Methods, devices, systems, and computer programs for estimating road condition information

Abstract

Methods, apparatus, systems, and computer program products for estimating personalized traffic information for a specific vehicle are disclosed. The method includes receiving general traffic information indicating at least one condition of a road segment. Furthermore, it receives personalized information representing an estimation method performed on the general traffic information to obtain the personalized traffic information for the specific vehicle. The personalized traffic information for the vehicle is then estimated, wherein the estimation method is applied to the received general traffic information to obtain the personalized traffic information.

Description

Technical Field

[0001] This invention generally relates to estimating road condition information for vehicles. Background Technology

[0002] In modern vehicles, a wide variety of information is measured, received, collected, and processed for further use. For example, vehicle sensors can collect dynamic or static information about road conditions (e.g., potential friction, the presence of speed bumps, etc.) and use this information to adjust vehicle behavior accordingly. For instance, in the case of a slippery road (low potential friction), the parameters used for potential ABS braking can be adjusted accordingly.

[0003] With the rise of connected driving, this information is no longer confined to isolated vehicles: instead, measured road condition information can be sent from some vehicles to other vehicles, to (centralized or decentralized) infrastructure (such as internet-based cloud systems that manage road maps), and vice versa.

[0004] However, with the exchange of road condition information, difficulties can arise in the proper processing and interpretation of the data: as an illustrative example, a vehicle with summer tires might encounter a particular slippery section of road and transmit the corresponding information to other vehicles—even though these other vehicles might be equipped with winter tires and therefore wouldn't find the road (so) slippery. Conversely, given that road condition maps in the cloud are constructed using data from vehicles with winter tires, the friction values ​​transmitted to another vehicle using summer tires might not be appropriate for that vehicle.

[0005] In other words, differences in vehicle characteristics can lead to the transmission of road conditions that differ significantly from those actually experienced by a particular vehicle or those applicable to that vehicle. Such differences can compromise safety, as illustrated by the comparison between summer and winter tires above, for example, if a vehicle with summer tires experiences high friction on a snowy road.

[0006] These examples illustrate that a common problem in traffic information data processing is the impact of individual vehicles. Therefore, providing personalized traffic information is of great importance.

[0007] Therefore, the purpose of this invention is to provide an enhanced solution for estimating personalized road condition information for vehicles. Summary of the Invention

[0008] Methods, apparatuses, systems, and computer program products are disclosed to at least partially overcome the disadvantages of the above and other types.

[0009] In the first aspect, a method for estimating personalized road condition information for a specific vehicle is disclosed.

[0010] The method includes receiving general road condition information indicating at least one condition of a road segment. For example, general road condition information may indicate the condition of a "normal" vehicle. Non-limiting examples of road condition information include one or more of the following: potential friction value, road weather condition information, road roughness information, pothole information, and speed bump information.

[0011] The method also includes receiving personalized information, which represents an estimation method performed on general traffic information to obtain personalized traffic information for a specific vehicle. Non-limiting examples of personalized information include: mathematical functions, sets of one or more parameters, algorithms, program code, neural networks, and lookup tables. For example, an algorithm may deterministically specify how to compensate for individual differences between general vehicles and a specific vehicle. Alternatively, a neural network may have been trained (using a historical experience dataset) to output personalized traffic data based on inputs of general traffic data and personalized information. In the case of personalized information in parametric form, the parameters may describe the scaling behavior of traffic conditions according to the differences between general vehicles and a specific vehicle.

[0012] The method also includes estimating personalized road condition information for the vehicle, wherein an estimation method represented by the received personalized information is applied to the received general road condition information to obtain personalized road condition information. This allows for compensation for any differences between "general" vehicles and specific vehicles, thereby obtaining a more appropriate estimate of the road conditions experienced by a specific vehicle.

[0013] For example, in some implementations, the estimation method can be configured to take one or more personalized characteristics as input variables. In this case, the method may further include inputting at least one of the personalized characteristics into the estimation method to obtain personalized traffic information as an output dependent on the input variables.

[0014] In particular, personalization can include vehicle characteristics. Examples of vehicle characteristics include one or more of the following: number of wheels, number of drive wheels, number of steerable wheels, manufacturer, engine type, and nominal power. This allows for compensation for static differences between vehicles that lead to variations in the effect of road conditions on these different vehicles. For example, a sports car may experience more frictional changes than a truck.

