DE102021104303A1 - Device and method for assessing driving behavior - Google Patents

Abstract

The present disclosure relates to automotive technology. More particularly, the present disclosure relates to safe operation of a vehicle. In addition, the present disclosure relates in particular to a mobile device and a method for determining a driving behavior of a driver of a vehicle. Accordingly, a mobile device (100) is provided for determining a driving behavior of a driver of a vehicle, the mobile device comprising the following: at least one sensor element (150a) to obtain first sensor data, a communication element that is set up to communicate with a vehicle ( 120) to communicate (160) and receive second sensor data from the vehicle, a processing element configured to process the first sensor data and the second sensor data to determine at least one driving parameter from the first sensor data and the second sensor data, wherein the driving parameter is an indicator for the driving operation of the vehicle, and a signal element (130a) which is set up to deliver an information signal (140) to a user of the mobile device, in particular the driver, which is determined from the driving parameter, wherein the information signal is an indicator of the driving operation of the vehicle by the driver.

Description

TECHNICAL AREA

The present disclosure relates to automotive technology. More particularly, the present disclosure relates to safe operation of a vehicle. In addition, the present disclosure relates in particular to a mobile device and a method for determining a driving behavior of a driver of a vehicle.

BACKGROUND

Urban transport has changed a lot in recent years, particularly with the emergence of on-demand vehicle sharing services. In particular, sharing services for two-wheelers such as e-scooters have increased significantly with general legalization in various countries in Europe and around the world. Such two-wheelers are often used for the last stretch and therefore have a high degree of utilization. As the number of users increases, so does the number of accident victims. Statistics show that many accidents are single-vehicle accidents, mostly due to the driver and his/her driving behavior.

The statistics also show that such single-vehicle accidents often involve drug abuse, e.g. B. alcohol, was involved. Furthermore, even in single vehicle accidents, head injuries are quite common and can be attributed to the driver not wearing a helmet and engaging in improper behavior.

Therefore, it might be necessary to analyze the driving behavior of a driver of a vehicle, especially with regard to the safety aspects of vehicle operation.

In addition, it may be necessary to inform the driver and/or relevant third parties about driving behavior in order to bring about a change in behavior in the event of incorrect or reckless behavior and thus increase the operational safety of the vehicle.

The present invention was worked out in light of the above circumstances.

SUMMARY

At least such a need can be met by the subject matter of the independent claims. Preferred embodiments are specified in the dependent claims and are explained in more detail in the following description.

The present invention relates to a mobile device and a method for determining a driving behavior of a driver of a vehicle and a computer-readable storage medium according to the independent claims.

According to a first aspect of the present disclosure, a mobile device is provided for determining a driving behavior of a driver of a vehicle, the mobile device comprising the following: at least one sensor element to obtain first sensor data, a communication element that is set up to communicate with a vehicle communicate and receive second sensor data from the vehicle, a processing element that is set up to process the first sensor data and the second sensor data and to determine at least one driving parameter from the first sensor data and the second sensor data, the driving parameter being an indicator for the driving operation of the vehicle, and a signal element that is set up to deliver an information signal, which is determined from the driving parameter, to a user of the mobile device, in particular the driver, the information signal being an indicator of the driving operation of the vehicle by the driver .

According to a second aspect of the present disclosure, a method for determining a driving behavior of a driver of a vehicle is provided, comprising the following steps: obtaining first sensor data of a mobile device that is assigned to 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 an indicator for the driving operation of the vehicle, and delivering one the driving parameters specific information signal to a user, in particular the driver, the information signal is an indicator of the driving operation of the vehicle by the driver.

According to a third aspect of the present disclosure, there is provided a computer-readable storage medium that includes instructions that, when executed by a computer, cause the computer to perform the steps of the method of the present disclosure.

According to the technical concept of the present application, the mobile device and the method judge how a driver operates a vehicle. This assessment is based, at least in part, on sensor data collected during actual operation of the vehicle. The data source can be the vehicle and/or separate devices, e.g. B. a mobile device that the driver carries with him when operating the vehicle. In other words, the vehicle and mobile devices may include elements that collect sensor data that is then analyzed. From the analyzed data, the manner in which the driver uses the vehicle can be determined and driving behavior can be derived from this.

