TECHNICAL FIELD

[0001] The present disclosure relates to vehicle technology. In particular, the present disclosure relates to safely operating a vehicle. Further in particular, the present disclosure relates to a mobile device and a method for determining a driving behaviour of a driver of a vehicle.

BACKGROUND

[0002] City traffic has changed significantly over the recent years, in particular with the advent of on-demand vehicle sharing services. Especially sharing services of two-wheeled vehicles like e- scooters rose significantly with the general legalisation in various countries throughout Europe and all over the world. Such two wheeled vehicles are often used for last mile trips and thus have a high usage ratio. With the increase of users, an increase of casualties is observed. Statistics show that many crashes are so-called single vehicle crashes and as such may be mainly attributed to the driver and their behaviour when driving the vehicle.

[0003] Statistics also show that in such single vehicle crashes substance abuse, e.g. alcohol, was often involved. Further, even in single vehicle crashes, head injuries are quite common and may be explained by not wearing a helmet while exhibiting a behaviour unfit for driving.

[0004] Thus, there may be a need for analysing the driving behaviour of a driver of a vehicle, in particular regarding the safety aspects of vehicle operation.

[0005] Further, there may be a need for informing the driver and/or relevant third parties about the driving behaviour, in order to effect a behaviour change in case of a wrong or reckless behaviour, thereby increasing operation safety of a vehicle.

[0006] The present invention has been devised in light of the above considerations.

SUMMARY

[0007] At least one such need may be met by the subject-matter of the independent claims. Preferred embodiments are provided in the dependent claims and are explained in detail in the following description.

[0008] The present invention relates to a mobile device and a method for determining a driving behaviour of a driver of a vehicle as well as a computer-readable storage medium according to the independent claims.

[0009] According to a first aspect of the present disclosure, there is provided a mobile device for determining a driving behaviour of a driver of a vehicle, the mobile device comprising at least one l sensor element for obtaining first sensor data, a communication element adapted for communicating with a vehicle and arranged for receiving second sensor data from the vehicle, a processing element adapted for processing the first sensor data and the second sensor data and for determining at least one driving parameter from the first sensor data and the second sensor data, wherein the driving parameter is indicative of the driving operation of the vehicle and a signal element adapter for providing an information signal to a user of the mobile device, in particular the driver, determined from the driving parameter, wherein the information signal is indicative of the driving operation of the vehicle by the diver.

[0010] According to a second aspect of the present disclosure, there is provided a method for determining a driving behaviour of a driver of a vehicle, comprising the steps obtaining first sensor data of a mobile device associated with a driver of a vehicle, obtaining second sensor data of the vehicle, processing the first sensor data and the second sensor data, determining at least one driving parameter from the first sensor data and the second sensor data, wherein the driving parameter is indicative of the driving operation of the vehicle, and providing to a user, in particular the driver, an information signal determined from the driving parameter, wherein the information signal is indicative of the driving operation of the vehicle by the diver.

[0011] According to a third aspect of the present disclosure, there is provided a computer- readable storage medium comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method of the present disclosure.

[0012] In accordance with the technical concept of the present application, the mobile device and the method assess how a driver is operating a vehicle. This assessment is at least in part based on sensor data that is acquired during the actual operation of the vehicle. The data source may be the vehicle and/or separate devices like e.g. a mobile device carried by the driver while operating the vehicle. In other words, the vehicle and the mobile devices may comprise elements that acquire sensor data, which is then analysed. From said analysed data the way a driver is using the vehicle may be determined and therefrom, a driving behaviour may be deduced.

[0013] A simplified information signal may in turn be calculated from the analysed data to convey in a simple and easily understandable manner a score about the driving behaviour to the driver and/or other interested third parties. Such a score may be a number of a defined range which is related to a crash probability, based on the acquired sensor data and thereby based on the rider’s behaviour.

