Method for generating an adaptation data set for adapting a target parameter for an electronic distance control system of a vehicle

Abstract

The invention relates to a method for generating an adaptation data set (2) for adapting a target parameter for an electronic distance control system (12) of a vehicle (13), comprising the steps: - Conducting an initial reference drive with the first fleet vehicle of a vehicle fleet with at least two fleet vehicles; - Recording at least one initial influencing factor and an initial distance from the first fleet vehicle to a preceding vehicle during the first reference run; - Conducting a second reference run with a second fleet vehicle from the vehicle fleet; - Recording at least one second influencing factor and a second distance from the second fleet vehicle to another preceding vehicle during the second reference run; - Determining the adaptation data set (2) depending on the first influencing variable and the second influencing variable and on the first distance and the second distance.

Description

[0001] One aspect of the invention relates to a method for generating an adaptation data set for adapting a target parameter for an electronic distance control system of a vehicle. Further aspects of the invention relate to a method for operating an electronic distance control system for a vehicle, an electronic analysis system, and an electronic distance control system.

[0002] Electronic distance control systems for controlling the distance to a vehicle ahead are known, commonly referred to as Adaptive Cruise Control (ACC). The distance to be controlled from a vehicle ahead is often based on the vehicle's current speed. In addition, the driver may have the option of varying the distance gradually.

[0003] DE 10 2017 213 278 A1 discloses a method for dynamically and situation-specific setting of a target distance between two vehicles.

[0004] DE 10 2017 216 408 A1 relates to a method for setting a situation-specific optimized distance to a motor vehicle driving ahead.

[0005] The invention is based on the object of providing a method to enable an improved adaptation of a target parameter for an electronic distance control system of a vehicle.

[0006] The object is achieved by the subject matter of the independent claims. Advantageous developments of the invention are defined by the dependent claims, the following description, and the figures.

[0007] One aspect of the invention relates to a method for generating an adaptation data set for adapting a target parameter for an electronic distance control system of a vehicle. The method comprises the following steps: - Carrying out a first reference drive with a first fleet vehicle of a vehicle fleet with at least two fleet vehicles; - detecting at least one first influencing variable and a first distance from the first fleet vehicle to a preceding vehicle during the first reference run; - Carrying out a second reference drive from a second fleet vehicle of the vehicle fleet; - detecting at least one second influencing variable and a second distance from the second fleet vehicle to another preceding vehicle during the second reference run; - Determining the adaptation data set as a function of the first influencing variable and the second influencing variable and of the first distance and the second distance.

[0008] The adaptation data set generated in this way can be used, for example, to operate the electronic distance control system. In other words, the method generates an adaptation data set that can be used to dynamically and situation-specifically adjust the distance between the vehicle and a vehicle ahead, depending on the target parameter.

[0009] It is advantageous to perform a large number of reference runs. In particular, a large number of measured values ​​of the influencing variables are recorded during each reference run. The more reference runs performed, the more different influencing variables, and the more measured values ​​of the respective influencing variables are recorded, the more meaningful and differentiated the adaptation data set can be.

[0010] The target parameter is, in particular, a target distance, which is to be set during operation of the electronic distance control system, between the vehicle equipped with the electronic distance control system and a vehicle currently traveling ahead. Alternatively, the target parameter can be, for example, a time gap. The time gap is, in particular, the time required for the vehicle to reach the vehicle in front.

[0011] The influencing factors influence a driver's behavior with regard to distance. The method according to the invention can achieve more natural or human-like driving behavior. Using the adaptation data set generated in this way, driver interaction with a vehicle system can also be reduced, since the electronic distance control system can independently adjust the distance to the vehicle ahead based on the influencing factors. In contrast to conventional distance control systems, for example, the driver does not need to select a distance level, since, in particular, the adaptation of the target parameter takes place automatically depending on the influencing factors.