A simplified information signal can in turn be calculated from the analyzed data in order to convey an evaluation of the driving behavior to the driver and/or other interested third parties in a simple and easily understandable manner. Such an evaluation can be a number in a defined range, which is related to an accident probability on the basis of the recorded sensor data and thus on the behavior of the driver.

The sensor data can in turn enable the driver of the vehicle to precisely determine the individual driving behavior during a specific journey. When analyzing the data, external data can also be used, so that e.g. B. it can be determined how a driver behaves in a specific traffic situation. In a two-wheeled vehicle, e.g. B. an e-scooter, it can be determined how a driver approaches a specific traffic situation, z. B. when changing from pedestrian walkway to street level. The driver can z. For example, stopping the vehicle and lifting it off the sidewalk onto the street, or simply driving off the sidewalk onto the street. Analysis of such behavior enables the design of the vehicle to be adjusted to develop a vehicle that requires less maintenance and is less prone to damage, taking into account a driver's usual driving behavior. In the above example, if the analysis of the sensor data leads to the conclusion that a driver does not normally stop when changing from a slightly elevated pavement to a road, but simply continues on, a vehicle can be designed with a specially adapted suspension, thereby avoiding damage to the vehicle during such an operation can be reduced, thus extending the life of the vehicle. Alternatively, an assessment can indicate how an analyzed operation of the vehicle can affect the safe operation and thus the service life of the vehicle.

In other words, according to the concept of the present disclosure, a rating, also referred to as a safety score, is provided to the driver of a vehicle, which collects sensor data to describe the driving behavior of the driver. As already mentioned, the driving behavior can be an indicator of requirements for the technical design of the vehicle in order to technically optimize the design of the vehicle and/or can alternatively be used for third-party providers such as insurance companies, fleet management companies or mobility sharing services that use the respective vehicles rent, be relevant.

Various individual behaviors affect the safe operation of the vehicle and thus the information signal delivered to the driver. Looking again at an e-scooter application, while little scientific or statistical information is available, it has been shown that the majority of accidents involving a single vehicle are mainly due to the driver's driving behavior and/or driving condition.

The most important influencing factors that lead to a vehicle accident are summarized below. Significant factors relating to driving behavior and contributing to an accident or collision are one for a particular driving condition, e.g. B. the local infrastructure, the current traffic and traffic flow, inappropriate speed, a lighting situation (day, night, darkness), a certain weather condition, etc. The personal condition or state of mind of the driver can also be an important factor and e.g. B. from substance abuse (alcohol, drugs, medication). Non-compliance with (local) traffic regulations can also be an important factor leading to an accident, as other road users can be surprised by behavior that is not necessarily expected in a given traffic situation, which is possible sometimes leads to a conflict in the use of the available traffic space. In addition, a lack of foresight when driving the vehicle can lead to a traffic situation that is difficult for a driver to cope with. For example, a driver can B. driving at top speed most of the time instead of anticipating other road users and their behavior, e.g. B. a pedestrian emerging from behind and an obstacle. In addition, incorrect operation of the vehicle can lead to unsafe operation of the vehicle. So e.g. B. in an e-scooter, which is normally intended for a single driver, use by several drivers lead to an unstable driving movement and in particular to a longer braking distance. In addition, a driver may be distracted by secondary tasks unrelated to actual driving operations. Thus, a driver who is simultaneously operating a cellular phone may become so distracted that he is unable to react in a normal manner and in a normal time frame to a situation that arises during operation of the vehicle. A distracted driver may take longer to realize that a particular action, e.g. B. braking is required to avoid an accident.

Sensor data from various sensor types can be recorded and used to analyze driving behavior and to calculate an evaluation. Such sensor data can relate to absolute and/or relative position data of the vehicle and/or the driver, acceleration data, speed data, angular velocity data and angle data. The data can be collected from a global positioning system such as Galileo, GPS, GloNass or BaiDou. It can also collect sensor data from accelerometers, gyroscopes, and inclinometers. These sensor elements can either be attached to the vehicle and/or arranged in a mobile device which, for. B. is worn by the driver. Information such as the position of the vehicle, the speed, the acceleration, the direction of travel and the inclination of the vehicle can be processed from the sensor elements and in particular their sensor data. In addition, third-party data, e.g. B. via the mobile device from a z. B. via the Internet connected third-party server, such. B. weather information, traffic flow information, infrastructure information and other information about the environment of the current position, z. B. Construction site information or lighting information (daylight, artificial light, darkness). The adequacy of the third-party information can e.g. B. be determined based on position, direction and speed information to determine a route of the vehicle and then to determine relevant third-party information in relation to this route. So z. B. several construction sites are in the vicinity of the vehicle, but only the construction site that is on the (expected or calculated) route can be relevant for determining the driving behavior.

