Method for managing a vehicle fleet, computer program and / or computer-readable storage medium, data processing device and vehicle-external server

Abstract

Method (100) for managing a vehicle fleet (250), wherein the fleet comprises at least one vehicle (200a), in particular utility vehicle (200b), and the method (100) comprises: obtaining (110), from the at least one vehicle (200a), in particular utility vehicle (200b), vehicle specific and driving related real-time vehicle information (210); determining (120) a vehicle specific and / or fleet related emission indicator (220) based on the vehicle information (210) and on an emission factor (215); determining (130) an adjustment measure (230) related to the vehicle information (210), wherein the adjustment measure (230) is adapted to lower the emission indicator (220) to a lowered emission indicator (221); and outputting (140) the lowered emission indicator (221) and / or the adjustment measure (230).

Description

[0001] Method for managing a vehicle fleet, computer program and / or computer-readable storage medium, data processing device and vehicle-external server

[0002] The disclosure relates to a method for managing a vehicle fleet, wherein the fleet comprises at least one vehicle, in particular utility vehicle. The disclosure also relates to a computer program and / or computer-readable storage medium, to a data processing device for a vehicle-external server and to a vehicle-external server.

[0003] Managing a fleet of vehicles is typically performed, at least to some extent, by a vehicle-external server, i.e., a backend and / or cloud. The server is adapted to obtain information about the vehicles and to process the information to determine potentially useful data for managing and / or evaluating the vehicle fleet and / or one or more of the vehicles thereof. The server provides a fleet managing platform.

[0004] Known fleet managing platforms, also called fleet orchestration platforms, may offer basic carbon footprint tracking. A carbon footprint of a vehicle may refer to the total amount of greenhouse gases, primarily carbon dioxide (CO2) and other emissions, that are produced directly or indirectly as a result of the vehicle's manufacturing, operation, and end-of-life disposal. Thus, the carbon footprint may be a measure of the vehicle's impact on the environment in terms of contributing to climate change.

[0005] The carbon footprint of a vehicle fleet, i.e., of a plurality of vehicles, may refer to the combined impact of the vehicles of the vehicle fleet. Such a vehicle fleet is typically operated and / or owned by a company and / or a public transportation system. Carbon footprint tracking in these contexts may comprise monitoring, recording and / or evaluating the carbon footprint associated with the vehicle fleet and / or the vehicles over their entire life cycle. This tracking can help individuals, companies and / or governments to understand the environmental impact of their transportation choices and identify areas for improvement. By tracking and managing the carbon footprint of vehicles and fleets, it may become possible to make more informed decisions to mitigate the environmental impact of transportation. Vehicle fleet operators and / or owners typically have the incentive to improve the efficiency of the vehicle fleet, in particular during an operation of the vehicles. The efficiency may be improved for economic purposes but also to reduce the carbon footprint. Reducing the carbon footprint may be an important asset for a company, however, a reliable reporting thereof is necessary. The reduction of the carbon footprint may be rewarded, which may in turn constitute an economic incentive.

[0006] Further, fleet managing return-of-invest (ROI) calculators typically rely on manual data collection and periodic updates of data, which may not reflect the actual emissions during specific trips or events, i.e., during the operation of the vehicles. Thus, fleet owners may invest in the efficiency of the vehicle fleet and / or in emission adjustment measures for operating the vehicles without a clear understanding of the potential financial returns or cost savings.

[0007] US 10,223,655 B2 discloses a fleet management system for managing a plurality of vehicles. The system includes means for receiving data related to a deposit of a personally owned vehicle from an owner of the vehicle, the deposit being of a predetermined duration, means for receiving a travel request from a traveler other than the owner, and means for assigning the personally owned vehicle for use by the traveler during the predetermined duration based on the travel request. By providing such a system, a corporation or other entity may mitigate its emissions footprint (e.g., carbon, NOx, etc.) by facilitating the ownership of vehicles possessing the latest emissions reduction technologies by employees of the corporation, and by assigning vehicles that are best suited to performing functions associated with the requested travel plan. Carbon emissions associated with the vehicle fleet resulting in credits may be tracked, stored, traded, and used based on excess emissions capacity provided by the vehicle fleet.

[0008] In the light of the prior art, the object of the present disclosure is to provide a contribution to the prior art, and to provide a method being suitable for improving at least one of the above-mentioned aspects of the prior art, respectively. In particular, it is an object of the disclosure to provide a comprehensive and real-time solution for fleets to measure, manage and / or optimize in particular carbon emissions. The object is solved by the features of the independent claim. The dependent claims have further embodiments of the disclosure as their subject matter.

