The invention regards to a method for identifying geographical zones with high wind activities and a device for creating a route of a vehicle. The vehicle can be a heavy or small commercial vehicle as well as a passenger car.

High winds, especially cross winds, are of high danger to trucks as they interfere with the maneuverability of the vehicle and can topple the truck and I or jack-knifing a truck trailer and I or can cause serious life and material damage. It’s known that winds as low as 25 mph can affect the maneuverability of trucks, so when caught by surprise in a high wind situation can put even skilled driver to test.

It is the object of the invention to provide a method and a device for identifying geographical zones with high wind activities that work reliably and error-free in as many wind-influenced traffic situations as possible.

This object is achieved by a method for identifying geographical zones with high wind activities according to claim 1 , in which locally measured wind speed data is provided with location information, whereby this wind speed data is catalogued in geographical zones and a dynamically adaptable geographical wind zone model for the prediction of regional strong wind zones is created. The geographical wind zone model is also intended to mean a map-like overview in which regional strong wind zones of a region or a country or a continent are marked. It is also possible to display the strong wind zones across the globe. The geographic wind zone model is able to predict, when and where the wind speeds are dangerous.

By collecting real time windspeed data, location data and weather service data and time stamp data a geographic location can be predicted. The geographic wind zone model can be adapted in a periodic rhythm of the wind activities. So high wind geographic zones can be predicted which can dynamically change throughout day and night and can swift with seasons and weather forecasts. The object is also achieved by a device for creating a driving route of a vehicle, comprising a computing unit that is designed to create the driving route depending on at least one wind speed occurring along the route. Taking wind speed into account when creating the vehicle's route reduces dangerous situations in traffic. A driver warning can also be issued at any time. It is possible to prepare the vehicle for pre wind assist systems. By making such a prediction, the driver can be better prepared for strong wind situations. The vehicle can also be better equipped for such situations, for example by anchoring the load to be transported by the vehicle in a windproof manner.

In a further embodiment the locally measured wind speed data, characterized by the location and I or time information and compared with the regional weather service data, are collected over a given period of time or until a predetermined number of wind speed data is reached and the geographical wind zone model is created by means of an artificial intelligence algorithm. By using an artificial intelligence algorithm, a variety of provided data can be processed, resulting in a particularly accurate geographic wind zone model. At the same time, the geographic wind zone model can be constantly adjusted by newly measured wind speed data as well as corresponding regional weather service data. Such a dynamization of the geographical wind zone model contributes to the fact that a reliable statement about actually occurring strong wind zones in a corresponding region is possible at any time.

Artificial intelligence should also be understood as software, robots and other systems that behave similarly to humans. One area of artificial intelligence is machine learning algorithms, which is particularly suitable for creating the dynamic geographic wind zone model. They use data to derive and learn general concepts from it and then apply them to new data. As examples of the methods within machine learning, regressions, decision trees, neural networks are known. As a specialization of machine learning, deep learning algorithms can be used.

In a further embodiment, the categorisation of the geographical zones of the geographical wind zone model is carried out by comparing the wind speed data measured locally in the respective geographical zone with at least one predetermined wind speed threshold and at least one geographical zone in which the locally measured wind speed data exceed the largest wind speed threshold is categorised as a high- wind zone. Thus, individual clusters can be categorized as weak, medium and highly dangerous wind zones. Once a highly dangerous wind zone has been modelled, it is delineated using artificial intelligence algorithms using a regional map that geographically corresponds to this determined wind zone.

This geographical boundary, called Geo Fence, uses the respective native map features to geofence dynamically. Dynamic Geo Fencing of High Wind Zones means that the Geo Fence zones are not fixed for 24x7 hours or 365 days for the location. The High Wind Zones are affected by time of the day and season of the year. When the weather service data are integrated additionally, it is possible to predict the High Wind Zones more reliably real time. So, the geo fence zone for high wind will appear in one location for some time and in another location for another time.

In a further embodiment, current and past wind speed data transmitted by the vehicles of the vehicle fleet, characterized by the location and I or time information, and locally measured wind speed data are stored centrally together with the assigned local weather service data and fed to the artificial intelligence algorithm in order to continuously adapt the geographical wind zone model. Through this machine learning, the geographic wind zone model can be adapted with high precision to the geographic location as well as the time of occurrence of the strong wind zone.

In a further embodiment, the identified strong wind zones are geographically delimited using a native map and displayed in the geographic wind zone model. By processing only data that is measured directly in the regions where the vehicles are moving, the geographic wind zone model can be created in real time and used to issue alerts to vehicles about to enter a strong wind zone. Such warnings can be issued by warning devices of the vehicle. Alternatively, warnings are possible via an app via a smartphone.