The mobile device and the method according to the present disclosure, which can be implemented as a client application running on a mobile device, can provide a safety assessment of the driving behavior as a driver's safety score, which is associated with the driver's accident risk caused by the driving behavior correlating metric is defined. Accordingly, if the driver receives the safety score, he can continuously improve his driving behavior in order to reduce damage to the vehicle and avoid accidents and, in particular, the endangerment of human life. The mobile device or mobile application can continuously record and retain the determined rating. So e.g. B. during a specific trip at defined intervals, z. B. every second, every 10 seconds, every minute, etc., a rating can be calculated, which is displayed to the driver and also locally in the mobile device or remotely in a server connected to the mobile device and / or the vehicle, in particular safe and tamper-proof , can be saved.

As previously mentioned, the rating or safety score may depend on a driver's behavior and, in particular, may depend on the probability of an accident or the average probability of an accident during driving, taking into account the driver's behavior. The accident probability may be related to a driveability metric, a strategic behavior metric, a context adaptation metric, a traffic compliance metric, and an operational behavior metric. The use of only some of the metrics mentioned or other metrics is conceivable.

$$$$

In general, the safety score can be calculated using Equation 1: $$\begin{array}{l}SafetyScO\mathrm{right}e=\\ 1000\times [1-\\ P\left(faH\mathrm{right}ta\mathrm{and}GlicH,St\mathrm{right}ateGiscHesVe\mathrm{right}Halten,KOntextAnpass\mathrm{and}nG,EinHaltenDe\mathrm{right}Ve\mathrm{right}keH\mathrm{right}s\mathrm{right}eGeln,Bet\mathrm{right}iebsve\mathrm{right}Halten\right)]\end{array}$$

$$$$

In Equation 1, P is the average accident probability while driving based on driver behavior, where P is defined by Equation 2: $$P\left(X\right)=\frac{{\displaystyle {\int }_{t_{faH\mathrm{right}tbeGinn}}^{t_{faH\mathrm{right}ten\mathrm{i.e}e}}p\left(X,t\right)\mathrm{i.e}t}}{t_{faH\mathrm{right}ten\mathrm{i.e}e}-t_{faH\mathrm{right}tbeGinn}}$$

In Equation 2, X is a vector containing all the necessary information describing the driver's behavior, e.g. B. All or some of the previously mentioned metrics. Furthermore, p(X,t) is defined according to Equation 3: $$p\left(X,t\right)=\frac{1}{1+e^{ƒ\left(X\left(t\right)\right)}}$$

Specifically, in Equation 3, the expression f(X(t)) can take the form of a polynomial function as expressed in Equation 4: $$ƒ\left(X\left(t\right)\right)={\beta }_0+{\displaystyle \sum _{i=1}^{N_1}{\beta }_{\mathrm{1,}i}{X}_i\left(t\right)+}{\displaystyle \sum _{i=N_1+1}^{{\displaystyle {\sum }_{j=1}^2{N}_j}}{\beta }_{\mathrm{2,}i}{X}_i{\left(t\right)}^2+...+}{\displaystyle \sum _{i={\displaystyle {\sum }_{j-1}^{n-1}{N}_j+1}}^{{\displaystyle {\sum }_{j=1}^2{N}_j}}{\beta }_{n,i}{X}_i{\left(t\right)}^n}$$

In Equation 4, only continuous predictors β _i are given. For a specific calculation and application, the use of categorical predictors is also conceivable, which are then defined as β _{X i} ( _t ) can be expressed.

An example of a continuous predictor can be the speed of the vehicle, which can be expressed by a numerical value, e.g. B. between 0 and 30. In this case, the value corresponds to the value β _i . An example of a categorical predictor would be night or day. This predictor returns one data value per item in this category. If there are three elements, e.g. B. small, medium and large drivers, then β _i can have a value for each element.

The model is based on real data, e.g. B. from incoming accident data evaluated. After comparing the model data with the real data, the model is re-evaluated, possibly changing and thereby improving the model. One way of improvement can be to include more sub-scores that are added to Equation 4. The mathematical principle can remain the same, but e.g. B. the number of predictors may change. The number of predictors corresponds to the number of X in the equation, while the vector X includes the predictors.