[0012] The first and / or the second influencing variable is, for example, at least one of the following variables: speed of the respective fleet vehicle and / or of the respective preceding vehicle, a change in the speed of the respective fleet vehicle and / or of the respective preceding vehicle, a weather situation, a traffic density, a temperature, a set driving profile of the fleet vehicle, a vehicle type of the fleet vehicle and / or of the preceding vehicle, a time of day and / or brightness and / or time of year, a road category, a technology concept of a drive system of the fleet vehicle, a driver type of the fleet vehicle and / or of the preceding vehicle, a lane of a roadway being used, an operating state of a distance control system of at least the fleet vehicle, a road friction coefficient, a speed limit, a road condition, a distance to a vehicle driving behind.The listed quantities can also be referred to as a type of influencing variable or as labels or descriptors.

[0013] The adaptation data set is determined in particular by evaluating the reference runs and the influencing variables and distances recorded during them. In particular, the recorded influencing variables are each assigned to a distance. The adaptation data set is, for example, an assignment rule that assigns a target parameter, in particular a target distance, to the recorded influencing variables. The target parameter, in particular the target distance, is determined in particular from the recorded distances. Optionally, the adaptation data set is a multi-dimensional characteristic map, wherein, in particular, each type of influencing variable corresponds to a dimension and the target parameter represents a further dimension. The adaptation data set for two different types of influencing variables is, for example, a three-dimensional characteristic map.

[0014] The recorded influencing factors and distances of the reference trips can be referred to as fleet data.

[0015] The steps of the method can be carried out in a temporally reversed order or at least partially overlapping.

[0016] In one exemplary embodiment, a reference run is only considered for determining the adaptation data set if a maximum relative speed between the first fleet vehicle and the, in particular constant, leading vehicle is not exceeded for a minimum period, in particular for at least 30 seconds. In particular, the leading vehicle does not change during a homogeneous following run. This means, for example, that the first fleet vehicle follows one and the same leading vehicle during the homogeneous following run.

[0017] Such a reference trip can be referred to as a homogeneous following trip. For example, the maximum relative speed is less than 5 km / h, for example 2 km / h or 3 km / h. In particular, the reference trips to be considered are filtered from a large number of recorded trips. For example, a reference trip is only taken into account for determining the adaptation data set if the maximum relative speed between one of the fleet vehicles and the respective vehicle in front is not exceeded for the minimum period. In other words, the adaptation data set is determined only from the fleet data recorded during a homogeneous following trip. During homogeneous following trips, the recorded influencing factors and distances are more meaningful than during non-homogeneous following trips. A non-homogeneous following trip is, for example, an overtaking maneuver or a vehicle merging.

[0018] In one embodiment, an environmental influencing variable and / or an influencing variable of the fleet vehicle and / or an influencing variable of the leading vehicle are considered as the first influencing variable and / or as the second influencing variable. In particular, at least one of the following variables is considered: - a speed of the respective fleet vehicle and / or the preceding vehicle, and / or , - a change in the speed of the respective fleet vehicle and / or the vehicle in front, - a weather situation, for example sun, clouds, rain, snowfall, hail, in particular a precipitation intensity, - a traffic density, for example next to the fleet vehicle and / or the vehicle in front, in front of the fleet vehicle and / or the vehicle in front and / or behind the fleet vehicle and / or the vehicle in front, - a temperature, - a set driving profile, especially of the fleet vehicle, - a vehicle type of the fleet vehicle and / or the front vehicle, for example whether it is a truck or a passenger car, - a time of day and / or a brightness, and / or a season, - a road category, for example motorway, country road or urban road, - a technology concept for a drive system for the fleet vehicle, - a driver type of the fleet vehicle and / or the lead vehicle, - a lane of a roadway in use, - a road friction coefficient, - a speed limit, - a road condition, for example whether a roadwork is currently being driven through or whether a road currently being used has damage, - a distance to a vehicle following behind, - an operating state of a distance keeping system of at least the fleet vehicle, for example active or inactive distance keeping system.

[0019] In particular, at least two of the aforementioned influencing factors are taken into account. Specifically, two different types of influencing factors are used to determine the adaptation data set. The more influencing factors are taken into account, the more specifically a situation can be captured and a distance assigned to the respective situation.

