Methods of predicting destinations from partial trajectories employing open- and closed-world modeling methods

FIELD: physics.

SUBSTANCE: destinations of a trip are based on at least one of a prior and a likelihood based at least in part on the received input data. The destination estimator component can use one or more of a personal destinations prior, time of day and day of week, a ground cover prior, driving efficiency associated with possible locations, and a trip time likelihood to probabilistically predict the destination. In addition, data gathered from a population about the likelihood of visiting previously unvisited locations and the spatial configuration of such locations may be used to enhance the predictions of destinations and routes. The group of inventions make easier probabilistic prediction of destinations.

EFFECT: output of distributions of probabilities on destinations and routes of a user from observations on content and partial trajectories.

 

The level of technology

Location can be an important part of the human environment. A huge amount of information can be associatively linked to the geographical location of the person and if the person moves, the geographical location of its destination. Usually people moving from one location to another location, in a typical embodiment uses the card as a guide. However, the use of the card may require the person to identify the route by which you can move from its current location to its destination. Additionally, such a traveler in a typical embodiment, is notified only to the information related to its current location or destination, on the basis of the oral notification, personal acquaintances, etc. To illustrate, if the traveler is in a location where it previously was not, he may not know about the location of gas stations, restaurants, etc. and thus may initiate the request for assistance or view the signs along the road. As an additional illustration of a driver who uses the card, you may learn about a traffic jam in traffic, just listening to the radio, which provides this information.

Usually there are a number of applications, which shall support the creation of a map from the start point to the destination. For example, such applications in a typical embodiment can provide the user with directions and a map that shows the route from the start position to the destination. To illustrate, the user can enter a start point and an end point, and the application may display the associated driving directions and/or map(s) (e.g., route selection). These applications can be used in connection with devices such as personal computers, laptop computers, handheld computers, cell phones, etc.

Recently devices global positioning system (GPS), which can determine the location associated with the device, have become more widely used. For example, GPS can be used with the navigation system of the vehicle to provide direction to the driver of the vehicle. Following this example, the navigation system can display a map, which is updated according to the change of position of the vehicle. Additionally, the navigation system can provide the driver with step-by-step directions while the vehicle is moving (e.g., via a display, speaker, etc). However, traditional systems that use GPS (as well as other traditional technologies, in typical form, require the user directly is directly enter a destination. For example, the GPS device usually will not provide direction to the driver of the vehicle, while the driver will not indicate the location of the destination. Additionally, users can enter a destination each time they moved; therefore warnings associated with the destination and/or an associated route may not be provided to users. For example, the user may not enter a destination during its movement in the location to which he often moves, such as work, home, school and so on; accordingly, an appropriate warning may not be provided to the user.

The invention

The following text presents a simplified summary, to provide a basic understanding of some aspects described in this document. This brief description is not a comprehensive review stated the essence of the invention. It is intended neither to identify key or critical elements of the declared nature of the invention nor to delineate its scope. Its sole purpose is to present in a simplified form some of the concepts as a prelude to the more detailed description that is presented later.

The claimed invention relates to systems and/or methods that clothe given probabilistic prediction point(s) of destination. Can be obtained input data that may be relevant to the user, the history of the user (e.g., historical data), to individual users, the topography of the geographical area (for example, data about the properties area), optimal routes, timing of movement, the current movement (e.g., location, change in location, time), etc. it is Assumed that the input data can be received from any source (for example, component location, component, timer, data warehouse, Internet). The prediction may be made using one or more of a priori data and/or one or more probabilities. For example, a priori data can be a priori data about personal destinations and/or a priori information about the properties area. Additionally, the probability may be a probability of optimal movement and/or the possibility of time travel. You should take into account that one or more a priori data, one or more probabilities, or a combination of a priori data and probability can be used to form the predicted one(s) paragraph(s) of destination.

In accordance with various aspects of the claimed of the invention component of the evaluation of the destination can probabilistically predict the destination point is to move on the basis of a priori data and/or likelihood(s). Evaluation of destination can be used to select and/or combine the a priori data and/or likelihood(s)to display the predicted destinations. According to the example, any combination of a priori data and/or probabilities can be used by the component of the evaluation of the destination by using the Bayes rule.

Following one or more aspects of the claimed subject matter, a component of the evaluation of the destination can apply a priori data about personal destinations, a priori information about the properties of the terrain, the probability of optimal movement and/or the possibility of time travel. A priori data about personal destinations can be based on previous destinations of the user; thus, the historical data can be evaluated to derive a priori data about personal destinations. For example, the open-world modeling and/or simulation of the closed world can be applied in connection with obtaining a priori data about personal destinations. Analysis of open-world and/or analysis of the closed world can be United in the forecast location; thus the analysis may include a prediction as to the likelihood that the driver will visit not previously observed location (as a function of th is of Santa observations), and spatial relationships new locations, specified a priori locations. The parameters for the logical conclusion based on the open world can be obtained from observations of many people over time and can then be mapped to specific individuals. Also when modeling open world can be considered demographic information. Additionally, the a priori data about the properties of the terrain can be based on data about the properties of the terrain, which provide the probability that a single cell is a destination on the basis of the properties of the terrain in a single cell. In addition, the likelihood of effective movement can be based on the change of time before arriving at a possible destination, where we can assume that the traveler will continue to reduce the amount of time until arrival as a continuation of the movement. For example, the calculated efficiency of motion associated with each possible destination, can be used as information about the final destination. The probability of time travel can be based on the timing of movement and/or past time. According to additional example as part of the analysis can be used contextual characteristics such as time of day, day of the week (the Sabbath. for example, weekend versus weekday), holiday, season, month, year, etc.

According to various aspects of the claimed of the invention rationale can be applied to identify the destinations, the routes that people will probably take as their journey to the destination, etc. in Addition, applications can use the identified destinations and/or routes, to provide appropriate information to the user. According to the example application can provide warnings about traffic, construction, security issues ahead of the displayed pointer, to provide direction, advice on the route, updates, etc. for Example, information provided to the user may refer to the predicted time point(s) of destination. Additionally or alternatively, the predicted routes to(s) paragraph(s) of the destination can be estimated so that the information may relate to the locations associated with the routes (e.g., location, passing along the route). Relevant information may include, for example, warnings related to motion, navigation, events, targeted advertising, agencies, road signs, etc. Should be taken into account that the relevant information can be is derived in any manner (for example, through audio, visual information, and so on). In addition, the information provided can be individually adjusted on the basis associated with user preferences.

The following description and accompanying drawings detail certain illustrative aspects of the claimed of the invention. These aspects, however, indicate only some of the many ways that can be used the principles of such of the invention, and the claimed invention includes all such aspects and their equivalents. Other advantages and new features of the invention will become apparent from the following detailed description of the invention, when considered together with the drawings.

Brief description of drawings

Figure 1 illustrates a block diagram of an exemplary system that facilitates determination of the item(s) of the destination user.

Figure 2 illustrates a block diagram of an exemplary system that generates probabilistic grid and/or route(s) between the locations, which can be used in connection with probabilistic prediction point(s) of the assignment.

Figure 3 illustrates a block diagram of an exemplary system that predicts the item(s) of destination on the basis of historical data.

Figure 4 illustrates a block diagram of an exemplary system that uses mod the regulation of open world, to predict the item(s) of destination.

Figure 5 illustrates a block diagram of an exemplary system that predicts the item(s) to the destination based at least in part, data about the properties of the terrain.

6 illustrates an example of a 4-level probability distribution with a sampling rate of four threshold radii from a previously visited location.

7 illustrates a block diagram of an exemplary system that displays the predictions of the item(s) of the destination based at least in part, data about effective route.

Fig illustrates a block diagram of an exemplary system that estimates the travel time in connection with the prediction of the item(s) of the assignment.

Fig.9 illustrates a block diagram of an exemplary system that allows you to combine the a priori data and/or likelihood(s)to facilitate the prediction of item(s) of the assignment.

