WO2021052586A1

WO2021052586A1 - A device for synchronizing location information

Info

Publication number: WO2021052586A1
Application number: PCT/EP2019/075146
Authority: WO
Inventors: Shay Horovitz; Yair Arian; Noam Peretz
Original assignee: Huawei Technologies Co., Ltd.
Priority date: 2019-09-19
Filing date: 2019-09-19
Publication date: 2021-03-25
Also published as: CN114424264A; CN114424264B

Abstract

A device configured to synchronize location information with another device and a corresponding method are disclosed., A first device for synchronizing location information with a second device being configured to predict a location of the first device according to an existing model, wherein the existing model is based on one or more parameters of the first device, detect the one or more parameters and/or a location of the first device, verify the existing model based on the detected one or more parameters and/or the detected location, determine the existing model is verified if the predicted location fits the detected location and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters, or send an updated model and/or the one or more detected parameters to the second device, if the model

Description

A DEVICE FOR SYNCHRONIZING LOCATION INFORMATION

TECHNICAL FIELD The present disclosure relates to a device configured to synchronize location information with another device and to a corresponding method.

BACKGROUND

Keeping location data synchronous with high frequency is beneficial for many applications, such as vehicle fleet control, road traffic congestion monitoring, traffic jam prevention, and autonomous vehicles.

For example, geolocation data transmissions of many vehicles is expensive for both the Cloud provider and the customer. Location or movement data is expected to be transmitted frequently by each vehicle, resulting in high transmission costs. The movement data can comprise weather related data (temperature, wind speed direction, humidity, precipitation, etc.), road signs, road conditions, congestion, road images and vehicle data. It is desirable to reduce the amount of data transmitted between the vehicle and the cloud, in order to reduce the OPEX and allow applications that require near real-time synchronization of vehicle location.

The emergence of connected cars and autonomous vehicles greatly increases the necessity of high resolution collected data or metrics from each vehicle, and may require real-time synchronization of such data with the Cloud.

Large fleets of driverless vehicles will produce unprecedented amounts of data, such as vehicle diagnostics and journey details and will also rely on large amounts of real-time data to navigate around.

Some conventional solutions are related to compression methods that are not targeted for the behavior of geospatial data. Other conventional solutions are focused on geospatial yet using less efficient methods. However, the conventional solutions require high and costly throughput or bandwidth and in large scale may become even more challenging.

Thus, there is a need for an improved device for synchronizing location information.

SUMMARY

In view of the above-mentioned challenges and disadvantages, embodiments of the present invention aim to improve conventional devices and methods for synchronizing location information. An objective is thereby to provide an improved device for synchronizing location information, which can provide accurate synchronization of the location information without high and costly throughput.

The objective is achieved by the embodiments provided in the enclosed independent claims. Advantageous implementations of the embodiments are further defined in the dependent claims.

According to a first aspect, the invention relates to a first device for synchronizing location information with a second device, the first device being configured to: predict a location of the first device according to an existing model, wherein the existing model is based on one or more parameters of the first device, detect the one or more parameters and/or a location of the first device, verify the existing model based on the detected one or more parameters and/or the detected location, determine that the existing model is verified if the predicted location fits the detected location and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters, or send an updated model and/or the one or more detected parameters to the second device, if the model cannot be verified.

This provides the advantage that data or parameters, which have to be sent from the first device (such as a vehicle) to the second device (such as a central location, e.g. a Cloud or data center) are reduced, while the location information can accurately synchronized. In an implementation form of the first aspect, the first device is configured to calculate the updated model based on the detected one or more parameters of the first device.

This provides the advantage that the model can accurately be calculated on the basis of detected (e.g. measured) parameters.

In a further implementation form of the first aspect, the one or more parameters of the first device comprise a velocity and/or an acceleration of the first device.

This provides the advantage that the model can be calculated in a simple way, e.g. based on a linear model taking into account velocity and/or acceleration of the first device.

In a further implementation form of the first aspect, the existing model is further based on one or more historical locations and/or one or more historical parameters of the first device.The server can use historical data of the first device and of the other devices that took the same road. It can, therefore, produce a better model.

This provides the advantage that resources of the first device can be saved, since use is made of already stored data. Further, the accuracy of the model and thus of predicting location information can be improved.

