage system, a host corresponding to the storage system, or hardware in other computing devices that are independent of the storage system.

9 Figure 3 shows a schematic block diagram of an exemplary device 900 that can be used to implement embodiments of the present disclosure. The device 900 can be used to do the in 1 shown navigation device 120 to implement.. As in 9 shown includes the device 900 a central processing unit (CPU = Central Processing Unit) 901 performing various appropriate actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 902 stored or from the storage unit 908 in a random access memory (RAM) 903 can be loaded. In the RAM 903 can also contain various programs and data necessary for the operation of the device 900 are necessary to be saved. The CPU 901 , the ROM 902 and the RAM 903 are to each other by a bus 904 connected. One input / output interface (I / O interface) 905 is also by bus 904 connected.

A number of components in the device 900 are with the I / O interface 905 connected. The components include an input unit 906 such as a keyboard, a mouse, or the like; an output unit 907 such as various types of display, speakers, and the like; a storage unit 908 such as a magnetic disk, a compact disk (CD) and the like; and a communication unit 909 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 909 enables the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

A processing unit 901 performs the various procedures and processes described above, as well as the procedures 200 , 400 and or 600 . For example, in some exemplary embodiments, the methods 200 , 400 and or 600 be implemented as a computer software program that is physically stored in a machine readable medium such as the storage device 908 is included. In some exemplary embodiments, some or all of the computer program can be stored in the ROM 902 and / or the communication unit 909 on the device 900 loaded and / or installed. When the computer program in the RAM 903 loaded and by the CPU 901 carried out, one or more steps of the methods described above can be performed 200 , 400 and or 600 are executed. Alternatively, in other exemplary embodiments, the CPU 901 be configured in any other suitable way (e.g. by means of firmware) to the procedures 200 , 400 and or 600 to execute ..

The functions described above can be performed at least in part by one or more hardware logic components. Exemplary types of hardware logic components that can be used include, for example, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System-on-Chip (SOC) Complex Programming Logic Device (CPLD) etc.

The program code used to implement the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to the processors or controllers of general purpose computers, special purpose computers or other programmable data processing devices so that the functions / operations indicated in the flowcharts and / or block diagrams are implemented when the program code is executed by the processor or the controller. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on the remote machine as an independent software package, or entirely on the remote machine or server.

Within the scope of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by the instruction execution system, device or device, or in combination with the instruction execution system, device or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine readable medium may include, and is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include cable-based electrical connections, portable computer disks, hard drives, random access memory (RAM), read only memory (ROM) and electrically programmable read only memory (EPROM or flash memory), fiber optic, portable compact disc read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

While the operations are shown in a particular order, it should be understood that such operations must be performed in the specific order given or in a sequential order, or that all of the operations shown must be performed to achieve the desired results. In certain circumstances, multitasking and parallel processing can be beneficial. While some specific implementation details are included in the discussion above, they should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in connection with a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination.

Although the subject matter has been described in a manner specific to structural features and / or logical acts of the method, it should be understood that subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and measures described above are merely exemplary forms of implementing the claims.

Claims (20)

A method of navigation comprising: Receiving a first user input relating to a navigation destination from a user; Determining at least one destination address associated with the navigation destination and parking difficulty information in a vicinity of the at least one destination address; and Providing the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user. Procedure according to Claim 1 wherein determining the parking difficulty information comprises: determining a time at which the user reaches the at least one destination address; and predicting the parking difficulty information in the vicinity of the at least one destination address at that time. Procedure according to Claim 1 or 2 , wherein providing the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address simultaneously for the user comprises: presenting the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the User. Procedure according to Claim 1 or 2 wherein the parking difficulty information comprises at least one of the following: an index indicating a parking difficulty; and the number of empty parking spaces. Procedure according to Claim 1 or 2 further comprising: receiving a second user input related to a selected destination address from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information; Determining a navigation route from a current position of the user to a selected destination address; and providing the navigation route to the user. Procedure according to Claim 2 wherein predicting the parking difficulty information in the vicinity of the at least one destination address at that time comprises: determining a parking area which corresponds to the at least one destination address, the parking area comprising one or more parking spaces in the vicinity of the at least one destination address; Predicting a parking requirement for the parking area at this time based on historical parking information from at least one parking space at a historical time corresponding to this time; and predicting the parking difficulty information in the vicinity of each of the at least one destination address at that time based on the parking need. Procedure according to Claim 6 wherein predicting the parking requirement for the parking area at that time comprises: determining a historical parking requirement of the at least one parking space at the historical time based on the historical parking information; Determining a mapping relationship between the historical parking requirement and a spatial feature of the at least one parking space; and predicting the parking needs of the parking area at that time based on the spatial feature of the parking area and the mapping relationship. Procedure according to Claim 7 wherein the spatial feature comprises at least one of the following: a distance to at least one place of interest; and the number of surrounding points of interest. Procedure according to Claim 6 , further comprising: predicting the parking difficulty information for each of the one or more parking spaces at that time based on the parking requirement, the one or more parking spaces including a parking space about which no historical parking information can be provided. A device for navigation, comprising: a first receiving module which is configured to receive a first user input relating to a navigation destination from a user; a first determination module that is configured to determine at least one destination address associated with the navigation destination and information about parking difficulty in an area around the at least one destination address; and a first provision module which is set up to provide the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user. Device according to Claim 10 wherein the first determination module comprises: a determination unit which is configured to determine a time at which the user reaches the at least one destination address; and a prediction unit configured to predict the parking difficulty information in the vicinity of the at least one destination address at that time. Device according to Claim 10 or 11 wherein the first provision module comprises: a presentation unit which is configured to present the at least one destination address and the parking difficulty information in the vicinity of the at least one destination address at the same time for the user. Device according to Claim 10 or 11 wherein the parking difficulty information comprises at least one of the following: an index indicating a parking difficulty; and the number of empty parking spaces. Device according to Claim 10 or 11 , further comprising: a second receiving module configured to receive a second user input relating to a selected destination address from the user in response to the user selecting one of the at least one destination address based on the parking difficulty information; a second determination module which is configured to determine a navigation route from a current position of the user to a selected destination address; and a second provision module which is configured to provide the navigation route to the user. Device according to Claim 11 , wherein the prediction unit is further configured to carry out the following: determining a parking area which corresponds to the at least one destination address, the parking area comprising one or more parking spaces in the vicinity of the at least one destination address; Predicting a parking requirement for the parking area at this time based on historical parking information from at least one parking space at a historical time corresponding to this time; and predicting the parking difficulty information in the vicinity of each of the at least one destination address at that time based on the parking need. Device according to Claim 15 wherein the prediction unit is further configured to perform the following: determining a historical parking requirement of the at least one parking space at the historical time based on the historical parking information; Determining a mapping relationship between the historical parking requirement and a spatial feature of the at least one parking space; and predicting the parking needs of the parking area at that time based on the spatial feature of the parking area and the mapping relationship. Device according to Claim 16 wherein the spatial feature comprises at least one of the following: a distance to at least one place of interest; and the number of surrounding points of interest. Device according to Claim 15 wherein the prediction unit is further configured to perform the following: Predicting the parking difficulty information for each of the one or more parking spaces at that time based on the parking requirement, the one or more parking spaces including a parking space that is not historical Parking information can be provided. A navigation device comprising: at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the device to enter a Method according to one of the Claims 1 - 9 execute when the instructions are executed by the at least one processing unit. Computer-readable storage medium on which a computer program is stored, the computer program causing a device to implement a method according to one of the Claims 1 - 9 when the computer program is executed by the device.

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