DE102019109095A1 - Arrival forecasting procedures and systems - Google Patents

Abstract

The present disclosure relates to a method for predicting the arrival of a vehicle at a target position based on a data source. The data source includes: a plurality of road segments; a plurality of intersections, each intersection of the plurality of intersections connecting at least one starting road segment to at least one destination road segment based on a maneuver; a reference maneuver for each intersection of the plurality of intersections; and for each maneuver other than the reference maneuver at an intersection of the plurality of intersections, a first value and a second value. The method comprises determining a route based on a starting position and a target position, the route at least a subset of road segments of the plurality of road segments and at least a subset of Intersections includes the plurality of intersections; Determining, for each intersection of the subset of intersections, an anticipated time interval in which the vehicle will drive through the respective intersection and, based on the data source, the first value of the second value and a third value u (T), in each case as a function of the anticipated time interval; and determining the arrival prognosis based on the determined first, second and third values for each intersection of the subset of intersections. The present disclosure further relates to a system comprising a control unit which is configured to carry out the method and a vehicle comprising the system.

Description

The disclosure relates to arrival forecasting methods and systems. More particularly, the disclosure relates to arrival forecasting methods and systems for calculating expected arrival times for vehicles.

State of the art

In the prior art, navigation systems are known which process real-time traffic information (“Real Time Traffic Information” or RTTI), for example in the course of visualization in a digital geomap or for the purpose of precisely calculating the arrival time of a route provided in the navigation system. Such navigation systems are known as systems integrated in vehicles, as well as in the form of stand-alone apps for mobile devices such as smartphones or mobile navigation systems.

Due to the high dynamics of the RTTI, it is not or only insufficiently possible to predict individual traffic speeds with conventional systems or processes. Rather, they have to be determined continuously, usually at least temporarily stored at a central location (e.g. on a central server) and finally transferred to the navigation systems. Such services are usually provided by private operators.

A major disadvantage of available products that use RTTI is that typically only a single traffic speed is determined for each road segment. A street segment can, for example, be part of a street, preferably the part between two consecutive intersections (e.g. Schleissheimerstrasse in Munich from the intersection with Max-Diamand-Strasse to the intersection with Frankfurter Ring).

So-called "locations" are used for georeferencing RTTI. These can consist of a single road segment or a chain of consecutive connected road segments. The definition of "locations" is often subject to the sovereignty of state authorities (e.g. the Federal Highway Research Institute, BASt, in Germany) and therefore represents a recognized standard that is widely used among RTTI providers. In the following, the terms “street segment”, “street link” and “location” are used synonymously, unless otherwise expressly stated in individual cases.

However, the limitation of locations affects the quality and usability of the RTTI in at least two ways. First, different speeds on, if necessary, several lanes of one and the same road segment must be averaged to a single average speed, since only one speed is provided per location. This is problematic in that the speeds on different lanes of the same road segment can potentially be very different. Examples are different traffic light phases turning left or right turning and vehicles driving straight ahead.

Furthermore, based on locations alone, the time required to traverse an intersection, to make a turn, or the like cannot be taken into account. Instead, only average speeds for individual locations are available. For example, for a maneuver in Munich from Schleissheimerstraße from north to south right into Moosacherstraße, only the average speeds on Schleissheimerstraße (or on the segment involved) and on Moosacherstraße (or on the segment involved) are used for the calculation considered a maneuver speed. An additional waiting time that is necessary to carry out the maneuver (e.g. due to pedestrian traffic) or the respective speeds of individual lanes cannot be taken into account.

There are RTTI providers who are active in the area of so-called high-resolution maps ("HD maps"). Under this term, products are offered that contain a proprietary modeling of road data with a higher resolution, the higher resolution referring to several lanes per road segment. Based on the proprietary model, it is possible to model average speeds for individual lanes. The disadvantage here can be that such a modeling contains fundamental changes that cannot be mapped by the classic RTTI structures. There is also no provision for a detailed model of the transition between individual road segments.

The present disclosure describes methods and systems which make it possible to map different maneuver speeds and lane speeds even when using classic, non-high-resolution maps and classic RTTI structures. With the methods and systems disclosed here, the RTTI speed for a location can be broken down according to different maneuver speeds. In this way, a discrepancy between the real and the calculated speed forecast can be reduced compared to the current application of RTTI and the accuracy of the arrival time forecast can be increased.

In a representative example, traverse within a unit of time 80 Vehicles at the intersection Schleißheimer Straße / Moosacher Straße from north to south (ie driving straight ahead) at a speed of 50 km / h. At the same time, 20 vehicles cross the same intersection from north to west (ie turn into Moosacher Strasse) at a speed of 10 km / h. Based on the conventional principle of average speeds and the aforementioned speeds, an RTTI provider can achieve an average speed of (50 * 80 + 10 * 20) / (80 + 20) = 42 for the location (ie the segment involved) on "Schleißheimer Straße" Calculate km / h. When comparing this value with the actual speed of the vehicles in the right lane for the purpose of turning right into Moosacher Straße, an error of (42-10) / 10 = 320% results.

There is therefore a need for arrival prognosis methods and systems that enable an improved mapping of the actual conditions in the modeling. An arrival prognosis and / or a more reliable function of navigation systems can thus be improved.

In particular, there is also a need for an improved energy consumption forecast for both electric and conventional drives. Since consumption depends on the speed and duration of the journey, an improved speed forecast can also improve the energy consumption forecast.

Disclosure of the invention

It is an object of the present disclosure to provide arrival forecast methods and arrival forecast systems which avoid one or more of the disadvantages described and / or enable one or more of the advantages described.