[0020] In one embodiment, a plurality of values ​​of the first and / or second influencing variable are determined to determine the adaptation data set. This plurality is classified into value classes. Each of the value classes is assigned a plurality of distance values. From the plurality of assigned distance values, one, in particular a single, target distance is calculated, for example using a statistical method. In particular, the target distance corresponds to a median or a mean of the plurality of assigned distance values. Alternatively or in addition to the statistical method, a machine learning algorithm can be used, for example in the form of a decision tree, in particular a Random Forest algorithm, or a neural network.

[0021] For example, the plurality is classified before determining the adaptation data set. Alternatively, the plurality is classified before providing the adaptation data set to an electronic distance control system. In particular, a target distance is assigned to each of the value classes.

[0022] For example, if the influencing factor is the speed of the respective fleet vehicle, the speed values ​​can be divided into value classes with a range of, say, 20 km / h. For example, the first value class is for speeds up to 20 km / h, and a second value class is for speeds from 21 km / h to 40 km / h.

[0023] In another example, the influencing variable can describe a weather situation, specifically the intensity of precipitation. It is possible that sensor values ​​from a vehicle's rain sensor are evaluated for this purpose. The evaluated sensor values ​​can, if necessary, be divided into three precipitation value classes, for example, "dry," "light precipitation," and "heavy precipitation."

[0024] It is possible to consider traffic density as an influencing factor. For this purpose, radar information from the vehicle is evaluated, for example. Possible value classes for traffic density can be divided into ten-percent increments, especially one-percent increments. For example, all surrounding vehicles that can be detected by the vehicle within the detection interval correspond to 100 percent. If, for example, the vehicle can detect a maximum of twelve surrounding vehicles within the detection interval, then twelve surrounding vehicles correspond to 100 percent. For example, six detected vehicles then correspond to 50 percent. The detection interval can last, in particular, 30 seconds.

[0025] For example, the brightness of the fleet vehicle's surroundings is considered as an influencing factor. For this purpose, a brightness sensor can be evaluated. For example, brightness measurements recorded by the vehicle's brightness sensor are divided into the value classes "light" and "dark."

[0026] It is possible that the temperature of the vehicle's surroundings can be considered as an influencing factor. For this purpose, the outside temperature can be determined using a temperature sensor in the vehicle. Temperature values ​​recorded by the temperature sensor can, for example, be divided into two value classes, specifically less than four degrees Celsius and greater than four degrees Celsius. In particular, the temperature values ​​are divided into finer value classes, for example, into five value classes. The five value classes cover, for example, the following intervals: less than -5°C, greater than -5°C to 0°C, greater than 0°C to 4°C, greater than 4°C to 10°C, and greater than 10°C.

[0027] In particular, at least two of the aforementioned influencing variables are determined. For example, speed and brightness are recorded as influencing variables. For example, a target parameter is assigned to each combination of the value classes for an influencing variable with the value classes of other influencing variables. In the example mentioned, a target parameter can therefore be assigned to each combination of the value classes for speeds with the value classes for brightness. For example, such a combination can be referred to as “50 km / h to 70 km / h + bright”. This combination includes, for example, all recorded speeds of the fleet vehicles that were recorded during a “bright” vehicle environment according to the measured value of the brightness sensor, in particular during the same recording interval. Another combination can be referred to, for example, as “50 km / h to 70 km / h + dark”.In particular, a minimum number of reference runs, in particular 700 to 800 reference runs, are carried out per combination of value classes.

[0028] By classifying the multitude of influencing variable values, the amount of data and thus the storage space required for the determined influencing variables are reduced. In particular, not a continuous spectrum of influencing variable values ​​is analyzed, but rather the value classes. This can increase the speed of the analysis.

[0029] In one embodiment, a median is calculated from the distance values ​​assigned to a value class. In particular, a single median is calculated for each value class. This allows each combination to be assigned a single target distance.

[0030] In one embodiment, the calculated medians of the value classes are interpolated so that a coherent value line or value map is generated.