[0014] The sensor data in turn may give a precise determination of the individuals driving behaviour throughout a defined trip, taken by the driver of the vehicle. When analysing the data, also external data may be employed, thereby allowing to determine e.g. how a driver behaves in a certain traffic situation. In an example of a two wheeled vehicle, exemplarily an e-scooter, it may be determined how a driver approaches a specific traffic situation like changing from the pedestrian walkway to the street level. E.g., a driver may stop the vehicle and lift the vehicle from the walkway to the road or may simply continue to drive from the walkway onto the road. Analysis of such a behaviour allows the adaptation of the build of the vehicle, in order to develop a vehicle that requires less maintenance and is less prone to damage considering the usual driving behaviour of a driver. In the above example, in case analysis of the sensor data leads to the conclusion that a rider normally does not stop when going from a slightly raised walkway to a road but simply continues to drive, a vehicle with a specifically adapted suspension may be developed, thereby reducing damage to the vehicle during such operation and thus prolonging the life time of the vehicle. Alternatively, a score may indicate how an analysed operation of the vehicle may impact the safe operation and thus the lifetime of the vehicle.

[0015] In other words, in accordance with the concept of the present disclosure, the driver of a vehicle is provided with a score, also referred to as Safety Score, that acquires sensor data to describe the driving behaviour of the driver. Like mentioned above, the driving behaviour may be indicative of requirements regarding the technical construction of the vehicle, in order to technically optimise the construction of the vehicle, and/or alternatively may be relevant for third- party providers like insurance companies, fleet management companies or mobility sharing services renting the respective vehicles.

[0016] Various individual behaviours influence the safe operation of the vehicle and thus the information signal provided to the driver. Again considering an e-scooter application, while little scientific or statistical information is available, it has been shown that the majority of crashes are single vehicle crashes that can mainly be attributed to the driving behaviour and/or driving condition of the driver.

[0017] The main influencing factors resulting in a vehicle crash are summarised in the following. Significant factors relating to riding behaviour and contributing to an accident or crash are inadequate speed for a certain riding condition, e.g. the local infrastructure layout, current traffic and traffic flow, a lighting situation (day, night, darkness), a certain weather condition, et cetera. Further, the personal condition or state of mind of the driver may be considered an important factor and may depend on e.g. substance abuse (alcohol, drugs, medication). The non-adherence to (local) traffic rules may also be a significant factor leading to an accident as other road users may be surprised by the exhibited behaviour not necessarily expected in a specific traffic situation, possibly resulting in a conflict in the use of the available traffic space. Further, a lack of anticipation while driving the vehicle may result in a traffic situation that is difficult to handle for a driver. E.g., a driver may be riding with the top out speed for most of the time instead of anticipating other traffic participants and their behaviour, e.g. a pedestrian appearing from behind and obstruction. Further, a misuse of the vehicle may attribute to an unsafe operation of the vehicle. E.g., in the scenario of an e-scooter, normally provisioned for a single rider, the use by multiple riders may lead to instability in the driving motion and in particular a longer braking distance. Further, a driver may be distracted by secondary tasks not related to the driving operation itself. E.g., a driver operating at the same time a mobile phone may be distracted to an extent that they cannot react in a normal manner and timeframe to a situation arising during the operation of the vehicle. A distracted driver may require longer until realising that a certain action is required, e.g. braking, to avoid an accident.

[0018] In order to analyse the driving behaviour, and to calculate a score, sensor data of different sensor types may be acquired and employed. Such sensor data may relate to the absolute and/or relative position data of the vehicle and/or the driver, acceleration data, velocity data, angular velocity data and angle data. The data may be acquired by a global position determining system like Galileo, GPS, GloNass, or BaiDou. Further, sensor data may be acquired by accelerometers, gyroscopes and inclinometers. These sensor elements may be arranged either attached to the vehicle and/or arranged in a mobile device, e.g. carried by the driver. From said sensor elements and in particular their sensor data, informational data may be processed like a position of the vehicle, speed, acceleration, heading and inclination of the vehicle. Further, third-party data may be acquired, e.g. via the mobile device from a third-party server, e.g. connected through the Internet, like weather information, traffic flow information, infrastructure information and further information about the surroundings of the current position, e.g. construction work information or lighting information (daylight, artificial light, darkness). The appropriateness of the third-party information may be determined by e.g. positional information, heading information and speed information in order to determine a path of travel of the vehicle and subsequently determining relevant third-party information related to said path of travel. E.g. while a plurality of construction work may be arranged in the vicinity of the vehicle, only the construction work that is in the (expected or calculated) path of travel may be relevant for determining the driving behaviour.