When calculating the SafetyScore according to Equation 1, various metrics or sub-evaluations are used. The fitness to drive rating refers to the driver's fitness to drive. Depending on their physiological and psychological condition, a rider may be more or less at risk of falling. For example, if a driver B. is under the influence of alcohol, he can react more slowly, lose balance and perceive his surroundings as such less well; an important factor in the assessment of fitness to drive can be an assessment of the influence of alcohol. For simplicity, this example uses the following predictor: Fitness to Drive = {Alcohol Influence} .

While it's fairly easy these days to determine blood alcohol levels through tests such as an on-site breath analyzer or blood tests, there may be times when these are unavailable or unacceptable to a driver prior to driving. Alternatively, a driver can be asked to take a certain number of steps, e.g. B. five steps to walk in a line while using his mobile phone at a suitable place, z. placed in a pocket, to obtain sensor data for a rough estimate of its condition. This estimation prior to the journey can be included in the evaluation and/or can also be used to determine whether the unlocking of the vehicle, which is necessary for the start of the journey, can be carried out. During and/or after the trip, sensor data may be collected and/or analyzed to determine whether the trip exhibits the characteristics of a driver who is driving under the influence of alcohol.

Another predictor related to strategic behavior can be used as follows: StrategicBehavior = {InfrastructureChoice}

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 that position to determine the rating. if e.g. For example, if a bike lane and a road are available at the driver's position, it may be less risky in the case of an e-scooter to use the bike lane instead of the road. It follows that using the bike lane reduces the likelihood of accidents compared to the road, as potentially dangerous vehicles such as cars on the road are avoided on the bike lane, which in turn influences the SafetyScore.

Another predictor related to context-matching can be used, such as by context-matching = {speed, acceleration, turn}. expressed.

With this predictor, the driver's speed, acceleration and turning behavior can be used to determine the SafetyScore. Here, too, position information and in particular third-party data such as infrastructure data, traffic flow data, weather data such as rain, fog, snow, etc. and light conditions (darkness) can be used to determine whether driving behavior, e.g. B. is prescient or reckless.

$$$$

Compliance with traffic regulations can be used as a further predictor, e.g. B. according to $$\begin{array}{l}EinHaltenDe\mathrm{right}Ve\mathrm{right}keH\mathrm{right}s\mathrm{right}eGeln=\\ \left\{StOppscHil\mathrm{i.e},faH\mathrm{right}enA\mathrm{and}ffalscHe\mathrm{right}faH\mathrm{right}sp\mathrm{and}\mathrm{right},AmpelROt\right\},\end{array}$$

$$$$

Here, in particular when using high-precision sensor elements to precisely determine the position and speed of the driver and in particular when using additional infrastructure information such. B. Time information about traffic signals (i.e. time information when a certain traffic signal shows green or red) can be used to determine the SafetyScore. Determining an absolute position of the driver within 25 cm can be particularly advantageous, e.g. B. to determine whether a driver is using the correct lane. Alternatively, matchmaking techniques using predefined infrastructure maps and a global position can also be used.

As a further predictor, the operating behavior can be determined by: $$\begin{array}{l}Bet\mathrm{right}iebsve\mathrm{right}Halten=\\ \left\{ReaktiOns\mathrm{e.g}eit,Anti\mathrm{e.g}ipatiOn,Stabilit\ddot{a}t\left\{ZweiH\ddot{a}n\mathrm{i.e}iGesfaH\mathrm{right}en,GleicHm\ddot{a}ßiGkeitDesBet\mathrm{right}iebs\right\}\right\}.\end{array}$$

A reaction time can be defined as how long it takes the driver to initiate an evasive maneuver from the time a triggering event occurs. The reaction time can be recorded in particular for starting, e.g. B. also in connection with the determination of a state of intoxication, z. B. by the requirement to operate a specific application on the mobile device, which in turn tests the reaction time of the driver. The anticipation can be determined by analyzing the sensor data from and during the journey, e.g. B. to determine that a driver brakes before a critical point, z. B. when he approaches a traffic light, an intersection or a pedestrian crossing. In addition, the stability of the driving operation can be determined using recorded position data and sensor data from a filtered inertial measurement unit. In this case, stability may be related to the rider having both hands on the handlebars, which can also be verified by determining whether a mobile device connected to the rider is being used while driving, and may also be related to how smoothly the driver drives, e.g. B. by not accelerating, braking or turning abruptly. The latter can also be determined by analyzing sensor data from an IMU, in particular filtered IMU data.