[0009] According to an aspect of the present disclosure a method for managing a vehicle fleet is provided, wherein the fleet comprises at least one vehicle, in particular utility vehicle, and the method comprises: obtaining, from the at least one vehicle, in particular utility vehicle, vehicle specific and driving related real-time vehicle information; determining a vehicle specific and / or fleet related emission indicator based on the vehicle information and on an emission factor; determining an adjustment measure related to the vehicle information, wherein the adjustment measure is adapted to lower the emission indicator to a lowered emission indicator; and outputting the lowered emission indicator and / or the adjustment measure.

[0010] In other words, the disclosure suggests a real-time data integration of the vehicle information to enable a comprehensive determination of the lowered emission indicator and / or the adjustment measure. The carbon footprint-based method addresses the need for a comprehensive and real-time solution for vehicle fleets to measure, manage and optimize their carbon emissions. By integrating the related real-time vehicle information as dynamic data and an advanced determination of adjustment measures and / or the lowered emission indicator, the disclosure enables fleet owners and / or fleet operators to make informed decisions, in particular which may reflect environmental responsibility. Therein, in other words, the advanced determination may be to be understood as an analysis of the real-time vehicle information in contrast to obtaining information for a pre-defined and / or specific time window and analyzing the information afterwards.

[0011] The use of the vehicle specific and driving related real-time vehicle information enables an insight and processing of real-time data. In other words, the method provides real-time data insights, allowing fleet owners and / or operators to track emissions as they occur, i.e., during the operation of the vehicle fleet. This may enable supporting proactive decision-making and immediate action for lowering the emissions by implementing the adjustment measure. By incorporating dynamic data, i.e., the real-time vehicle information, an advanced analysis of adjustment measures is possible to mitigate further carbon emission. The method may offer a holistic approach to sustainability, enabling fleet owners and / or operators to balance environmental impact and financial performance effectively. The method further improves compliance and reporting capabilities, since the method may simplify compliance with, e.g., environmental regulations, by providing accurate carbon footprint data and facilitating comprehensive reporting for regulatory purposes.

[0012] Optionally, the method comprises: determining an implementation indicator that relates to resources for the implementation of the adjustment measure; and outputting a return of investment based on a difference between the emission indicator and the lowered emission indicator and on the implementation indicator. Therein, it is realized that the implementation and / or realization of the adjustment measure to lower the carbon footprint may require a certain amount of resources. Therein, a resource may relate in particular to the cost-intensity of the adjustment measure, of the technological contribution which may be necessary to implement the adjustment measure and / or to a time quantity to implement the adjustment measure. The return of investment may then relate the effect of the adjustment measure, i.e., the difference between the emission indicator and the lowered emission indicator, to the implementation indicator which indicates and / or quantifies the resources.

[0013] Optionally, the vehicle specific and driving related real-time vehicle information relates to at least one of the following vehicle information types: traffic conditions, vehicle load, driving behavior, vehicle fuel type, fuel efficiency, mileage and / or engine performance. Each of the aforementioned vehicle information types may be obtained by the vehicle and / or its components and may thus indicate and / or represent the vehicle specific and driving related real-time vehicle information. Each of the vehicle information types may be used to evaluate the carbon footprint of the specific vehicle. Further, each of the vehicle information types may relate to a vehicle information that may be improvable by one or more adjustment measures.

[0014] Optionally, the real-time vehicle information is based on an ADAS sensor of the vehicle, in particular utility vehicle. Therein, an ADAS sensor is an advanced driver assistance system sensor, i.e., a sensor of the vehicle that may be used to obtain sensor data for an advanced driver assistance system (ADAS). It is realized that vehicles may typically comprise one or more ADAS sensors and that the sensor data of the ADAS sensor may be used as real-time vehicle information to gain insight into the carbon footprint of the vehicle. Optionally a plurality of vehicles and / or each of the vehicles comprises an ADAS sensor, and the real-time vehicle information for any of the vehicles is based on the ADAS sensor of the vehicle.

[0015] Optionally, the adjustment measure is determined for a plurality of vehicle information types. Therein, it is realized that the vehicle may produce the carbon footprint based on different mechanisms to produce the carbon footprint. These different mechanisms may be reflected in the different vehicle information types. Thus, a specific adjustment measure may be determined for each of the vehicle information types to improve the contribution of the respective vehicle information type to the carbon footprint of the vehicle.

