DE102019003963A1 - Method for determining a driving strategy of a vehicle, in particular a commercial vehicle - Google Patents

Abstract

The invention relates to a method for determining a driving strategy of a vehicle, in particular a commercial vehicle, in which a forecast horizon depending on a current position of the vehicle is determined on the basis of stored data from a digital map (7) and driving conditions of the vehicle are predicted, using the forecast horizon and the driving conditions, consumption-optimized control interventions are generated on a drive train of the vehicle. In a method in which erroneous data in the digital map are reliably corrected, when the vehicle travels through a predetermined section of the route (21), the data stored in the digital map (7) as setpoints is compared with the data stored by the vehicle sensor system (23). Measured actual values are carried out, in the event of deviations of the target values from the actual values, a specific event characterizing an exact position of the vehicle in the digital map (7) is stored in a local learning database (9) for correcting the data of the digital map (7), which of the Determining the driving strategy for future journeys on the specified route section (21) is used as a basis.

Description

The invention relates to a method for determining a driving strategy of a vehicle, in particular a commercial vehicle, in which a foresight horizon is determined as a function of a current position of the vehicle on the basis of stored data from a digital map and driving conditions of the vehicle are predicted, with the aid of the foresight horizon and the driving conditions consumption-optimized control interventions are generated on a drive train of the vehicle

From the DE 10 2011 121 853 A1 A device for regulating the longitudinal dynamics of a vehicle, taking into account a leading vehicle in front, is known, in which position requests for distance rules for the intervention in a drive train of the vehicle are generated by means of a distance control in a distance control step, with predictive position requests for consumption optimization in a prediction control step in a prediction control step Control interventions are generated on the drive train.

Such devices, known as driver assistance systems, include predictive cruise control (PCC). This PCC works on the basis of digital maps, which, in order to function correctly, provide information about a section of the route ahead in the form of an electronic horizon, as described in 7 is shown. This electronic horizon can range from a few meters to a few kilometers. Digital map data contain information on the course of the road, the gradient, the curvature and the speed limits of a section of the route to be traveled. On this basis, the PCC calculates in advance which driving speed can be set for the section of the route ahead with minimal fuel consumption. The desired fuel savings can be realized for different applications. The driver specifies a speed band in which the vehicle should move. The upper and lower limit of the speed band is called hysteresis, which can be changed dynamically by the driver while driving. The desired speed corresponds to the speed at which the PCC is regulated. Outside of this regulation, the PCC predicts the speed on the basis of the speed limits stored in the maps and the curvature values of the route, as described in 8th is shown. Depending on its physical properties, such as training as a solo vehicle, articulated lorry, articulated lorry, as well as loading condition and center of gravity, a vehicle, in particular a commercial vehicle, can only negotiate curves safely at a certain speed. The greater the curvature of the curve, the lower the maximum possible speed and vice versa. To prevent the vehicle from tipping over, the PCC uses a map in which a planning speed is plotted against the curvature. The vehicle speed is below the theoretically maximum possible cornering speed. In the prediction, the planning speed results from the minimum of the speed permitted due to the curvature and the speed limit applicable for the section of the route.

Digital map data are recorded on the basis of real test drives or created by the mathematical evaluation of satellite images. The digital map data created in this way have potential errors, which depend on the driving style of the vehicle user, temporary changes in the course of the road caused by the construction site and several different directional lanes. In the case of satellite images, optical covers caused by building or shading by trees cause inaccuracies in the GPS data.

The object of the invention is to provide a method for determining a driving strategy of a vehicle, in which the erroneous deviations contained in the data of the digital map are compensated and a suitable driving strategy for driving the vehicle for a predetermined route section is thus output.

The invention results from the features of the independent claims. Advantageous further developments and refinements are the subject of the dependent claims. Further features, possible applications and advantages of the invention result from the following description and the explanation of exemplary embodiments of the invention, which are shown in the figures.

The object is achieved with a method in that when the vehicle travels through a predetermined section of the route, a comparison is made of the stored data which are regarded as setpoints in the digital map with current actual values measured by a vehicle sensor system, with a deviation in the setpoints from the actual values an exact position of the vehicle in the digital map characterizing specific event is stored in a local learning database for correcting the data of the digital map, which is used as a basis for determining the driving strategy for future trips on the specified route section. This has the advantage that deviations in the data of the digital map stored locally in the vehicle are detected by using the local learning database are corrected so that the vehicle functionalities are continuously improved using this additional content for future journeys on the same route section.