[0019] The mobile device and the method according to the present disclosure, which method may be implemented as a client application executed on a mobile device, may provide a driving behaviour safety assessment as a driver’s safety score, being defined as a metric correlated to the driver’s crash risk caused by the driving behaviour. Accordingly, when receiving the safety score, the driver may continuously improve the driving behaviour, to reduce damage to the vehicle, to avoid accidents and in particular the harm of human life. The mobile device or a mobile application may log and retain continuously the determined score. E.g., throughout a specific trip or right, a score may be calculated at defined intervals, e.g. every second, every 10 seconds, every minute et cetera, may be presented to the driver and may likewise be stored locally in the mobile device or remotely in a server connected to the mobile device and/or the vehicle, in particular in a secure and tamperproof manner. [0020] As mentioned previously, the score or Safety Score may be dependent on the behaviour of a rider and may in particular be related to the crash probability or the average crash probability over the trip, considering the rider’s behaviour. Here, the crash probability may be related to a fit to ride metric, a strategical behaviour metric, a context adaptation metric, a traffic law adherence metric and an operational behaviour metric. The use of only a part of said metrics or further metrics is conceivable.

[0021] Generally, the safety score may be calculated in accordance with Equation 1: SafetyScore = 1000 x [1 —

P(FitToRide, StrategicalBehaviour, Context Adaptation, TrafficLaw Adherence, OperationalBehauvioi

[0022] In Equation 1 , P is the average crash probability over the trip based on the rider’s behaviour, with P being defined by Equation 2:

[0023] In Equation 2, X is a vector containing all the required information describing the rider behaviour, e.g. all or some of the metrics mentioned previously. Further, p X, t) is defined in accordance with Equation 3:

[0024] In Equation 3, the expression f(X t )) may in particular be in the form of a polynomial function as expressed in Equation 4:

[0025] In Equation 4 only continuous predictors bi are shown. For a specific calculation and application, the use of categorical predictors is also conceivable which will then be expressed ^as bc _ί (ί) ·

[0026] An example for a continuous predictors may be the speed of the vehicle, which can be expressed by a numerical value, e.g. between 0 and 30. In this case, the value corresponds to the value of ? _j . An example for a categorical predictor may be night or day. This predictor gives one value of data per item in this category. If there are three items, e.g. small, medium and large riders, then bi may have one value for each.

[0027] The model is evaluated against real-life data, e.g. incoming from crash data. Upon comparison of the model data and the real-life data, the model is re-evaluated, potentially changing the model, thereby improving it. One way of improving may be the inclusion of further sub-scores that are added to Equation 4. The mathematical principle may remain the same, but e.g. the number of predictors may change. The number of predictors corresponds to the number of X in the equation, while the vector X comprises the predictors.

[0028] In the calculation of the SafetyScore according to Equation 1, different metrics or sub- scores are employed. The FitToRide score relates to the rider’s fitness to ride. Depending on their physiological and psychological states a rider may be more or less prone to crash. E.g., when under the influence of e.g. alcohol, a rider may exhibit slower reaction times, may lose balance and may have a reduced perception of the environment as such, a significant factor in the FitToRide score may be an AlcoholUsage score. For simplification, in this example, the following predictor is used: itToRide = {AlcoholUsage} .