[0016] Optionally, the emission indicator is vehicle specific, and a plurality of vehicle specific emission indicators is determined; and the method further comprises: comparing vehicle specific emission indicators with each other; and determining the adjustment measure is based on the comparing of the vehicle specific emission indicators. Therein, it is realized that different vehicles may produce different carbon footprints based on different mechanisms to produce the overall carbon footprint. Thus, for each vehicle of the vehicle fleet, a vehicle-specific adjustment measure may be determined. This enables a reliable and comprehensive comparison of different vehicles. This may achieve to identify a best practice and / or to spot the vehicle or vehicles that disproportionally contribute to the carbon footprint of the vehicle fleet.

[0017] Optionally, the emission factor is based on a regulatory quantity and / or on a particulate matter sensor of the vehicle, in particular utility vehicle. Therein, it is realized that the emission factor may represent a weight and / or a contribution of the vehicle information to the emission indicator and that such a factor may need to comply with regulatory aspects, e.g., of a norm, standard, law and / or other regulation, and / or may be a result of a measurement of a particulate matter sensor. Therein, the particulate matter sensor may provide reliable information in relation to an effect of the vehicle information. Optionally a plurality of vehicles and / or each of the vehicles comprises a particulate matter sensor, and the emission factor for any of the vehicles is based on the particulate matter sensor of the vehicle.

[0018] According to an aspect of the disclosure, a computer program and / or computer- readable storage medium is provided, comprising instructions which, when being executed by a data processing device, causes the data processing device to carry out the method as described above. Optionally, the computer program and / or computer-readable storage medium comprises an instruction which, when being executed by a data processing device, causes the data processing device to realize one or more optional and / or preferred technical features of the method as described above to achieve a technical effect relating thereto.

[0019] According to an aspect of the disclosure, a data processing device for a vehicleexternal server is provided, wherein the data processing device is adapted to perform the method as described above. Optionally, the data processing device is adapted to realize one or more optional and / or preferred technical features of the method as described above to achieve a technical effect relating thereto.

[0020] According to an aspect of the disclosure, a vehicle-external server is provided, comprising the data processing device as described above. Optionally, the data processing device and / or the vehicle-external server is adapted to realize one or more optional and / or preferred technical features of the method as described above to achieve a technical effect relating thereto.

[0021] An embodiment according to an aspect of the present disclosure is described with reference to the Figures below.

[0022] Fig. 1 shows schematically a vehicle, in particular utility vehicle, of a vehicle fleet and a vehicle-external server according to an embodiment of the disclosure;

[0023] Fig. 2 shows a schematic chart of a method according to an embodiment of the disclosure; and

[0024] Fig. 3 shows a schematic of a computer-readable storage medium according to an embodiment of the disclosure. ln the following embodiments are described with reference to the Figures, wherein the same reference signs are used for the same objects throughout the description of the Figures and wherein the embodiment is just one specific example for implementing the disclosure and does not limit the scope of the disclosure as defined by the claims.

[0025] Figure 1 shows schematically a vehicle 200a, in particular utility vehicle 200b, of a vehicle fleet 250 and a vehicle-external server 400 according to an embodiment of the disclosure.

[0026] The vehicle fleet 250 is a plurality of vehicle 200a, in particular utility vehicles 200b. In the following, the vehicle 200a, in particular utility vehicles 200b, are referred to as vehicles 200a, 200b. The vehicles 200a, 200b are land vehicles. The vehicle fleet 250 may comprise any number of vehicles 200a, 200b as schematically indicated by the dots.

[0027] One of the vehicles 200a, 200b is schematically indicated with more detail and comprises a vehicle data processing device 201 , a vehicle communication interface 201 , an ADAS sensor 202 and a particulate matter sensor 203. The other vehicles 200a, 200b may comprise at least the data processing device 201 and the communication interface 201 but in another embodiment may comprise the same components and / or a subset thereof.