This object is achieved by the subject matter of the independent claims. Advantageous refinements are given in the subclaims.

und einen zweiten Wert ${{\displaystyle Z}}_i^{L_2}.$

Das Verfahren umfasst: Ermitteln einer Route basierend auf einer Startposition und der Zielposition, wobei die Route mindestens eine Untermenge von Straßensegmenten der Vielzahl von Straßensegmenten und mindestens eine Untermenge von Kreuzungen der Vielzahl von Kreuzungen umfasst; Ermitteln, für jede Kreuzung der Untermenge von Kreuzungen, eines voraussichtlichen Zeitintervalls T_k in dem das Fahrzeug die jeweilige Kreuzung befahren wird und, basierend auf der Datenquelle, des ersten Wertes ${{\displaystyle Z}}_i^{L_1}\left(T_k\right),$

des zweiten Wertes ${{\displaystyle Z}}_i^{L_2}\left(T_k\right)$

und eines dritten Wertes u_i(T_k), jeweils in Abhängigkeit des voraussichtlichen Zeitintervalls (T_k); und Ermitteln der Ankunftsprognose basierend auf den ermittelten ersten, zweiten und dritten Werten für jede Kreuzung der Untermenge von Kreuzungen.In a first aspect according to embodiments of the present disclosure, an arrival forecast method is specified. The method for predicting the arrival of a vehicle at a target position is based on a data source, the data source comprising: a plurality of road segments; a plurality of intersections, each intersection of the plurality of intersections connecting at least one starting road segment to at least one destination road segment based on a maneuver; a reference maneuver for each intersection of the plurality of intersections; and a first value for each maneuver other than the reference maneuver at an intersection of the plurality of intersections ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}$

and a second value ${{\displaystyle Z}}_i^{{\mathrm{L.}}_2}.$

The method comprises: determining a route based on a starting position and the destination position, the route including at least a subset of road segments of the plurality of road segments and at least a subset of intersections of the plurality of intersections; Determining, for each intersection of the subset of intersections, a probable time interval T _k in which the vehicle will drive through the respective intersection and, based on the data source, the first value ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}\left(T_k\right),$

of the second value ${{\displaystyle Z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

and a third value u _i (T _k ), in each case as a function of the probable time interval (T _k ); and determining the arrival prognosis based on the determined first, second and third values for each intersection of the subset of intersections.

basierend auf einem Durchschnittswert ermittelt; und/oder der zweiten Wert ${{\displaystyle Z}}_i^{L_2}$

basierend auf einem Durchschnittswert ermittelt.In a second aspect according to the preceding aspect 1, for each intersection of the plurality of intersections in the data source: the first value ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}$

determined based on an average value; and / or the second value ${{\displaystyle Z}}_i^{{\mathrm{L.}}_2}$

determined based on an average value.

basierend auf ${{\displaystyle Z}}_i^{L_1}\left(T_k\right)={{\displaystyle v}}_i^{L_1}\left(T_k\right)\text{/}{{\displaystyle v}}_1^{L_1}\left(T_k\right),$

mit ${{\displaystyle v}}_i^{L_1}\left(T_k\right)$

gleich der wahrscheinlichsten Geschwindigkeit auf dem Straßensegment L₁, wenn das Manöver i im Zeitintervall T_k durchgeführt wird, und ${{\displaystyle v}}_1^{L_1}\left(T_k\right)$

gleich der Geschwindigkeit auf dem Straßensegment L₁, wenn das Referenzmanöver im Zeitintervall T_k durchgeführt wird.In a third aspect according to one of the preceding aspects 1 or 2, the first value is determined ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

based on ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)={{\displaystyle v}}_i^{{\mathrm{L.}}_1}\left(T_k\right)\text{/}{{\displaystyle v}}_1^{{\mathrm{L.}}_1}\left(T_k\right),$

With ${{\displaystyle v}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

equal to the most likely speed on the road segment L ₁ if the maneuver i is carried out in the time interval T _k , and ${{\displaystyle v}}_1^{{\mathrm{L.}}_1}\left(T_k\right)$

equal to the speed on the road segment L ₁ if the reference _{maneuver is} carried out in the time interval T _k .

basierend auf ${{\displaystyle v}}_i^{L_2}\left(T_k\right)={{\displaystyle v}}_i^{L_2}\left(T_k\right)\text{/}{{\displaystyle v}}_1^{L_2}\left(T_k\right),$

mit ${{\displaystyle v}}_i^{L_2}\left(T_k\right)$

gleich der wahrscheinlichsten Geschwindigkeit auf dem Straßensegment L₂, wenn das Manöver i im Zeitintervall T_k durchgeführt wird, und ${{\displaystyle v}}_1^{L_2}\left(T_k\right)$

gleich der Geschwindigkeit auf dem Straßensegment L₂, wenn das Referenzmanöver im Zeitintervall T_k durchgeführt wird.In a fourth aspect according to one of the preceding aspects 1 to 3, the second value is determined ${{\displaystyle Z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

based on ${{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)={{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)\text{/}{{\displaystyle v}}_1^{{\mathrm{L.}}_2}\left(T_k\right),$

With ${{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

equal to the most likely speed on road segment L ₂ when maneuver i is performed in time interval T _k , and ${{\displaystyle v}}_1^{{\mathrm{L.}}_2}\left(T_k\right)$

equal to the speed on the road segment L ₂ if the reference _{maneuver is} carried out in the time interval T _k .

für das Manöver i basierend auf folgenden Gleichungen ermittelt:

• • wenn i = 1, $${{\displaystyle v}}_1^{{\widehat{L}}_1}={{\displaystyle v}}_{RTTI}^{L_1}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,..}{I}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,..}I\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{L_1}\left(T_k\right)\right)}},{{\displaystyle v}}_1^{{\widehat{L}}_2}={{\displaystyle v}}_{RTTI}^{L_2}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,..}{I}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,..}I\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{L_2}\left(T_k\right)\right)}}$$
  
  mit / gleich der Anzahl von Manövern i an der jeweiligen Kreuzung vom Straßensegment L₁ kommend.
• • wenn $i>\mathrm{1,}{{\displaystyle v}}_l^{{\widehat{L}}_1}={{\displaystyle v}}_1^{{\widehat{L}}_1}\ast u_i\left(T_k\right).$
  