With the described predictors, individual sub-evaluations of individual aspects of driver behavior can be determined, which in turn can be combined to form a general individual evaluation such as the SafetyScore according to Equation 1. Both the driver and interested third parties can thus obtain a simple metric summarizing the driving behavior of a specific driver. About that In addition, the analysis of the sensor data can allow the determination of critical aspects and elements, especially technical elements of the vehicle, in order to meet the driving requirements of the vehicle as expressed by the drivers, making it possible to design and to a vehicle Build to withstand the harsh environments and rider usage.

According to an embodiment of the present disclosure, the information signal may be provided to the user substantially upon termination of a driving operation, at selected times, at selected intervals, and/or substantially continuously during the driving operation.

In particular, if an essentially continuous and up-to-date calculation of the SafetyScore and thus feedback to the driver takes place, the behavior of the driver while driving can be influenced, which leads to driving behavior that puts less strain on the vehicle, thereby extending the life of the vehicle can be.

According to a further embodiment of the present disclosure, the first sensor data and/or the second sensor data can be at least one data type from the group consisting of absolute location data, relative location data, acceleration data, speed data, angular velocity data and angular data and/or the at least one sensor element can be a sensor element 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 accelerometer sensor element, a velocity sensor element, an accelerometer, a gyroscope, and an inclinometer.

According to a further embodiment of the present disclosure, the communication element can be configured to communicate with a third-party information source in order to obtain third-party information that is specific to the environment of the vehicle, and wherein the processing element can also be configured to use the third-party information to determine the at least one driving parameter to process.

According to another embodiment of the present disclosure, the third-party information may be at least one type of information from the group consisting of weather information, information about the surroundings of the vehicle, traffic information, traffic flow information, and infrastructure information.

According to a further embodiment of the present disclosure, the at least one driving parameter can be a vector X(t) which comprises at least one vector element X _i (t) as an evaluation, the at least one vector element X _i (t) consisting of at least one of the first sensor data, the second sensor data and the third-party information can be determined, and wherein the at least one vector element X _i (t) is an element from the group consisting of an assessment of the fitness to drive, an assessment of the strategic behavior, an assessment of the context adaptation, an assessment of compliance with traffic rules and an evaluation of the operating behavior.

According to another embodiment of the present disclosure, the processing element may be further configured to calculate a probability score P(X) using Equation 2 with Equation 3 and Equation 4, wherein the information signal may depend at least in part on the probability score P(X). .

According to another embodiment of the present disclosure, the information signal may be a numerical value S calculated by taking the equation S=1000×(1-P(X)).

According to a further embodiment of the present disclosure, the communication link can be a short-distance communication link and/or a near-field communication link, in particular a direct communication link between the mobile device and the vehicle.

In particular by providing a communication link, e.g. B. a two-way communication link between the vehicle and the mobile device, the calculation of the SafetyScore can be shared between the vehicle and the mobile device of the driver. So e.g. B. both the vehicle and the mobile device process their respective sensor data and only transmit the processed information to the other device. This can increase information security as no raw sensor data is transmitted, and reduce the bandwidth required for communication between the vehicle and the mobile device. It is also conceivable for raw sensor data to be transmitted from the vehicle or the mobile device to the other device for processing, while a processing result is transmitted back to the other device. For example, the vehicle can transmit its sensor data to the mobile device for processing, while the mobile device provides a result, e.g. B. the SafetyScore, for display on a display element of the vehicle back to the vehicle. This allows the driver to get immediate feedback on their driving behavior, especially without having to check their mobile device, which could potentially lead to an increased risk of accidents, which in turn affects the SafetyScore.

According to a further embodiment of the present disclosure, the information signal can be an indicator for a driving behavior of the driver of the vehicle.

According to a further embodiment of the present disclosure, the driving parameter and/or the driving behavior can be determined by the mobile device on the mobile device.

According to a further embodiment of the present disclosure, the communication element can also be set up to communicate with a remote computing system, in which case at least part of the first sensor data and the second sensor data can be transmitted to the remote computing system, and/or in which at least part of the third-party information can be transmitted from the remote computing system is obtained.