[0015] Additionally or alternatively, personalized features may include detected vehicle sensor data. Examples of detected vehicle sensor data include one or more of the following: tire pressure, tire temperature, tire stiffness, wheel slip ratio, ambient temperature, axle height, suspension pressure, suspension height, tire type (e.g., manually entered via a human-machine interface; derived from a tire classification algorithm; or estimated based on other sensor signals), estimated potential friction (e.g., based on microslip, or from ABS braking, or from TCS events), normalized traction on the wheel, friction-related values, torque applied to the wheel, longitudinal acceleration, lateral acceleration, vertical acceleration, braking pressure, yaw rate, vehicle speed, wheel speed, steering wheel angle, wheel angle, wiper speed, ambient humidity, air pressure, rain sensor data, brightness, radar data, camera data, and laser data. This allows compensation for dynamic differences in environmental conditions, which can lead to variations in the impact of specific environmental conditions on road conditions. For example, road conditions such as potential friction often exhibit a dependence on tire temperature.

[0016] In some implementations, the method may further include (a) determining one or more personalized characteristics indicative of vehicle behavior, and (b) inputting the determined personalized characteristics as input variables into an estimation method to obtain personalized road condition information. For example, where detected vehicle sensor data is part of a personalized characteristic, determining the personalized characteristic may include making actual measurements using vehicle sensors. For example, tire temperature may be measured using appropriate sensors (directly or indirectly). Additionally or alternatively, where a vehicle characteristic is part of a personalized characteristic, determining the personalized characteristic may include retrieving that information from a vehicle's internal registry. For example, a field including manufacturer information may be examined to determine the manufacturer.

[0017] In some implementations, the method may further include (e.g., after estimating personalized traffic information) measuring individual traffic information at vehicles on specific road segments. The individual traffic information indicates at least one condition of the vehicle's road segment. In this case, the method may further include transmitting the measured individual traffic information to a server. This allows for further construction of traffic maps and / or better refinement of the personalized information.

[0018] Therefore, in some embodiments, the method may further include comparing measured individual road condition information with estimated personalized road condition information. This comparison can occur on a vehicle or a server. This allows identification of whether the personalization provides a good estimate. If the comparison shows good consistency, the personalized information can be considered reliable. If the comparison shows reduced consistency, the source of discrepancy can be identified, for example, through statistics on multiple vehicles. For example, discrepancy might be due to changes in road conditions, such as changes in actual road friction due to recent snowfall. In this case, the general road condition information can be updated. Alternatively, discrepancy might be due to overcompensation or undercompensation in the personalization. For example, the effect of tire temperature on friction might be overestimated (or underestimated). In this case, the personalized information can be updated.

[0019] These two types of discrepancies can be identified statistically: if virtually all vehicles are giving inconsistent signals (e.g., friction is lower / higher than expected), then general road condition data can be updated. If only a subset of vehicles (e.g., all vehicles with low tire temperatures) are giving such inconsistent signals, then personalized information (for that subset of vehicles, i.e., for low tire temperatures) can be updated.

[0020] Therefore, additionally or alternatively, the method may also include updating personalized information (or estimation methods) based on measured individual road condition information. Again, the update can occur at the vehicle or server. This allows for further refinement of personalized characteristics and the construction of a system capable of dynamic self-correction.

[0021] In some implementations, the method may further include (a) transmitting measured individual traffic information to a server, and (b) transmitting comparison and / or update results to a server. This allows for effective management of general traffic data.

[0022] In particular, the transmission of results can occur separately from the transmission of individual road condition information measured. This allows for the reduction of privacy issues by avoiding the transmission of location data along with data that allows for the identification of specific vehicles.

[0023] In some implementations, the method may further include outputting personalized traffic information. This personalized traffic information can be output to the vehicle's autonomous driving system, vehicle control system, and / or driver assistance system. This allows the vehicle's driving behavior to be adjusted based on the personalized traffic information, thereby improving safety.

[0024] In some implementations, the method may further include (a) generating general traffic information and generating personalized information, and (b) transmitting the generated general traffic information and the generated personalized information from a server to a vehicle. This allows for distributed personalization on the vehicle.