In a fifth aspect according to one of the preceding aspects 1 to 4, the most likely first and second speeds ${{\displaystyle v}}_l^{\widehat{\mathrm{L.}}}\text{and}{{\displaystyle v}}_l^{{\mathrm{L.}}_2}$

determined for maneuver i based on the following equations:

• • if i = 1, $${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}={{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,\; ..}{\mathrm{I.}}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,\; ..}\mathrm{I.}\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{{\mathrm{L.}}_1}\left(T_k\right)\right)}},{{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_2}={{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,\; ..}{\mathrm{I.}}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,\; ..}\mathrm{I.}\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{{\mathrm{L.}}_2}\left(T_k\right)\right)}}$$
  
  with / equal to the number of maneuvers i at the respective intersection from the road segment L ₁ .
• • if $i>\mathrm{1,}{{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_1}={{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}\ast u_i\left(T_k\right).$
  

und ${{\displaystyle v}}_1^{{\widehat{L}}_2}.$

In a sixth aspect according to the preceding aspect 5, the arrival prognosis is determined based on the first and second speed values ${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}.$

and ${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_2}.$

und ${{\displaystyle v}}_{RTTI}^{L_2}$

basierend auf einem RTTI Dienst. Ermitteln der Ankunftsprognose erfolgt basierend auf den ersten und zweiten RTTI-Geschwindigkeiten ${{\displaystyle v}}_{RTTI}^{L\widehat{{}_1}}\text{ und }{{\displaystyle v}}_{RTTI}^{L_2}.$

In a seventh aspect according to one of the preceding aspects 1 to 4, the method further comprises determining first and second RTTI speeds ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}$

and ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}$

based on an RTTI service. The arrival forecast is determined based on the first and second RTTI speeds ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{\mathrm{L.}\widehat{{}_1}}\text{and}{{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}.$

In an eighth aspect according to one of the preceding aspects 1 to 7, the third value u _i (T _k ) is determined based on u _i (T _k ) = n _i (T _k ) / n ₁ (T _k ), with u _i ( T _k ) equals the most probable ratio between a number n _i of vehicles which carry out maneuver i in a predetermined time unit and a number n ₁ of vehicles which carry out reference maneuver i = 1 in the predetermined time unit; preferably wherein the predetermined time unit is 1 minute.

In a ninth aspect according to embodiments of the present disclosure, a system for predicting the arrival of a vehicle is specified. The system comprises a control unit which is configured to carry out the method according to embodiments of the present disclosure, in particular according to one of the preceding aspects 1 to 8.

In a tenth aspect according to embodiments of the present disclosure, a vehicle is provided. The vehicle comprises a system according to embodiments of the present disclosure, in particular according to the preceding aspect 9.

According to a further aspect, a software program is described. The software program can be set up to run on a processor and thereby to carry out methods disclosed herein.

According to a further aspect, a storage medium is described. The storage medium can comprise a software program which is set up to be executed on a processor and to thereby execute methods disclosed herein.

The methods and systems disclosed herein provide one or more of the advantages noted below.

Methods and systems according to the present disclosure enable a significantly improved mapping of the actual relationships in the modeling. This results in a significant improvement in an arrival prognosis and / or a more reliable function of navigation systems.

Methods and systems according to the present disclosure further enable an improvement in the energy consumption forecast.

Figure list

• 1 illustriert schematisch den Aufbau eines Systems gemäß Ausführungsformen der vorliegenden Offenbarung; und
• 2 zeigt ein Flussdiagramm eines Verfahrens gemäß Ausführungsformen der vorliegenden Offenbarung.

Exemplary embodiments of the disclosure are shown in the figures and are described in more detail below. Unless otherwise noted, the same reference symbols are used below for elements that are the same or have the same effect.

• 1 schematically illustrates the structure of a system according to embodiments of the present disclosure; and
• 2 12 shows a flow diagram of a method according to embodiments of the present disclosure.

Embodiments of the disclosure

Unless otherwise noted, the same reference symbols are used below for elements that are the same and have the same effect.

1 schematically illustrates the structure of a system 100 in accordance with embodiments of the present disclosure.

The term “vehicle” includes cars, trucks, buses, mobile homes, motorcycles, etc., which are used to transport people, goods, etc. In particular, the term includes motor vehicles for passenger transport. vehicles 80 with electric drive (in particular hybrid or electric vehicles) are driven by an electric motor and include one or more electrical energy storage devices (e.g. batteries, high-voltage storage devices), which are connected to a vehicle charging device 80 can be connected to a charging station and charged. Various charging techniques can be used to charge the electrical storage of such hybrid or electric vehicles.

The vehicle preferably includes an environment sensor system which is set up to record the environment data. The environmental sensor system preferably comprises at least one GPS for measuring the vehicle position and speed, a LiDAR system and / or at least one radar system and / or at least one camera and / or at least one ultrasonic system. The environment sensor system can provide the environment data (also referred to as “environment data”), which depict an area around the vehicle.

In the context of the present disclosure, the term “automated driving” can be understood to mean driving with automated longitudinal or lateral guidance or autonomous driving with automated longitudinal and lateral guidance. The automated driving can be, for example, driving on the motorway for a longer period of time or driving for a limited time as part of parking or maneuvering. The term “automated driving” includes automated driving with any degree of automation. Exemplary degrees of automation are assisted, partially automated, highly automated or fully automated driving. These degrees of automation were defined by the Federal Highway Research Institute (BASt) (see BASt publication “Research compact”, edition 11/2012). With assisted driving, the driver continuously performs longitudinal or lateral guidance, while the system takes on the other function within certain limits. With partially automated driving (TAF), the system takes over the longitudinal and lateral guidance for a certain period of time and / or in specific situations, whereby the driver has to continuously monitor the system as with assisted driving. At the highly automated driving (HAF), the system takes over the longitudinal and lateral guidance for a certain period of time without the driver having to permanently monitor the system; however, the driver must be able to take over driving the vehicle within a certain period of time. With fully automated driving (VAF), the system can automatically cope with driving in all situations for a specific application; a driver is no longer required for this application.