It is conceivable that either the raw sensor data is transmitted to the remote computing system for storage and/or processing. For example, if significant computational effort is required to determine the SafetyScore from the sensor data, it may be advantageous to calculate the score in a remote computing system such as a large server farm connected to the internet.

According to a further embodiment of the present disclosure, the mobile device can be assigned to the vehicle driven by the driver, in particular it can be an integral part of the vehicle.

Determining a driving parameter can be understood in particular as calculating a driving parameter. Driving operation can be understood to mean the manner in which a driver operates a vehicle and can refer in particular to the driving behavior of the driver. A mobile device associated with a driver may be a mobile device owned by the driver and/or in particular a mobile device carried by the driver, e.g. B. in his clothes. An assigned mobile device can be a device that assumes a defined relative relationship or position to the driver. A processing of sensor data can be understood in particular as a use of the sensor data, in particular for determining the driving parameter.

character list

• 1A,B ein beispielhaftes Anwendungsszenario gemäß der vorliegenden Offenbarung zeigen und
• 2 ein beispielhaftes Flussdiagramm des Verfahrens gemäß der vorliegenden Offenbarung zeigt.

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

• 1A,B show an exemplary application scenario according to the present disclosure and
• 2 Figure 12 shows an example flow chart of the method according to the present disclosure.

DETAILED DESCRIPTION

1A,B 12 show an exemplary application scenario according to the present disclosure.

In 1A and 1B, a vehicle 120 is operated by a user 110, a driver of the vehicle 120, by way of example. The vehicle 120 is, for example, a two-wheeled vehicle, in particular an e-scooter. Such a vehicle has only recently appeared on the road and, as a result, many users of such vehicles are still learning how to operate such a vehicle. While e-scooters are used in many areas where a bicycle would normally be used, an e-scooter needs to be operated very differently.

Most bicycles are still powered by human muscle power, while most e-scooters, particularly those on a shared mobility service infrastructure, are electrically powered den and therefore do not require any significant effort from the user when driving. This can easily lead the driver to overestimate their driving and traffic perception abilities, potentially leading to an increased risk of an accident.

Therefore, the user pulls 110, as in 1A shown, a mobile device 100 to determine the extent to which it is able to operate the two-wheeled vehicle 120, here the e-scooter, safely. The scenario in 1A could be a post-trip scenario where the user 110 has arrived at their destination and via the mobile device 100, specifically a special application running on the mobile device 100, is checking the previous operation of the vehicle 120. As in 1B As can be seen, the mobile device 100, the application, displays an information signal 140 containing a safety score of 890 to the user 110 via the display element 130a. In the context of this disclosure, a Safety Score of 890 corresponds to an 11% accident risk.

In order to obtain the safety score value, the mobile device 100 is in communicative connection 160 with the vehicle 120. Both the mobile device 100 and the vehicle 120 have sensor elements 150a, b that provide suitable sensor data for calculating the information signal 140 receive. Such sensor data may be data related to the position of the vehicle 120 and/or mobile device 100, e.g. B. absolute position data obtained via a satellite navigation system such as the European system Galileo. Additionally or alternatively, the sensor data may relate to acceleration or deceleration of the vehicle 120, a speed of the vehicle 120, and angle data, e.g. B. obtained by a gyroscope or an inclinometer, and be an indicator of the extent of operation of the vehicle 120 by the user 110. So e.g. B. Large fluctuations in the angle data while driving are an indicator of unstable operation of the vehicle due to driving under the influence of alcohol or a lack of forward-looking or anticipatory driving style/vehicle operation.

Although it is conceivable that the safety score is calculated either in the mobile device 100 or in the vehicle 120, the calculation or at least the collection of all sensor data, i. H. Sensor data from the vehicle 120 and the mobile device 100 in the mobile device 100 is preferred because the mobile device 100 is regularly user-specific and thus enables the creation of a long-term user profile, while in particular in a shared mobility service infrastructure a calculation in the vehicle 120 is regularly only can reflect the current journey.

