EP3895086A1

EP3895086A1 - Method, computer program, device, vehicle, and network component for estimating a departure time for a user using a vehicle

Info

Publication number: EP3895086A1
Application number: EP19820737.5A
Authority: EP
Inventors: Kord Lühr; Johannes Keller; Stefan Brosig; Stephan Menzel
Original assignee: Volkswagen AG
Current assignee: Volkswagen AG
Priority date: 2018-12-13
Filing date: 2019-12-11
Publication date: 2021-10-20
Also published as: DE102018221688A1; WO2020120538A1; US20220146275A1; CN113474771A

Abstract

The embodiments relate to a method, a computer program, a device, a vehicle, and a network component for estimating a departure time for a user using a vehicle. The method (10) for estimating a departure time for a user using a vehicle (100) has the steps of obtaining (12) vehicle-related data on the vehicle (100) and obtaining (14) personal data on the user. The method (10) additionally has a step of estimating (16) the departure time on the basis of the vehicle-related data and the personal data.

Description

description

Method, computer program, device, vehicle and network component for estimating a user's departure time with a vehicle

The present invention relates to a method, a computer program, a device, a vehicle and a network component for estimating a

Departure time of a user with a vehicle, in particular but not exclusively, to a concept for determining a departure time of a user with a vehicle based on personal data of the user and vehicle-related data of the vehicle.

Means of transportation or vehicles are subject to constant further development. The aim is to make them increasingly intelligent, for example, more and more communication components and more and more computing capacity (processors,

Control devices, controllers etc.) integrated. The vehicles can turn on

set different situations. One of the objectives can be to increase safety, comfort and mobility.

Document DE 10 2009 054 002 A1 describes a concept for estimating a

Travel time for a driver to travel from the current position to the location of an appointment according to the driver's route preferences. A notification period is determined on the basis of the estimated travel time and the time of the appointment and the user is reminded of the appointment at the notification time. This addresses the problem of promptly reminding a user who has an appointment at a location that is distant from his current position, so that his departure and arrival at the location of the appointment are timely but not too early. This is difficult for the user to estimate if he is not familiar with the area or is not sure where he will be immediately before the appointment.

A concept for determining and displaying available vehicles in a car sharing system is set out in the document DE 10 2017 111 711 A1. A user can select a vehicle taking into account the arrival time. A list of information regarding the types of the vehicles and the arrival times of the vehicles is displayed on a display of a user terminal, and the user can select a desired type of the vehicle in consideration of the arrival times.

Document DE 10 2015 007 490 A1 describes a method for operating a vehicle, in which a time period is determined which indicates a parking time permitted in a parking space provided for the vehicle. A user of the vehicle is informed of an impending expiration of the time period. To determine the time span, at least one piece of information is evaluated by an evaluation device, which of the

Evaluation device is transmitted by the vehicle and / or by at least one other vehicle. To record the parking conditions, vehicles are used that take part in the traffic anyway, record relevant information here and transmit it to the evaluation device.

EP 2 772 876 A1 is concerned with a parking guidance system. The concept there provides for the monitoring of parking spaces and their status (free / occupied). Based on a travel destination of a user, an arrival time is estimated and the user is made a reservation offer for a parking space.

State parameters or behavior parameters of a user of a vehicle with regard to temperature, lighting, air humidity, etc. are taken into account in a concept for adapting a corresponding room in document GB 2552360 A. For example, an average length of time the user stays in a room or building can be determined. The concept provides an evaluation of current and past navigation data of the user.

The concepts described in the prior art deal with the automated transmission of data which are intended to facilitate the handling of a vehicle. It does not take into account that a vehicle can be prepared for a corresponding application before the start of the journey.

This gives rise to the task of creating an improved concept for controlling a vehicle.

This object is achieved in accordance with the pending independent claims. Embodiments of the present invention are based on the knowledge that the

Arrival time (of a user at the vehicle) or departure time of a user with a

Vehicle can represent important information. If this information is made available, measures can be taken in the vehicle that prepare the departure or boarding of the user. Examples include ventilation, cooling, heating, de-icing the windscreen, calling up current traffic information, etc. In addition, personalization settings can be made to greet the vehicle, start up systems and much more. The parking time can also be estimated and the parking space can be arranged early so that a searcher can be navigated to the vacant parking space before the parking space becomes available.

Embodiments create a method for estimating a user's departure time with a vehicle. The method comprises receiving vehicle-related data about the vehicle and receiving personal data about the user.