The four degrees of automation mentioned above correspond to SAE levels 1 to 4 of the SAE J3016 standard (SAE - Society of Automotive Engineering). For example, highly automated driving (HAF) Level 3 corresponds to the SAE J3016 standard. In addition, SAE J3016 provides SAE level 5 as the highest level of automation, which is not included in the definition of BASt. SAE level 5 corresponds to driverless driving in which the system can automatically handle all situations like a human driver during the entire journey; a driver is generally no longer required.

The system 100 can essentially be on a control unit 120 of the vehicle 80 and / or on one or more backend components 150 (e.g. server, services) are implemented.

The vehicle 80 includes in addition to the control unit 120 further a communication unit 130 used for data communication 140 with to the vehicle 80 external components (e.g. backend 150 ) is configured and a user interface 110 for example as a touchscreen in the vehicle 80 can be implemented (for example on or in the dashboard area of the vehicle 80 or as part of a rear seat entertainment system in the rear of the vehicle 80 ; Not shown). The communication unit 130 can also be configured to provide a data connection to mobile devices, in particular to those located in the vehicle 80 and / or in the vicinity of the vehicle 80 and / or by the user 60 be used.

The system 100 can be partially or fully through the user interface 110 in the vehicle 80 to be served. The user interface 110 can have one or more multimodal user interfaces, in particular user interfaces that are used for operating the vehicle 80 configured (e.g. communication, infotainment, air conditioning, seat settings, vehicle settings). The user interface 110 enables the multimodal recording of input from a user 60 , for example via a graphical user interface (e.g. touchscreen), via classic vehicle controls 80 (e.g. buttons, switches, iDrive controller), by voice control (e.g. by means of sensors 210 ), by scanner (e.g. for fingerprints, retina) and the like. The user interface 110 further enables the multimodal output of information to a user 60 , for example via graphic display elements (e.g. touchscreen, head-up display, instrument cluster, central information display or CID), via tactile elements (e.g. vibration of the steering wheel or parts of the seat), by voice output via a loudspeaker system in the vehicle (e.g. infotainment system ) or acoustic signal generators (e.g. gong, beeper) and the like. The user interface 110 can implement a graphical user interface based on corresponding configuration data, in which display elements and operating elements are shown that the user 60 for operating the vehicle 80 can be used. Additionally or alternatively, the user interface can contain (further) display and operating elements, for example switches, buttons and displays.

Via the communication unit 130 can the control unit 120 with (external) backend components and services 150 in data communication 140 and so for example with backend servers and / or services 150 communicate. Alternatively or additionally, the control device 120 via the communication unit 130 with a user's mobile device 60 enter into data communication and use data and / or sensors provided by the mobile terminal device (as described above).

The system can continue 100 one or more to the vehicle 80 external backend components 150 and / or have an infrastructure that provides one or more resources (eg servers, services). The one or more backend components 150 can temporarily or permanently with the control unit 120 of the vehicle 80 and / or in data communication with the mobile terminal 140 stand. Resource-intensive processing steps (for example processing natural language and / or image or video data, speech recognition, processing large external data volumes) can preferably be sent to the external backend component 150 that are outsourced by the control unit 120 in the vehicle 80 and / or could only be carried out with difficulty or not at all by the mobile terminal. In this context, possible requirements with regard to computing power, storage capacity, available bandwidth, connection to external data sources and the like can also be taken into account.

According to the present disclosure, a computation model is implemented, based essentially on “big data” concepts, which can determine the most likely speed distribution between all permissible driving maneuvers at an intersection. Permissible driving maneuvers at an intersection are defined by appropriate signs, signs, lane markings and / or based on the road traffic regulations STVO. For example, intersections can have separate lanes for turning, right of way and / or other rules (e.g. prohibition to turn left).

Initially, in a first step, it is provided that measurement data relating to vehicle position and speed from a large number of vehicles are collected by means of environmental sensors built into the vehicles and sent to the back-end 150 through the communication unit 130 and to record them there in a data source (e.g. database, especially persistent database). These measurement or fleet data can be recorded over a longer period of time and / or updated at cyclical intervals. Methods according to the present disclosure are based on data which are stored in the data source and which characterize a large number of journeys over a longer period of time. The period is preferably a few weeks or months. As an alternative or in addition, the time period comprises a number of individual journeys for each road segment under consideration and / or for each transition from one road segment to another road segment. The number is preferably at least 1,000 trips, more preferably at least 100,000 trips.

The recorded fleet data, which characterize a large number of individual journeys, are preferably examined for outliers, incomplete data (e.g. interrupted journeys and / or obviously incorrect data, and individual data or data records (e.g. individual journeys) are optionally discarded In a preferred embodiment, each data record of the data source includes at least one preferably anonymized vehicle identification number (VIN) of the vehicle from which the measurement data originate, a measured position (preferably length and width according to WGS84) for each successful measurement and a time stamp which contains the time of the measurement. Preferably, the data source further comprises the measured speed of the vehicle. The vehicle identification number makes it possible to determine all measurement points for a vehicle based on the data source be it is possible to transfer travel data of a location (e.g. Road segment or intersection) in the data source. The time stamp makes it possible to assign individual measuring points in the data source to a trajectory. This enables a reconstruction of trajectories, based on which, in turn, measuring points can be assigned to individual maneuvers at specific intersections.

The data in a first data source can be processed in a second data source based on the aforementioned assignments. These assigned data are preferably used for outliers, incomplete data (e.g. trip data that cannot be clearly assigned to an intersection, for example because the distance from a nearest intersection is too great, or driving data that cannot be assigned to a maneuver at an intersection due to incomplete measurement points let) and, optionally, individual data or data sets (e.g. measuring points that cannot be assigned at intersections and / or maneuvers) are discarded. An analysis of the possibly processed data from the second data source then takes place.