Historical data and device or vehicle network data can be further used to improve the prediction models for the geo-location data and thereby save further on transmission cost.

In a further implementation form of the first aspect, the existing model is further based on a map and/or service information.

This provides the advantage that the model can be calculated more accurately on the basis of available map and/or service information. In a further implementation form of the first aspect, the existing model is further based on one or more parameters of one or more other first devices.

In a further implementation form of the first aspect, the first device is further configured to receive the one or more parameters of the one or more other first devices, verify the existing model based on the received parameters, and determine that the existing model is verified, if the one or more parameters, on which the model is based, fit the one or more received parameters.

In a further implementation form of the first aspect, the first device is configured to calculate the updated model based on the one or more parameters of the one or more other first devices.

Advantageously, the first device thus reduces the number of devices such as vehicles that need to transmit information using, for example, the compressive sensing approach. Further, the model can be calculated and updated more accurately.

Many devices, such as vehicles, normally transmit similar data or parameters because they drive on the same road. In addition, roads are typically straight or regularly curved and, therefore, are likely to have a simple model. Taking advantage of the redundancy in both the road pattern and the correlation between drivers on the same road sharing the same movement behavioral patterns, data transmitted from the Fog/Edge to the Cloud can be reduced substantially, thereby reducing expensive transmission cost and making the synchronization between the vehicle and the cloud far more efficient.

In a further implementation form of the first aspect, the one or more parameters of the one or more other first devices indicate a correlation between a location and/or movement of the one or more other first devices and the location and/or movement of the first device. In a further implementation form of the first aspect, the first device is configured to send the updated model and/or the one or more detected parameters to the one or more other first devices.

In a further implementation form of the first aspect, the existing model is based on at least one of the following: a machine learning model, for example a regression model, a kinematic model, and a machine-learning model.

In a further implementation form of the first aspect, the first device is configured to send the updated model and/or the one or more detected parameters to the second device on the basis of a determined protocol between the first device and the second device.

In a further implementation form of the first aspect, the updated model and/or the one or more detected parameters are sent to the second device only, if the predicted location and/or the one or more parameters, on which the model is based, deviate from the detected location and/or the one or more detected parameters by more than a first and/or second threshold value, respectively.

In a further implementation form of the first aspect, the first device is configured to receive an updated model from the second device and replace the existing model by the received updated model.

In a further implementation form of the first aspect, the first device is configured to receive a location from the second device and to verify the existing model based on the received location, wherein the existing model is verified, if the received location fits the detected location.

In a further implementation form of the first aspect, a first device is a vehicle or a mobile phone.

According to a second aspect, the invention relates to a second device for synchronizing location information with a first device, the second device being configured to predict a location of the first device according to an existing model, wherein the existing model is based on one or more parameters of the first device, and wherein the existing model is used for a predefined time interval DT or until receiving an updated model and/or one or more updated parameters from the first device.

This means that the first device can accumulate data over a DT time period in order to produce a model for this DT time period. It can then send the model representing this time period to the second device. The second device can use the received model to deduce the data for that DT time period i.e. the DT time period prior to the time of reception of the model by the second device, because that is the data that the model represents. In addition, the model may predict the next time interval in which case first device does not send anything more.

In an implementation form of the second aspect, the second device is further configured to compute an updated model based on the one or more updated parameters and to transmit the updated model to the first device.

In a further implementation form of the second aspect, the second device is configured to send a predicted location to the first device.

In a further implementation form of the second aspect, the second device is configured to receive an updated model from the first device and to replace the existing model by the received updated model.

In a further implementation form of the second aspect, the existing model is based on at least one of the following: a kinematic model, a map, and a machine-learning model, for example, a regression model.

In a further implementation form of the second aspect, the existing model is further based on one or more parameters of one or more other first devices.

According to a third aspect, the invention relates to a method for synchronizing location information with a second device, the method comprising predicting a location of a first device according to an existing model, wherein the existing model is based on one or more parameters of the first device, detecting the one or more parameters and/or a location of the first device, verifying the existing model based on the detected one or more parameters and/or the detected location, determine the existing model is verified if the predicted location fits the detected location and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters, or sending an updated model and/or the one or more detected parameters to the second device, if the model cannot be verified.