The method also includes estimating the time of departure based on the vehicle-related data and the personal data. A departure time can be estimated more reliably based on the vehicle-related data and the personal data than would only be possible with vehicle-related data or only with personal data. In addition to the advantages already mentioned, exemplary embodiments can also enable mobility providers to recognize the arrival of their customers at the stopping point at an early stage. This means that vehicle fleets and routes can be planned better or even optimally and services can be made more convenient and efficient.

In further exemplary embodiments, the method can include receiving the personal data from a mobile device of the user and receiving the vehicle-related data from the vehicle. This means that data from mobile devices such as smartphones, cell phones, etc. can continue to be used. In particular, this data can be combined with the vehicle-related data, e.g. directly from the vehicle or from a corresponding database for the storage of such data. This results in an increased reliability of the estimate, since both data histories and current data are made available.

Estimating the time of departure may also include determining an arrival time of the user to the vehicle. The determination of the arrival time gives an indication of the presumed departure time. The arrival time can, for example, from a movement profile of the Users can be estimated. This can improve the accuracy of the estimated departure time.

In some further exemplary embodiments, a location of the vehicle can be determined based on the vehicle-related data and a determination of an arrival of the user at the location of the vehicle based on the personal data. The arrival time can be reliably estimated from the current location of the vehicle together with the data of the user.

For example, a determination of a user behavior routine based on the personal data and a determination of a vehicle routine based on the vehicle-related data can also be carried out in some exemplary embodiments. The departure time can then be estimated based on a comparison of the user behavior routine and the vehicle routine. In this way, an estimate can be made at low cost by comparing these data.

The determination of the vehicle routine can include evaluating vehicle starts and vehicle destinations over a period of time based on the vehicle-related data. Determining the user behavior routine can include evaluating the personal data with regard to mobility of the user. Correlations are thus made possible, which enable an improved estimate of the departure time.

The personal data can include one or more elements of the group from a mobility status of the user, a location of the user, a movement of the user, a data connection of the user, a time profile of the mobility status of the user, a time profile of the location of the user, a time profile the movement of the user and a temporal course of data connections of the user. This means that a variety of data, which is already available at least in some cases, can be used to estimate the departure time and thus prepare the vehicle for the upcoming journey and the user.

In some exemplary embodiments, real-time events can also be determined based on the personal data and the departure time can also be estimated based on the real-time events. For example, events such as the termination of data connections can be used to detect an impending departure of the vehicle with a specific user. An example of this would be leaving an office by a user on the way to his vehicle, one

Data connection to a cordless network (e.g. wireless local area network, WLAN) breaks down and can be used to recognize that you have just left the office. Further

Real-time events are the availability of radio networks that e.g. can repeat in a regular way a user to his vehicle and thus also one

Indicating an upcoming departure.

Depending on the data used to estimate the departure time, the reliability of the estimate may vary. It is therefore also possible to determine reliability information for estimating the time of departure. Based on this

Reliability information can then be adapted to any measures on / in the vehicle or a request to confirm the estimated departure time can be sent to the user.

Forwarding the estimated departure time to the vehicle (and / or the user) or activating a measure preparing for the departure in / on the vehicle can also take place in further exemplary embodiments. In this way it can be achieved that the vehicle is already prepared when the user arrives and begins the journey, e.g. can already be ventilated, heated, cooled, defrosted or a seat and / or mirror adjustment as well as one or more other personalization settings can be made.

Since it is also foreseeable that the parking space of the vehicle will become available on the basis of the estimated departure time, a parking space of the vehicle can also be arranged based on the estimated departure time. The earlier thus made possible

Mediation or occupancy planning of the parking lot can be more efficient

Parking space can be used.

A further exemplary embodiment is a computer program for performing one of the methods described herein if the computer program is on a computer, a

Processor, or a programmable hardware component expires. A device with a control module that is designed to carry out one of the methods described herein is also a further exemplary embodiment. Embodiments also create a network component, which comprises a corresponding device, and a system with a vehicle, a mobile device and a network component according to the present description. Further advantageous embodiments are described in more detail below with reference to the exemplary embodiments shown in the drawings, to which exemplary embodiments are generally not limited as a whole. Show it:

1 shows a flowchart of an exemplary embodiment of a method for estimating a time of departure of a user with a vehicle; and

2 shows a diagram to illustrate exemplary embodiments of a device, a network component and a system for estimating a time of departure of a user with a vehicle.