[0025] In a second aspect, a computer program product is provided, comprising program code configured to perform the steps of the method according to the first aspect when executed in a computing device.

[0026] In a third aspect, an apparatus is provided for estimating personalized traffic information for a specific vehicle. The apparatus includes a communication interface and a processing unit. The communication interface is used to receive general traffic information and to receive personalized information. General traffic information includes, for example, at least one condition of a road segment indicated for a "normal" vehicle. Personalized information represents an estimation method performed on the general traffic information to obtain personalized traffic information for a specific vehicle.

[0027] The processing unit is communicatively connected to the communication interface and is configured to estimate personalized road condition information for the vehicle, wherein an estimation method (represented by the received personalized information) is applied to the received general road condition information to obtain personalized road condition information.

[0028] In some embodiments of the device, the processing unit is also configured to perform method steps according to the method of the first aspect.

[0029] In a fourth aspect, a system for estimating personalized road condition information for a vehicle is provided. The system includes the means according to the third aspect and further includes at least one of a sensor, a bus interface, and / or a server.

[0030] Sensors can be used to estimate one or more personalized characteristics, particularly vehicle sensor data. This allows for consideration of dynamic changes in environmental conditions, thus providing a more comprehensive view of personalization.

[0031] The bus interface can be configured to output personalized traffic information to the vehicle's vehicle bus. This allows for efficient use of the acquired data and seamless integration of the teachings of this invention into existing vehicle systems.

[0032] The server can be configured to generate general traffic information and transmit it to vehicles, and can also be configured to generate personalized information and transmit it to vehicles.

[0033] In addition, the system may include a storage device configured to store general traffic information and / or personalized information.

[0034] In the fifth aspect, the use of the system based on the fourth aspect for estimating personalized road condition information for vehicles is provided.

[0035] On another front, the present invention also relates to methods, systems, and computer program products for constructing road condition maps. For this purpose, individual road condition information is received. This individual road condition information indicates the road conditions perceived by a particular vehicle. Furthermore, one or more personalized characteristics of that vehicle are received. Based on the received individual road condition information and personalized characteristics, general road condition data indicating the road conditions of (hypothetical) general vehicles can be estimated. This allows for mitigation of the impact of vehicle-specific errors. In other words, the process compensates for the vehicle-specific portion of the road condition measurement (i.e., performs "reverse" personalization, i.e., generalization). This compensation can be performed on the vehicle or on a server. Based on general road condition data from multiple vehicles, a map of general road condition data can be constructed.

[0036] When compensation is performed at the server level, the transmission of traffic information from each of multiple vehicles can occur separately from the transmission of personalized features. This allows for the reduction of privacy issues where a particular vehicle can be identified through its personalized information and where individual traffic information includes location information. Attached Figure Description

[0037] The following detailed description refers to the accompanying drawings, in which:

[0038] Figure 1 The illustration schematically depicts a road and vehicle situation in which methods, apparatus, or systems according to the embodiments can be used.

[0039] Figure 2 A block diagram of a system having an apparatus according to an embodiment is shown.

[0040] Figure 3 A flowchart of a method according to an embodiment is shown.

[0041] Figure 4 A diagram illustrating personalized information according to an implementation method is shown schematically.

[0042] Figure 5 The use of the apparatus according to the embodiment is illustrated schematically. Detailed Implementation

[0043] Figure 1 The illustration schematically depicts a road and vehicle situation in which methods, apparatus, or systems according to the embodiments can be used.

[0044] Vehicle 10 and vehicle 11 are traveling on the road. Specifically, both vehicles 10 and 11 are approaching position a on the first road segment 12. Subsequently, they will approach position b on the second road segment 13.

[0045] Both vehicles communicate with the device 15 according to the invention. In particular, in the example shown, the device 15 is a central server that is linked to both vehicles 10 and 11 via a wireless communication link through the Internet 14.

[0046] Typically, the teachings of this invention can be implemented at various points in such a system. In the illustrated example, device 15 is centrally located away from vehicles and roads. Referring to the accompanying drawings, teachings focusing on this centralized implementation are exemplarily shown. In other examples, the server functionality can be implemented within a distributed cloud infrastructure.

[0047] In other embodiments, device 15 may be located at a vehicle (e.g., vehicle 10). In this case, vehicles 10 and 11 can be directly linked via a vehicle-to-vehicle communication link. In yet another embodiment, some of the taught functions can be implemented on a server, while others can be implemented on vehicle(s).