In order to enable the exchange of sensor data between the vehicle 120 and the mobile device 100, both the vehicle 120 and the mobile device 100 include a communication element (not shown separately), which z. B. establishes a short-distance or short-range communication link for direct communication between the vehicle 120 and the mobile device 100 . In 1B the vehicle 120 also includes a signal/display element 130b which can display the calculated safety score. In order to provide the information signal 140 to the user 110 throughout the journey, it may be preferable for the vehicle 120 to include a display element 130b. In this way, the user 110 receives essentially continuous and immediate feedback on his driving behavior and in particular the associated risk of accidents and can also take countermeasures, e.g. B. slow down when the information signal 140 indicates a significant risk of accidents. The use of a display element 130B on the vehicle 120 is preferred since the user 110 does not have to operate the mobile device 100, which in turn could significantly increase the risk of an accident.

In order to determine the information signal 140, the mobile device 100 and/or the vehicle comprises/comprise a processing element (not shown separately), which is set up to determine at least one driving parameter from the sensor data, which is an indicator for the driving operation of the vehicle 120 , in particular for a driving behavior of the user 110. The respective processing element can thus calculate an evaluation on the basis of the sensor data received from the mobile device 100 and/or the vehicle 120, as previously described in this disclosure, in order to subsequently provide it to the user either via one of the signal/indicating elements 130a,b of the mobile Device 100 or alternatively the vehicle 120 display.

As in 1B shown, the mobile device 100 is also in communication with a third party information source 170, e.g. B. by using cellular communication technology, as it is common in a mobile phone, and connects to the third party information source 170 via the Internet. The third party information source 170 can provide additional information such as information specific to the vehicle environment, e.g. B. weather information, information about the environment of the vehicle, traffic information, traffic provide flow information, infrastructure information, construction site information and the like. This third-party information can flow into the determination of the information signal 140 or the evaluation, for example by analyzing whether the user 110 adjusts the operation of the vehicle 120 to a current weather situation, a current traffic situation, local infrastructure or construction site information and the like.

2 FIG. 12 shows an exemplary flow diagram of the method according to the present disclosure.

Method 200 for determining the driving behavior of a driver of a vehicle includes the step of obtaining 210 first sensor data from a mobile device that is assigned to a driver 110 of a vehicle 120 . The method 200 further includes the step of obtaining 220 second sensor data of the vehicle. Furthermore, the method 200 includes 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, the driving parameter being an indicator for the driving operation of the vehicle. Following the determination of the at least one driving parameter, the method 250 supplies a user, in particular the driver, with an information signal 140 determined from the driving parameter, the information signal being an indicator of the driving operation of the vehicle by the driver. In particular, the information signal is an indicator of a driving behavior or an operating behavior of the driver.

Steps 210 through 250 may suitably be looped, thereby enabling the information signal 1402 to be provided to the user 110 in a substantially continuous or quasi-continuous manner. In 2 not shown separately, additional third-party information can be obtained and then, e.g. B. in the processing of the sensor data, to determine 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 received from a third-party information source 170 .

It should be understood that the invention is not limited to the embodiments described above and that various changes and improvements can be made without departing from the concepts described herein. Each of the features described above and below may be used separately or in combination with 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.

Finally, it should be noted that the term "comprising" does not exclude other elements or steps and that "a/an" does not exclude the plural. Elements described in relation to different types of embodiments can be combined. Any reference signs in the claims shall not be construed as limiting the scope of a claim.

Reference List

100

mobile device

110

user

120

vehicle

130a,b

signal/display element

140

information signal

150a,b

sensor elements

160

communication link

170

Third Party Information Source

200

Method for determining a driving behavior of a driver of a vehicle

210

Step: Obtaining the first sensor data

220

Step: Obtain second sensor data

230

Step: Processing the sensor data

240

Step: determining at least one driving parameter

250

Step: Delivering an information signal

Claims (15)