Various embodiments will now be described in more detail with reference to the accompanying drawings, in which some embodiments are shown. Optional features or components are shown in dashed lines.

Although exemplary embodiments can be modified and modified in various ways, exemplary embodiments are shown in the figures as examples and are described in detail herein. However, it should be clarified that it is not intended

To limit exemplary embodiments to the forms disclosed in each case, but rather that exemplary embodiments should rather cover all functional and / or structural modifications, equivalents and alternatives which are within the scope of the invention.

Note that an element called "connected" or "coupled" to another element may be directly connected or coupled to the other element, or there may be elements in between. If, on the other hand, an element is called “directly connected” or “directly coupled” to another element, there are no elements in between. Other terms used to describe the relationship between elements should be interpreted in a similar way (e.g., "between" versus "directly between", "adjacent" versus "directly adjacent", etc.).

The terminology used herein is used only to describe certain ones

Exemplary embodiments and are not intended to limit the exemplary embodiments. As used herein, the singular forms "one", "one", "one" and "the", "the", "that" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be clarified that the terms such as “includes”, “including”, “has”, “comprises”, "Comprehensive" and / or "comprising" as used herein indicate the presence of said features, integers, steps, workflows, elements and / or components, but the presence or addition of one or more features, whole Do not exclude numbers, steps, workflows, elements, components and / or groups.

1 shows a flow chart of an exemplary embodiment of a method 10 for estimating a time of departure of a user with a vehicle. The method 10 comprises obtaining vehicle-related data about the vehicle and obtaining 14 personal data about the user. The method also includes estimating 16 the time of departure based on the vehicle-related data and the personal data.

In embodiments, any means of transportation can be considered as vehicles, examples are passenger cars, trucks, two-wheelers, etc., but also water or aircraft. An estimate in the sense of execution games means here

Predicting, predicting or extrapolating a departure time of the vehicle, which may be somewhat inaccurate. Examples are a determination of the

Departure time with an accuracy of one or more seconds, minutes, etc. or with a certain accuracy. The user or user can be one of a number of potentially potential users, drivers or passengers of the vehicle. An example is a family-used vehicle or a vehicle used by a company that is used by several drivers. In this respect, the presentations are aimed at one of possibly several conceivable users. The vehicle-related data can, for example

Sensor data or corresponding data histories. Examples are travel times,

Speeds, parking processes, positions, etc. This data can be acquired and recorded via components in the vehicle itself, such as sensors, navigation devices, etc.

are provided, but it is also conceivable to record via a device inserted into the vehicle by the user, such as mobile radio devices.

The personal data relate to the user and can also be recorded using appropriate devices, such as mobile devices or other sensors. Scenarios are also conceivable in which the exit of an office, a shopping center, an apartment of the user is announced in the corresponding sensor data. Examples include shutting down a computer, switching off lighting in the office / apartment, etc., which can provide information about an impending use of the vehicle. Furthermore The personal data can also be recorded by the vehicle itself, for example which user uses the vehicle at what time.

FIG. 2 shows a diagram to illustrate exemplary embodiments of devices, network components 200, 300 and a system 400 for estimating a

Departure time of a user with a vehicle 100. FIG. 2 illustrates a vehicle 100, a network component 200 and a mobile radio device 300, these components also forming an exemplary embodiment of a system 400. However, exemplary embodiments, in particular those of a system 400, are not restricted to the presence of all three components, as the following explanation will show. The method 10 described above can run on any of the components shown. As shown in FIG. 1, the vehicle-related data and the personal data are used to estimate a time of departure of the vehicle 100. This data can be in the vehicle 100, in a network component 200 or in a mobile device 300

converge / be recorded and the method 10 can then be carried out there.

For example, common wireless communication technologies are used to communicate the data, and corresponding radio modems or other components that allow data communication can be used. Thus, the network component 200 shown in FIG. 2 above can receive the vehicle-related data from the vehicle 100 and the personal data from the mobile device 300. Additionally or alternatively, the vehicle 100 can receive this data from the mobile device 300, for example via a network component provided therefor 200 (base station, access point, Internet) or also directly from the mobile device 300. With direct communication, cordless technologies also come into consideration, for example mobile radio, WLAN, Bluetooth, etc. or else other interfaces, for example USB (from Universal Serial Bus), if the mobile device 300 is coupled in the vehicle, for example. In addition or as an alternative, the data can also be transmitted from the vehicle 100 directly or via a network to the mobile device 300. Another variant is the acquisition of the vehicle-related data directly by the mobile device 300. Here and in the following it is emphasized that the mobile device 300 itself is also a network component. In this respect, the methods 10 described herein can be carried out in each of the components shown and the dashed arrows shown in FIG. 2 indicate the various communication paths or possibilities. In addition, in some exemplary embodiments, the acquisition of the personal data and the acquisition of the vehicle-related data can also be carried out solely by the vehicle 100 or solely by the mobile device 300. In exemplary embodiments, the method 10 can also be implemented as a computer program. A computer program for performing one of the methods 10 described herein when the computer program is on a computer, processor, or

programmable hardware component runs, is therefore also an embodiment.