Figure 10 illustrates a block diagram of an exemplary system that provides information which may relate to the predicted point(s) of the assignment.

11 illustrates a block diagram of an exemplary system that predicts probabilistic point(s) of destination during the movement.

Fig illustrates a block diagram of an exemplary system that facilitates the formation of the predicted point(s) of the assignment.

Fig illustrates an exemplary methodology that region is gchat probabilistic prediction point(s) of the assignment.

Fig illustrates an exemplary methodology that provides information relating to the destination, which can be predicted on the basis of a priori data and/or likelihood(s), which can be combined.

Fig-18 illustrate an exemplary grid and the relevant maps depicting various aspects of the associative connection with the driver behavior model and the predictions of the destination.

Fig illustrates a typical network environment, which can be used new aspects of the claimed subject matter.

Fig illustrates an exemplary operating environment that can be used in accordance with the claimed subject matter.

Detailed description of embodiments of the invention

The claimed subject matter is described with reference to the drawings, in which identical reference positions are used to refer to the same elements. In the following description, for purposes of explanation, many of the specific details set forth in order to provide a complete understanding of the present invention. Nevertheless, it is clear that the claimed invention can be used in practice without these specific details. In other instances, well known structures and devices are shown in the form of block diagrams to simplify the description of this the century of invention.

When used in this document, the terms "component," "system" and the like are intended to refer to a computer object, either hardware or software (e.g., during execution), and/or firmware. For example, the component may be a process running on a processor, a processor, an object, an executable file, the program and/or computer. As an illustration, the application running on the server, and the server can be a component. One or more components may reside within a process, and a component can be localized on the computer and/or distributed between two or more computers.

Additionally, the claimed invention can be implemented as a method, device, or product using standard programming and/or engineering to produce software, firmware, hardware or any combination thereof to control a computer to implement the disclosed the invention. The term "product" when used in this document is used to encompass a computer program accessible from any computer-readable device, carrier or medium. For example, machine-readable media may include, but not about ranicoats this, magnetic storage device (e.g. hard disk, floppy disk, magnetic tape etc), optical disks (for example, compact disc (CD), digital multi-function disk (DVD)), a smart card and flash memory devices (for example, card, flash drives). Additionally, should be taken into account that a carrier wave can be used to carry computer-readable electronic data, such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN). Of course, specialists in the art will realize that many modifications can be made to this configuration without leaving the scope and essence of the claimed invention. In addition, the word "exemplary" is used in the materials of the present application, to mean "serving as an example, instance or illustration". Any aspect or design described in the materials of the present application as "exemplary"is not necessarily to be construed as preferred or predominant over other aspects or designs.

Figure 1 illustrates a system 100 that facilitates determining the point(s) of the destination user. The system 100 includes a component 102 interface that accepts input data that m is able to relate to the user, user history, topography, geographic region, movement, optimal routes, etc. Component 102 interface can accept input from any source. For example, the interface 102 can receive input from any component (not shown), which allows determination of the location and/or change in location of the user, such as, for example, a component that supports the global positioning system (GPS), satellite navigation system, GLONASS, Galileo, the European geostationary optional navigation system (EGNOS), Beidou navigation system Decca Navigator, triangulation between towers, etc. as an additional illustration, the component interface 102 can receive input data, associative associated with the destination to which the user moved earlier (for example, from a data store, via user input). Additionally or alternatively, the component interface 102 can receive input from the component timer (not shown)that can determine the amount of time during which the user moves at the present time (for example, during the current movement). Additionally, the interface 102 can receive data about the properties of the terrain; for example, such data could the t can be obtained from a data store (not shown). You should take into account that the component interface 102 can receive input data at any time; for example, input data can be received by the component interface 102 during movement of the user (e.g. in real time) before the user starts moving, etc.

The input data can be provided by the component 102 of the interface component 104 evaluation of destination, who can evaluate the input data and the probabilistic prediction of the item(s) of destination. Component 104 evaluation of the destination can generate predicted(s) item(s) to the destination using a priori data and/or likelihood(s) based, at least partially, of the input data. For example, the component 104 evaluation of destination can apply a priori data about personal destinations, a priori information about the properties area, the likelihood of effective movement and/or the possibility of time travel. You should take into account that any number of a priori data and/or probabilities can be used in combination to display the predicted(s) paragraph(s) of destination. As an illustration, the component 104 evaluation of the destination can only use a priori information about the properties area, in order of probability to predict the destination associative movement is the group. According to another example, the component 104 evaluation of destination can apply a priori data about personal destinations, a priori information about the properties area, the likelihood of effective movement and the possibility of time travel, in order of probability to predict the item(s) of destination. You should take into account that the claimed invention is not limited to these examples.

Component 104 evaluation of destination can evaluate data from various sources to predict the location to which you are moving people. According to the illustration component 104 evaluation of destination has the probability to predict the destination before you travel (for example, when the user sits in the car) or at any time during movement. Thus, the input data may include data related to the current movement (e.g., current location, change in location, any number of locations associated with the current movement, the amount of time associated with the current movement etc). In addition, according to the example in the process of such movement component 104 evaluation of the destination can use the input from the movement to dynamically update the prediction(I) of paragraph(s) of destination. Alternate is but component 104 evaluation of destination may analyze the input data, which lack information associated with the current user's movement, and respectively output the predictions based on heterogeneous information (for example, data about the properties of the terrain, historical data, etc).

Component 104 evaluation of destination may display the predicted point(s) of destination, as illustrated. Additionally, it is assumed that the predicted(s) item(s) assignment may be granted(s) component 102 of the interface component 104 evaluation of the destination and the component interface 102 may display a prediction(I) of paragraph(s) of destination. You should take into account that the predicted destinations can be provided to the user. According to the example, the user may be given a map, which displays the predicted destination. Additionally, the map may include information such as the course covered so far in the current movement, and/or areas associated with the remaining part of the movement to achieve predicted(s) paragraph(s) of destination. This card may also provide information for targeted advertising; the advertising content may be selectively displayed based on considerations of the user is Liski preferences (for example, the user prefers gasoline A gasoline B, rather a restaurant with fast food than restaurant D fast food). It is assumed that the predicted destinations can be provided to the user using any type of audible and/or visual signal. In addition, the user can provide feedback associated with the predicted destination (for example, selecting one destination from the set of predicted destinations, indicating that the predicted destination is incorrect and so on). According to another illustration, the prediction of(I) paragraph(s) of the destination may be transmitted(s) separate component (not shown), which may use prediction(I)to display relevant information (for example, close points of interest, services, location-based, the meteorological data related to point(s) of destination information of traffic associated with the item(s), destination advertising, information associated with events and other), which may subsequently be presented to the user (for example, through warnings).

Component 104 evaluation of the destination can estimate the most likely destinations based at least in part, data on the vegetative cover, the fact that travelers (e.g., drivers) typically the variant using efficient routes and/or the measured distributions of times of movement. Additionally, the component 104 evaluation of destination can combine these signals (e.g., input data) using the Bayes rule, in order of probability to predict the item(s) of destination. In addition, the component 104 evaluation of destination may take into account previous(e) item(s) destination (e.g., historical data) of the user and/or other users; however, the claimed invention is not limited to the above examples. Component 104 evaluation of destination may also improve accuracy over time, as the received training data associated with the user. According to another example, the component 104 evaluation of destination may allow to determine the location of possible destinations, anywhere. According to another illustration, the component 104 evaluation of destination may limit the possible destinations of the road network; thus the accuracy can be increased, as many of the actual destinations are on the road or near the road. However, the claimed invention is not limited thus. Also component 104 evaluation of destination may take into account contextual information, such as, for example, time of day, day of week (for example, the weekend about the Yves weekday), holiday, season, month, year, etc.