According to a fourth aspect, the invention relates to a method for synchronizing location information with a first device, the method comprising predicting a location of the first device according to an existing model, wherein the existing model is based on one or more parameters of the first device, wherein the existing model is used for a predefined time interval AT or until receiving an updated model and/or one or more updated parameters from the first device.

This means that the first device can accumulate data over a AT time period in order to produce a model for this AT time period. It can then send the model representing this time period to the second device. The second device can use the received model to deduce the data for that AT time period i.e. the AT time period prior to the time of reception of the model by the second device, because that is the data that the model represents. In addition, the model may predict the next time interval in which case first device does not send anything more.

According to a fifth aspect, the invention relates to a computer program comprising computer readable code instructions which, when run in a computer will cause the computer to perform the method according to the third aspect or fourth aspect. Further, the invention also relates to a computer program product comprising a computer readable medium and said mentioned computer program, wherein said computer program is included in the computer readable medium, and comprises of one or more from the group: ROM (Read-Only Memory), PROM (Programmable ROM), EPROM (Erasable PROM), Flash memory, EEPROM (Electrically EPROM) and hard disk drive. According to a sixth aspect, the invention relates to a computer readable storage medium comprising computer program code instructions, being executable by a computer, for performing a method according to the third aspect or fourth aspect when the computer program code instructions run on a computer.

The methods of the third and fourth aspect and their respective implementation forms provide the same advantages and effects as described above for the devices of the first and second aspect, respectively, and their respective implementation forms.

According to a seventh aspect, the invention relates to a first device for synchronizing location information with a second device, the first device includes a processor and a memory. The memory is storing instructions that cause the processor to perform the method according to any one of the third aspect and possible implementations of the third aspect.

According to an eighth aspect, the invention relates to a second device for synchronizing location information with a first device, the second device includes a processor and a memory. The memory is storing instructions that cause the processor to perform the method according to any one of the fourth aspect and possible implementations of the fourth aspect.

It has to be noted that all devices, elements, units and means described in the present application could be implemented in the software or hardware elements or any kind of combination thereof. All steps which are performed by the various entities described in the present application as well as the functionalities described to be performed by the various entities are intended to mean that the respective entity is adapted to or configured to perform the respective steps and functionalities. Even if, in the following description of specific embodiments, a specific functionality or step to be performed by external entities is not reflected in the description of a specific detailed element of that entity which performs that specific step or functionality, it should be clear for a skilled person that these methods and functionalities can be implemented in respective software or hardware elements, or any kind of combination thereof. BRIEF DESCRIPTION OF THE DRAWINGS

The above described aspects and implementation forms of the present invention will be explained in the following description of specific embodiments in relation to the enclosed drawings, in which:

FIG. 1 shows a schematic representation of a first device for synchronizing location information with a second device, and of the second device, according to embodiments;

FIG. 2 shows maps that can assist in building a model for a road by a first device for synchronizing location information with a second device according to an embodiment; FIG. 3 shows examples of a regression model used by a first device for synchronizing location information with a second device according to an embodiment;

FIG. 4 shows a schematic diagram of a model transmission synchronization (a) and a corresponding map (b) for a first device for synchronizing location information with a second device according to an embodiment;

FIG. 5a shows a schematic representation of clusters of vehicles comprising a first vehicle for synchronizing location information with a second device or server according to an embodiment;

FIG. 5b shows a schematic representation of clusters of vehicles comprising a first vehicle for synchronizing location information with a second vehicle according to an embodiment; FIG. 6 shows a schematic representation of clusters of mobile phones comprising a first mobile phone for synchronizing location information with a second device according to an embodiment; FIG. 7 shows a schematic diagram of a method for synchronizing location information with a second device according to an embodiment; and

FIG. 8 shows a schematic diagram of a method for synchronizing location information with a first device according to an embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 shows a schematic representation of a first device 101 for synchronizing location information with a second device 102, and of the second device 102, according to embodiments.