[0029] While it is nowadays quite easy to determine the blood alcohol content by tests like a breath analysis in the field as well as blood tests, these may be unavailable or not accepted by a driver prior to commencing the ride. Alternatively a rider may be asked pre-ride to walk a certain number of steps, e.g. five steps, on a line with their mobile phone placed appropriately, e.g. in a pocket, in order to obtain sensor data for a rough estimation of their state. This pre-ride estimation may be used in the score and/or may also be used to determine whether the unlocking of the vehicle, necessary in order to commence the ride, can be performed. During the ride and/or post-ride, sensor data may be acquired and/or analysed to determine if the riding motions the characteristics of a driver riding under the influence of alcohol.

[0030] A further predictor related to the strategical behaviour may be used, as expressed in the following: StrategicalBehaviour = [InfrastructureChoice]

[0031] Here, the position of the vehicle and/or the mobile device, e.g. an absolute global position may be correlated with third-party data indicative of the infrastructure surrounding said position may be used in determining the score. E.g., in case a bike lane and a road is available at the position of the rider, it may be less risky to use the bike lane rather than the road, in case of an e-scooter. It follows, that the use of the bike lane reduces the crash probability versus the road due to the avoidance of potentially dangerous vehicles, like cars, on the road versus the bike lane, which reduce crash probability in turn influences the SafetyScore. [0032] Yet another predictor related to the context adaptation may be used, as expressed by

Context Adaptation = [Speed, Acceleration, Turn}.

[0033] In this predictor, the speed, the acceleration and the turn behaviour of the driver may be used to determine the SafetyScore. Again, this may employ position information and in particular third-party data, like infrastructure data, traffic flow data and whether data like rain, fog, snow et cetera and lighting condition (darkness) to determine whether the driving behaviour is e.g. forward-looking or reckless.

[0034] Further, the adherence to traffic law may be used as a further predictor in accordance e.g. with TrafficLaw Adherence = [StopSigns, RidingWringLand, RedLights}.

[0035] Here, in particular when employing high precision sensor elements to precisely determine the position and the speed of the rider and in particular employing additional infrastructure information like timing information on traffic signals (i.e. timing information when a certain traffic signal shows green or red), may be used when determining the SafetyScore. In particular beneficial may be the determination of an absolute position of the rider within 25 cm, e.g. in order to determine whether a rider uses the correct lane. Alternatively matchmaking techniques using predefined maps of infrastructure and a global position may be used.

[0036] As a further predictor, the operational behaviour may be determined by: OperationalBehauviour =

[ReactionTime, Anticipation, Stahility[TwoHandOperation, SmothnessOf Operation}}.

[0037] A reaction time may be the defined by how long the rider requires to start an evasive manoeuvre from the moment a precipitating event starts. The reaction time may be acquired in particular be for starting a, e.g. also in relation to the determination of an intoxication status e.g. by requiring to operate a certain application on the mobile device, which in turn tests the reaction time of the driver. Anticipation may be determined by analysing the sensor data of and throughout the ride to determine e.g. a rider decelerates already ahead of a critical location, e.g. when approaching a traffic lights, an intersection ora pedestrian crossing. Still further the stability of the driving operation may be determined from acquired position data and sensor data from a filtered inertial measurement unit. Here, stability may be related to the rider using two hands on the handle to ride, which can also be verified by ascertaining whether a mobile device associated with the rider is used during the ride, and further may be related to how smoothly the rider is driving, e.g. by not exhibiting harsh acceleration, braking or hard turns. The latter may also be determined by analysing sensor data from an IMU, in particular filtered IMU data.

[0038] With the described predictors, individual sub-scores of individual aspects of the rider’s behaviour may be determined, which in turn may be composed to a general single score like the SafetyScore in accordance with Equation 1. The rider as well as interested third parties may thus obtain a simple metric summarising the riding behaviour of a particular rider. Further, analysis of the sensor data may allow the determination of critical aspects and elements, in particular technical elements of the vehicle, in order to comply with the driving demand of the vehicle as exhibited by the drivers, thereby allowing to design and build a vehicle that further with stands the harsh environments and use of the riders.

[0039] According to an embodiment of the present disclosure, the information signal may be provided to the user substantially after concluding a driving operation, at select instances, at select intervals and/or substantially continuously throughout the driving operation.