[0048] Return to Figure 1 The device 15 is configured to provide personalized road condition information to each vehicle 10, 11.

[0049] For example, the device can be configured to provide individualized potential friction estimates for each vehicle 10, 11. The estimated potential friction is a measure of the smoothness of a specific road segment (e.g., the first road segment 12).

[0050] To this end, device 15 utilizes general road condition information and personalized information. In the illustrated example, general road condition information may be general potential friction data for the first road segment 12. This general potential friction data may be based on statistical processing of friction data collected from vehicles previously passing through the first road segment. Alternatively or additionally, the general potential friction data may be based on a meteorological model configured to estimate local road conditions. In this sense, general potential friction data can be considered an indication of the condition of "normal" vehicles—regardless of whether such normal vehicles are physically present.

[0051] In an example of statistical data collection, a typical vehicle could be considered, for example, as the statistical average of previously passed vehicles. Alternatively, a typical vehicle could have defined characteristics. For example, it could be defined as having (in particular) winter tires and a certain tire temperature (e.g., 20°C). In this case, statistical processing of empirical data from previously passed vehicles could include adjusting the actual measurements of previously passed vehicles to compensate for differences from the assumed typical vehicle (e.g., compensating for the presence of summer tires and / or any differences in tire temperature). For illustrative purposes, Figure 1 In this case, ordinary vehicles can be considered as having winter tires with a tire temperature of 20°C.

[0052] In any case, ordinary potential friction data indicates at least one condition (e.g., smoothness indicated by potential friction) for a first road segment 12 for a general vehicle. However, ordinary potential friction data may not be a good estimate for an individual vehicle (e.g., the first vehicle 10). For example, the first vehicle 10 may be driving with summer tires at a tire temperature of 10°C, and therefore significantly different from a general vehicle. The currently disclosed teachings provide a solution for personalizing road friction data for the first vehicle 10, so that personalized road friction data can be used by the first vehicle 10 and provide a more suitable estimate than "ordinary" data.

[0053] Meanwhile, the general potential friction data could be a slightly more suitable estimate for the second vehicle 11, which can operate on winter tires at a tire temperature of 22°C, and is therefore relatively similar to the assumed general vehicle. In this case, the personalization of the general potential friction data might involve minor compensations, such as compensation only for small differences in tire temperature.

[0054] For illustrative purposes, potential friction is used as an example. However, the description of the condition of the first section 12 may include other or further information, such as information about the current weather, information about road roughness, information about potholes or speed bumps, etc.

[0055] As described above, in addition to general road condition information, device 15 also utilizes personalized information. Personalized information refers to the estimation method performed on general road condition information to obtain personalized road condition information for a specific vehicle, as will be described in further detail with reference to the following figures. Personalized information may describe the dependence of road conditions on vehicle characteristics or vehicle sensor data. For example, personalized information may describe how potential friction scales with tire temperature and / or how it depends on tire type.

[0056] Based on personalized information and general potential friction data, device 15 estimates personalized potential friction data for the first road segment 12 for the first vehicle 10. In this process, the estimation method represented by the received personalized information is applied to the received general road condition information (general potential friction data) to obtain personalized road condition information (personalized potential friction data).

[0057] The personalized potential friction data can then be transmitted to that specific vehicle, such as the first vehicle 10. The vehicle can utilize this personalized information as it approaches the first road segment 12 to, for example, adjust the vehicle control system accordingly. As an illustrative example, the personalized potential friction data can be forwarded to the ABS braking module via the vehicle bus interface to adjust the ABS braking parameters in consideration of the personalized potential friction data. The personalized friction data can also be advantageously used in autonomous driving scenarios.

[0058] The above process can then be repeated for another road segment (e.g., the second road segment 13 at the second location b).

[0059] In other examples not shown, personalization can also occur at the vehicle level. For instance, personalized information and general traffic information can both be transmitted from the server to the corresponding vehicle.

[0060] Figure 2 A block diagram of a system 20 having device 24 is shown. Specifically, device 24 may be... Figure 1 The implementation of device 15 is shown schematically in the figure.