[0028] The vehicle communication interface 201 of the vehicle 200a, 200b is adapted to communicate with a vehicle-external server 400. The server 400 comprises a communication interface 401 . The vehicle communication interface 201 and the communication interface 401 of the server 400 are adapted to communicate with each other to transmit and / or receive data and / or information. For example, each of the vehicle communication interface 201 and the communication interface 401 is adapted to communicate with each other over a wireless local area network (WLAN), a cellular network and / or a vehicle-to-everything (V2X) connection including V2V communication (vehicle-to-vehicle) and V2I communication (vehicle-to- infrastructure). The vehicle 200a, 200b is adapted to acquire vehicle specific and driving related realtime vehicle information 210. The vehicle specific and driving related real-time vehicle information 210 relates to at least one of the following vehicle information types 211 : traffic conditions 21 1 a, vehicle load 21 1 b, driving behavior 21 1 c, vehicle fuel type 21 1 d, fuel efficiency 21 1 e, mileage 21 1 f and / or engine performance 21 1 g. The real-time vehicle information 210 is based on the ADAS sensor 202. For example, real-time traffic conditions 211 a may be acquired through GPS and / or traffic APIs. Real-time vehicle load 211 b may be monitored using loT and / or ADAS sensors. Real-time driving behavior 21 1 c may be acquired and / or collected through tachograph data.

[0029] The vehicle data processing device 201 is adapted to process the vehicle information 210 and to transmit the vehicle information 210 via the vehicle communication interface 201 a and the communication interface 401 to the server 400. The server

[0030] 400 obtains the vehicle information 210 from the vehicle 200a, 200b and from the other vehicles 200a, 200b of the vehicle fleet 250.

[0031] The server 400 comprises a data processing device 401 . The data processing device

[0032] 401 is adapted to process the vehicle information 210. The data processing device is adapted to determine a vehicle specific and / or fleet related emission indicator 220 based on the vehicle information 210 and on an emission factor 215. The emission factor 215 is based on a regulatory quantity and / or on information from the particulate matter sensor 203 of the vehicle 200a, 200b. For example, a real-time CO2 emission factor 215 may be logged by the server 400 and may be set as a specific amount of CO2 emission per distance, e.g., for a light commercial vehicle (LCV): 147g / km.

[0033] The data processing device 401 is adapted to determine the emission indicator 220 as a vehicle-specific quantity for the vehicles 200a, 200b. Thus, the data processing device 401 is adapted to determine a plurality of vehicle specific emission indicators 220. The emission indicator 220 may be based on a real-time carbon footprint calculation. This enables a real-time and accurate carbon footprint calculation for individual vehicles 200a, 200b of the vehicle fleet 250, considering specific vehicle information 210 as dynamic factors. Therein, the vehicle information types 21 1 of dynamic and real-time vehicle information 210 is considered to enable a precise carbon footprint assessment. The data processing device 401 is adapted to compare the vehicle specific emission indicators 220 with each other.

[0034] The data processing device 401 is adapted to determine an adjustment measure 230 related to the vehicle information 210. The adjustment measure 230 is adapted to lower the emission indicator 220 to a lowered emission indicator 221 . The adjustment measure 230 is determined for a plurality of vehicle information types 21 1 .

[0035] The determining 130 the adjustment measure 230 is based on comparing of the vehicle specific emission indicators 220. Therein, a machine learning model for predictive maintenance for improving fuel economy for deriving the adjustment measure 230 may be deployed. A fleet-wide comparison allows fleet owners and / or operators to compare the carbon footprint and a return of investment ROI of adjustment measures 230 relating to different vehicles 200a, 200b within the vehicle fleet 250, identifying high performing vehicles 200a, 200b and sharing best practices across the fleet. This includes information about a certain driver behavior which is found to be optimal with regard to the degree of fuel efficiency. In another embodiment a specific load management strategy may be another best practice to be shared for optimizing fuel efficiency, respectively carbon footprint optimization. In a third embodiment an advanced route optimization may be shared as best practice.

[0036] The data processing device 401 is adapted to determine an implementation indicator 240 that relates to resources 245 for the implementation of the adjustment measure 230. The implementation indicator 240 enables an advanced analysis of a return of investment ROI to estimate a financial impact of implementing the adjustment measures 230 as emission reduction strategies, providing fleet owners and / or operators with data-driven insights to optimize both environmental and financial performance.

[0037] The server 400 is adapted to output the lowered emission indicator 221 and / or the adjustment measure 230. The server 400 is adapted to output the return of investment ROI based on a difference between the emission indicator 220 and the lowered emission indicator 221 and on the implementation indicator 240. Outputting by the server 400 may be achieved via the communication interface 401 a. The server 400 may be adapted to output, e.g., the adjustment measure 230 to one or more of the vehicles 200a, 200b individually, i.e., a vehicle-specific adjustment measure 230 for a specific vehicle 200a, 200b. This enables sharing best practices across the vehicle fleet 250. Further, the server 400 may be adapted to output the lowered emission indicator 221 and / or the return of investment ROI to another interface (not shown) which is accessible by an owner and / or operator of the vehicle fleet 250.