The first device 101 is configured to predict a location of the first device 101 according to an existing model, i.e. using the existing model. The existing model is based on one or more parameters of the first device 101. Further, the first device 101 is configured to detect the one or more parameters and/or a location of the first device 101. For instance, it may measure the one or more parameters and/or may measure the location of the first device 101. Further, the first device 101 is configured to verify the existing model based on the detected one or more parameters and/or the detected location. The existing model is verified by the first device 101, if the predicted location fits the detected location (e.g. the predicted location is the same as the detected location; or the predicted location has a certain relation to the detected location) and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters (e.g. if a value of the detected parameter is the same as used for the existing model). Then, the first device 101 is configured to send an updated model and/or the one or more detected parameters to the second device 102, if the model cannot be verified. The updated model may be calculated by the first device 101 based on the detected parameters and/or based on the detected location.

The prediction of the location of the first device 101 can be done by using self prediction based methods. In the so-called local self-path prediction methods, the locally collected location of the first device 101, such as a vehicle (in the following, without limiting the present disclosure, a vehicle is described as an example of the first device 101) is used, in order to predict its location in a future time interval. This can be done using the following models: model the vehicle path with time by means of time series models, for example linear regression or ARIMA, and infer the next locations of the first device 101 or vehicle from the model. Moreover, a delay buffer can be used and a regression model from the data in the buffer can be constructed;

- use a kinematic model (e.g. based on acceleration; speed) that fits the driver kinematic behavior, for example, one driver may accelerate and/or break faster, and another one may drive more calmly (i.e. may accelerate and/or break slower); and

- use previous rides of the same vehicle which can serve in order to identify repeating patterns of lanes/paths of the specific vehicle (such as driving from home to office and back). This data can be used in order to define the predicted lane and save on transmission cost.

Moreover, the prediction of the location of the first device 101 can be done by using a map assisted self-path prediction method. In this method, the above mentioned method “local self-path prediction” is supported by an additional source of data, namely maps of the expected driving area.

Using the maps and the kinematic model of the vehicle (or generally first device 101), this method can address cases where the path cannot be predicted by previous location data only. For example, in case of a curved and/or twisted roads, a prediction that is based solely on previous location cannot anticipate the next location of the vehicle.

In the so-called traffic service provider assisted self-path prediction, the previously mentioned method “local self-path prediction” is supported by an additional source of data, at least one service provider that provides map, traffic, congestion, speed data, other driver data and correlation with them, traffic lights, road conditions, historical data of first device and of other drivers.

Moreover, the second device 102 can be configured to predict a location of the first device 101 according to an existing model, wherein the existing model is based on one or more parameters of the first device 101. The second device 102 is thereby configured to use the existing model (only) for a predefined time interval DT or until receiving an updated model and/or one or more updated parameters from the first device 101.

FIG. 2 shows maps that can assist in building a model for a road by the first device 101 for synchronizing location information with the second device 102 according to an embodiment.

In the embodiment shown in Fig. 2, thee self-prediction model refers to a curved road. In the case of a curved road without a map, a non-linear model can be used in order to predict the position of the first device 101, as elucidated in the following: radius of curvature, by finding the intersection of two normals to the road and from their intersection deduce the curvature;

Bezier curves: https://en.wikipedia.org/wiki/B%C3%A9zier_curve It can be fit with a quadratic Bezier curve or a cubic Bezier curve:

- polynomial regression: quadratic for a simple curve and cubic for a twisted road: https://en.wikipedia.org/wiki/Polynomial_regression (see Fig. 3):

Moreover, a map can assist in building a correct model for a road that is curved or twisted. Historical data of the same vehicle around the curve can also assist in building the model that can help in case of a fork. Based on the current location, using historical data, the next location of the vehicle can be computed.

In one embodiment, both the first device 101 and the second device 102 share the same model, and while the first device 101 actual location follows that model, there is no need to transmit movement data. Historical data of previous drivers around the curve can also assist in building the model.

In one embodiment, the second device 102 builds the model using information of other devices (e.g. drivers), historical data, stored maps and transmits the model to the first device 101. The first device 101 does not transmit movement data to the second device 102 as long as the actual data fits the model.

In another embodiment, the first device 101 stores map data and builds a model for the movement data. The movement data can comprise weather related data (temperature, wind speed direction, humidity, precipitation, etc.), road signs, road conditions, congestion, road images and vehicle data. It synchronizes the model with the second device 102 and does not transmit a new model to the second device 102 as long as the model is valid.

FIG. 4 shows a schematic diagram of a model transmission synchronization (a) and a corresponding map (b) for the first device 101 for synchronizing location information with the second device 102 according to an embodiment.