[0031] For example, each value class is assigned an identifier and a single value. For example, the value class for speeds, which corresponds to a speed range between 50 km / h and 70 km / h, is assigned the identifier "60 km / h" and the value 60 km / h. This reduces the data volume of the values ​​of the influencing variables. A continuous value line is then optionally interpolated between the points of the identifiers and their respective single values. For example, a value gap is created between the values ​​of the identifiers, which is closed by interpolation. A linear or logarithmic function is used for interpolation, for example.

[0032] In one embodiment, a characteristic curve and / or a characteristic map is generated as the adaptation data set, in which a target parameter is presented as a function of values ​​and / or states of at least one influencing variable. In other words, the influencing variables, in particular a respective combination of the influencing variables, describe a specific situation to which a target parameter is assigned. In particular, the adaptation data set contains this assignment. For example, within the acquisition interval, n different influencing variables and distance values ​​for each combination of the influencing variables are acquired. If necessary, the adaptation data set is stored as an n + 1 dimensional array, where one dimension corresponds to the distance.

[0033] A further aspect of the invention relates to a method for operating an electronic distance control system for a vehicle. An adaptation data set is provided to the electronic distance control system. The adaptation data set was generated according to the inventive method for generating the adaptation data set for adapting the target parameter for the electronic distance control system of the vehicle. At least one actual influencing variable is recorded using a recording unit. A target parameter associated with the actual influencing variable is determined from the adaptation data set. The determined target parameter is adjusted during active operation of the electronic distance control system. This can be referred to, for example, as dynamic adjustment.

[0034] By using the adaptation data set, the target parameter can be set dynamically and situation-specifically. If necessary, the target parameter is determined solely based on the provided adaptation data set and at least one actual influencing variable. In particular, the same influencing variables as those already listed are recorded as actual influencing variables. This does not mean the values ​​of the actual influencing variable and the influencing variable, but rather their type. For example, the same influencing variables are recorded as actual influencing variables. For example, the vehicle speed and the brightness are recorded as actual influencing variables if the speed of the fleet vehicle and the brightness were recorded as influencing variables during the reference runs.

[0035] In one embodiment, during active operation of the distance control system, a current target parameter is defined and a comparison is made with at least one reference target parameter. The current target parameter is adjusted based on the comparison. In this embodiment, in particular, an existing distance control system is improved, in particular overridden, using the adaptation data set. If, for example, the actual target parameter does not match the reference target parameter, the reference target parameter can be adopted as the new current target parameter. It is advantageous if the reference target parameter is determined from the adaptation data set, in particular by comparing the actual influencing variables with the influencing variables of the characteristic map of the adaptation data set.For example, the comparison of the current target parameter with the reference target parameter is only performed if at least one value of one of the actual influencing variables exceeds a predefined threshold. In another example, the reference target parameter is only adopted as the new current target parameter if a difference between the current target parameter and the reference target parameter determined from the adaptation data set exceeds a difference threshold. These examples can reduce computational effort.

[0036] In one embodiment, the vehicle has the detection unit. The detection unit has sensors on the vehicle. This allows the actual influencing variable to be detected by the detection unit, if necessary in real time. This allows a realistic actual scenario to be assessed. Using a realistic actual scenario, the target parameter can be set specifically for this actual scenario.

[0037] In one embodiment, the adaptation data set is both provided and trained during active operation of the electronic distance control system. This allows the fleet data from which the adaptation data set is determined to be improved during active operation of the electronic distance control system. Thus, the adaptation data set and, consequently, the operation of the electronic distance control system are also improved.

[0038] The invention also includes further developments of the method according to the invention for operating the electronic distance control system, which have features as already described in connection with the further developments of the method according to the invention for generating an adaptation data set. For this reason, the corresponding further developments of the method according to the invention for operating the electronic distance control system are not described again here.

[0039] A further aspect of the invention relates to an electronic analysis system with at least one evaluation unit. The electronic analysis system is designed to carry out the method according to the invention for generating an adaptation data set for adapting a target parameter for an electronic distance control system of a vehicle. In particular, the electronic analysis system carries out the method according to the invention.

[0040] A further aspect of the invention relates to an electronic distance control system designed to implement a method according to the invention for operating an electronic distance control system for a vehicle. In particular, the electronic distance control system implements the method according to the invention. In particular, the analysis system is a component of the distance control system.