[0040] In particular when providing a substantially continuous and live calculation of the SafetyScore and thus feedback to the driver, the behaviour of the driver may be influenced while driving, thereby resulting in a driving behaviour that may be less demanding on the vehicle, thereby prolonging the life span of the vehicle.

[0041] According to a further embodiment of the present disclosure, the first sensor data and/or the second sensor data may be at least one type of data out of the group consisting of absolute location data, relative location data, acceleration data, velocity data, angular velocity data and angle data and/or the at least one sensor element may be a sensor element out of the group consisting of a relative location sensor element, an absolute location sensor element, a GPS sensor element, an inertial measurement unit sensor element, an acceleration sensor element, a velocity sensor element, an accelerometer, a gyroscope, and an inclinometer.

[0042] According to a further embodiment of the present disclosure, the communication element may be adapted for communication with a third-party information source for obtaining third-party information specific to the vicinity of the vehicle and wherein the processing element may be further adapted for processing the third-party information for determining the at least one driving parameter.

[0043] According to a further embodiment of the present disclosure, the third-party information may be at least one type of information out of the group consisting of weather information, information about the surroundings of the vehicle, traffic information, traffic flow information, and infrastructure information.

[0044] According to a further embodiment of the present disclosure, the at least one driving parameter may be a vector X(t ) comprising at least one vector element Xi(t) as a score, wherein the at last one vector element Xi(t ) may be determined from at least one of the first sensor data, the second sensor data and the third-party information, and wherein the at least one vector element Xi(t) may be one element out of the group consisting of a fit-to-ride score, a strategical-behaviour score, a context-adaptation score, a traffic-law-adherence score and an operational-behaviour score.

[0045] According to a further embodiment of the present disclosure, the processing element may further be adapted to calculate a probability score P X) by employing Equation 2 with Equation 3 and Equation 4, wherein the information signal may at least in part be dependent on the probability score P(X .

[0046] According to a further embodiment of the present disclosure, the information signal may be a numerical score S calculated by employing the equation

S = 1000 x (1 — P ( )).

[0047] According to a further embodiment of the present disclosure, the communication connection may be a short-range communication connection and/or near-field communication connection, in particular a direct communication connection between the mobile device and the vehicle.

[0048] In particular by providing a communication connection, e.g. a bidirectional communication connection between the vehicle and the mobile device, the calculation of the SafetyScore may be shared between the vehicle and the mobile device of the driver. E.g., each of the vehicle and the mobile device may be processing their respective sensor data and only transmit processed information to the respective other device. Such may increase information security by not transmitting raw sensor data, as well as reduce bandwidth required for communication between the vehicle and the mobile device. Likewise, it is conceivable that raw sensor data is transmitted from one of the vehicle and the mobile device to the other for processing while a processing result is transmitted back to the respective other device. E.g., the vehicle may transmit its sensor data to the mobile device for processing while the mobile device transmits back to the vehicle a result of e.g. the SafetyScore for display on a display element of the vehicle. Thereby, the driver may receive instantaneous feedback about his driving behaviour, in particular without requiring to review their mobile device, which could potentially result in an increased risk of a crash, thereby impacting the SafetyScore.

[0049] According to a further embodiment of the present disclosure, the information signal may be indicative of a driving behaviour of the driver of the vehicle.

[0050] According to a further embodiment of the present disclosure, the driving parameter and/or the driving behaviour may be determined by the mobile device on the mobile device.

[0051] According to a further embodiment of the present disclosure, the communication element may be further arranged for communication with a remote computing system, and wherein at least some of the first sensor data and the second sensor data may be transmitted to the remote computing system, and/or wherein at least part of the third-party information is obtained from the remote computing system.

[0052] Here, it is conceivable that either the raw sensor data is transmitted to the remote computing system for storage and/or processing. E.g., in case a significant computational demand is required to ascertain the SafetyScore from the sensor data, it may be beneficial to compute the score in a remote computing system like a big server farm connected to the Internet.