The deviations between setpoints and actual values are advantageously determined by threshold value comparisons, wherein if at least one threshold value is exceeded, a problem marker characterizing the specific event is set in the local learning database. The threshold value comparison represents a particularly simple trigger for determining whether incorrect digital data are stored. By setting this problem marker, the local learning database is made aware that the corresponding threshold value comparison must always be carried out for the specified specific event in the future, since an error is present ,

In one embodiment, a curvature of the predetermined route section is considered as a problem marker, a comparison being carried out between a curvature of the digital map representing the desired value and a curvature describing the actual value and calculated using the data from the vehicle sensor system, the problem marker being exceeded when a curvature threshold value is exceeded. Curvature ”is set in the local learning database, which then learns a new curvature for the digital map and stores it in it. In this way, the causes of curvature errors, which can be attributed, for example, to the noise of the sensors when recording the measured values or to the driving style and driving style of the driver of a measuring vehicle, which measures the specified route section, can be reliably prevented.

In one variant, the curvature describing the actual value and calculated using the data from the vehicle sensor system is obtained from position data of a GPS system and / or from a lateral acceleration of the vehicle. In this way, algorithms per se in the vehicle are used in determining the driving strategy of the vehicle, as a result of which the process costs are reduced.

In one embodiment, an initial value of a curve is used to learn the new curvature in the learning database, the curvature of which is evaluated as constant in the digital map, a corrected one being corrected from the learned new curvature and a weighting factor when the position of the predetermined route section corresponding to the initial value is passed Curvature of the predetermined route section is determined for the digital map. This is particularly important for commercial vehicles, where the curvature errors due to a wide commercial vehicle, which due to its size and width must follow the course of the road exactly, are considerable.

In a further embodiment, the corrected curvature of the predetermined route section for the digital map is redetermined each time the predetermined route section is crossed, as long as the problem marker is set. As a result, the corrected curvature is continuously adapted when the same predetermined route section is crossed several times, since the corrected curvature determined in the previous driving cycle is always stored as stored data in the digital map. However, this only takes place as long as the problem marker "curvature" indicates that the threshold value is exceeded in the target / actual comparison of the original data.

A start / stop event of an automatic start / stop of the vehicle is advantageously considered as the problem marker, and when this problem marker is set, "start / stop event" in the learning database depending on the occurrence of a current dynamic and a current static traffic situation and a current one Driving style of the vehicle user when the vehicle is at a standstill is decided whether the automatic start / stop is activated or deactivated. The data stored in the learning database is vehicle-dependent and thus results from the driving style of the vehicle user.

In one variant, the direction of travel of the vehicle and a traffic situation are considered as the current dynamic traffic situation, while in the current static traffic situation a street type and / or intersections or roundabouts and / or traffic control systems are taken into account. Thus, a variety of possible influences are taken into account when determining a correction value, which increases the accuracy of the correction value.

In a further embodiment, the “start / stop event” problem marker identifies positions on the digital map at which the vehicle user switches the automatic start / stop on or off, which are in the learning database for automatically switching the start / stop on and off. Automatically be saved if the specified section of the route is passed repeatedly. Here too, the behavior of the driver is learned by repeatedly driving over a predetermined section of the route, so that the driver no longer has to be active in the corresponding section of the route.

In a further development, the driving strategy is determined as a function of a current weather situation, starting from maps for classified weather situations, a target speed shown in the digital map is compared with a current actual speed of the vehicle, measured by the vehicle sensors, each time the specified section of the route is crossed, whereby by setting a "weather situation" problem marker, the driving strategy is optimized as a function of weather data when the specified section of the track is repeatedly crossed becomes. Incorrect speed limits within the digital map, which can be extremely problematic for road traffic, can thus be corrected.

Further advantages, features and details result from the following description, in which - if necessary with reference to the drawing - at least one exemplary embodiment is described in detail. Described and / or illustrated features can form the subject matter of the invention individually or in any meaningful combination, possibly also independently of the claims, and can in particular also be the subject of one or more separate applications. Identical, similar and / or functionally identical parts are provided with the same reference symbols.