[0061] System 20 includes a database 21 that stores general road condition data. In the example shown, the database includes multiple database entries 22 that link general potential friction values ​​to corresponding location data. For example, database 21 stores a first entry that indicates... Figure 1 The first ordinary potential friction μ at location a is the first segment of the road. gen,a Furthermore, database 21 stores a second entry indicating the second common potential friction force of the second road segment at location b. Therefore, database 21 can be visualized as a map of common road condition data, storing the common friction force value for each (or at least some) location.

[0062] Furthermore, system 20 includes personalized information 23. Personalized information 23 represents an estimation method performed on general road condition information to obtain personalized road condition information for a specific vehicle. In the example shown, the personalized information is represented by a mathematical function that incorporates the individual's potential frictional force μ. indiv Represented as ordinary friction force data μ gen The functions and a set of functions with personalized features, as shown below, will be referenced. Figure 4 Detailed description. As mentioned above, an illustrative example of a set of personalized characteristics may include tire type (e.g., summer tires or winter tires) and tire temperature.

[0063] Other examples of personalized information not shown include sets of one or more parameters, algorithms, program code, neural networks, and lookup tables. In any case, personalized information allows for the estimation of personalized traffic information based on general traffic information.

[0064] For example, personalized information 23 and database 21 containing entries 22 about general road conditions can both be stored on the storage device (not shown) of system 20.

[0065] Furthermore, system 20 includes a device 24 for estimating personalized traffic information for a specific vehicle. Device 24 includes a communication interface 25 and a processing unit 26. The communication interface 25 is used to receive general traffic information from database 21 and to receive personalized information 23. The received general traffic information includes at least general traffic information (e.g., general traffic data μ at location a). gen,a Optionally, the received general traffic information may also include receiving corresponding location data (e.g., location a). In the latter case, communication interface 25 receives one of the database entries 22 of database 21. In a further example, the reception may involve the entire database or at least a portion thereof.

[0066] The processing unit 26 is communicatively connected to the communication interface 25, and the processing unit 26 is configured to estimate personalized traffic information of the vehicle, wherein the estimation method (represented by the received personalized information) is applied to the received general traffic information to obtain personalized traffic information.

[0067] Figure 3 A flowchart illustrating an implementation of a method 30 for estimating personalized road condition information for a specific vehicle is shown.

[0068] Method 30 includes receiving 32 general road condition information. The general road condition information indicates at least one condition of a road segment. As described above, examples of general road condition information include one or more of the following: potential friction value, road weather condition information, road roughness information, pothole information, and speed bump information.

[0069] Method 30 also includes receiving 34 personalized information. Personalized information represents an estimation method performed on general traffic information to obtain personalized traffic information for a specific vehicle. Non-limiting examples of personalized information include: mathematical functions, sets of one or more parameters, algorithms, program code, neural networks, and lookup tables.

[0070] Method 30 also includes estimating personalized traffic information for the 36 vehicles. To this end, the estimation method represented by the received personalized information is applied to the received general traffic information to obtain personalized traffic information.

[0071] Figure 4 A graph 40 schematically illustrates personalized information according to an implementation method. The personalized information represents an estimation method performed on general road condition information to obtain personalized road condition information. The graph is a visual representation of the relationship, representing the general friction force data μ. gen The personalized potential friction μ is a function of the tire temperature as an example of a personalized characteristic. indiv .

[0072] For any given common friction value 42 and a given tire temperature 44, Figure 40 generates a corresponding personalized potential friction value 46.

[0073] like Figure 4 As shown, personalized information can be given as a numerical relationship (the shaded surface in Figure 40). In other examples, this relationship can be mathematically represented by parameters, such as μ. ind =A*(T) tire -B)*μ gen The function is defined in the form of , where A and B represent tuning parameters, such as B representing the (hypothetical) tire temperature of a (simulated) ordinary vehicle, and A representing the scaling parameter for the tire temperature dependence of the potential friction force.

[0074] In this respect, the personalized information represented by Figure 40 is similar to the reference above. Figure 2 Personalized information described 23.

[0075] exist Figure 4 In other embodiments not shown, the mathematical function can take more than one personalized characteristic (e.g., a set of personalized characteristics) as input variables. However, for illustrative purposes, a single input variable, such as the three-dimensional representation in Figure 40, is possible.