[0053] According to a further embodiment of the present disclosure, the mobile device may be associated with the vehicle driven by the driver, in particular may be an integral part of the vehicle.

[0054] The determining a driving parameter may in particular be understood as calculating a driving parameter. A driving operation may be understood as at the way a driver is operating a vehicle and may in particular be related to the driving behaviour of the driver. A mobile device associated with a driver may be a mobile device in possession of the driver and/or may in particular be a mobile device carried by the driver, e.g. in their clothing. As such, and associated mobile device may be a device that maintains a defined relative relation or position to the driver. Processing sensor data may in particular be understood as using the sensor data, in particular for determining the driving parameter.

BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The present invention will now be described with reference to the accompanying drawings, in which:

[0056] Fig. 1A,B show an exemplary application scenario according to the present disclosure, and

[0057] Fig. 2 shows an exemplary flowchart of the method according to the present disclosure. DETAILED DESCRIPTION

[0058] Now referring to figures 1A,B, showing an exemplary application scenario according to the present disclosure.

[0059] In figures 1A and 1B exemplary, a vehicle 120 is operated by a user 110, a driver of the vehicle 120. The vehicle 120 is exemplarily a two wheeled vehicle, specifically an e-scooter. Such a vehicle has only recently been introduced to the traffic landscape, and thus many users of such vehicles need to get accustomed to the way such a vehicle needs to be operated first. Specifically, while e-scooters share many of the applications where normally a bicycle is used, an e-scooter however needs to be operated substantially differently. [0060] The majority of bicycles are still human powered, whereas the majority of e-scooters, in particular those of a shared mobility service infrastructure, are electrically powered and thereby not requiring any substantial effort of the user when driving. This can easily lead to a situation, where the driver overestimates their abilities as far as vehicle handling and traffic awareness is concerned, possibly resulting in an increased crash risk.

[0061] Therefore, as depicted in figure 1A, the user 110 is employing a mobile device 100 to assist in determining the extent they are capable of safely operating the two wheeled vehicle 120, here the e-scooter. The scenario of figure 1A could be a post-ride scenario, where the user 110 has arrived at their destination and verifies via the mobile device 100, specifically a dedicated application running on the mobile device 100, the past operation of the vehicle 120. As seen in figure 1B, the mobile device 100, the application, presents an information signal 140 via display element 130a, a safety score of 890 to the user 110. In the context of this disclosure, a safety score of 890 correlates with a crash risk of 11%.

[0062] In order to obtain the safety score value, the mobile device 100 is in communicative connection 160 with the vehicle 120. Here, both the mobile device 100 and the vehicle 120 comprise sensor elements 150a,b, which obtain suitable sensor data for calculation of the information signal 140. Such sensor data may be data related to the position of the vehicle 120 and/or the mobile device 100, e.g. absolute position data obtained via a satellite navigation system like the European system Galileo. Additionally or alternatively, sensor data may relate to the acceleration or deceleration of the vehicle 120, a velocity of the vehicle 120 and angle data, e.g. obtained by a gyroscope or inclinometer, indicative of the magnitude of operation of the vehicle 120 by the user 110. E.g. large variations in angle data throughout the ride may be indicative of an instable vehicle operation due to driving under the influence or lack of forward- looking or anticipatory driving/ vehicle operation.

[0063] While it is conceivable that the calculation of the safety score can be performed in either one of the mobile device 100 or the vehicle 120, the calculation or at least the collection of all sensor data, i.e. sensor data from the vehicle 120 and from the mobile device 100 in the mobile device 100 is preferred, since the mobile device 100 is regularly user-specific and thus allows the generation of a long term user profile, whereas, in particular in a shared mobility service infrastructure, a calculation in the vehicle 120 may regularly only reflect the current ride.