In the same way, the determined strong wind zones can be taken into account in the generation of a route of at least one individual vehicle in the vehicle fleet. If the geographic wind zone model shows such a strong wind zone along a predicted route of the vehicle, the route can be automatically changed to bypass the local area of the strong wind zone. This increases the road safety of the vehicle. Fleet safety is also ensured when crossing geographic and time zones with strong winds. There is also the possibility of forward-looking planning for a safe route when transporting sensitive cargo.

In a further embodiment an insurance premium for a vehicle in the vehicle fleet is determined depending on the frequency of driving through high-wind zones. This is always an advantage if it is known that vehicles in a vehicle fleet are mainly on the road in areas where strong wind zones regularly occur.

With regard to the disclosed device, the computing unit is designed to change the route of the vehicle if at least one strong wind zone occurs along the predetermined route of the vehicle. This ensures that wind speeds measured or received by the vehicle can be taken into account in route planning in order to increase the vehicle's road safety. Wind speeds can be measured in the vehicle itself by wind sensors or calculated by weather data transmitted to the vehicle.

In a further embodiment, the computing unit connected to a position determination unit for recording the current position of the vehicle is designed to determine the driving route to a destination starting from the current position of the vehicle by means of a digital map, wherein the computing unit is wirelessly coupled with a dynamically adaptable geographical wind zone model for the prediction of regional strong wind zones vehicle backend, to determine the route depending on the at least one strong wind zone occurring on the route. Such a geographical Wind zone model includes wind speed in a variety of vehicles, preferably a vehicle fleet, which are traveling at different geographical coordinates, which are compared with the wind characteristics over time (morning, noon, evening at night) and over the seasons in one place. This wind speed knowledge is based on weather forecasts and can be matched with a local weather service. The geographic wind zone model thus provides wind data for vehicles located in different geographic locations, which allows the device to select a route with a reduced wind volume. Further features, advantages and properties of the invention are explained by the description of preferred embodiments of the invention with reference to the figures showing:

Fig. 1 A principle representation of the influence of wind on a truck,

Fig. 2 a schematic representation of a system for carrying out the method according to the invention,

Fig. 3 an embodiment for geographically limited wind zones,

Fig. 4 Embodiments for the application of the geographic wind zone model,

Fig. 5 an embodiment for warning the driver in the event of the occurrence of strong wind zones,

Fig. 6 a principle representation of the device according to the invention for determining the route of the vehicle.

For a better understanding of the embodiment of a method according to the invention to be described subsequently, Fig. 1 is considered. In the present case the vehicle 1 is designed as a truck, consists of a semitrailer tractor 2 and a trailer 3. In particular, the trailer 3, which has large areas, offers a large attack surface for the wind that occurs outdoors. Because the wind can attack the vehicle 1 from different sides, a distinction is made between crosswind 4 and tailwind 5.

Fig. 2 shows a system for carrying out the method according to the invention. System 6 consists of a large number of vehicles 1 , which form a vehicle fleet 1 a to 1 n. Each vehicle 1 comprises a control unit 7 for electronic stability control (ESC), which prevents vehicle 1 from breaking out by deliberately braking individual wheels of vehicle 1 . The control unit 7 is designed to calculate a current wind speed from the vehicle parameters available as input signals, such as yaw rate, lateral acceleration, pitch angle, vehicle acceleration and steering angle, which acts on vehicle 1 . For the purpose of determining the wind speed hitting vehicle 1 , a speed sensor 8 may alternatively be positioned on vehicle 1 , with which the wind speed is measured directly. The wind speeds are continuously calculated or measured. Each determined wind speed is forwarded to a telematics control unit 9 of vehicle 1 , which enables a position determination of vehicle 1 , for example by means of GPS, together with geographic information, such as topology-, road-, air- and nautical maps. So, a destination guidance allows to a selected location or a route determination in compliance with certain criteria. For position determination, the telematics control unit 9 is wirelessly connected to a global positioning system 10. The telematics control unit 9 combines the wind speed data determined by the control unit 7 or the wind sensor 9 with the respective position data. Thus, a local relationship between the position of the respective vehicle 1 and the current wind speed is established. At the same time, this data is provided with a time stamp 14.

The wind speeds of all vehicles 1 of the vehicle fleet 1 a prepared in this way by the telematics control units 9 of the vehicles 1 of the vehicle fleet 1 a to 1 n are sent to a cloud-designed central computer 1 1 . For this purpose, the telematics control unit 9 is equipped with a mobile communication modem, such as e.g., GSM-, LTE- or 5G modem. This central computer 1 1 has a memory 12 for historical data, which stores the wind speed information provided by vehicles 1 of the vehicle fleet 1 a to 1 n. Storage 12 is also wirelessly connected to local weather service 13 and merges the received wind speed information with local weather information. The information summarized in this way, consisting of wind speed with time stamp 14, position 15 and weather service data 16, is fed from memory 12 to an algorithm 17 for machine learning. This algorithm 17 is preferably a density-based geo spatial clustering model. The machine learning algorithm 17 uses the data provided to generate a dynamic geographic wind zone model 18, from which strong wind zones 19 are inferred.