[0076] Furthermore, Figure 40 is shown as an example of a continuous distribution or continuous range of input variables (tire temperature and potential friction can take any value from the interval). However, in other examples, the input and / or output variables can be discrete.

[0077] For example, in Figure 4 In other embodiments not shown, the mathematical function can use tire type (e.g., summer tires or winter tires) as another input variable. In the case of summer tires, an additional (negative) offset can be added to the estimate of the individualized potential friction (assuming the typical potential friction of winter tires has already been determined).

[0078] Figure 5 The device 55 is schematically shown in a manner similar to Figure 1 The situation described herein is being used. First vehicle 50 and second vehicle 51 are approaching the first section 52.

[0079] The system has a typical potential friction value of 1.1 at the first road segment 52. However, this estimate is only valid for "normal" vehicles with winter tires at a tire temperature of 20°C, while the first vehicle 50 is driving with summer tires at a tire temperature of 10°C. The second vehicle 51 is driving with winter tires at a tire temperature of 22°C.

[0080] To compensate for these differences between the actual characteristics of ordinary vehicles and the first vehicle 50 and the second vehicle 51, the above references Figures 2-4 The teachings described are applicable.

[0081] Device 55 is communicatively connected to storage device 56, which includes similar components. Figure 2 The storage device 56 includes a database containing entries for general friction data. It also includes personalized information, such as... Figure 2 Mathematical function 23 or according to Figure 4 The chart.

[0082] Similar to a reference Figure 3 The method described involves device 55 receiving general friction data and personalized information to estimate personalized friction data.

[0083] The personalized friction data can then be transmitted from device 55 to the corresponding vehicle. For example, device 55 can transmit a personalized friction estimate of 0.9 to the first vehicle 50 at position 52. Furthermore, device 55 can transmit a personalized friction estimate of 1.1 to the second vehicle 51 at position 52.

[0084] In the example shown, device 55 is located away from the vehicle and road. Consistent with this, device 55 can be implemented as a server or cloud infrastructure. In other examples not shown, device 55 can be located at a vehicle such as the first vehicle 50. In this case, storage device 56 transmits general friction data and personalized information to the vehicle, such as the first vehicle 50. At the first vehicle 50, device 55 can then locally generate a personalized friction estimate, for example, a friction estimate with a value of 0.9 in the example of the first vehicle 50.

[0085] Return to Figure 5 The system is centrally located so that once one of the vehicles (e.g., the first vehicle 50) reaches road segment 52, (actual) individual road condition information can be measured at that vehicle. For example, the actual potential friction force can be measured during an ABS event or estimated by processing data from other sensors (e.g., the wheel speed sensors of vehicle 50). The individual road condition information then indicates the condition of the road segment as perceived by vehicle 50.

[0086] Then these measured individual road condition information ( Figure 5(Not shown) The data is transmitted to device 55 and compared with previously estimated personalized traffic information (at device 55, whether device 55 is located at the center or at the vehicle). The result of this comparison allows for updating the personalized information. In particular, the currently disclosed teachings allow the result of this comparison to be transmitted separately from the transmission of the measured individual traffic information, or separately from any privacy-sensitive data such as location information or information that allows identification of a specific vehicle. In short, the inconsistency between the estimated value and the actual measured value allows for updating the personalized information based on the measured individual traffic information.

[0087] For example, the first vehicle 50 might show an inconsistency between the estimated and actual measurements, while the second vehicle 51 might indicate good consistency. Therefore, the comparison might suggest that the effect of tire temperature on personalized friction data is either overestimated (in cases where the actual individual road condition information for the first vehicle deviates from the estimated personalized road condition information and is actually closer to the general road condition information) or underestimated (in cases where the actual individual road condition information deviates from the estimated personalized road condition information and is actually farther from the general road condition information). Therefore, the temperature dependence in the personalized information can be adjusted, for example, by changing the corresponding tuning parameters representing the temperature dependence. Such adjustment of the personalized parameters can be particularly advantageous by considering statistical analysis from multiple vehicles.