[0041] The invention also includes further developments of the analysis system according to the invention and the electronic spacing system according to the invention, which have features already described in connection with the further developments of the methods according to the invention. For this reason, the corresponding further developments of the analysis system according to the invention and the electronic spacing system according to the invention are not described again here.

[0042] Another aspect of the invention relates to a method for generating an adaptation data set for adapting a target parameter for an electronic distance control system of a vehicle. The method comprises the following steps: - Providing to an evaluation unit first reference data which comprise at least a first influencing variable and a first distance from a first fleet vehicle of a vehicle fleet comprising at least two fleet vehicles to a preceding vehicle during a first reference run; - Providing second reference data to the evaluation unit, which data comprise at least a second influencing variable and a second distance from a second fleet vehicle of the vehicle fleet to another preceding vehicle during a second reference run; and - Determining the adaptation data set with the evaluation unit depending on the first reference data and the second reference data.

[0043] Data generated from the reference runs can also be provided to the evaluation unit. In particular, the reference data and, if applicable, the data generated from other sources are received by the evaluation unit.

[0044] Another aspect of the invention relates to a method for generating an adaptation data set for adapting a target parameter for an electronic distance control system of a vehicle. The method comprises the following steps: - Carrying out a reference drive with a vehicle with its electronic distance control system activated; - Detecting at least one first influencing variable and a first distance parameter from the vehicle to a preceding vehicle during the first reference run; a distance level set by the driver can be detected as the distance parameter. Alternatively, a time gap set by the driver can be detected as the distance parameter; in particular, a distance value can be calculated from the time gap; - Carrying out a second reference run with a vehicle with its electronic distance control system deactivated; - Detecting at least one second influencing variable and a second distance from the vehicle to another vehicle ahead during the second reference run; if necessary, one or more distance values ​​in meters are determined as the second distance. - Determining the adaptation data set as a function of the first influencing variable and the second influencing variable and of the distance parameter and the distance.

[0045] In particular, the first and second reference runs are considered to be those in which identical or essentially identical driving situations prevail. A driving situation can, for example, be characterized by the influencing factors already mentioned. It is possible to perform a comparison between the first and second reference runs. Furthermore, the influencing factors and the distances can be weighted depending on the active and inactive states of the adaptive cruise control system.

[0046] For use cases or application situations that may arise in one of the methods and which are not explicitly described here, it may be provided that, in accordance with the method, an error message and / or a request to enter user feedback is issued and / or a default setting and / or a predetermined initial state is set.

[0047] The invention also includes further developments of the inventive method that have features already described in connection with the further developments of the inventive method for generating an adaptation data set. For this reason, the corresponding further developments of the inventive method are not described again here.

[0048] The invention also includes combinations of the features of the described embodiments.

[0049] Exemplary embodiments of the invention are described below. Shown are: Fig. 1 a schematic representation of an analysis system for carrying out the method according to the invention for generating an adaptation data set; Fig. 2 shows a schematic representation of an example of an adaptation data set generated according to the method according to the invention; Fig. 3 a schematic representation of an exemplary situation in which an embodiment of the method according to the invention for operating an electronic distance keeping system is carried out.

[0050] The exemplary embodiments explained below are preferred exemplary embodiments of the invention. In the exemplary embodiments, the described components each represent individual, independently considered features of the invention, which also further develop the invention independently of one another and are thus also to be considered as components of the invention, either individually or in a combination other than that shown. Furthermore, the described exemplary embodiments can also be supplemented by further features of the invention already described.

[0051] In the figures, functionally identical elements are provided with the same reference numerals.

[0052] In Fig. 1 schematically shows an analysis system 1 for carrying out a method for generating an adaptation data set 2. The analysis system 1 has, for example, an evaluation unit 3. For example, first reference data 4 is provided to the evaluation unit 3. To generate the first reference data 4, for example, a first reference drive is carried out with a first fleet vehicle of a vehicle fleet with at least two fleet vehicles. In this process, for example, a first influencing variable and a first distance between the first fleet vehicle and a vehicle traveling ahead are recorded. The first reference data includes, for example, the first influencing variable and the first distance.