Although the interface 102 is depicted as separate from the component 104 evaluation of destination, it is assumed that the component 104 evaluation of the destination may include a component interface 102 or its part. The interface 102 can provide various adapters, components, channels, communication channels, etc. to provide the opportunity to interact with the component 104 evaluation of the destination.

Knowledge of the individual (e.g., the driver) of the destination can be an important parameter for obtaining useful information, while the person moves (for example, during the trip). For example, the navigation system in the car can automatically display traffic jams, gas stations, restaurants and other points of interest that the driver expects to meet during the movement. Additionally, if the navigation system can create an accurate assumption about the General region, which is sent to the driver, it can effectively filter out the information that it displays, thereby reducing cognitive load. In addition, although it may be possible to explicitly request the driver his destination, it is useful to decrease the polling driver to provide this information at the beginning of each movement. System 100 ways is to implement the automatic prediction of destinations, for example, by use of an algorithm to predict the destination of the movement based on the intuition that the driver will take quite efficient route to your destination. According to the aspect of prediction can be formed without modeling the behavior of the individual in the process of movement (for example, assuming no a priori knowledge about the normal destination of the driver, such as work, home, school etc); however, the claimed invention is not limited thus. According to this example, the system 100 can be used in a new transport vehicle, rent car rental vehicle or in the city, which the driver had not previously visited.

Figure 2 illustrates a system 200 that generates probabilistic grid and/or route(s) between the locations, which can be used in connection with probabilistic prediction point(s) of destination. The system 200 may include a component interface 102 that receives input data and provides the input component 104 evaluation of destination. Component 104 evaluation of destination can probabilistically predict the item(s) appointments associate(s) to the input data. Component 104 evaluation of the destination can use probabilistic grid formed by the component 20 coordinate grid, and/or any number of routes between locations (and any data associated with them), derived component 204 route planning, to identify the probable point(s) of destination.

According to the example component 202 grid can generate probabilistic grid, which can be associated with the card. For example, a two-dimensional grid of squares (e.g., cells) can be associated with the card so that the squares (e.g., cells) may refer to any actual physical geographic area (for example, 1 kilometer is associated with each side of each of the grid squares). Additionally, it is assumed that the coordinate grid, derived component 202 grid may include cells of any shape (for example, a polygon with M sides, where M is a positive integer greater than two, circle, etc) or forms other than or in addition to the square cells. Cells may represent a discrete location and can be associated with any portion of the image, size and number. Each cell can be assigned a unique index (for example, z'=1, 2, 3,..., N, where N is any positive integer), and the component 104 evaluation of destination can identify the cell or cells in which the user is likely to finish the movement (e.g., destination).

Component 104 evaluation of destination can calculate for each cell the probability that it is a destination. For example, the probability can be determined by evaluating P(D=i|X=x), where D is a random variable representing the destination, and X is a random variable representing the vector of observed characteristics of movement to the present time. Additionally, you can use the probability and/or a priori data, and can be used Bayes rule to display the following:

Accordingly, N may be the number of cells in a grid, and P(D=i) can be a priori probability that the destination is the cell i. The a priori probability can be calculated, for example, by using a priori data about personal destinations and/or a priori information about the property location. In addition, P(X=x|D=i) can be the probability that cell i is the destination based on the observed measurements of X, which can be computed map information from various sources. For example, the probability may be a probability of efficient movement and/or the possibility of time travel. The denominator can be normalizing multiplier, which can be what icicles to sum the probabilities of all cells that amount is equivalent to the unit.

Component 204 route planning can provide routes between pairs of cells and/or evaluation times of travel between each pair of cells in a grid, formed component 202 grid. Component 204 route planning can be approximated motion using Euclidean distance and approximation of the velocity between each pair of cells. Additionally or alternatively, the component 204 route planning can plan a route between Central points (latitude, longitude) pairs of cells to display a more accurate assessment of the movement of time. Thus, the component 204 route planning can provide output data based at least in part, the road network and speed limits between cells.

Figure 3 illustrates a system 300 that provides predicted(s) item(s) to the destination based at least in part, data about the properties area. The system 300 includes a component 102 interface that accepts input data that may include data about the properties area. The system 300 also includes a component 104 evaluation of the destination, which can generate predicted(s) item(s) to the destination based at least in part, a priori data about the properties of the terrain, the shaped component 302 properties of the area.

Component 302 properties area may facilitate the assessment of the probability that the cell is the destination, based on the properties of the terrain associated with a single cell. The a priori data about the properties of the terrain can be associated with the topology related to the location. For example, the middle of lakes and oceans are rare destinations for drivers, and commercial areas are more attractive destinations than places, covered with ice and snow. Component interface 102, for example, may facilitate the receipt of the card with the data on the properties of the terrain, which may allow component 302 properties area to characterize the cells in a grid-based maps of geographic imagery of the United States (USGS); however, the claimed invention is not limited thereby, and it is assumed that the cells may be characterized using any data about the properties area. For example, a map of the property area USGS can categorize each square with a size of 30 m × 30 m of the United States one type of twenty-one type (for example, detailing herbaceous wetlands, forested wetlands, orchards, perennial ice, cereals, arable land, bare rock, fallow lands, urban areas, residential areas with high quality, the second intensity, transitional areas, quarries, water, meadows, mixed forest, shrubs, deciduous forest, evergreen forest, residential areas with low intensity, commercial area etc) properties area. Component 302 properties of the terrain can estimate the latitude and/or longitude of each of the destination of movement in the data set, to create a sorted histogram types of properties location (for example, twenty-one property type area), for example. Water may be unpopular destination, although it is more popular than some of the other categories (e.g., herbaceous wetlands, forested wetlands, and so on), and commercial areas may be more attractive than covered with ice and snow. Two of the most popular destination can be a types of "commercial" and "residential area with low intensity, which the USGS describes how

Commercial/industrial/transportation" includes infrastructure (e.g. roads, railroads) and all highly developed areas not classified as "residential area with high intensity.

"Residential area with low intensity" includes areas with a mixture of established materials and vegetation. Created materials comprise 30-80% of the surface. Vegetation may be 20-70% of the surface. This region is STI the most generally include a dwelling unit for one family. The population density is lower than in residential areas with high intensity.

Category "water" can be associated with a non-zero probability as the square of the USGS 30 m × 30 m can be categorized as water, even if it has up to 25% of the land, which may include properties of beaches and shores, depending on how are squares. It is assumed that different regions can be associated with different combinations of properties, areas, and permanent residents disparate regions may possibly have a different line of conduct in respect of property types in the area.

According to the example component 302 properties location can determine the probability of a cell of the destination, if it is completely covered with a type j properties location for j= 1, 2, 3, ..., 21 by evaluating P(D=i|G=j). According to the illustration, if the component 302 properties of the terrain uses a probabilistic grid with cells of 1 km × 1 km, each cell can contain about 1111 labels property location 30 m × 30 m (for example, often the cells may not be fully covered by one and the same type). For each cell component 302 properties of the terrain can calculate the distribution of property types of terrain, which may be referred to as Pi(G=j). As an illustration, the component 302 properties of the terrain can calculate the a priori probability for the each cell through the elimination of property types, location in the cell:

The probability PG(D=i) can be associated with a priori probability of a cell to a destination on the basis of the properties of the terrain. Accordingly, the component 302 properties area may determine that water and rural areas, for example, may be less likely destinations. The a priori data about the properties of the terrain, formed component 302 properties of the terrain (and a priori data about personal destinations obtained by using the history of the user, such as described below)can provide a priori probability distribution, as they are in a typical embodiment, not based on measured characteristics of the current trip of the user.

Figure 4 illustrates a system 400, which predicts the item(s) of destination on the basis of historical data. The system 400 includes a component 102 interface that accepts input data. The input data may include, for example, the historical data. Historical data may relate to an individual user, a different user and/or the number of users. For example, historical data may refer to a number of previous destinations of the user. As an illustration, the component interface 102 may retrieve historical data from a data store (not shown). Complement is Ino, component interface 102 may provide the input data, including historical data, component 104 evaluation of the destination, which predicts probabilistic point(s) of destination on the basis of such data.