In this respect, exemplary embodiments also include a device with a control module, which is designed to carry out one of the methods 10 described herein.

For example, such a method can be implemented in software and can then be carried out by appropriate hardware. In exemplary embodiments, a control module can correspond to one or more arbitrary controllers or processors or a programmable hardware component. For example, a device can also be implemented as software that is suitable for a corresponding hardware component

is programmed. In this respect, a control module can be implemented as programmable hardware with correspondingly adapted software. Any processors such as digital signal processors (DSPs) can be used. Exemplary embodiments are not restricted to a specific type of processor. Any processors or even multiple processors or microcontrollers for implementing the device or the control module are conceivable. Implementations in an integrated form with other control units are also conceivable, for example in a control unit for a vehicle, an ECU (from Electronic Control Unit), a user terminal (e.g. a mobile radio device)

Network component), a server (e.g. network component), which can also include one or more other functions. Embodiments also create a network component (base station, vehicle, mobile device, server) with a corresponding device or a control module.

In some exemplary embodiments, a position of a smartphone can also be one

Be made known to users and there may be many other options with the time remaining until boarding. For example, messages or messages can be sent to the user in the last minutes before the start. The window of time made available in this way can be valuable because once it arrives in the vehicle, the user's attention is probably no longer on the smartphone. E.g. any reports or messages can be triggered by advertising agencies,

Office management services, etc. If the entry time is known, it can also be used

Derive the length of stay of the vehicle at the parking lot. This makes further services possible. Some examples are software updates, refueling, cleaning, packages, washing from the Deliver cleaning, charging management for e-cars, charging before departure saves you that

Preheating in winter, etc.

The method 10 is further described below from the point of view of the network component 200 in FIG. 2. In other exemplary embodiments, these details apply analogously if the data converge in the vehicle 100 or in the mobile device 300. In this respect, the method 10 can include receiving the personal data from a mobile device 300 of the user and receiving the vehicle-related data from the vehicle 100. For example, the data are transmitted to the network component 200 via cordless interfaces and corresponding access points via the Internet.

In some exemplary embodiments, the method 10 realizes, on the one hand, an algorithm that predicts the arrival at a specific location (parking lot or stop) from customer-related / personal data (for example GPS movement data, from the Global Positioning System) and, on the other hand, a cloud service with which this Information is made available to all interested functions. Estimating 16 the time of departure may include determining an arrival time / arrival time of the user on the vehicle 100. It should be pointed out that in many cases the arrival time of the user on the vehicle 100 is shortly before the departure time of the vehicle. However, other cases are also conceivable in which the user initially lingers in or on the vehicle before a journey begins. Scenarios are conceivable in which it is foreseeable that there will be waiting for several passengers, in which certain user routines or habits or even scheduled trips occur.

The method 10 may include, for example, determining a location of the vehicle 100 based on the vehicle-related data. In addition to the GPS data already mentioned, other location mechanisms can also be used, especially where no GPS signal is available, e.g. in underground garages, parking garages, garages, etc. Alternative mechanisms are, for example, location based on available mobile radio or WLAN networks, sensor data of the vehicle (for example optically recorded parking space number), etc. In addition, the method 10 can determine an arrival of the user at the location of the Include vehicle 100 based on the personal data. GPS-based or location-based mechanisms can also be used.

In this respect, in some exemplary embodiments, determining a user behavior routine based on the personal data and determining a vehicle routine based on the vehicle-related data. Estimating 16 of the

Departure time can then be based on a comparison of the user behavior routine and the

Vehicle routine based. Routines can be learned here, for example by capturing temporal information about vehicle starts and driving destinations, as is also used for navigation prediction, also P-NAV (from predictive navigation). User behavior can also be learned via smart phone data, for example, in order to suggest the next destinations and to provide navigation instructions.