In a 1^st step, the first device 101 (here a vehicle) transmits a model Ml to the second device 102 (here a Cloud);

In a 2^nd step, the first device 101 transmits the location C2 and a new curve model CM based on map data (between 1^st and 2^nd step, the second device 102 follows the model Ml); In a 3^rd step, the first device 101 transmits a new linear model M2;

In a 4^th step, the second device 102 notifies a probable trajectory T1 (turn right) based on the history of the first device 101 (see Fig. 6 (b));

In a 5^th step, the first device 101 verifies the trajectory T1 proposed by the second device 102 and remains silent;

In a 6^th step, the second device 102, on the basis of a common traffic behavior, sends the model M3 with reduced speed; and

In a 7^th step, the second device 102 elects the first device 101 as traffic jam cluster head, notifies the other devices (here vehicles) in the cluster about the new model M4 and the role of the first device 101 as cluster head. The other devices verify the second device 102 proposed M4 and remain silent. The first device 101, on the other hand, transmits location or speed data to the second device 102.

Therefore, prediction methods can be used, in order to learn the model of geospatial data to be transmitted, both from local data of the vehicle and other vehicles that used similar paths in the past, and utilize that model in order to allow the vehicle to only send data that are beyond the computer model or in cases that the model has significant errors.

Summarizing, a method for synchronization between a first device 101 (e.g. vehicle) and a second device 102 (e.g. server) in order to reduce movement data transmission volume is proposed, wherein the method comprises the steps of:

1^st collecting data of the first device 101 (location, speed, sensors);

2^nd computing a model for the data;

3^rd transmitting the model; 4^th using the model to infer the data for the time interval DT by the second device 102 or with the use of the predictive model inferring the data until receiving a new model or raw data from the first device 101.

5^th comparing the predictive model to the real data by the first device 101 and computing a new model when required. The new model is computed based on DT time starting at the moment that the last model is found to be not applicable and transmitting the new model to the second device 102. While the model represents the actual data, no transmission is required.

The model can be computed by the second device 102 and transmitted to the first device 101, optionally assisted by a kinematic model and/or a map and/or machine learning model.

The model and the compression relative to the model can be transmitted only if the number of bits to be transmitted is smaller than when a conventional compression algorithm is used for the current time interval

Any machine learning or other modeling technique may be used to produce a model of the data such as autoregressive method.

A protocol between the first device 101 and second device 102 can communicate the model and its parameters and the compressed data to the second device 102.

The data of one first device 101 can be correlated with the data of another device (e.g. other vehicle). The correlation model is communicated to the second device 102 and the second device 102 infers the data of the other device from the model of the first device 101 and from the correlation model.

FIG. 5a shows a schematic representation of clusters of vehicles, the vehicles comprising the first device 101 (in this case the first device 101 is a vehicle 101) for synchronizing location information with the second device according to an embodiment. In particular, Fig. 5 a shows a common prediction model, wherein the server 102 instructs each designated single vehicle 101, in each direction, to report movement data information for all the vehicles around it, such as, for example, in a traffic jam, as shown in Fig. 5a.

In particular, each of the vehicles 101 and 500 synchronizes location information with the server 102 (each one for a different traffic direction and each one is asked by the server to report). All the other vehicles do not need to synchronize direction with the server. Furthermore, they also do not communicate with each other.

Fig. 5b shows a vehicular ad-hoc network (VANET) model, wherein the vehicles in the network communicate among themselves and report to a designated vehicle 101. The designated vehicle 101 is a single vehicle in the VANET communicating movement data to the cloud or second device 102.

In an embodiment, the vehicles are connected, as elucidated in the following two scenarios:

1) a group of vehicles travel together, the server (the second device 102) identifies a cluster, notifies one of the vehicles to transmit the “group’s location” instead of receiving locations from all group members. This elected vehicle 101 notifies the other group members about its location, and they will remain silent until they differ from the common (group) location.

2) vehicles in the same vicinity can choose a representative (the vehicle 101) to transmit their location. This representative can compute clusters, regression, or correlation between the “client” vehicles and transmit their location efficiently. This not only can compress the location data, but also dramatically reduce the number of connections to the cloud (the second device 102). Furthermore, not only location data can be compressed, but also “local-aware” data such as current traffic information, parking lot or optimized fuel consumption. For the sake of completeness, in the following a summary of common prediction based models is given, wherein the models specified below are valid in general irrespective of a VANET scenario. They are part of common prediction irrespective if the cars are connected.