To create a reliable geographic wind zone model 18, the wind speed data is clustered. These clusters are categorized, for example in low-, medium- or strong dangerous wind zones. For this purpose, several wind speed thresholds of different wind forces are used, with which the wind speed data of a zone are compared. Depending on the result of the comparison, the wind speed data is assigned to the corresponding clusters. All wind speed data that exceeds the largest wind speed threshold is assigned to the cluster for strong wind zones 19. Once highly dangerous wind zones clusters are identified, each cluster is modeled into geographical boundaries, called GEO fence zone 20. Thereto the areas of Clusters are compared with the respective native map characteristics for GEO fence zones 20. These GEO fence zones 20 are dynamic based on weather service data and received time stamps for identifying daytime. Dynamic geo fencing of strong wind zones 19 means that strong wind zones are affected by the time of the day and season of the year. So, the GEO fence zones 20 for strong wind will appear in one location for some time and in another location for another time (Fig. 3). The geographic wind zone model 18 shall keep on updating by continuously applying daily data. The zones are changed with latest data.

The determination of the strong wind zones 19 can be used, as shown in Fig. 4, to issue an alarm 22 to individual vehicles 1 to warn them of the strong wind zone 19. At the same time, a back office 21 of vehicles 1 of the vehicle fleet 1 a to 1 n can be informed about the strong wind zones 19, where a fleet manager intervenes if the driver of the warned vehicle 1 does not react. Alternatively, a vehicle route generator can be activated by the back office 21 to adapt the route of vehicle 1 approaching the strong wind zone 19.

In addition, the knowledge of the strong wind zones 19 can be used to derive monetization’s 23 from them. In this way, this information can be transmitted to road construction authorities to draw attention to possible wind damage. For users of autonomous vehicles in which no driver is present, maps with the wind speed information can be created so that these vehicles can be automatically warned of strong wind zones 19. Insurance companies can take this information into account when calculating insurance premiums, especially whenever it is known that a vehicle 1 often drives through such high-wind zones 19.

An embodiment for warning the driver in the event of the occurrence of strong wind zones 19 is shown in Fig. 5. Vehicle 1 continuously transmits its current location position 15 to the central computer 1 1 via its telematics control unit 9. In this, the current location information is merged with the local weather service information 16 and compared with the currently determined strong wind zones 19 of the geographical wind zone model 18. Whenever a vehicle 1 of vehicle fleet 1 a to 1 n enters the strong dangerous wind zone 19 an alarm 22 will be displayed either by the display unit 28 of the vehicle 1 or via mobile phone by app to warn as entering high wind zone 19 and to careful drive by high winds. If the alert is not respected by the driver, fleet manager receives a notification.

Fig. 6 shows a principle representation of the device according to the invention for determining the route of vehicle 1 . This device 24 comprises a computing unit 25 which is connected to a positioning unit 26 for detecting the current position of vehicle 1 . Digital cards are stored in a memory 27 connected to the computing unit 25. In addition, the computing unit 25 is coupled (in wired form or wirelessly) with a display unit 28 and a receiving unit 29 of the vehicle 1 . This receiving unit 29 receives wireless data from the vehicle backend (central computing unit 11 ), which has the dynamically adaptable geographic wind zone model for predicting regional strong wind zones 19.

The computing unit 25 determines a route to a destination based on the current position 15 of vehicle 1 by means of the digital maps. The computing unit 25 transmits the data into the digital maps, which is why the position or the route can not only be specified in coordinates, but also a graphical image can be displayed in the display unit 28. When determining the vehicle route, the predicted strong wind zones 19 are taken into account and a route is provided that bypasses these strong wind zones 19.

It is also possible to adjust a once created route of vehicle 1 if a new strong wind zone 19 is reported on the once selected route while vehicle 1 is driving.

Parts of device 1 , such as the computing unit 24, the positioning unit 25 and the memory 26 for digital cards, may be part of the telematics control unit 7.

List of references (Part of the description):

1 vehicle

2 semitrailer tractor

3 trailer

4 crosswind

5 tailwind

6 system

7 control unit for electronic stability control

8 speed sensor

9 telematics control unit

10 global positioning system

1 1 central computing unit

12 storage for historical data

13 local weather service

14 time stamp

15 current vehicle configuration

16 weather service data

17 machine Learning Algorithm

18 dynamic geographic wind zone model

19 strong wind zones

20 GEO fence zone

21 backoffice

22 alarm

23 monetization

24 device

25 computing unit

26 positioning unit

27 memory for digital maps

28 display unit

29 receiver unit