Component 104 evaluation of destination may further comprise component 402 history of the user, which evaluates historical data to generate a priori data about personal destinations. Thus, a priori data about personal destinations can be applied component 104 evaluation of destination, in order to facilitate the prediction of item(s) of destination. You should take into account that the component 104 evaluation of the destination can use a priori data about personal destinations obtained by the application component 402 history of the user and/or in combination with other a priori data and/or one or more probabilities.

Component 402 history of the user may be based on the intuition that drivers often move to places where they were previously, and that such areas should be given higher probability of destination. For example, the component 402 history of the user may use the loss of the GPS signal, to indicate that the user has entered the building. If the user is in the same building neko is / establishment, which number of times, this location can be marked as a possible destination for future predictions. Additionally, measured using GPS location where the user spent more than a threshold amount of time (for example, 10 minutes), can be grouped to extract probable destinations. In addition, the destinations can be extracted by combining the group of locations with long stops. Also potential destinations may be implied together with the explicit consideration of changes in the scale size of the destination and length of stay.

According to another illustration component 402 history of the user can simulate personal destinations as cell grid, which includes the end point of the segmented movement. Essentially, the spatial scale of possible destination may be the same as the cell size and the residence time should be considered so that the destination can be defined by segmentation of movement (for example, five minutes).

Component 402 history of the user may use a separate assumptions based on personal data about destinations: the assumption of closedness of the world and the assumption about the openness of the world. Component 402 of storieprivate can use the analysis of open-world and/or analysis of a closed world, and can combine both in the prediction of the location; thus, the analysis performed by component 402 history of the user may include a prediction of the likelihood that the driver will visit not previously observed location (as a function of the horizon of observation), and spatial relationships new locations, specified a priori locations. In addition, the parameters for the logical conclusion based on the open world can be derived from the observations of many people over time and can then be displayed to the people. Can also be taken into consideration demographic information (e.g. age, gender, type of work, religion, political affiliation, etc.) when modeling open world. As an illustration, the assumption of closedness of the world can be associated with the assumption that drivers are only visiting the destinations which they have determined to visit in the past. This may be called the assumption of a closed world, and the corresponding analysis can be used as the analysis based on closed world. Making the assumption of a closed world, component 402 history of the user can explore the points at which ended the movement of the driver, and creates a histogram with N cells. Normalization gives the function of probability measures Pclosed(D=i), i=1, 2, 3,...,N, where the subscript closed indicates that this likely is here based on personal destinations. If the user has not been observed visiting the cell, then the probability of personal destinations for this cell may be zero. This is due to the fact that this probability is multiplied by the other probability for N cells in Bayesian computation in order to compute the posterior probability of the destination for each cell. If the cell has a zero a priori data, the cell cannot be maintained as a possible destination.

The assumption of a closed world naively that people can actually visit the locations that they have never been considered for a visit. This is a common case, but every consideration of new destinations can be especially noticeable in the early stages of observation by the driver. In the latter case, the "new" locations include places visited by the driver before, but not visited during the survey (for example, observation/monitoring of the user), as well as truly new destinations for this driver. Thus, a potentially more accurate approach to infer the likely destination of the driver, may take into account the possibility to see the destinations that have not been addressed previously, thereby enhancing the model of the "open world". Modeling this effect, the function of probability measures is a closed world, taken at an early point for the study (for example, monitor driver)can be transformed into an approximation of the steady-state probabilities, which can be observed at the end of the survey and later. This model is an open world then replaces Pclosed(D=i), and may display a more accurate model places that the entity intends to visit.

Figure 5 illustrates a system 500 that uses the open-world modeling to predict the item(s) of destination. The system 500 includes a component 102 of the interface component 104 evaluation of destination. In addition, the component 104 evaluation of the destination includes a component 402 history of the user, which evaluates the historical data contained in the received input data. Component 402 history of the user may additionally include the component 502 modeling of open world, which explicitly takes into account the likelihood and location of new destinations that have not yet been considered (for example, the destinations are not included as part of the historical data).

Component 502 of the open-world modeling can simulate not visited location in different ways. For example, you have not visited location can be modeled using component 502 modeling of open world on cos the ve observations, what destinations have a tendency to unite the group. As an example, drivers may intend to move to places that are close to each other, to save time, or in a common area with which they are familiar, for example, drivers can choose gas stations and grocery stores that are near your place of work. Component 502 modeling open world can model this effect as a discretized probability distribution for the distance from the previously visited points. This distribution may have the overall shape of the set levels "wedding cake", as shown in Fig.6. (6 illustrates an example of a 4-tier distribution 600 with sampling probabilities for the four threshold radii from the previous visited location). Each level can have the likelihood of new destinations around the previously visited. Each level of the "wedding cake" may be concentric circle of constant probability at a certain radius from the center and is designed to simulate a possible Association to a group of destinations in steady state.

In accordance with figure 5 according to another example component 502 modeling open world can measure the desire to grouping by viewing sorted histograms points appointed the Oia grid during different days of the GPS observations of each subject. For each destination on this day can be calculated the probability that haven't visited the destination will appear in a possible steady state for each round 10-tier cake around this destination. Each level can be a ring width of one kilometer and a radius r from the center={1, 2, ..., 10} kilometers, and the steady state can be taken from all of the destinations visited during the whole observation. Day 1 observation probability of finding not visited destinations in steady state near the already visited destinations can be relatively high. As the days pass, each subject gradually pays more than its normal objects, so the probability is reduced. For each day of the levels near the center are higher than near the outer edge. Indeed, for a given probability Pclosed(D=i), based on the closeness of the world, from this day can be calculated another possibility with non-visited neighbors of each nonzero Pclosed(D=i), substituted "wedding cake" with probability values for the corresponding day. It can simulate the expected space in steady state. After normalization to unit "wedding cakes" can be defined as W(D=i). This may be the accomplished separately for each individual.

Although the destination steady state tend to group together, can also meet isolated destinations. This effect can be characterized by calculating the probability that the destination steady state will not be covered 10-tier wedding cake" around the destination visited before steady state. This probability may be referred to as β. The probability of new, isolated destinations may fall over time. One way to model this low-priority probability is a method that uses a uniform distribution over all the grid cells. However, this may contribute to the probability of places where no one goes, like the middle of lakes. Instead of a uniform distribution component 502 modeling open world can take the background as PG(D=i), which is a priori data about the properties of the terrain, as described previously.

These effects can be combined to calculate the probability distribution of destinations that more accurately simulates the steady-state probability value. Three components can be a priori data Pclosed(D=i) a closed world, a parameterized space, as represented by the distributions W(D=i) in the form of a "wedding cake"as described above, the background probability P G(D=i), to simulate isolated destinations. The proportion α of the total probability to W(D=i) can be distributed, where α is the sum of the levels for the corresponding day. We studied the proportion of β the probability of exciting the likelihood that users will move to places beyond the arranged levels of distributions assigned to the background. Version of the open world for the probability of the destination of the driver, which can be a priori probability can then be computed as follows

Popen(D=i)=(1-α-β)Pclosed(D=i)+αW(D=i)+βPG(D=i)

It can be called a priori probability distribution of the open world. In addition, component 502 modeling open world can use it in the formula of Bayes.

Over time, α and β tend to decrease, weakening the amendment on the group and the background probability in favor studied the actual destination of each individual. This represents a wealth of open world, which takes into account the fact that people can visit new locations, especially immediately in the period of observation, but also extended travel.

The a priori probability distribution open world, Popen(D=i), can be approximated steady-state distribution of destinations is of better than naive, the a priori probability Pclosed(D=i) a closed world. In addition, the a priori data of the open world can work with a priori data closer to the actual steady state than the closed model of the world.