Common path prediction method: in this method, the location of other vehicles that have driven in the same road/lane as the vehicle 101 is used in order to predict the next location of the vehicle 101. This can be done using the following: cluster lanes of vehicles that have driven in the same location, extract the common lane patterns and use for prediction of the next location. For example, the experience of other drivers can be used also to predict the next turn of the vehicle. If 90% of the drivers turn left at the next intersection, then the model will be wrong only 10% of time by assuming a left turn in the next intersection; collect the lanes of neighboring vehicles that precede the current vehicle 101. For example, when driving in a convoy or in a traffic jam the vehicles in front are likely to predict the future location of the vehicle 101. In another embodiment, the road usually dictates the speed and driving behavior of the drivers. For example, on the right lane the cars are usually slower than on the left lane. On small roads, cars usually have the same driving pattern. The experience of other drivers can be used in the model predicting the location and other sensor information of the vehicle 101; employ correlation/regression methods in order to find similarities between the current vehicle 101 and neighboring vehicles that preceded it.

In one embodiment, data are transmitted explicitly only, if they deviate from the model by a configured error threshold.

In one embodiment, the model can be built based on the vehicle speed and acceleration to predict (or fit) the vehicle’s data within the allowed error. In one embodiment, the model can be built based on the vehicle location to predict (or fit) the vehicle’s data to within the allowed error.

In one embodiment, the sampling time can be fixed for all vehicles and, therefore, has a relatively low cost, since it should be transmitted once per many vehicles.

In an embodiment, in the case of a traffic jam, a single representative can report exact location or environmental data for many vehicles and save a lot of transmission cost. The election of the representative can be made according to connected vehicles known algorithms or by the central server. Each vehicle can either follow a model that the central server (the second device 102) transmits to the vehicle 101, or periodically transmit its location. A vehicle starts transmitting its location only when the model is not valid anymore.

In an embodiment, the data of the vehicle 101 correlates with other vehicles data. For instance, vehicles in adjacent lanes are correlated. They drive the same road pattern. Even though the left lane is faster than the right lane, they are correlated. A regression model can, for example, capture this correlation and predict the location of one car based on the other lane car. The correlation with the other lane is most useful when the vehicles are not driving in a cluster. When in a cluster, a better alternative is to choose a representative for the cluster (e.g. the car at the head of the cluster).

In the embodiment shown in Fig. 5, the self-prediction model refers to a straight road. In this example, a simple kinematic model with acceleration and velocity is enough to describe the location of the first device.

Therefore, the model is composed of only two parameters, acceleration and velocity. The second device 102 deduces the location of the first device 101, e.g. a vehicle or car, according to the model and verifies it with the first device 101 periodically.

A more complicated case is, when the acceleration is not constant. A time series model can predict the next location of the vehicle 101 (e.g. Autoregressive Integrated Moving

Average model, ARIMA). Again, the second device 102 verifies the vehicle location periodically. A regression model can be used together with a delay buffer storing the last N locations as a function of time. The vehicle 101 can transmit the model to the second device 102 (e.g. server 102) and the vehicle 101 verifies that the new data conforms to the model. When the model changes, the vehicle 101 updates the server 102 with the new model.

Summarizing, the following method is proposed, in order to reduce vehicle data transmission volume when in a cluster with other vehicles comprising:

1^st electing a cluster head (the first device 101) representing the cluster;

2^nd computing a model for common data for the cluster head 101, such as a regression model;

3^rd transmitting the model;

4^th using the model by the receiver (the second device 102) to infer the data for all vehicles in the cluster;

5^th informing the other vehicles in the cluster by the receiver 102, in order to transmit the said data periodically with a period longer than when there is no cluster head representative;

6^th informing a vehicle to renew regular transmission of data from the receiver 102 when its data differs from the cluster head data.

In an embodiment, the receiver 102 transmits a model to each vehicle in the cluster using connected vehicle protocols and the vehicle renews regular data transmission when the actual data differs from the model.

FIG. 6 shows a schematic representation of clusters of mobile phones comprising a first device 101 (in this case the first device 101 is a mobile phone 101) for synchronizing location information with the second device 102 according to an embodiment.