The vehicle routine can thus be determined by evaluating vehicle starts and / or vehicle destinations over a period of time on the basis of the vehicle-related data (e.g. regular commuting between two destinations at certain times). The user behavior routine can be determined by evaluating the personal data with regard to the mobility of the user (e.g. regularly leaving a house / apartment / office or regularly moving house / apartment to a specific work address on weekdays - also using different ones

Means of transport). Exemplary embodiments enable a combination of personal data such as smart phone data with vehicle data and can thus create a central availability of the information “time of entry” or “time of departure” in a cloud service.

For example, a smart phone (mobile device 300 in FIG. 2) collects data such as mobility status (resting, walking, running, driving), location and movement

(GPS positions) and data connections (WLAN, Bluetooth, mobile radio) and sends them to the cloud backend (to the network component 200 in FIG. 2).

The data is processed on the server (network component 200) and, at least in some exemplary embodiments, analyzed according to two principles. On the one hand, after

Behavior routines searched for a prediction at a specific time on a

enable a specific location (mobility patterns are learned). On the other hand

Real-time events are observed and the connection with a subsequent entry / exit event is learned. Neural networks and mechanisms of artificial intelligence can also be used. A learning mechanism can be created, for example, by combining the estimated departure times with the

actual departure times are compared and the respective algorithms are adapted or trained based on the successes or failures.

The personal data can include one or more elements of the group from a mobility status of the user, a location of the user, a movement of the user, a data connection of the user, a time profile of the mobility status of the user, a time profile of the location of the user, a time profile of the movement of the user and a time profile of data connections of the user.

Further examples are the mobility status, the geo-position, WLAN, Bluetooth connections, etc. Further examples of vehicle data used are ignition start and end (terminal 15), parking position, time routines, etc.

Furthermore, exemplary embodiments of the method 10 can provide a determination of real-time events based on the personal data, and the estimation 16 of the departure time can additionally be based on the real-time events. A real-time event can be, for example, the disconnection of the home WLAN connection or the approach of the smart phone (mobile device 300) to the parking position.

As a result, the method 10 of the service outputs the arrival or departure time of a person at an entry point. In addition to the time, the probability of this prediction being met can also be specified. In some exemplary embodiments, the smaller the time interval at the time of entry, the larger the

Probability for the prediction to come true as the information becomes more and more reliable. In other words, the method 10 can also determine a

Include reliability information for estimating the departure time.

Some exemplary embodiments implement a connection of smart phone data and

Vehicle data. The entry or departure forecast can be made available centrally for use for a large number of functions. The provision of the boarding / departure time can be made available at any time at a central point with a probability. As already explained above, the vehicle-related data can also be recorded via the mobile device 300. Some exemplary embodiments create a pure smart phone application without directly capturing vehicle data from the vehicle, but based on vehicle-related data that are captured by the smartphone itself and its sensors. Then the local sensor system of the mobile device 300 (infrastructure) can be like

Cameras, microphones can be used to predict user behavior.

Because the data, at least in some exemplary embodiments, have a direct personal or vehicle reference, a high accuracy of the estimate 16 can therefore be made possible. Since the number of personal data (according to the principle of data economy) remain manageable and the number of partners in the chain of effects is small, the estimate 16 can be made efficiently. Data security, transparency and erasability can be avoided

are comprehensibly granted and thus a solid relationship of trust with the customer can be built. Exemplary embodiments can be used with a wide variety of vehicles. Examples are bicycles, scooters, motorcycles, trucks (trucks), buses, segways, aviation, railroad, etc. Many services can be provided by

Examples benefit. Examples are carpooling services, taxi services, shuttle services, car sharing services, car park switching services,

Charging station switching services, traffic forecasting services (where and how many vehicles start), local advertising, city planning, etc.

In a further exemplary embodiment, the method provides for forwarding the estimated departure time to the vehicle 100 or activating one that prepares the departure

Measure in the vehicle 100 before. For example, the preparatory

Measures are activated remotely or by vehicle 100 itself. Examples of such measures are rules for heating, cooling, ventilation, settings for seats, mirrors or user-specific settings of the navigation system (destinations, route preferences) or the entertainment system (radio stations, online services, etc.) . Also can in

Embodiments a switching of a parking space of the vehicle 100 based on the estimated departure time. This can require more efficient parking placement and use.