[0053] In particular, second reference data 5 is provided to the evaluation unit 3. To determine the second reference data 5, for example, a second reference drive is performed by a second fleet vehicle of the vehicle fleet. In this process, for example, at least a second influencing variable and a second distance between the second fleet vehicle and another vehicle traveling ahead are recorded. In particular, the second reference data 5 includes the second influencing variable and the second distance.

[0054] The evaluation unit 3 can determine the adaptation data set 2 based on the first reference data 4 and the second reference data 5. For this purpose, for example, a statistical value for a target distance can be calculated from several recorded distance values. In particular, the recorded distance values ​​were all recorded at essentially the same values ​​of the respective influencing variable. For example, an average value from all distance values ​​recorded at a temperature of less than 4 °C is calculated as the target distance. In particular, temperature can represent an influencing variable.

[0055] Fig. 2 shows a schematic representation of an example of the adaptation data set 2, which was generated by the analysis system 1. The adaptation data set 2 can optionally be generated as a characteristic map 6. In the Fig. In the example shown in Figure 2, the influencing variables are the speed of the fleet vehicles and the brightness of the fleet vehicles' surroundings. A large number of reference runs can be carried out. In this example, at least one speed value and one brightness value are recorded for each reference run. For each combination of speed values ​​and brightness values ​​recorded in the same time interval, a distance value is determined. Optionally, the speed values ​​can be divided into value classes 7a, 7b, 7c, 7d, 7e, 7f for the speeds. If necessary, each of the value classes 7a, 7b, 7c, 7d, 7e, 7f is assigned an identifier and a single value 8. The brightness values ​​can, for example, be divided into the brightness value classes "dark" 9a and "light" 9b.

[0056] For example, the value class for speeds with the identifier "40 km / h" is assigned to speeds up to 50 km / h. For example, there is the combination "40 km / h + light" and the further combination "40 km / h + dark." The recorded distances are assigned to each combination if necessary. In this example, all distances recorded when the fleet vehicle was traveling at a speed of less than 50 km / h and in a "bright" vehicle environment according to a brightness sensor of the fleet vehicle can be assigned to the combination "40 km / h + light." Optionally, a median 11 or another statistical value can be calculated from the assigned distances. The median 11 can then be assigned to the combination. The median 11 forms the target distance for the respective combination. This means, for example, that each combination is assigned a single target distance.

[0057] In Fig. Figure 3 schematically illustrates an exemplary situation in which an embodiment of the method for operating an electronic distance control system 12 is implemented. The electronic distance control system 12 is, for example, part of a vehicle 13. The vehicle 13 is traveling at a distance 14 behind a preceding vehicle 15.

[0058] In one embodiment, the electronic distance control system 12 may comprise the adaptation data set 2 and a detection unit 16. For example, a detection unit 16 of the vehicle 13 may comprise a plurality of sensors, for example, a speed sensor and a brightness sensor. For example, the electronic distance control system 12 may comprise an evaluation unit 3. The evaluation unit 3 may, if appropriate, be the same evaluation unit 3 as in Fig. 1 of the analysis system 1. Alternatively, the evaluation unit 3 can be Fig.3 of the electronic distance control system 12 may be a different evaluation unit.

[0059] For example, the detection unit 16 detects an actual influencing variable, which can also be referred to as an environmental variable. The actual influencing variable can, for example, comprise brightness and / or speed. The actual influencing variable can, in particular, be the same as one of the influencing variables. In particular, a type of the actual influencing variable, for example a temperature, is the same as the type of influencing variable. The detection unit 16 can provide the detected actual influencing variable to the electronic distance keeping system 12, in particular to the evaluation unit 3. The adaptation data set 2 can also be provided to the electronic distance keeping system 12, in particular to the evaluation unit 3, and / or stored there. The electronic distance keeping system 12 can determine a target distance from the provided actual influencing variable and the adaptation data set 2. The electronic distance keeping system 12 can adjust the determined target distance.For this purpose, the vehicle 13 can, for example, be braked or accelerated in such a way that the distance 14 to the vehicle 15 in front corresponds to the target distance to be set.