In a preferred embodiment, each data record of the second data source comprises at least one vehicle identification number, an intersection identification number (KIN), a maneuver identification number (MIN), the position (preferably length and latitude according to WGS84) and the time stamp (ZS) of the measurement points that map a trajectory, which represents the execution of the maneuver (MIN) at the intersection (KIN) around the time around the time stamp (ZS).

For each intersection K _s , the second data source preferably comprises at least one start road segment L _s with at least one destination road segment L _z based on a legal maneuver i that connects L _s with L _z ; a reference maneuver for each intersection K of the multiplicity of intersections.

berechnet, wobei:

• • L₁ ist die Location, von dem die Kreuzung erreicht wird;
• • L₂ ist die Location, in die die Kreuzung nach Durchführung des Manövers i verlassen wird;
• • ${{\displaystyle v}}_i^{L\widehat{{}_1}}$
  
  ist die wahrscheinlichste Geschwindigkeit auf der Location L₁, wenn das Manöver i durchgeführt wird;
• • ${{\displaystyle v}}_i^{L_2}$
  
  ist die wahrscheinlichste Geschwindigkeit auf der Location L₂, wenn das Manöver i durchgeführt wird;
• • T_k ist ein definiertes Zeitintervall; beispielsweise kann T₂₀ das Zeitintervall zwischen 9:00 und 9:30 von Montag bis Freitag und T₅₅ das Zeitintervall zwischen 14:00 und 14:30 sonntags darstellen; der maximale Wert von k, d.h. k_max, bzw. die Größe vom Vektor T_k, hängt von der Auflösung des Modells ab; beispielsweise im Fall des einfachsten Modells (d.h. k_max=1) wird lediglich eine Geschwindigkeit pro Manöver unabhängig von Uhrzeit und Wochentag angenommen;
• • ${{\displaystyle v}}_i^{L\widehat{{}_1}}\left(T_k\right)$
  
  ist dann beispielsweise die wahrscheinlichste Geschwindigkeit auf der Location L₁, wenn das Manöver i im Zeitintervall T_k durchgeführt wird;

In a second step, the most probable speeds are determined for each legal maneuver i at an intersection based on the data from the second data source ${{\displaystyle v}}_i^{\mathrm{L.}\widehat{{}_1}}\left(T_k\right)\text{and}{{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

calculated, where:

• • L ₁ is the location from which the intersection is reached;
• • L ₂ is the location into which the intersection is left after maneuver i has been carried out;
• • ${{\displaystyle v}}_i^{\mathrm{L.}\widehat{{}_1}}$
  
  is the most likely speed at location L ₁ when maneuver i is performed;
• • ${{\displaystyle v}}_i^{{\mathrm{L.}}_2}$
  
  is the most likely speed at location L ₂ when maneuver i is performed;
• • T _k is a defined time interval; for example, T _{20 can represent} the time interval between 9:00 and 9:30 from Monday to Friday and T ₅₅ the time interval between 14:00 and 14:30 on Sundays; the maximum value of k, ie k _max , or the size of the vector T _k , depends on the resolution of the model; for example, in the case of the simplest model (ie k _max = 1), only one speed per maneuver is assumed regardless of the time and day of the week;
• • ${{\displaystyle v}}_i^{\mathrm{L.}\widehat{{}_1}}\left(T_k\right)$
  
  is then, for example, the most likely speed at location L ₁ if maneuver i is carried out in time interval T _k ;

The data from the second data source is then analyzed further in a third step. For every legal maneuver i at an intersection, the most likely number of journeys n _i (T _k ) in a reference time unit (eg 1 minute) in the time interval T _{k is} calculated using the data from the second data source.

berechnet.In a fourth step, a reference maneuver is defined for each intersection, preferably driving straight ahead. This maneuver is given the index i = 1. Further maneuvers are given indexes i> 1. For every legal maneuver at an intersection that is not the same as the reference maneuver, ie ∀ i: i> 1, the relationships are determined based on the results from the second step ${{\displaystyle Z}}_i^{\mathrm{L.}\widehat{{}_1}}\left(T_k\right)={{\displaystyle v}}_i^{\mathrm{L.}\widehat{{}_1}}\left(T_k\right)/{{\displaystyle v}}_1^{\mathrm{L.}\widehat{{}_1}}\left(T_k\right)\text{and}{{\displaystyle Z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)={{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)/{{\displaystyle v}}_1^{{\mathrm{L.}}_2}\left(T_k\right)$

calculated.

For every legal maneuver at an intersection that is not the same as the reference maneuver, ie ∀ i: i> 1, the ratio u _i (T _k ) = n _i (T _k ) / n ₁ (T _k ) determined, ie the most probable ratio between the number n _i of vehicles which perform maneuver i in a unit of time (e.g. 1 minute, 5 minutes, 10 minutes, 30 minutes or 60 minutes) and the number n ₁ of vehicles, which perform the reference maneuver i = 1 in the same time unit.