7 illustrates a system 700 that displays the predictions of the item(s) of the destination based at least in part, data about effective route. The system 700 includes a component 102 of the interface that receives the input data. The input data may include data about effective route, which can be persisted in a data store (not shown)formed component route planning (e.g., component 204 route planning figure 2), etc. in Addition or alternatively, the component interface 102 can receive data associated with the current user's movement (e.g., data related to the location, change in location, the amount of time of movement and so on). The input data can be assessed component 104 evaluation of destination, which may additionally contain a component 702 efficiency. Component 104 evaluation of destination may respectively generate the predicted point(s) of destination, based on the probability of effective movements, provided by the component 702 efficiency. component 702 efficiency can calculate the efficiency of movement, associated with a set of possible destinations (e.g., each possible destination), as data about the target location/destination, which can be used by the component 104 evaluation of the destination.

The likelihood of effective traffic generated by the component 702 effectiveness may be based on the change of time before arriving at a possible destination. For example, the likelihood of effective movement can be based on the current user's movement. The likelihood of effective movement (as well as any other probability) can be a form P(X=x|D=i), where x is some measured characteristic of the current movement. The measured characteristic associated with the probability of optimal movement, can be a list of cells that the driver has already crossed, and the intuition behind probability suggests that drivers usually do not abandon the easy cases to reach their destinations in an efficient way.

Component 702 efficiency can be quantitatively determine the effectiveness of using the time of travel between points on the route of the driver and possible destinations. Thus, for each pair of cells (i,j) in the probabilistic grid component 702 efficiency can estimate the time Tijmovement between them and/or to accept the assessment as part of the data about effective route. As illustrated a first approximation to the time the motion may be formed component 702 efficiency using a simple Euclidean distance and approximation of the velocity between each pair of cells. Additionally or alternatively, the component 702 effectiveness can use mapping software to display on the screen to plan a route between Central (latitude, longitude) points of pairs of cells. Mapping software can provide a software interface, which can provide an estimated time of movement of the planned routes. The scheduler routes allows to take into account the road network and speed limits between cells, giving a more accurate assessment of the movement of time. For N cells can be N(N-1) different ordered pairs, not including a pair of identical cells. In addition, routes can be planned by the assumption that the travel time from location i to j is the same as from cell j in i, i.e. Ti,j=Tj,i. It is noted that this calculation can be performed only once for a single grid.

Component 702 efficiency can be assumed that drivers will not refuse the opportunity to quickly reach their destination. For example, if Italy comes close to their destination at one point during the movement, he is unlikely to subsequently go further away from your destination. In other words, during the movement, we can assume that the time associated with reaching the destination, may decrease monotonically. According to the example component 702 efficiency may allow testing this hypothesis using data on the movement. According to this example, each movement can be converted into a sequence of traversed cells (without adjacent repeating cell), and each sequence can be considered one cell at a time. When considering each sequence can be traced minimum time before cell destination intersected still cells. Efficient route may reduce this minimum time during the sequence. For each cell can be calculated transition in the sequence, Δt, which may be a change in the estimated time of motion obtained by moving to a new location with minimal time to destination, met so far. Time can be negative, and thus the movement of the cell can reduce the time to destination.

Ordered histogram of Δt can be an estimate for P(ΔT=Δt), which gives the probability of changes in the time of movement that will cause a failover driver is to the next cell, with reference to the closest to the destination, in which the driver was still. The likelihood that the driver will reduce the minimum time to the destination, may bep==of 0.625, for example. According to this example, 1-p=the 0.375 or 37.5% of the time during which the driver moves to a new cell is actually increasing time to the destination. However, this number may be artificially high due to the specialized knowledge of the driver that the route planner may not have changes in the road network, such as an abbreviated route, and the traffic conditions. The space discretization may mean that the routes of cell-cell depending on where are the cell centers, sometimes must take into account the entrances and exits on the highway that the driver is not required to agree, if he moves just right. The mean and median P(ΔT=Δt) can be

-22,2 seconds and -39,0 seconds, respectively; thus, on average, the data can illustrate that drivers tend to continue to move towards their destination with each transition to a new cell in the grid.

Component 702 efficiency can calculate PE(S=s|D=i), which can be the probability S that the movement up to the present time specified destination. Re is the movement S can be represented as a sequence of cells, crossed still no adjacent repetitions:S={s1s2s3,...,sn}.We can assume that in each cell of thesjthe driver takes an independent decision as to which cell to move forward, meaning that this probability will be computed as

Here n may be the number of grid cells crossed when moving still. This equation is multiplied by p, if the new cell is closer to the point i destination than the previous cell, indicating that the driver made a motion, which reduces the estimated time to cell i, otherwise it is multiplied by 1-p. While p>0.5, and the probability favors the cells in the direction which moves the driver.

Using this probability and uniform a priori data distribution can be obtained by using the component 702 efficiency. As an illustration, when the movement begins in a certain direction (e.g., South) from a specific location, specific cells can be excluded as a destination (for example, the cell to the North can be excluded as destinations); the probability of the destination can be displayed on the map, for example. After driving further South all the options, except the South, can be is excluded.

According to the example, the driver can choose more randomly and winding road from the beginning of the movement compared to when its actual destination is near (for example, at the end of the movement). Thus, the probability that the driver will move closer to its final destination at each point of time, p, may vary as a function of time in the movement. Accordingly, P may be lower at the beginning of the movement, compared with the end of the movement.

According to another example component 702 efficiency can measure the effectiveness of cell-based s the beginning of the movement and cell j possible destination. If the route driver effective, then the total time required to travel between the two cells should be ~ Ti,j. If the driver is currently in cell j, then the time to reach a possible item i of destination, should be Tj,i. If i really is a destination and if the driver should be efficient route, the driver must take time Ts,i- Tj,iin order to achieve the current cell j. The actual time of movement of the driver to this point is equal to Δt, which is longer than Ts,i- Tj,iif the driver takes an inefficient route. Thus, the component 702 effective the spine can measure efficiency as the ratio how much time the driver has to spend, moving towards the eventual destination divided by the amount of time that actually passed:

It is assumed that this relation can be approximately unity for efficient movement between s and i. Using data from GPS observations, the distribution of values of efficiency can be calculated based on the known movements and their respective destinations. The probability PE(E=e|D=i) efficiency efficiency is the fact that drivers actually make your way to your destination. If possible destination is the efficiency with low probability, then likely to be appropriately low when PE(E=e|D=i) in the United Bayes rule. The probability of effectiveness may change as a function of the proportion of the movement; thus, the distribution near the beginning of such movement may be unrealistic due to the inability to provide accurate times of movement for short movements. For all of the shares of movement some drivers can improve their efficiency over the range of 1.0 or due to speeding, either because of errors in estimates of time to perfect the movement. The effect of such probabilities for before the punishment destination can be what if you see that the driver moves from a possible destination, then the probability of destination will decrease.

On Fig illustrated system 800, which estimates the travel time in connection with the prediction of the item(s) of destination. The system 800 includes a component 102 of the interface that receives the input data and the component 104 evaluation of the destination, which evaluates the input data, to output the predicted point(s) of destination. Component 104 evaluation of destination may additionally include a component 802 of time travel, which estimates the probability associated with an estimated time to the destination and/or elapsed time of movement associated with the current movement.

Component 802 times and movement can generate the probability of time travel based at least in part, using data distribution time of movement, which can be included as part of the input data. For example, the data distribution time of movement can be obtained from the National service of supervision of the movement of the family unit (NHTS); however, the claimed invention is not limited thus. For example, NHTS 2001 may include data related to the daily movement and/or movement on the far R is sloanie approximately 66000 family units in the USA. Additionally, the results of observation can be accessed via the web interface, and can be formed histogram of times of movement.