[0038] As a non-limiting, fictional example: The vehicle fleet 250 may comprise a number of vehicles 200a, 200b, e.g., 50 vehicles. From a specific time point until now, a cumulative fuel consumption as a real-time fuel consumption, e.g., being based on a fuel efficiency 211 e of 13 km / l and on a real-time mileage 211f of 50000 km, of a specific vehicle 200a, 200b amounts to 3846,15 I. The real-time fuel consumption of the vehicle fleet 250 may thus amount to 192307,5 I. With an CO2 emission factor 215 of 147 g CO2 / km, the cumulative vehicle-specific emission indicator 220 is 7350 kg CO2 and the fleet related emission indicator 200 is 367500 kg CO2. An adjustment measure 230 may relate to the driving behavior. After implementing the driving behavior related adjustment measure 230, the fuel efficiency 211e and / or the mileage 211f may be improved which leads to a reduction of 10% of the emission indicator 220. This leads to, e.g., a fleet related lowered emission indicator 211 of 330750 kg CO2. After further implementing a traffic related adjustment measure 230, the traffic conditions 211 a may be considered which leads to a reduction of 10% of the lowered emission indicator 221 . This leads to, e.g., a fleet related further lowered emission indicator 211 of 297375 kg CO2. After further implementing a load management related adjustment measure 230, the vehicle load 211 b may be improved which leads to a further reduction of 10% of the lowered emission indicator 221 . This leads to, e.g., a fleet related further lowered emission indicator 211 of 267905 kg CO2. After further implementing a route optimization related adjustment measure 230, the mileage 211 f and / or the traffic conditions 211 a may be further considered which leads to a further reduction of 10% of the lowered emission indicator 221 . This leads to, e.g., a fleet related further lowered emission indicator 211 of 24114 kg CO2. With an assumption of the resources 245 of the adjustment measures 230, the implementation indicator 240 and the return of investment ROI is computed. Figure 2 shows a schematic chart of a method 100 according to an embodiment of the disclosure. The method of Figure 2 is a method 100 for managing a vehicle fleet 250, wherein the fleet comprises at least one vehicle 200a, in particular utility vehicle 200b. Such a method is typically carried out by a vehicle-external server 400. Such a vehicle-external server 400, a vehicle fleet 250 and a vehicle 200a, 200b is described with reference to Figure 1 . Figure 2 is described under reference to Figure 1 .

[0039] The method 100 of Figure 2 comprises: obtaining 110, from the at least one vehicle 200a, in particular utility vehicle 200b, vehicle specific and driving related real-time vehicle information 210. The vehicle specific and driving related real-time vehicle information 210 relates to at least one of the following vehicle information types 211 : traffic conditions 211 a, vehicle load 211 b, driving behavior 211 c, vehicle fuel type 211 d, fuel efficiency 211 e, mileage 211 f and / or engine performance 211 g.

[0040] The real-time vehicle information 210 is based on an ADAS sensor 202 of the vehicle 200a, in particular utility vehicle 200b.

[0041] The method 100 comprises: determining 120 a vehicle specific and / or fleet related emission indicator 220 based on the vehicle information 210 and on an emission factor 215. The emission factor 215 is based on a regulatory quantity and / or on a particulate matter sensor 203 of the vehicle 200a, in particular utility vehicle 200b.

[0042] The emission indicator 220 is vehicle specific, and a plurality of vehicle specific emission indicators 220 is determined.

[0043] The method 100 comprises: comparing 129 vehicle specific emission indicators 220 with each other.

[0044] The method 100 comprises: determining 130 an adjustment measure 230 related to the vehicle information 210, wherein the adjustment measure 230 is adapted to lower the emission indicator 220 to a lowered emission indicator 221 . The adjustment measure 230 is determined for a plurality of vehicle information types 211 .

[0045] The determining 130 the adjustment measure 230 is based on the comparing 129 of the vehicle specific emission indicators 220. The method 100 comprises: outputting 140 the lowered emission indicator 221 and / or the adjustment measure 230.

[0046] The method 100 comprises: determining 135 an implementation indicator 240 that relates to resources 245 for the implementation of the adjustment measure 230.

[0047] The method 100 comprises: outputting 140a a return of investment ROI based on a difference between the emission indicator 220 and the lowered emission indicator 221 and on the implementation indicator 240.