A mobile device (100) for determining a driving behavior of a driver of a vehicle, the mobile device comprising: at least one sensor element (150a) to obtain first sensor data, a communication element that is set up to communicate (160) with a vehicle (120) and to receive second sensor data from the vehicle, a processing element that is set up to process the first sensor data and the second sensor data and to determine at least one driving parameter from the first sensor data and the second sensor data, the driving parameter being an indicator for the driving operation of the vehicle, and a signal element (130a) that is set up to deliver an information signal (140) to a user (110) of the mobile device, in particular the driver, which is determined from the driving parameter, the information signal being an indicator of the driving operation of the vehicle by the driver. Method (200) for determining a driving behavior of a driver of a vehicle, comprising the following steps: Obtaining (210) first sensor data from a mobile device that is assigned to a driver (110) of a vehicle (120), receiving (220) second sensor data of the vehicle, processing (230) the first sensor data and the second sensor data, Determining (240) at least one driving parameter from the first sensor data and the second sensor data, the driving parameter being an indicator for the driving operation of the vehicle, and Supplying (250) an information signal (140) determined from the driving parameter to a user (110), in particular the driver, the information signal being an indicator of the driving operation of the vehicle by the driver. Mobile device or method according to the preceding claim, wherein the information signal is provided to the user substantially after the termination of a driving operation, at selected times, at selected intervals and/or substantially continuously during the driving operation. Mobile device or method according to at least one of the preceding claims, wherein the first sensor data and/or the second sensor data is at least one data type from the group consisting of absolute location data, relative location data, acceleration data, velocity data, angular velocity data and angular data; and or wherein the at least one sensor element is a sensor element from 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 accelerometer sensor element, a velocity sensor element, an accelerometer, a gyroscope, and a inclinometer is. Mobile device or method according to at least one of the preceding claims, wherein the communication element is configured to communicate with a third party information source (170) to obtain third party information specific to the environment of the vehicle; and wherein the processing element is further configured to process the third-party information to determine the at least one driving parameter. Mobile device or method according to the preceding claim, wherein the third-party information is at least one information type from the group consisting of weather information, information about the surroundings of the vehicle, traffic information, traffic flow information and infrastructure information. Mobile device or method according to at least one of the preceding claims, wherein the at least one driving parameter is a vector X(t) which comprises at least one vector element X _i (t) as an evaluation, the at least one vector element X _i (t) consisting of at least one the first sensor data, the second sensor data and the third-party information is determined, and wherein the at least one vector element X _i (t) is a member from the group consisting of a driveability rating, a strategic behavior rating, a context adaptation rating, a traffic compliance rating, and a performance rating. Mobile device or method according to the preceding claim, wherein the processing element is further arranged to calculate a probability score P(X) by solving the equation $$P\left(X\right)=\frac{{\displaystyle {\int }_{t_{faH\mathrm{right}tbeGinn}}^{t_{faH\mathrm{right}ten\mathrm{i.e}e}}p\left(X,t\right)\mathrm{i.e}t}}{t_{faH\mathrm{right}ten\mathrm{i.e}e}-t_{faH\mathrm{right}tbeGinn}}$$

With $$p\left(X,t\right)=\frac{1}{1+e^{ƒ\left(X\left(t\right)\right)}}$$

and $$ƒ\left(X\left(t\right)\right)={\beta }_0+{\displaystyle \sum _{i=1}^{N_1}{\beta }_1{X}_i\left(t\right)+}{\displaystyle \sum _{i=N_1+1}^{{\displaystyle {\sum }_{j=1}^2{N}_j}}{\beta }_{\mathrm{2,}i}{X}_i{\left(t\right)}^2+...+}{\displaystyle \sum _{i={\displaystyle {\sum }_{j-1}^{n-1}{N}_j+1}}^{{\displaystyle {\sum }_{j=1}^2{N}_j}}{\beta }_{n,i}{X}_i{\left(t\right)}^n}$$

and wherein the information signal depends at least in part on the probability score P(X). Mobile device or method according to the preceding claim, wherein the information signal is a numerical value S calculated by using the following equation: $$S=100\times \left(1-P\left(X\right)\right).$$

Mobile device or method according to at least one of the preceding claims, wherein the communication link is a short-distance communication link and/or a near-field communication link, in particular a direct communication link between the mobile device and the vehicle. Mobile device or method according to at least one of the preceding claims, wherein the information signal is an indicator of a driving behavior of the driver of the vehicle. Mobile device or method according to at least one of the preceding claims, wherein the driving parameter and/or the driving behavior is/are determined by the mobile device on the mobile device. Mobile device or method according to at least one of the preceding claims, wherein the communication element is further set up for communication with a remote computing system, and wherein at least part of the first sensor data and the second sensor data is transmitted to the remote computing system, and / or wherein at least a part the third party information is obtained from the remote computing system. Mobile device according to at least one of the preceding claims, wherein the mobile device is assigned to the vehicle driven by the driver, in particular is an integral part of the vehicle. A computer-readable storage medium containing instructions which, when executed by a computer, cause the computer to perform the steps of the method (200) of any one of claims 2 until 14 to execute.

Images