Depending on the variance of the values z _i and u _i for different T _k and depending on the accuracy requirement of the application, the model can be simplified, ie k _max can be reduced. For example, k _max = 1 could be set, in favor of the simplicity and efficiency of the following steps, in order to eliminate the dependence of the model on T _k without significant loss of accuracy if the values z _i and u _i for all T _k differ by a maximum of +/- 5% different. An accuracy requirement can be defined individually. For example, if the data analysis shows that the variance of the values z and u over all T fluctuates a maximum of 5% from the respective mean values and the user of the arrival time forecast accepts a tolerance of 10% in relation to the best possible estimate, then the dependency can be T can be neglected and k _max = 1 can be assumed because the maximum error in this case would be 5%, which is smaller than the accuracy requirement (10%).

werden für jede Kreuzung in einer persistenten Datenbank gespeichert. Persistent bedeutet in diesem Fall, dass die entsprechenden Daten statisch und unveränderbar gespeichert werden, und dass die Datenbank als ein „Produkt“, unabhängig von einer Entwicklungsumgebung exportierbar und anwendbar ist. Beispielsweise können die Daten vom Back-End zum Fahrzeug über Luftschnittstelle übertragen werden, oder von USB auf die Fahrzeug-Festplatte. Die Datenbank enthält mindestens eine Anzahl von Werten ${{\displaystyle Z}}_i^{L_1}\left(\text{bzw}\text{. }{{\displaystyle Z}}_i^{L_2}\text{ und }{u}_i\right)$

gleich N = k_max ∑_j=1,...,_J ∑_i=0,...,_Ij-1i, mit J gleich der Anzahl aller Kreuzungen und I_j gleich der Anzahl aller legalen Manövern an der Kreuzung j.The values ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1},{{\displaystyle Z}}_i^{{\mathrm{L.}}_2}\text{and}{u}_i$

are stored in a persistent database for each intersection. In this case, persistent means that the corresponding data is stored statically and unchangeably, and that the database can be exported and used as a "product", independent of a development environment. For example, the data can be transferred from the back end to the vehicle via the air interface, or from USB to the vehicle's hard drive. The database contains at least a number of values ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}\left(\text{or}\text{.}{{\displaystyle Z}}_i^{{\mathrm{L.}}_2}\text{and}{u}_i\right)$

equal to N = k _max ∑ _{j = 1} , ..., _J ∑ _{i = 0} , ..., _Ij-1 i, with J equal to the number of all intersections and I _j equal to the number of all legal maneuvers at intersection j.

A navigation function in the vehicle 80 (e.g. a navigation system, an app on a mobile device or indirectly via a server application) receives access to the database. In the course of determining a route and a corresponding arrival time forecast, for each intersection that belongs to the route, the location L ₁ from which the intersection is reached, the location L ₂ into which the intersection is left, and the maneuver i that is carried out at the intersection are determined. In addition, it is preferably determined (approximately) in which time interval the respective intersection will be reached.

An approximate estimate of the respective time intervals is regarded as sufficient here, since an exact arrival time prognosis is not yet available at the time the route is determined. In particular, it cannot be assumed that slight temporal deviations (due to an approximate estimate) usually lead to only insignificantly different results in the respective calculations. In some embodiments, instead of an approximate estimate, a current time of day can be used to determine the relevant time interval).

und u_i(T_k) auslesen. Des Weiteren kann die Navigationsfunktion Informationen von einem RTTI-Provider erhalten, welche RTTI-Geschwindigkeiten ${{\displaystyle v}}_{RTTI}^{L_1}\text{ und }{{\displaystyle v}}_{RTTI}^{L_2}$

an den entsprechenden Locations L₁ und L₂ gemessen wurden (vorzugsweise unter Berücksichtigung der Fahrtrichtung im Bezug zum Manöver i).The navigation function can now read the values from the database ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)\text{,}{{\displaystyle Z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

and read out u _i (T _k ). Furthermore, the navigation function can receive information from an RTTI provider, which RTTI speeds ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\text{and}{{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}$

were measured at the corresponding locations L ₁ and L ₂ (preferably taking into account the direction of travel in relation to maneuver i).

für das Manöver i werden basierend auf folgenden Gleichungen berechnet:

• wenn i = 1, $${{\displaystyle v}}_1^{{\widehat{L}}_1}\text{ = }{{\displaystyle v}}_{RTTI}^{L_1}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,..}{I}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,..}I\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{L_1}\left(T_k\right)\right)}}\text{,}$$
  
  $${{\displaystyle v}}_1^{{\widehat{L}}_2}\text{ = }{{\displaystyle v}}_{RTTI}^{L_2}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,..}{I}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,..}I\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{L_2}\left(T_k\right)\right)}}\text{,}$$
  
  mit / gleich der Anzahl legaler Manövern an der Kreuzung von der Location L₁ kommend, und
• wenn i > 1, $${{\displaystyle v}}_l^{{\widehat{L}}_1}={{\displaystyle v}}_1^{{\widehat{L}}_1}\ast u_i\left(T_k\right).$$
  

The most likely maneuver speeds ${{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_1}\text{and}{{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_2}$

for maneuver i are calculated based on the following equations:

• if i = 1, $${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}\text{=}{{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,\; ..}{\mathrm{I.}}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,\; ..}\mathrm{I.}\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{{\mathrm{L.}}_1}\left(T_k\right)\right)}}\text{,}$$
  
  $${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_2}\text{=}{{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}\ast \frac{1+{\displaystyle \sum j=\mathrm{2,\; ..}{\mathrm{I.}}^{u}j\left(T_k\right)}}{1+{\displaystyle \sum j=\mathrm{2,\; ..}\mathrm{I.}\left(uj\left(T_k\right)\ast {{\displaystyle Z}}_j^{{\mathrm{L.}}_2}\left(T_k\right)\right)}}\text{,}$$
  
  with / equal to the number of legal maneuvers at the intersection coming from location L ₁ , and
• if i> 1, $${{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_1}={{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}\ast u_i\left(T_k\right).$$
  

anstelle von ${{\displaystyle v}}_{RTTI}^{L_1}\text{ und }{{\displaystyle v}}_1^{{\widehat{L}}_2}$

anstelle von ${{\displaystyle v}}_{RTTI}^{L_2}$

bei der Berechnung der Ankunftszeitprognose.The navigation function then uses the speed values ${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}$

instead of ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\text{and}{{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_2}$

instead of ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}$

when calculating the arrival time forecast.

und einen zweiten Wert ${{\displaystyle z}}_i^{L_2}$

2 shows a flow diagram of a method 200 in accordance with embodiments of the present disclosure. The procedure 200 for the arrival forecast of a vehicle 80 at a target location is based on a data source. The data source includes: a plurality of road segments L; a plurality of intersections, each intersection of the plurality of intersections connecting at least one starting road segment L _s to at least one target road segment L _z based on a maneuver i; a reference maneuver for each intersection of the plurality of intersections; and a first value for each maneuver i other than the reference maneuver at an intersection of the plurality of intersections ${{\displaystyle Z}}_i^{{\mathrm{L.}}_1}$

and a second value ${{\displaystyle z}}_i^{{\mathrm{L.}}_2}$

The procedure starts at step 201 .