• 1 ein Ausführungsbeispiel eines Steuergerätes für eine Geschwindigkeitssteuerung mit einem Integral Predictive Power Control-System zur Durchführung des erfindungsgemäßen Verfahrens,
• 2 Ausführungsbeispiele für fehlerhafte Ereignisse, die die Auswahl der Fahrstrategie beeinflussen,
• 3 ein Ausführungsbeispiel für die Aufnahme einer Krümmung einer Fahrstrecke durch ein Messfahrzeug und ein Nutzfahrzeug,
• 4 ein Ausführungsbeispiel für eine in der digitalen Karte beschriebene fehlerhafte Krümmung, welche von der realen Straßenkrümmung abweicht,
• 5 ein Ausführungsbeispiel des erfindungsgemäßen Verfahrens,
• 6 ein Ausführungsbeispiel für das Lernprinzip einer Lerndatenbank für Krümmungen bei mehrmaligem Befahren eines vorgegebenen Streckenabschnittes,
• 7 ein Ausführungsbeispiel für eine grafische Darstellung eines elektronischen Horizonts,
• 8 ein Ausführungsbeispiel für die Prädiktion einer Geschwindigkeit anhand der in einer digitalen Karte hinterlegten krümmungsabhängigen maximalen Kurvengeschwindigkeiten und Geschwindigkeitsbegrenzung gemäß dem Stand der Technik.

Show it:

• 1 1 shows an exemplary embodiment of a control device for speed control with an integral predictive power control system for carrying out the method according to the invention,
• 2 Exemplary embodiments for faulty events that influence the selection of the driving strategy
• 3 An exemplary embodiment for recording a curvature of a route by a measuring vehicle and a commercial vehicle,
• 4 an exemplary embodiment of a faulty curvature described in the digital map, which deviates from the real road curvature,
• 5 an embodiment of the method according to the invention,
• 6 an exemplary embodiment of the learning principle of a learning database for curvatures when driving a given section of the route several times,
• 7 an exemplary embodiment of a graphic representation of an electronic horizon,
• 8th an embodiment for the prediction of a speed based on the curvature-dependent maximum curve speeds and speed limit stored in a digital map according to the prior art.

In 1 An embodiment of an IPPC control device (Integrated Protective Powertrain Control) is shown, which over a data bus 3 with a vehicle sensor system 23 of the vehicle communicates. This IPPC control unit 1 comprises three main components: a card module 5 in which a digital map 7 and a learning database 9 stored, a reconstructor 11 as well as a core module 13 which is a predictive cruise control 15 , a card validator 17 and a problem marker handler 19 includes.

A foresight horizon, which is a section of the digital map 7 from the immediate surroundings of the vehicle and reaching up to a predetermined foresight distance, contains not only data about existing roads but also associated terrain information, in particular curvature data. This foresight horizon forms the entrance to the reconstructor 11 , The reconstructor converts individual groups of information, such as the curvatures and slopes 11 in tables. The data are in relation to the specified route section 2 applied, where 2a an incline 2 B a curvature and 2c Problem marker shows, where A represents a problem marker "slope" and B a problem marker "curvature".

The learning database 9 can set, transfer or delete problem markers. Both setting and deleting a problem marker is done by the models developed for the use cases in a problem marker handler 19 , These models analyze the vehicle sensors 23 collected data. Based on the analysis results, the models request the learning database 9 to set or delete corresponding problem markers. Each problem marker has its own area of application. Problem markers that describe the same area of application are referred to as problem marker types. If there is a problem marker on the most likely path, the card module transmits 5 this to the reconstructor 11 , The problem marker exists for storing new digital map data 7 within the learning database 9 and contains a link identifier and a link offset.

The core module 13 contains the actual function of the IPPC control unit 1 , Based on the tables of the reconstructor 11 as well as current vehicle parameters, such as the vehicle speed, via the data bus 3 to the IPPC control unit 1 be transmitted calculates the core module 13 a driving strategy of the vehicle. This driving strategy is also via the data bus 3 to a higher-level vehicle control unit 25 output. This vehicle control unit 25 uses the driving strategy for the function of a cruise control (ACC, CC) or an automatic gear selection (AG). At the same time, the higher-level vehicle control unit is responsible 25 and other control devices 27 the data they collect for the problem marker handler 19 back to the core module 13 , The card validator too 17 provides results about the correctness of the map data to the problem marker handler 19 , These serve as the basis for the decision-making process as to whether and if so which problem marker is required. The card module 5 gets the results of the problem marker handler 19 based on these results and intervenes in the learning database 9 in front.