In one embodiment, the cluster is composed of mobile phones and a representative (i.e. the mobile phone 101) is chosen by the server (the second device 102), wherein the server is, for example, a server in the cloud. If a protocol exists between the mobile phones in the cluster, then it can be used to select the cluster representative.

In a train, bus, taxi or ferry a cluster of devices that travel together on the same path or lane can be identified and the transmission of locations can be reduced. In addition, the train or bus may use connected vehicle protocol to represent its passengers ' location. Moreover, a base station or server in the cloud can identify local clients and transmit their location in bus or taxi for example.

FIG. 7 shows a schematic diagram of a method 700 for synchronizing location information with a second device.

The method 700 comprises the steps of: predicting 701 a location of a first device 101 according to an existing model, wherein the existing model is based on one or more parameters of the first device; detecting 702 the one or more parameters and/or a location of the first device

101; verifying 703 the existing model based on the detected one or more parameters and/or the detected location; determine 704 that the existing model is verified if the predicted location fits the detected location and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters; or sending 705 an updated model and/or the one or more detected parameters to the second device 102, if the model cannot be verified.

FIG. 8 shows a schematic diagram of a method 800 for synchronizing location information with a first device 101.

The method 800 comprises the step of: predicting 801 a location of the first device 101 according to an existing model, wherein the existing model is based on one or more parameters of the first device 101, wherein the existing model is used for a predefined time interval DT or until receiving an updated model and/or one or more updated parameters from the first device 101.

The present disclosure has been described in conjunction with various embodiments as examples as well as implementations. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the independent claims A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation.

Other systems, methods, features, and advantages of the present disclosure will be or become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present disclosure, and be protected by the accompanying claims.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

It is expected that during the life of a patent maturing from this application many relevant keys will be developed and the scope of the term key is intended to include all such new technologies a priori.

As used herein the term “about” refers to ± 10 %.

The terms "comprises", "comprising", "includes", "including", “having” and their conjugates mean "including but not limited to". This term encompasses the terms "consisting of' and "consisting essentially of'.

The phrase "consisting essentially of' means that the composition or method may include additional ingredients and/or steps, but only if the additional ingredients and/or steps do not materially alter the basic and novel characteristics of the claimed composition or method.

As used herein, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a compound" or "at least one compound" may include a plurality of compounds, including mixtures thereof.

The word “exemplary” is used herein to mean “serving as an example, instance or illustration”. Any embodiment described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments and/or to exclude the incorporation of features from other embodiments.

The word “optionally” is used herein to mean “is provided in some embodiments and not provided in other embodiments”. Any particular embodiment of the invention may include a plurality of “optional” features unless such features conflict. Throughout this application, various embodiments of this invention may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein, it is meant to include any cited numeral (fractional or integral) within the indicated range. The phrases “ranging/ranges between” a first indicate number and a second indicate number and “ranging/ranges from” a first indicate number “to” a second indicate number are used herein interchangeably and are meant to include the first and second indicated numbers and all the fractional and integral numerals therebetween.

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination or as suitable in any other described embodiment of the invention. Certain features described in the context of various embodiments are not to be considered essential features of those embodiments, unless the embodiment is inoperative without those elements.

All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention. To the extent that section headings are used, they should not be construed as necessarily limiting.

Claims

1. A first device (101) for synchronizing location information with a second device (102), the first device (101) being configured to: - predict a location of the first device (101) according to an existing model, wherein the existing model is based on one or more parameters of the first device (101); detect the one or more parameters and/or a location of the first device

(101); - verify the existing model based on the detected one or more parameters and/or the detected location; o determine that the existing model is verified if the predicted location fits the detected location and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters; or o send an updated model and/or the one or more detected parameters to the second device (102), if the model cannot be verified.

2. The first device (101) of claim 1, wherein the first device (101) is further configured to calculate the updated model based on the detected one or more parameters of the first device (101).

3. The first device (101) of claim 1 or 2, wherein the one or more parameters of the first device (101) comprise a velocity and/or an acceleration of the first device (101).

4. The first device (101) of any one of the claims 1 to 3, wherein the existing model is further based on one or more historical locations and/or one or more historical parameters of the first device (101).