[0048] Figure 3 shows a schematic of a computer-readable storage medium 300 according to an embodiment of the disclosure. The computer-readable storage medium 300 comprises instructions (not shown) which, when being executed by a data processing device 401 , causes the data processing device 401 to carry out the method 100 as described with reference to Figure 2.

[0049] The instructions may be provided as a program code in any code and / or in any language, in particular as a program code that is suitable for fleet management and / or to be executed by the data processing device 401 of a vehicle-external server 400. The computer-readable medium 300 may be and / or comprise any digital data storage device, such as a USB flash drive, hard drive, CD-ROM, SD card and / or SSD card. The computer program does not necessarily have to be stored on such a computer-readable storage medium, but can also be accessed and / or provided via the internet or otherwise.

[0050] List of reference signs (Part of the

[0051] 100 method 110 obtaining real-time vehicle information

[0052] 120 determining an emission indicator 129 comparing

[0053] 130 determining an emission indicator 135 determining an implementation indicator

[0054] 140 outputting the lowered emission indicator and / or the adjustment measure 140a outputting a return of investment

[0055] 200a vehicle 200b utility vehicle 201 vehicle data processing device 201 a vehicle communication interface 202 ADAS sensor 203 particulate matter sensor 210 vehicle information

[0056] 211 information type 211 a traffic condition 211 b vehicle load 211 c driving behavior

[0057] 211d vehicle fuel type 211 e fuel efficiency 211f mileage 211 g engine performance 215 emission factor 220 emission indicator 221 lowered emission indicator

[0058] 230 adjustment measure 240 implementation indicator

[0059] 245 resources

[0060] 250 fleet 300 computer program and / or computer-readable storage medium 00 vehicle-external server 01 data processing device 01 a communication interface

[0061] ROI return of investment

Claims

Claims1 . Method (100) for managing a vehicle fleet (250), wherein the fleet comprises at least one vehicle (200a), in particular utility vehicle (200b), and the method (100) comprises:- obtaining (1 10), from the at least one vehicle (200a), in particular utility vehicle (200b), vehicle specific and driving related real-time vehicle information (210);- determining (120) a vehicle specific and / or fleet related emission indicator (220) based on the vehicle information (210) and on an emission factor (215);- determining (130) an adjustment measure (230) related to the vehicle information(210), wherein the adjustment measure (230) is adapted to lower the emission indicator (220) to a lowered emission indicator (221 ); and- outputting (140) the lowered emission indicator (221 ) and / or the adjustment measure (230).

2. Method (100) as claimed in claim 1 , wherein the method (100) comprises:- determining (135) an implementation indicator (240) that relates to resources (245) for the implementation of the adjustment measure (230); and- outputting (140a) a return of investment (ROI) based on a difference between the emission indicator (220) and the lowered emission indicator (221 ) and on the implementation indicator (240).

3. Method (100) as claimed in claim 1 or 2, wherein the vehicle specific and driving related real-time vehicle information (210) relates to at least one of the following vehicle information types (21 1 ): traffic conditions (211 a), vehicle load (21 1 b), driving behavior (211 c), vehicle fuel type (21 1 d), fuel efficiency (21 1 e), mileage (211 f) and / or engine performance (21 1 g).

4. Method (100) as claimed in any one of the preceding claims, wherein the real-time vehicle information (210) is based on an ADAS sensor (202) of at least the vehicle (200a), in particular utility vehicle (200b).

5. Method (100) as claimed in any one of the preceding claims, wherein the adjustment measure (230) is determined for a plurality of vehicle information types(21 1 ).

6. Method (100) as claimed in any one of the preceding claims, wherein- the emission indicator (220) is vehicle specific, and a plurality of vehicle specific emission indicators (220) is determined; and- the method (100) further comprises: comparing (129) vehicle specific emission indicators (220) with each other; and- determining (130) the adjustment measure (230) is based on the comparing (129) of the vehicle specific emission indicators (220).

7. Method (100) as claimed in any one of the preceding claims, wherein the emission factor (215) is based on a regulatory quantity and / or on a particulate matter sensor (203) of the vehicle (200a), in particular utility vehicle (200b).

8. Computer program and / or computer-readable storage medium (300), comprising instructions which, when being executed by a data processing device (401 ), causes the data processing device (401 ) to carry out the method (100) as claimed in any one of the preceding claims.

9. Data processing device (401 ) for a vehicle-external server (400), wherein the data processing device (401) is adapted to perform the method (100) as claimed in any one of claims 1 to 7.

10. Vehicle-external server (400), comprising the data processing device (401 ) as claimed in claim 9.

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