Furthermore, error-specific events that are caused by a problem marker in the card module are to be considered 5 Marked are. First of all, a problem marker "curvature" should be considered, with incorrect curvature values of the specified route section in the digital map 7 Getting corrected. In 3 is the driving style and driving style of a driver of a measuring vehicle P1 that the specified route section 21 measures through the line L1 shown. Since it is the measuring vehicle P1 the measuring vehicle can be a car P1 if the road is wide, the winding section 21 pass through almost in a straight line, so that the measured curvatures on this section of the route are determined to be too small, which is the result of the horizontal curve L3 in 4 equivalent. While the effects of the curvature error are minimal in a narrow vehicle, they are for a wide commercial vehicle P2 which, due to its size and width, must follow the course of the road considerably (curve L2 ).

The IPPC control unit 1 is caused by the incorrect curvature of the line L1 , which depends on the curvature of the section of the route traveled 21 (Curve L2 ) deviates, the vehicle speed for this section of the route 21 predict too high. The driver must intervene to correct the function of the IPPC control unit 1 is interrupted.

To correct the incorrect curvature, is related to 5 described an embodiment of the method according to the invention. In the block 100 the vehicle's own sensor data are recorded. It can be a video radar decision unit that detects objects, road markings and road users. At the same time, the control data of the IPPC control unit 1 used (block 110 ). The one in the blocks 100 and 110 captured data are in the block 120 evaluated, it being determined which vehicle positions the vehicle occupies in relation to lane markings and whether the vehicle is following the lane. In the block 130 the system asks whether the vehicle is driving in the lane. If this is not the case, the block 140 the procedure was canceled. But if the vehicle drives in the lane, it becomes a block 150 where a comparison is made between a map curvature and a calculated curvature. The calculated curvature can be a curvature that consists of image data from the block 100 mentioned sensors was determined or was determined from a lateral acceleration of the vehicle. The curvatures are determined using the difference in the individual wheel speeds in combination with a Kalman filter. The map curvature represents the target value, while the calculated curvature represents an actual value. If the deviation between the setpoint and actual value exceeds a curvature threshold, the block 160 set a problem marker "curvature". This arranges the learning database 9 the problem marker curvature ”an exact position in the digital map 7 to. If the problem marker "curvature" is set, then in the block 170 corrected a curvature learned in the learning database 9 is saved.

In 6 is an embodiment of the general learning principle in the learning database 9 shown. A curve is the starting position L3 before whose curvature in the digital map 7 constant with the value 0 Is evaluated. The first time the vehicle passes over the affected position, the problem marker handler sets 19 the problem marker "curvature" due to the large deviation between the curvature values from the curve L3 and the calculated curvature of the curve L4 , This starts the learning process in the learning database 9 , The learning database 9 now uses the one from the card validator 17 calculated values for the curvature and a confidence level. From the two values as well as that in the digital map 7 The learning database calculates the saved curvature 9 a corrected value of the curvature. The learning database 9 saves the corrected curvature as permanent.

In general, the higher the confidence level, the more the function weights the calculated corrected curvature. The level of trust is a measure of how reliable the correction is. With multiple crossings of the specified route section 21 there are always new calculations of the corrected curvature, which is due to the curve L4 "First crossing", the curve L5 "Second crossing", the curve L6 "Third crossing" and the curve L7 "Fourth crossing" is shown.

As long as the problem marker "curvature" is set, the learning database corrects 9 every time the specified section of the route is crossed, the curvature from the learning database 9 , The learning database 9 may delete the problem marker as soon as the difference between the calculated actual curvature and that in the learning database 9 stored target curvature is less than the curvature threshold.

Another exemplary embodiment is the use of the problem markers and the learning database associated therewith 9 in connection with an automatic start / stop of a vehicle. The aim here is to optimize the driving comfort, the fuel consumption and the wear of the vehicle.

• • die Fahrtrichtung
• • die Information über Kreuzungen, Kreisverkehr, Beschilderungen, Ampelanlagen
• • den Straßentyp, z.B. Autobahn, Bundesstraße, keine offizielle Straße, wie beispielsweise ein Parkplatz

The exact information of the problem markers set in this case include:

• • the direction of travel
• • Information about intersections, roundabouts, signs, traffic lights
• • the type of road, eg motorway, federal road, no official road, such as a parking lot

This information is provided by the learning database 9 the IPPC control unit 1 provided for the automatic start / stop. The IPPC control unit 1 contains a corresponding algorithm for interpreting this information. When the vehicle is at a standstill, this algorithm decides whether or not the drive of the vehicle should be switched off in accordance with the sense of the automatic start / stop.