Claims

1. A method of estimating individualized road condition information for a specific vehicle, the method comprising: - receiving general road condition information, the general road condition information being indicative of at least one condition of a road section; - receiving individualization information, the individualization information representing an estimation method to be performed on the general road condition information for obtaining the individualized road condition information for the specific vehicle, - estimating the individualized road condition information for the vehicle, wherein the estimation method is applied to the received general road condition information for obtaining the individualized road condition information, the method further comprising, after the estimating: - measuring individual road condition information at the vehicle, the individual road condition information being indicative of at least one condition of the road section for the vehicle; - comparing the measured individual road condition information with the estimated individualized road condition information; and - updating the individualization information based on the comparison.

2. The method according to claim 1, wherein - the estimation method is configured to take one or more individualization characteristics as input variables, and - the method further comprises inputting at least one of the individualization characteristics into the estimation method for obtaining the individualized road condition information as an output dependent on the input variables.

3. The method of claim 2, wherein, The individualization characteristics comprise vehicle features, in particular one or more of: number of wheels, number of driven wheels, number of steerable wheels, manufacturer, engine type, nominal power.

4. The method of claim 2 or 3, wherein, The individualization characteristics comprise detected vehicle sensor data, in particular one or more of: tire pressure, tire temperature, tire stiffness, wheel slip ratio, ambient temperature, vehicle axle height, suspension pressure, suspension height, tire type, estimated potential friction, normalized traction on a wheel, friction related values, torque applied on a wheel, longitudinal acceleration, lateral acceleration, vertical acceleration, brake pressure, yaw rate, vehicle speed, wheel speed, steering wheel angle, wheel angle, wiper speed, ambient humidity, air pressure, rain sensor data, brightness, radar data, camera data, laser data.

5. The method of claim 2, 3, or 4, wherein, The method further comprises: - determining one or more individualization characteristics, the one or more individualization characteristics being indicative of a vehicle behavior of a vehicle, and - inputting the determined individualization characteristics as input variables into the estimation method for obtaining the individualized road condition information.

6. The method of any one of claims 1 to 5, wherein, The method further comprises: - transmitting the measured individual road condition information to a server.

7. The method according to claim 6, the method further comprising: - transmitting a result of the comparison and / or the update to the server.

8. The method of claim 7, wherein, The transmission of the result occurs separately from the transmission of the measured individual road condition information.

9. The method according to any one of claims 1 to 8, wherein - the road condition information comprises one or more of: potential friction values, road weather condition information, road roughness information, pothole information, speed bump information, and / or - the individualization information comprises at least one of: a mathematical function, a set of one or more parameters, an algorithm, a program code, a neural network, a lookup table.

10. The method of any one of claims 1 to 9, wherein, The method further comprises outputting the personalized traffic information to an autonomous driving system, a vehicle control system, and / or a driver assistance system of the vehicle.

11. The method of any one of claims 1 to 10, wherein, The method further comprises, prior to the receiving, - generating general traffic information and generating personalized information, and - transmitting the generated general traffic information and the generated personalized information from a server to the vehicle.

12. A computer program product comprising program code configured to perform the steps of the method of any one of claims 1 to 11 when executed in a computing device.

13. An apparatus for estimating personalized traffic information for a specific vehicle, comprising: - a communication interface for: -- receiving general traffic information, the general traffic information being indicative of at least one condition of a road segment; and -- receiving personalized information, the personalized information representing an estimation method to be performed on the general traffic information for obtaining the personalized traffic information for the specific vehicle, - a processing unit: -- communicatively coupled with the communication interface, and -- configured to estimate the personalized traffic information for the vehicle, wherein the estimation method is applied to the received general traffic information for obtaining the personalized traffic information, the processing unit being further configured to: - measure individual traffic information, the individual traffic information being indicative of at least one condition of the road segment for the vehicle; - compare the measured individual traffic information with the estimated personalized traffic information; and - update the personalized information based on the comparison. The processing unit is further configured to perform the steps of the method of any one of claims 1 to 11.

15. A system for estimating personalized traffic information for a vehicle, comprising an apparatus according to claim 13 or 14, and further comprising:

14. The apparatus of claim 13, wherein, - a sensor for estimating one or more individual characteristics, and / or - a bus interface configured to output the personalized traffic information to a vehicle bus of the vehicle, and / or - a server configured to generate the general traffic information and to transmit the general traffic information to the vehicle, and configured to generate the personalized information and to transmit the personalized information to the vehicle.

16. Use of a system according to claim 15 for estimating personalized traffic information for a vehicle. ​ ​

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