[0064] In order to allow the exchange of sensor data between the vehicle 120 and the mobile device 100, both the vehicle 120 and the mobile device 100 comprises a communication element (not separately depicted), e.g. establishing a short range or near field communication connection for direct communication between the vehicle 120 and the mobile device 100. In figure 1B, also the vehicle 120 comprises a signal/display element 130b, which may display the calculated safety score. In order to provide the user 110 with the information signal 140 throughout the ride, it may be preferable that a vehicle 120 comprises a display element 130b. This way, the user 110 receives a substantially continuous and instantaneous feedback regarding their driving behaviour and in particular the associated crash risk and may also engage in countermeasures, e.g. slowing down, when the information signal 140 signifies a substantial crash risk. The use of a display element 130B on the vehicle 120 is preferred, since the user 110 need not operate the mobile device 100, which in turn could significantly increase a crash risk.

[0065] In order to determine the information signal 140, either one of the mobile device 100 and the vehicle, or both, comprises a processing element (not separately depicted) arranged for determining at least one driving parameter from the sensor data, which driving parameter is indicative of the driving operation of the vehicle 120, in particular of a driving behaviour of the user 110. The respective processing element may thus calculate a score as previously described in this disclosure based on the obtained sensor data from the mobile device 100 and/order the vehicle 120, for subsequent presentation to the user via either one of the signal/display elements 130a,b of the mobile device 100 or alternatively the vehicle 120.

[0066] As depicted in figure 1B, the mobile device 100 is also in communication connection with a third-party information source 170, e.g. by employing cellular communication technology as common in a mobile phone, connecting with the third-party information source 170 through the Internet. The third-party information source 170 may provide such additional information like information specific to the vicinity of the vehicle, e.g. weather information, information about the surroundings of the vehicle, traffic information, traffic flow information, infrastructure information, road work information and the like. This third-party information allows the inclusion in the determination of the information signal 140 or the score, e.g. by analysing whether the user 110 the operation of the vehicle 120 to a current weather situation, a current traffic situation, a local infrastructure or road work information and the like.

[0067] Now referring to figure 2, showing an exemplary flowchart of the method according to the present disclosure.

[0068] The method 200 for determining a driving behaviour of a driver of a vehicle comprises the step of obtaining 210 first sensor data of a mobile device associated with a driver 110 of a vehicle 120. Further, the method 200 comprises the step of obtaining 220 second sensor data of the vehicle. Still further, the method 200 comprises the step of processing 230 the first sensor data and the second sensor data and determining 240 at least one driving parameter from the first sensor data and the second sensor data, wherein the driving parameter is indicative of the driving operation of the vehicle. Subsequent to determining the at least one driving parameter, the method provides 250 to a user, in particular the driver, an information signal 140 determined from the driving parameter, wherein the information signal is indicative of the driving operation of the vehicle by the driver. In particular, the information signal is indicative of a driving behaviour or operation behaviour of the driver.

[0069] The steps 210 to 250 may be looped appropriately, thereby allowing a substantially continuous or quasi-continuous provision of the information signal 1402 the user 110. Not separately depicted in figure 2, additional third-party information may be obtained and subsequently employed, e.g. during processing of the sensor data, for determining the at least one driving parameter. In other words, the at least one driving parameter is determined from the first sensor data, the second sensor data and the third party information obtained from a third- party information source 170.

[0070] It is to be understood that the invention is not limited to the embodiments described above, and various modifications and improvements may be made without deviating from the concepts described here. Any of the features described above and below may be used separately or in combination with any other features described herein, provided they are not mutually exclusive, and the disclosure extends to and includes all combinations and sub-combinations of one or more features described herein.

[0071] Finally, it should be noted that the term "comprising" does not exclude other elements or steps, and that "a" or "one” does not exclude the plural. Elements that are described in relation to different types of embodiments can be combined. Reference signs in the claims shall not be construed as limiting the scope of a claim.

LIST OF REFERENCE NUMERALS

100 Mobile device

110 User

120 Vehicle

130. Signal/display element

140 Information Signal Sensor Elements

160 Communication connection

170 Third-party information source

200 Method for determining a driving behaviour of a driver of a vehicle

210 Step: obtaining first sensor data

220 Step: obtaining second sensor data

230 Step: processing sensor data

240 Step: determining at least one driving parameter

250 Step: providing an information signal