1. 1. Einer aktuellen dynamischen Verkehrssituation: Hierfür ist eine Analyse der Verkehrslage notwendig. Dies kann z.B. durch eine Bildverarbeitung (Kameras in Fahrtrichtung) und/oder einer Umgebungsdetektion, z.B. durch Ultraschallsensoren, Radarsensoren und Beschleunigungssensoren, geschehen. Anhand dieser Daten findet eine Analyse der Verkehrsteilnehmer im Umfeld des Fahrzeuges in der gleichen Fahrsituation statt.
2. 2. eine aktuelle statische Verkehrssituation: Diese betrifft Beschilderungen, Kreuzungen, Straßentyp, Fahrtrichtung, Ampelanlage (Zustandslose-Betrachtung) und ähnliches. Bestimmt werden hierdurch Verkehrssituationen, in denen eine Analyse des Fahrzeugverhaltens Sinn macht. Sinnvolle Verkehrssituationen sind z.B. lange Wartezeiten vor Ampelanlagen, nicht-sinnvolle Verkehrssituationen sind z.B. Einparkmanöver in mehreren Zügen.
3. 3. Einem aktuellen Fahrverhalten: Deaktiviert der Fahrer die Start/Stopp-Automatik regelmäßig an der gleichen Position bzw. fährt dieselbe Strecke regelmäßig mit deaktivierter Automatik bzw. ist das Start/Stopp-System dauerhaft deaktiviert, dann speichert das Fahrzeug dies langfristig in der Lerndatenbank 9 ab und deaktiviert an den entsprechenden Koordinaten die Automatik selbstständig.

The in the learning database 9 stored data are vehicle-dependent and thus result from the current driving style of the vehicle user. The data are obtained from the following sources:

1. 1. A current dynamic traffic situation: This requires an analysis of the traffic situation. This can be done, for example, by means of image processing (cameras in the direction of travel) and / or environmental detection, for example by means of ultrasonic sensors, radar sensors and acceleration sensors. Based on this data, an analysis of the road users around the vehicle takes place in the same driving situation.
2. 2. A current static traffic situation: This concerns signage, intersections, street type, direction of travel, traffic light system (stateless observation) and the like. This determines traffic situations in which an analysis of vehicle behavior makes sense. Sensible traffic situations are long waiting times in front of traffic lights, non-sensible traffic situations are parking maneuvers in several trains.
3. 3. Current driving behavior: If the driver regularly deactivates the automatic start / stop at the same position or drives the same route regularly with deactivated automatic or if the start / stop system is permanently deactivated, the vehicle stores this in the learning database for a long time 9 and automatically deactivates the automatic system at the corresponding coordinates.

If the automatic start / stop is activated, a deeper analysis must be carried out: Does the driver close repeated gaps as quickly as possible to a vehicle driving in front of him in repeated stop situations? What is the average downtime of the vehicle in the geographical area? Appropriately interpreted data is stored in the learning database using an algorithm with threshold values 9 filed, which are then used the next time the same section of the route is crossed.

In a further exemplary embodiment, a problem marker driving strategies is to be used as a function of weather data and traffic. The IPPC control unit determines the speed limit in the specified section of the route 1 a speed of the vehicle that is always less than or equal to this speed limit. It is problematic if the measuring vehicle records data on a section of a highway with a construction site. The route is signposted with adapted speed limits. The adjusted speed limits flow into the digital map due to the learning in the learning database 9 on. After completion of the construction site, the road traffic authorities will adjust the speed limits again. Now the vehicle is driving with this outdated digital map over this section of the route 21 , The IPPC control unit 1 is based on the old speed limits. There is a difference between the permitted and the driven speed.

A traffic sign recognition and an associated problem marker "weather situation" eliminate the incorrect speed limits. So another model can be found in the problem marker handler 19 changed speed limits in the learning database 9 to save. As a rule, a changed driving situation is the reason for the new speed limit. The learning database 9 can use the data previously collected on the road section to correct the driving strategy in the IPPC control unit 1 discard. In the current system, the speed limit and the map represent the maximum Speed depending on the curvature and vehicle mass are two limiting factors. In addition, different maps for different classified weather situations, such as light rain, heavy rain, fog, darkness and the like are taken into account. Via the video radar decision unit 23 , an ESP control unit 27 and other sensors (rain sensor, temperature sensor) the current weather class is determined. The IPPC control unit 1 calculates individual driving strategies based on the weather classification. For example, the IPPC control unit predicts 1 for a left turn in heavy rainfall a speed of 50 km / h. The driver corrects the proposed speed due to road construction, visibility and other factors by intervening at 35 km / h. About using the video radar decision unit 23 or an ACC unit, a dependency of the driver intervention due to other road users is excluded. If this is the case, the driver is obviously dissatisfied with the driving strategy chosen in this situation. The problem marker "Weather situation" stores the difference between the target and the actual speed. The “weather situation” problem marker can therefore use the previously classified weather situations for repeated trips on the specified section of the route 21 optimize.