The probability that regulates time travel, can be PT(Ts=ts|D=i), where Tsis a random variable representing the travel time to the present moment. To use this likelihood component 802 traveling time can quantize the time of movement according to the elements of sampling, associative associated with the histogram. The histogram can represent the distribution of times to the destination before the movement began, for example, P(TD=tD), where TDrepresents the total travel time. After some time has passed since the beginning of the movement, the likelihood of times gone by, drops to zero, and can be done ordering, to bring

To calculate the probability of possible destination, tsmay be the duration of the movement so far, and tDcan be assessed by the time to the destination from the current cell, based on Ti,jestimated times of travel. Using this probability and/or uniform a priori data can be obtained subsequent distribution.

On Phi is .9 shows a system 900, which allows you to combine the a priori data and/or likelihood(s)to facilitate the prediction of item(s) of destination. The system 900 includes a component 102 of the interface that receives the input data. Additionally, the system 900 includes a component 104 evaluation of the destination, which predicts probabilistic item(s) to the destination through the use of a priori data and/or likelihood(s). Component 104 evaluation of destination may further comprise component 402 history of the user, which displays a priori data about personal destinations, component 302 properties of the terrain, which forms the a priori data about the properties of the terrain, the component 702 efficiency, which represents the likelihood of effective movement, and/or component 802 of time travel, which creates the possibility of time travel.

Component 104 evaluation of destination may optionally be associated with a component 902 Association, which may allow selection of a priori data and/or likelihood(s)to be used in Association with the probabilistic prediction of item(s) of destination. Component 902 Association may merge selected a priori data and/or likelihood(s). For example, a component 902 Association may display a single probability distribution associated with the early priori data and/or likelihood(s). As an illustration, can be selected a priori data about personal destinations; accordingly, the component 902 of the Association may form a probability distribution based on the a priori data about personal destinations. According to another example can be selected a priori information about the properties area, the likelihood of effective movement and the possibility of time travel, and thus the component 902 Association may merge selected a priori data and probability. You should take into account that the claimed invention is not limited to these illustrations.

Component 902 Association may assume the independence of the probabilities of the effectiveness of the movement and duration of travel to these destinations and can combine these two elements and the a priori data in one of the subsequent probability for each destination using the Bayes rule. Thus, the probability of a destination can be the following:

The assumption of such Nezavisimosty is called the initial formulation of Bayes Bayesian update. Mitigation assumptions of independence to allow richer probabilistic dependencies can improve the accuracy of the predictions, since the input of realistic dependencies to minimize the duty to regulate "revaluation" probabilistic perturbations. In this case, the ratio between the efficiency of movement and duration may not be considered. In addition, the above equation is the probability of the destination can be estimated by calculating the coordinate grid of scalars for each of the probabilistic components, the multiplication of scalars in the appropriate cells and sequencing to create the sum of the results.

Probabilistic formulation of predictions destination means that the uncertainty of the true destination of the driver can be presented in a coherent way. Thus, applications that are built on the system, such as the device 104 evaluation of destination, can take into account the inevitable uncertainty in the destination of the driver. For example, an application that shows restaurants or gas stations near the destination, the driver may gradually show more or less detail area when the destination becomes more definite. Warnings about the problems of motion may be held up until confidence in their meeting will not exceed a certain threshold. Bewildered people who deviate from their planned destination, can be prevented only when the deviation becomes almost certain.

Figure 10 illustrates with the system 1000, which provides information which may relate to the predicted point(s) of destination. The system 1000 includes a component 102 interface that accepts input data and the component 104 evaluation of the destination, which is a probabilistic way predicts the item(s) to the destination based on the input data. For example, the component 104 evaluation of destination may use one or more of a priori data and/or one or more probabilities in order to generate predictions. Predicted(s) item(s) assignment may be granted(s) component 1002 content that provides relevant information associated with the predicted point(s) of destination. For example, a component 1002 content can provide alerts about the movement, construction, security problems ahead, the display guides the announcements, to provide direction, advice on the route, updates, etc.

Component 1002 content can provide any information that is relevant to the predicted point(s) of destination. For example, a component 1002 content may display information relating to restaurants, movement, navigation, petrol stations, road signs, retail institutions, etc. According to the example component 1002 content shall be given a separate destination. According to this example component 1002 content may provide an alert that includes information associated with a particular location, and/or information associated with any location that is close to the route between the current location and destination. Thus, the component 1002 content can specify what events are happening in the location that movement is difficult, etc. According to another example component 1002 content can provide promotional information associated with establishments located in the destination and/or in the vicinity of the route. According to another example, if the user is lost, the component 1002 content can provide a warning to allow the user to continue the correct route to the intended destination.

Component 1002 content may include component 1004 configuration that conforms to relevant information that is provided by a component 1002 content for a particular user based on the associated user preferences. For example, user preferences may indicate that the user does not wish to accept any advertisements; accordingly, the component 1004 settings m which can reduce the transfer of such relevant information. According to another example, the user wishes to be informed of any accidents when driving on the route to its destination; thus, the component 1004 settings may allow the component 1002 content to provide such information and/or may set the priority-related information flow in comparison with the disparate information provided by the component 1002 content. You should take into account that the claimed invention is not limited to the above examples.

Figure 11 illustrates a system 1100 that probabilistic way predicts the item(s) destination during the movement. The system 1100 includes a component 102 of the interface component 104 evaluation of destination. Additionally, the system 1100 includes a component 1102 location that identifies the current location and/or change in location of the user and/or device. For example, the component 1102 location may be associated with a GPS satellite navigation system, GLONASS, Galileo, the European geostationary optional navigation system (EGNOS), Beidou System Decca Navigator, triangulation between towers, etc. Component 1102 location can provide related to the location of the data component 102 interface to let the ü further evaluation.

The system 1100 may further include a component 1104 timer, which provides time-related information component 102 interface. Component 1104 timer may, for example, to provide time-related data, which include the amount of time associated with the current movement, the amount of time associated with slight movement or lack of movement, etc. Additionally, although depicted as separate components, it is assumed that the component 1102 location and component 1104 timer can be a single component.

Component interface 102 may also be associated with the store data 1106. Store 1106 data may include, for example, data related to the user, user stories, topography, geographic region, movement, efficient route, etc. is Additionally related to the location data provided by the component 1102 location and/or time-related data received from the component 1104 timer may be stored in the storage 1106 data. Store 1106 data may be, for example, either volatile or non-volatile memory, or it may include volatile and non-volatile memory. As an illustration, but not limitation, nonvolatile memory can include posto is the authorized storage device (ROM), programmable ROM (EPROM), electrically programmable ROM (EPROM), electrically erasable ROM (ASPSU) or flash memory. A volatile storage device may include random access memory (RAM), which acts as an external cache. As an illustration, but not limitation, RAM is available in many forms such as static RAM (POPS), dynamic RAM (DOSE), synchronous DOSE (SDSU), SDSU with double data rate (DDR SDSU), improved SDSU (ESDRAM), Rambus direct RAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM). Store 1106 data from these systems and methods is intended to include (but not limited to, these and any other suitable types of memory. You should also take into consideration that store data 1106 may be a server, a database, a hard disk, etc.

In addition, the component 104 evaluation of the destination may include a component 1108 movement in real time, which can use the data associated with the current movement to probabilistic way to predict the item(s) of destination; however, it is assumed that such data do not need to use to generate the predicted point(s) of destination. As an example, the component 1108 movement in real time may collect location data associated with the individual p is razvijanjem, which can be provided by the component 1102 location. The collected location data can then be used to generate predictions.