5. The first device (101) of any one of the claims 1 to 4, wherein the existing model is further based on a map and/or service information.

6. The first device (101) of any one of the claims 1 to 5, wherein the existing model is further based on one or more parameters of one or more other first devices (101). 7. The first device (101) of claims 6, wherein the first device (101) is further configured to: receive the one or more parameters of the one or more other first devices

(101), verify the existing model based on the received parameters, and - determine the existing model is verified, if the one or more parameters, on which the model is based, fit the one or more received parameters.

8. The first device (101) of claim 6 or 7, wherein the first device (101) is configured to calculate the updated model based on the one or more parameters of the one or more other first devices.

9. The first device (101) of any one of the claims 6 to 8, wherein the one or more parameters of the one or more other first devices indicate a correlation between a location and/or movement of the one or more other first devices and the location and/or movement of the first device (101).

10. The first device (101) of any one of the claims 6 to 9, wherein the first device (101) is configured to send the updated model and/or the one or more detected parameters to the one or more other first devices.

11. The first device (101) of any one of the preceding claims, wherein the existing model is based on at least one of the following: a regression model, a kinematic model, and a machine-learning model. 12. The first device (101) of any one of the preceding claims, wherein the first device (101) is configured to send the updated model and/or the one or more detected parameters to the second device (102) on the basis of a determined protocol between the first device (101) and the second device (102).

13. The first device (101) of any one of the claims 1 to 12, wherein the updated model and/or the one or more detected parameters are sent to the second device (102) only, if the predicted location and/or the one or more parameters, on which the model is based, deviate from the detected location and/or the one or more detected parameters by more than a first and/or second threshold value, respectively.

14. The first device (101) of any one of the preceding claims, wherein the first device (101) is configured to receive an updated model from the second device (102) and replace the existing model by the received updated model.

15. The first device (101) of any one of the preceding claims, wherein the first device (101) is configured to receive a location from the second device (102) and to verify the existing model based on the received location, wherein the existing model is verified, if the received location fits the detected location.

16. The first device (101) of any one of the claims 1 to 16, wherein a first device (101) is a vehicle or a mobile phone.

17. A second device (102) for synchronizing location information with a first device (101), the second device (102) being configured to: predict a location of the first device (101) according to an existing model, wherein the existing model is based on one or more parameters of the first device (101), and wherein the existing model is used for a predefined time interval AT or until receiving an updated model and/or one or more updated parameters from the first device (101).

18. The second device (102) of claim 17, wherein the second device (102) is further configured to compute an updated model based on the one or more updated parameters and to transmit the updated model to the first device.

19. The second device (102) of claim 17 or 18, further configured to send a predicted location to the first device (101).

20. The second device (102) of any one of the claims 17 to 19, wherein the second device (102) is configured to receive an updated model from the first device

(101) and to replace the existing model by the received updated model.

21. The second device (102) of any one of the claims 17 to 20, wherein the existing model is based on at least one of the following: a regression model, a kinematic model, a map, and a machine-learning model.

22. The second device (102) of any one of the preceding claims, wherein the existing model is further based on one or more parameters of one or more other first devices.

23. A method (700) for synchronizing location information with a second device (102), the method (700) comprising: predicting (701) a location of a first device (101) according to an existing model, wherein the existing model is based on one or more parameters of the first device (101); detecting (702) the one or more parameters and/or a location of the first device (101);

- verifying (703) the existing model based on the detected one or more parameters and/or the detected location; and o determining (704) that the existing model is verified if the predicted location fits the detected location and/or if the one or more parameters, on which the model is based, fit the one or more detected parameters; or o sending (705) an updated model and/or the one or more detected parameters to the second device (102), if the model cannot be verified.

24. A method (800) for synchronizing location information with a first device (101), the method (800) comprising: predicting (801) a location of the first device (101) according to an existing model, wherein the existing model is based on one or more parameters of the first device (101), wherein the existing model is used for a predefined time interval AT or until receiving an updated model and/or one or more updated parameters from the first device (101). 25. A computer program product comprising computer readable code instructions which, when run in a computer will cause the computer to perform the method according to claim 23 or 24.

26. A computer readable storage medium comprising computer program code instructions, being executable by a computer, for performing a method according to the method according to claim 23 or 24 when the computer program code instructions run on a computer.