In step 202 a route is determined based on a starting position and the target position, the route comprising at least a subset of road segments of the plurality of road segments and at least a subset of intersections of the plurality of intersections.

der zweite Wert ${{\displaystyle z}}_i^{L_2}\left(T_k\right)$

und ein dritter Wertes u_i(T_k) ermittelt, jeweils in Abhängigkeit des voraussichtlichen Zeitintervalls T_k.In step 204 becomes, for each intersection of the subset of intersections, a probable time interval T _k in which the vehicle 80 the respective intersection is being driven on. It will continue to be the first value based on the data source ${{\displaystyle z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

the second value ${{\displaystyle z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

and a third value u _i (T _k ) is determined, in each case as a function of the probable time interval T _k .

In step 206 the arrival prognosis is determined based on the determined first, second and third values for each intersection of the subset of intersections.

The procedure 200 ends at step 220 .

verwendet. Die realen Werte für ${{\displaystyle z}}_2^{L_1}\text{ und }{u}_2$

betragen somit ${{\displaystyle z}}_2^{L_1}=10\text{/}50=0.2\text{ und }{u}_2=20\text{/}80=\mathrm{0.25.}$

In order to quantitatively estimate the advantage of the method according to the present disclosure, it can be tested using a simulation. As an example, consider an intersection with two legal maneuvers i = 1 and i = 2. For the sake of simplicity, let k _max = 1. The values are used as real speed and vehicle frequency measurements ${{\displaystyle v}}_1^{{\mathrm{L.}}_1}=50km\text{/H}\text{,}{{\displaystyle v}}_2^{{\mathrm{L.}}_1}=10km\text{/}H,n_{{}_1}=\mathrm{80,}{n}_2=\mathrm{20th}$

used. The real values for ${{\displaystyle z}}_2^{{\mathrm{L.}}_1}\text{and}{u}_2$

amount to ${{\displaystyle z}}_2^{{\mathrm{L.}}_1}=10\text{/}50=0.2\text{and}{u}_2=\mathrm{20th}\text{/}80=\mathrm{0.25.}$

und u₂ von den realen Werten abweichen, in Folge ihrer statistischen Natur. Angenommen man verwendet beispielsweise ${{\displaystyle z}}_2^{L_1}=0.15\text{ und }{u}_2=0.2$

Das entspräche einem Modellfehler von jeweils 25% und 20%. Da ${{\displaystyle v}}_{RTTI}^{L_1}=42km\text{/}h,$

wie oben berechnet, ergibt sich folgende Geschwindigkeitsverteilung: $$-{{\displaystyle v}}_1^{{\widehat{L}}_1}={{\displaystyle v}}_{RTTI}^{L_1}\ast \frac{1+{\displaystyle {\sum }_{j=2\mathrm{,..}I}{u}_j}}{1+{\displaystyle {\sum }_{j=2\mathrm{,..}I}\left(u_j\ast {{\displaystyle z}}_j^{L_1}\right)}}=42\ast \frac{1+u_2}{1+u_2\ast {{\displaystyle z}}_2^{L_1}}=42\ast \frac{1.2}{1.03}=48.9km\text{/}h$$

$$-{{\displaystyle v}}_2^{L_1}={{\displaystyle v}}_1^{{\widehat{L}}_1}\ast u_2=48.9\ast 0.2=9.8km\text{/}h$$

However, it is further assumed (conservatively) that the statistically recorded values for ${{\displaystyle z}}_2^{{\mathrm{L.}}_1}\text{and}{u}_2$

and u ₂ differ from the real values due to their statistical nature. For example, suppose you use ${{\displaystyle z}}_2^{{\mathrm{L.}}_1}=0.15\text{and}{u}_2=0.2$

That would correspond to a model error of 25% and 20% respectively. There ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}=42km\text{/}H,$

as calculated above, the following speed distribution results: $$-{{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}={{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\ast \frac{1+{\displaystyle {\sum }_{j=2\mathrm{,\; ..}\mathrm{I.}}{u}_j}}{1+{\displaystyle {\sum }_{j=2\mathrm{,\; ..}\mathrm{I.}}\left(u_j\ast {{\displaystyle z}}_j^{{\mathrm{L.}}_1}\right)}}=42\ast \frac{1+u_2}{1+u_2\ast {{\displaystyle z}}_2^{{\mathrm{L.}}_1}}=42\ast \frac{1.2}{1.03}=48.9km\text{/}H$$

$$-{{\displaystyle v}}_2^{{\mathrm{L.}}_1}={{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}\ast u_2=48.9\ast 0.2=9.8km\text{/}H$$

Diese Werte bedeuten eine erhebliche Verbesserung gegenüber herkömmlichen Verfahren.The error from the calculated speeds compared to the real speeds is in each case ${{\displaystyle e}}_1^{{\mathrm{L.}}_1}=\frac{\left(48.9-50\right)}{50}=-2.2\text{\% and}{{\displaystyle e}}_1^{{\mathrm{L.}}_1}=\frac{\left(9.8-10\right)}{10}=-2.0\text{\%}\text{.}$

These values represent a significant improvement over conventional methods.

As already explained above, the error is -16% and 320%, respectively, without making a correction and with direct use of the RTTI speeds.