On Fig illustrates a system 1200 that facilitates the formation of the predicted point(s) of destination. The system 1200 may include a component interface 102 and the component 104 evaluation of destination, which may be essentially similar to the corresponding components described above. The system 1200 may further include an intelligent component 1202. The intellectual component 1202 may be used by the component 104 evaluation of destination, in order to facilitate the prediction of item(s) of the destination associated with the input data. For example, the intelligent component 1202 may identify that the user has applied a shorter route to cross the predicted destination. Thus, the identified short route can be saved and/or used in connection with the assessment of future point(s) of destination. According to another example, the intelligent component 1202 may determine a combination of a priori data and/or likelihood(s) (or some a priori data or a probability), which can lead to more accurate prediction of the destination (for example, compared with the current combination). Vpol is dctii component 104 evaluation of the destination can be used in combination, identified intellectual component 1202.

It should be understood that the intellectual component 1202 may provide a rationale or output state of the system, environment, and/or user from a set of observation data obtained through events and/or data. Logical inference can be used to identify a specific context or action, or can generate a probability distribution, for example, by States. The logical conclusion may be probabilistic, i.e. the calculation of the probability distribution of interest to the States based on the analysis of data and events. The logical conclusion may also refer to the methods used to link events to a higher level from a set of events and/or data. This logical conclusion leads to the creation of new events or actions from a set of observed events and/or stored event data, regardless of whether events are correlated in close temporal proximity and do events and data from one or more event sources and data. Various schemes and/or systems (for example, methods, support vector machine, neural networks, expert systems, Bayesian network representations, fuzzy logic, core synthesis data, etc) classification (explicitly and/or implicitly trained) can be used in connection with the eating of automatic and/or possible as inferences output in connection with the claimed invention.

A classifier is a function that maps an input vector of attributes of thex=(x1, x2, x3, x4, xn)with confidence that the input belongs to a class, i.e. f(x)=confidence(class). This classification can be used probabilistic and/or statistical analysis (for example, the decomposition efficiency analysis and costs)to prognose or infer an action that, according to the wishes of the user should be automatically executed. Support vector machine (SVM) is an example of a classifier that can be used. SVM operates by finding a hypersurface in the space of possible input data, and this hypersurface is trying to separate initialization criteria from reinitializing events. Intuitively this makes the correct classification for the test data, which are close but not identical to the training data. Other specified and not specified approaches classification models, for example, include simple algorithms Bayes, Bayesian network diagrams, decision trees, neural networks, fuzzy logic model and the model of probabilistic classification, providing different patterns of independence can be applied. Classification when used in this document also includes statistical regression, which is used for th is would be to develop a priority model.

Component 1204 views can provide various types of user interfaces to facilitate interaction between a user and any component coupled to the component 104 evaluation of destination. As shown, the component 1204 view is a separate object that can be used with the component 104 evaluation of destination. However, it should be taken into account that the component 1204 views, and/or similar components may be combined in a component 104 evaluation of destination (and/or the component interface 102) and/or a separate module. Component 1204 may provide one or more graphical user interfaces (GUI), command-line interfaces and similar. For example, a GUI can be rendered in a way that will provide the user with a region or means to load, import, read, etc. data, and can include a region to present the results of this. These areas may contain known text and/or graphics area that contains the dialog boxes, static controls, drop-down menus, list boxes, pop-up menu as edit boxes, combo boxes, dependent switches, independent switches, end keys and the graphics window. In addition, there may be used means the La facilitate submission, such as vertical and/or horizontal scroll bars to move and toolbar buttons to determine whether the visible region. For example, a user may interact with one or more components connected to the component 104 evaluation of the destination.

The user can also interact with the regions to select and provide information via various devices such as a mouse, trackball, keypad, keyboard, pen and/or voice control. In a typical embodiment, a mechanism such as a button or enter key on the keyboard, can be additionally used to enter information in order to start the search. However, it should be taken into account that the claimed invention is not limited to this. For example, just the highlighted radio button can start the movement of information. In another example, may be applied to the command-line interface. For example, the command line interface can prompt (e.g., via a text message on the display or audio tone) the user information, providing a text message. The user can then provide appropriate information, such as alphanumeric enter the appropriate option provided in the tip of the interface, what is the answer to the question, formulated in the tooltip. You should take into account that can be used in the command-line interface in connection with a GUI and/or API. In addition, the command line interface can be used in connection with the hardware (e.g. graphics cards) and/or displays (e.g., black and white and enhanced graphics adapter (EGA)) with limited support graphics, and/or communication channels with a narrow bandwidth.

Fig-14 illustrate methods in accordance with the claimed subject matter. To simplify the explanation of the methods depicted and described as a sequence of actions. You should understand and take into account that the invention is not limited to the illustrated actions and/or sequence of actions, such as actions can be performed in different sequences and/or concurrently, and with other acts not presented and described herein. Moreover, not all illustrated steps may be required to implement the methods in accordance with the claimed invention. In addition, specialists in the art should be understood that methods may be an alternative represented as a sequence of interrelated States or events through a scheme of States or events.

The piano is g illustrates a method 1300, which facilitates probabilistic prediction point(s) of destination. At step 1302 may be formed probabilistic grid is associated with a geographic location. It is assumed that the geographical location can be any size. For example, the geographic location may be associated with the city, the County, any number of urban neighborhoods, state, country, etc. Additionally, the grid may include any number of cells, and the cells can be any size, shape, etc.

At step 1304, the data associated with movement, can be evaluated to determine a priori data and/or likelihood(s). For example, the estimated data may be data about the properties of the terrain, historical data, data about effective route data on the distribution of drive time, your location in real time relating to the current movement, etc. Data, for example, can be obtained from any source. In addition, as an illustration, the location data in real time relating to the current movement can be considered when determining a priori data or probability. Alternatively, the a priori data or probability can be identified without the use of location data in real-time. At step 106, the destination, associated with movement, can be predicted using a coordinate grid by probabilistic merging of a priori data and/or likelihood(s). One or more a priori data and/or one or more probabilities can be selected for merging. Thus, according to the example of a priori information about the properties of the terrain and the possibility of time travel can be selected for use to predict the destination, but the claimed invention is not limited to this example. The combination of a priori data and/or likelihood(s) can then be used to form the predicted destination.

On Fig illustrates a method 1400 that provides information relating to the destination, which can be predicted on the basis of a priori data and/or likelihood(s), which can be combined. At step 1402 can be selected one or more of the a priori data about personal destinations, a priori information about the property location, the likelihood of effective movement and probability of time travel. For example, a priori information on personal destinations can be based on a set of previous destinations of the user (e.g., historical data). In addition, the a priori data about the properties of the terrain can be related is s with probability what cell in the probabilistic grid related to a geographical location, is a destination on the basis of the properties location in the cell. Additionally, the likelihood of effective movement can be based on the change in time before arriving at a possible destination. Thus, the routes between pairs of cells can be assessed in connection with the likelihood of effective movement. The probability of time travel, for example, can refer to elapsed time of movement and/or data on the distribution of time of movement.

At step 1404 selected a priori data and/or likelihood(s) can be combined. For example, can be used Bayes rule in connection with the Association selected a priori data and/or likelihood(s). At step 1406, the destination may be a probabilistic manner predicted by combining. Thus, for example, a single cell from a probabilistic grid can be identified as the destination. At step 1408 may be provided with information, which refers to the predicted destination. For example, the information may relate to the destination and/or location along the route to the destination. As an illustration, the corresponding information can be associated with the movement connected with weather related is targeted advertising, connected with assistance in navigation, is associated with potentially interesting event, etc.

Fig-18 illustrate an exemplary grid and the relevant maps depicting various aspects in connection with the modelling of driver behaviour and the predictions of the destination. You should take into account that these grids and maps are provided as examples and the claimed invention is not limited thus. On Fig illustrated grid 1500 depicting cell destination associated with a priori data about personal destinations associated with a particular user. On Fig depicts a grid of 1600, which demonstrates a priori information about the properties of the terrain, where darker contours show a higher probability of cell destination. Fig illustrates the grid 1700 related to the likelihood of effective movement; in particular, the grid 1700 illustrates that after driving four cells in a southerly direction a large part of the Northern section can be removed. In addition, Fig depicts the grid 1800, where the user continues to move further South, and additional cells may be removed from the grid 1800 compared to grid 1700.

in Order to provide additional context for various aspects of the claimed of the invention, Fig-20 and subsequent discussion are intended to provide a brief General description of a suitable computing environment in which can be implemented in various aspects of the present invention. Although the claimed invention is described above in the General context mashinostryenia instructions of a computer program that is running on the local computer and/or remote computer, specialists in the art should be understood that the invention can also be implemented in combination with other program modules. Software modules typically include procedures, programs, components, data structures, etc. that perform specific tasks and/or implement a separate abstract data types.