The IPPC control unit thus has 1 the possibility of individually adapting simple, mathematically illustrated routes with slopes and curvatures to the driver and the weather situation in the basic system.

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

LIST OF REFERENCE NUMBERS

1

IPPC controller

3

bus

5

card module

7

digital map

9

Learning database

11

reconstructor

13

core module

15

Predective cruise control control

17

Kartenvalidator

19

Problem markers handler

21

specified route section

23

Video Radar decision unit

25

higher-level vehicle control unit

27

ESP control unit

QUOTES INCLUDE IN THE DESCRIPTION

This list of documents listed by the applicant has been generated automatically and is only included for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102011121853 A1 [0002]

Claims (10)

Method for determining a driving strategy of a vehicle, in particular a commercial vehicle, in which a forecast horizon depending on a current position of the vehicle is determined on the basis of stored data from a digital map (7) and driving conditions of the vehicle are predicted, consumption-optimized on the basis of the forecast horizon and the driving conditions Control interventions are generated on a drive train of the vehicle, characterized in that when the vehicle travels through a predetermined section of the route (21), a comparison is made of the data stored in the digital map (7) as setpoints with actual values measured by the vehicle sensor system (23) In the event of deviations of the setpoints from the actual values, a specific event characterizing an exact position of the vehicle in the digital map (7) is stored in a local learning database (9) for correcting the data on the digital map (7) which is used as the basis for determining the driving strategy for future journeys on the specified route section (21). Procedure according to Claim 1 , characterized in that the deviations are determined by a threshold value comparison, wherein if at least one threshold value is exceeded a problem marker characterizing the specific event is set in the local learning database (9). Procedure according to Claim 1 or 2 , characterized in that a curvature of the predetermined route section (21) is considered as the problem marker, a comparison being carried out between a map curvature representing the target value and a curvature describing the actual value and calculated using the data of the vehicle sensor system, the curvature threshold being exceeded if the Problem marker "curvature" is set in the local learning database (9), which then learns a new curvature for the digital map (7) and saves it. Procedure according to Claim 3 , characterized in that the curvature describing the actual value and calculated using the data of the vehicle sensor system (23) is obtained from position data of a GPS system and / or from a lateral acceleration of the vehicle. Method according to at least one of the preceding claims, characterized in that for learning the new curvature in the learning database (9) an output value of a curve is used, the curvature of which is assessed as constant in the digital map (7), the output value being exceeded when the curve is passed Corresponding position of the predetermined route section (21) from the learned new curvature and a weighting factor, a corrected curvature of the predetermined route section (21) is determined in the digital map (9). Procedure according to Claim 5 , characterized in that the corrected curvature of the predetermined route section (21) for the digital map (7) is determined anew each time the predetermined route section (21) is passed, as long as the problem marker "curvature" is set. Method according to at least one of the preceding claims, characterized in that a start / stop event of an automatic start / stop of the vehicle is considered as the problem marker, wherein when this problem marker is set, "start / stop event" in the learning database (9) in Depending on a current dynamic and a current static traffic situation and a current driving style of the vehicle user when the vehicle is at a standstill, a decision is made as to whether the automatic start / stop is activated or deactivated when the predetermined route section (21) is repeatedly passed. Procedure according to Claim 7 , characterized in that a driving direction of the vehicle and a traffic situation are considered as the current dynamic traffic situation, while in the current static traffic situation a street type and / or intersections or roundabouts and / or traffic control systems are taken into account. Procedure according to Claim 7 or 8th , characterized in that the “start / stop event” problem marker indicates positions in the digital map (7) at which the vehicle user switches the automatic start / stop on or off, which in the learning database (9) for automatic switching on or switching off the automatic start / stop when the specified route section (21) is repeatedly passed. Method according to at least one of the preceding claims, characterized in that the driving strategy is determined as a function of a current weather situation, starting from characteristic diagrams for classified weather situations, a target speed shown in the digital map (7) with a current actual speed of the vehicle measured by the vehicle sensor system is compared each time the specified route section (21) is traveled over, the driving strategy depending on weather data being repeated when a problem marker is set Overcoming the specified route section (21) is optimized.

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