[0060] For example, the detection unit 16 can detect the actual influencing variable again after a predefined time interval. For example, the target distance can be determined again from the adaptation data set 2 after this time interval and / or if the actual influencing variable exceeds and / or falls below a predefined threshold. List of reference symbols 1 analysis system 2 Adaptation data set 3 Evaluation unit 4 first reference data 5 second reference data 6 map 7a - f value classes 8 Value 9a value class 9b value class 12 Distance keeping system 13 vehicles 14 Distance 15 front vehicle 16 Recording unit

Claims

[1] Method for generating an adaptation data set (2) for adapting a target parameter for an electronic distance control system (12) of a vehicle (13), comprising the steps: - Carrying out a first reference drive with a first fleet vehicle of a vehicle fleet with at least two fleet vehicles; - detecting at least one first influencing variable and a first distance from the first fleet vehicle to a preceding vehicle during the first reference run; - Carrying out a second reference drive from a second fleet vehicle of the vehicle fleet; - detecting at least one second influencing variable and a second distance from the second fleet vehicle to another preceding vehicle during the second reference run; - Determining the adaptation data set (2) as a function of the first influencing variable and the second influencing variable and of the first distance and the second distance. [2] Method according to claim 1, wherein a reference run is only taken into account for determining the adaptation data set (2) if a maximum relative speed between the first fleet vehicle and the, in particular constant, front vehicle is not exceeded for a minimum period of time, in particular for at least 30 seconds. [3] Method according to one of the preceding claims, wherein an environmental influencing variable and / or an influencing variable of the fleet vehicle and / or an influencing variable of the front vehicle is taken into account as the first influencing variable and / or as the second influencing variable, in particular at least one of the following variables is taken into account: - a speed of the respective fleet vehicle and / or the preceding vehicle, and / or , - a change in the speed of the respective fleet vehicle and / or the vehicle in front, - a weather situation, - a traffic density, - a temperature, - a set driving profile of the fleet vehicle, - a vehicle type of the fleet vehicle and / or the lead vehicle, - a time of day and / or a brightness and / or a season, - a road category, - a technology concept for a drive system for the fleet vehicle, - a driver type of the fleet vehicle and / or the lead vehicle, - a busy lane of a roadway, - a friction coefficient - an operating state of a distance control system of at least the fleet vehicle. [4] Method according to one of the preceding claims, wherein for the determination of the adaptation data set (2) a plurality of values ​​of the first and / or the second influencing variable are determined and this plurality is classified into value classes (7a, 7b, 7c, 7d, 7e, 7f, 9a, 9b) and distance values ​​are assigned to each of the value classes (7a, 7b, 7c, 7d, 7e, 7f, 9a, 9b). [5] Method according to claim 4, wherein a median (11) is calculated from the distance values ​​assigned to a value class (7a, 7b, 7c, 7d, 7e, 7f, 9a, 9b), in particular a median (11) is calculated for each value class (7a, 7b, 7c, 7d, 7e, 7f, 9a, 9b). [6] Method according to claim 5, wherein the calculated medians (11) of the value classes (7a, 7b, 7c, 7d, 7e, 7f, 9a, 9b) are interpolated so that a connected value line or a value characteristic field (6) is generated. [7] Method according to one of the preceding claims, wherein a characteristic curve and / or a characteristic map (6) is generated as an adaptation data set, in which a target parameter is presented as a function of values ​​and / or states of at least one influencing variable. [8] Method for operating an electronic distance control system for a vehicle (13), comprising the steps: - Providing the adaptation data set (2) to the electronic distance keeping system (12) which was generated according to one of the preceding claims; - detecting at least one actual influencing variable by means of a detection unit (16); - Determining a target parameter assigned to the actual influencing variable from the adaptation data set (2); and - Setting the determined target parameter during active operation of the distance control system (12). [9] Electronic analysis system (12) with at least one evaluation unit (3), wherein the electronic analysis system (12) is designed to carry out a method according to one of claims 1 to 7. [10] Electronic distance keeping system (12) which is designed to carry out a method according to claim 8.

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