Further exemplary embodiments are computer programs for performing one of the methods described herein if the computer program is on a computer, a

Processor, or a programmable hardware component expires. Depending on the specific implementation requirements, exemplary embodiments of the invention can be implemented in hardware or in software. The implementation can be carried out using a digital storage medium, for example a floppy disk, a DVD, a Blu-Ray disc, a CD, a ROM, a PROM, an EPROM, an EEPROM or a FLASH memory, a hard disk or another magnetic or optical memory are carried out, on which electronically readable control signals are stored, which can interact with a programmable hardware component in such a way or that the respective method is carried out.

A programmable hardware component can be a processor, a

Computer processor (CPU = Central Processing Unit), a graphics processor (GPU = Graphics Processing Unit), a computer, a computer system, an application-specific integrated circuit (ASIC = Application-Specific Integrated Circuit), an integrated circuit (IC = Integrated Circuit), a one-chip system (SOC = System on Chip), a programmable Logic element or a field programmable gate array with one

Microprocessor (FPGA = Field Programmable Gate Array) can be formed.

The digital storage medium can therefore be machine or computer readable. Some exemplary embodiments thus comprise a data carrier which has electronically readable control signals which are able to interact with a programmable computer system or a programmable hardware component in such a way that one of the methods described herein is carried out. One exemplary embodiment is thus a data carrier (or a digital storage medium or a computer-readable medium) on which the

Program for performing one of the methods described herein is recorded.

In general, exemplary embodiments of the present invention can be implemented as a program, firmware, computer program or computer program product with a program code or as data, the program code or the data being or being effective in performing one of the methods when the program is on a processor or programmable hardware component expires. The program code or the data can also be, for example, on a machine-readable carrier or data carrier

be saved. The program code or the data can be present, inter alia, as source code, machine code or byte code and as another intermediate code.

The above-described embodiments are merely illustrative of the principles of the present invention. It is understood that modifications and

Variations in the arrangements and details described herein will be apparent to those skilled in the art. Therefore, it is intended that the invention be limited to the

Scope of protection of the following claims and not by the specific

Details that were presented based on the description and explanation of the exemplary embodiments are limited. A list of methods for estimating a user's departure time with a

vehicle

Obtain vehicle-related data about the vehicle

Receive personal data about the user

Estimating the departure time based on the vehicle-related data and the personal data

vehicle

Network component

Mobile device

system

Claims

1. Method (10) for estimating a user's departure time with a vehicle (100) with

Obtaining (12) vehicle-related data about the vehicle (100);

Receiving (14) personal data about the user; and

Estimating (16) the time of departure based on the vehicle-related data and the personal data.

2. The method (10) according to claim 1, further comprising receiving the personal data from a mobile device (300) of the user and receiving the vehicle-related data from the vehicle (100).

3. The method (10) according to one of the preceding claims, wherein estimating (16) the departure time comprises determining an arrival time of the user at the vehicle (100).

The method (10) according to claim 3, further comprising determining a location of the vehicle (100) based on the vehicle-related data and determining an arrival of the user at the location of the vehicle (100) based on the personal data.

5. The method (10) according to one of the preceding claims, further comprising determining a user behavior routine based on the personal data and determining a vehicle routine based on the vehicle-related data and estimates (16) of the departure time based on a comparison of the user behavior routine and the

Vehicle routine.

6. The method (10) according to claim 5, wherein determining the vehicle routine evaluates vehicle starts and vehicle destinations over a period of time based on the vehicle-related Data includes, and wherein the determination of the user behavior routine includes an evaluation of the personal data regarding mobility of the user.

7. The method (10) according to any one of the preceding claims, wherein the personal data one or more elements of the group of a mobility status of the user, a location of the user, a movement of the user, a data connection of the user, a temporal course of the mobility status of the user , a time course of the location of the user, a time course of the movement of the user and a time course of data connections of the user.

8. The method (10) according to any one of the preceding claims, further comprising determining real-time events based on the personal data and estimates (16) of the departure time additionally based on the real-time events.

9. The method (10) according to any one of the preceding claims, further comprising determining reliability information for estimating (16) the departure time.

10. The method (10) according to one of the preceding claims, further comprising forwarding the estimated departure time to the vehicle (100) or activating the departure

preparatory action in the vehicle (100).

11. The method (10) according to one of the preceding claims, further comprising arranging a parking space for the vehicle (100) based on the estimated departure time.

12. Computer program for performing one of the methods (10) according to one of claims 1 to 11, when the computer program runs on a computer, a processor, or a programmable hardware component.

13. Device with a control module, which is designed to carry out one of the methods (10) of claims 1 to 11.

14. Network component (200) with a device according to claim 13.

15. Vehicle (100) with a device according to claim 13.