Although the invention has been illustrated and explained in more detail by preferred exemplary embodiments, the invention is not restricted by the disclosed examples and other variations can be derived therefrom by the person skilled in the art without departing from the scope of protection of the invention. It is therefore clear that there is a multitude of possible variations. It is also clear that embodiments cited by way of example really only represent examples that are not to be interpreted in any way as a limitation, for example, of the scope of protection, the possible applications or the configuration of the invention. Rather, the preceding description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without the To leave the scope of protection defined by the claims and their legal equivalents, such as further explanations in the description.

Claims (10)

A method (200) for forecasting the arrival of a vehicle (80) at a target position based on a data source, the data source comprising: a plurality of road segments (L); - A plurality of intersections, each intersection of the plurality of intersections connecting at least one starting road segment (L _s ) with at least one target road segment (L _z ) based on a maneuver (i); a reference maneuver for each intersection of the plurality of intersections; and a first value for each maneuver (i) unequal to the reference maneuver at an intersection of the plurality of intersections ${{\displaystyle z}}_i^{{\mathrm{L.}}_1}$

and a second value ${{\displaystyle z}}_i^{{\mathrm{L.}}_2}\text{;}$

The method comprising: determining (202) a route based on a start position and the destination position, the route including at least a subset of road segments of the plurality of road segments and at least a subset of intersections of the plurality of intersections; Determining (204), for each intersection of the subset of intersections, a probable time interval (T _k ) in which the vehicle (80) will drive through the respective intersection and, based on the data source, the first value ${{\displaystyle z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

of the second value ${{\displaystyle z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

and a third value u _i (T _k ), in each case as a function of the probable time interval (T _k ); and determining (206) the arrival prognosis based on the determined first, second and third values for each intersection of the subset of intersections. Method (200) according to Claim 1 , where for each intersection of the plurality of intersections in the data source: the first value ${{\displaystyle z}}_i^{{\mathrm{L.}}_1}$

is determined based on an average value; and / or the second value ${{\displaystyle z}}_i^{{\mathrm{L.}}_2}$

is determined based on an average value. Method (200) according to one of the preceding claims, wherein determining the first value ${{\displaystyle z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

based on $${{\displaystyle z}}_i^{{\mathrm{L.}}_1}\left(T_k\right)={{\displaystyle v}}_i^{{\mathrm{L.}}_1}\left(T_k\right)\text{/}{{\displaystyle v}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$$

success with ${{\displaystyle v}}_i^{{\mathrm{L.}}_1}\left(T_k\right)$

equal to the most likely speed on the road segment L ₁ if the maneuver i is carried out in the time interval T _k , and ${{\displaystyle v}}_1^{{\mathrm{L.}}_1}\left(T_k\right)$

equal to the speed on the road segment L ₁ if the reference _{maneuver is} carried out in the time interval T _k . Method (200) according to one of the preceding claims, wherein determining the second value ${{\displaystyle z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

based on $${{\displaystyle z}}_i^{{\mathrm{L.}}_2}\left(T_k\right)={{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)\text{/}{{\displaystyle v}}_1^{{\mathrm{L.}}_2}\left(T_k\right)$$

success with ${{\displaystyle v}}_i^{{\mathrm{L.}}_2}\left(T_k\right)$

equals the most likely speed on road segment L ₂ if maneuver i is performed in time interval T _k , and (T _k ) equals the speed on road segment L ₂ if the reference maneuver is performed in time interval T _k . Method (200) according to one of the Claims 3 or 4th , with the most likely first and second speeds ${{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_1}\text{and}{{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_2}$

for maneuver i can be determined based on the following equations: - if i = 1, $$\begin{array}{l}{{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}={{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\ast \frac{1+{\displaystyle {\sum }_{j=2\mathrm{,\; ..}\mathrm{I.}}{u}_j\left(T_k\right)}}{1+{\displaystyle {\sum }_{j=2\mathrm{,\; ..}\mathrm{I.}}\left(u_j\left(T_k\right)\ast {{\displaystyle z}}_j^{{\mathrm{L.}}_1}\left(T_k\right)\right)}},{{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_2}={{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}\ast \\ \frac{1+{\displaystyle {\sum }_{j=2\mathrm{,\; ..}\mathrm{I.}}{u}_j\left(T_k\right)}}{1+{\displaystyle {\sum }_{j=2\mathrm{,\; ..}}\left(u_j\left(T_k\right)\ast {{\displaystyle z}}_j^{{\mathrm{L.}}_2}\left(T_k\right)\right)}}\end{array}$$

with / equal to the number of maneuvers i at the respective intersection from the road segment L ₁ . - if $i>\mathrm{1,}{{\displaystyle v}}_l^{{\widehat{\mathrm{L.}}}_1}={{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}\ast u_i\left(T_k\right).$

Method (200) according to Claim 5 , wherein determining the arrival forecast based on the first and second speed values ${{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_1}\text{and}{{\displaystyle v}}_1^{{\widehat{\mathrm{L.}}}_2}$

he follows. Method (200) according to one of the Claims 1 to 4th , further comprising determining first and second RTTI velocities ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\text{and}{{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}$

based on an RTTI service (150); and determining the predicted arrival based on the first and second RTTI speeds ${{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_1}\text{and}{{\displaystyle v}}_{\mathrm{R.}TT\mathrm{I.}}^{{\mathrm{L.}}_2}$

he follows. Method (200) according to one of the preceding claims, wherein determining the third value u _i (T _k ) based on $$u_i\left(T_k\right)=n_i\left(T_k\right)\text{/}{n}_1\left(T_k\right)$$

takes place, with u _i (T _k ) equal to the most likely ratio between a number n _i of vehicles that perform the maneuver i in a predetermined time unit and a number n ₁ of vehicles that perform the reference maneuver i = 1 in the predetermined time unit , preferably wherein the predetermined time unit is 1 minute. System (100) comprising a control unit (120), wherein the control unit (120) is configured to carry out the method (200) according to one of the preceding Claims 1 to 8th . Vehicle (80) comprising a system (100) according to the preceding Claim 9 .

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