Moreover, specialists in the art will take into account that the methods according to the invention can be practiced with other computer system, including single-processor or multiprocessor computer systems, minicomputers, mainframes and personal computers, pocket-sized computing devices, microprocessor-based and/or programmable consumer electronics, and the like, each of which may operatively communicate with one or more associated mouth is the properties. Illustrated aspects of the claimed of the invention can also be implemented in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a data exchange network. However, some, if not all, aspects of the invention can be practiced on stand-alone computers. In a distributed computing environment, program modules may be stored on a local and/or remote storage devices.

Fig is a schematic block diagram of an example computing environment 1900, which can interact with the claimed invention. The system 1900 includes one or more clients 1910. The client(s) 1910 may be hardware and/or software (for example, threads, processes, computing devices). The system 1900 also includes one or more servers 1920. Server(s) 1920 can be hardware and/or software (for example, threads, processes, computing devices). Servers 1920 can post threads to perform transformations, for example, through the use of the invention.

One of the possible links between customer 1910 and 1920 server can be in the form of a data packet, PR is sposobnogo for communication between two or more computing processes. The system 1900 includes the structure of the 1940 data exchange, which can be used to facilitate communication between the client(s) 1910 and the server(s) 1920. The client(s) 1910 conveniently connected to one or more storage devices 1950 customer data, which can be used to save information locally on the client(s) 1910. Also, the server(s) 1920 conveniently connected to one or more storage devices 1930 data server, which can be used to save information locally on the servers 1920.

According Fig typical environment 2000 for the implementation of the various parties claimed the invention includes a computer 2012. The 2012 computer includes a processor 2014, system memory 2016 and the system bus 2018. The system bus 2018 connects the system components, including (but not only) system memory 2016 processor 2014. The processor 2014 may be any of various available processors. Architecture dual microprocessors and other multi-processor architectures may also be used as a processor 2014.

The system bus 2018 may be any of several types of structures(s) of the tire, including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any of a variety of bus architectures, including (but not only) W is well industry standard (ISA), the bus, a microchannel architecture (MCA), extended ISA (EISA), integrated drive electronics (IDE), local bus Association standards in the field of video electronics (VLB), the bus connection of peripheral components (PCI), universal serial bus (USB), accelerated graphics port (AGP)bus, an International Association of manufacturers of memory cards for personal computers (PCMCIA)standard for high performance serial bus IEEE 1394 (Firewire) and system small computer system interface (SCSI).

System memory 2016 includes volatile memory 2020 and non-volatile memory 2022. The system basic input / output system (BIOS), containing basic routines to transfer information between elements within the computer 2012, for example, at startup, is stored in nonvolatile memory 2022. As an illustration, but not limitation, nonvolatile memory 2022 may include a permanent storage device (ROM), programmable ROM (EPROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. A volatile storage device 2020 includes random access memory (RAM), which acts as an external cache. As an illustration, but not limitation, RAM is available in many forms such as static the RAM (POPS), dynamic RAM (DOSE), synchronous DOSE (SDSU), SDSU with double data rate (DDR SDSU), improved SDSU (ESDRAM), Rambus direct RAM (RDRAM), direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM).

Computer 2012 also includes removable/fixed, volatile/non-volatile storage media storing computer data. Fig illustrates, for example, the drive 2024 on the disks. Drive 2024 disks includes (but not limited to, such devices as storage on magnetic disks, floppy drive, tape drive, Jaz drive, Zip drive, tape drive, LS-100, a flash memory card or Memory Stick. In addition, the drive 2024 disks may include media data storage independently or in combination with other media storage, including (but not only) optical drive such as CD-ROM (CD-ROM), drive to a recordable compact discs (CD-R Drive), the drive to rewritable CDs (CD-RW Drive) or a memory stick reader, digital versatile disks (DVD-ROM). To facilitate the connection of disk storage devices 2024 to the system bus 2018, in a typical embodiment uses a removable or non-removable interface, such as interface 2026.

You should take into account that Fig describes software that acts the quality is TBE intermediary between users and the basic computer resources, described in the appropriate operational environment 2000. Such software includes the operating system 2028. Operating system 2028, which can be stored on the drive 2024 on disks that are used to control and allocate resources of the computing system 2012. System application 2030 advantage of resource management by the operating system through 2028 software modules 2032 and 2034 program data stored either in system memory 2016, or on the drive 2024 on the disks. You should take into account that the claimed invention can be implemented with various operating systems or combinations of operating systems.

The user enters commands or information into the computer 2012 through device(s) 2036 input. Device 2036 entry include (but not only) pointing device such as a mouse, trackball, pen, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, card, TV tuner, digital camera, digital video camera, web camera, etc. These and other input devices are connected to the processor 2014 via the system bus through 2018 interface(s) port(s) 2038. Interface(s) port(s) 2038 includes, for example, a serial port, a parallel port, game port Universalny serial bus (USB). Device(s) 2040 o uses(comply) with some of the same types of ports that the device(s) 2036 input. Thus, for example, the USB port can be used to provide input into the computer 2012, and to output information from computer 2012 on the device 2040 output. Adapter 2042 output is provided to illustrate that there are some devices 2040 output (such as monitors, speakers, and printers, among other devices 2040 output that require special adapters. Adapters 2042 output include, as an illustration, but not limitation, video and sound cards, which provide a means of connection between the device 2040 output and the system bus 2018. It should be noted that other devices and/or systems are devices provide opportunities for both input and output, such as a remote(s) computer(s) 2044.

The 2012 computer may operate in a networked environment using logical connections to one or more remote computers, such as remote computers 2044. Remote computer 2044 may be a personal computer, a server, a router, a network PC, a workstation, a device based on a microprocessor, a peer device or other standard network node and the like, and in a typical embodiment, includes most or all of the elements described relative to whom is lutera 2012. For brevity, only the memory device 2046 storage is illustrated with a remote computer(s) 2044. The remote computer 2044 logically connected to the computer 2012 through a network interface 2048 and thus physically connected through the connecting 2050 communication. Network interface 2048 comprises a wired and/or wireless data network, such as a local area network (LAN) and wide area network (WAN). Technology LAN include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ring, etc. WAN Technologies include, but not limited to, point-to-point links, circuit switched network channels, such as digital network integrated services (ISDN) and their variants, network with packet switching and digital subscriber line (DSL).

Connection(I) 2050 communication means(ut) hardware/software used(s) to connect the network interface 2048 bus 2018. Although the connection 2050 connection is shown for purposes of illustrative clarity inside your computer 2012, it can also be external to computer 2012. Hardware/software necessary for connection to the network interface 2048 include (only for typical purposes of internal and external technologies such as modems, including modems on a regular phone with the data lines, cable modems and DSL modems, ISDN adapters, and Ethernet cards.

What has been described above includes examples of the invention. Of course, it is impossible to describe every probable combination of components or methods for descriptive purposes stated of the invention, but an ordinary specialist in the art may recognize that many further combinations and permutations of the invention are valid. Therefore, the claimed invention is intended to include all such transformations, modifications and variations that covered entity and the scope of the attached claims.

In particular, concerning the various functions performed by the above described components, devices, circuits, systems, and the like, the terms (including a reference to a "means")used to describe these components are intended to correspond, unless otherwise stated) any component which performs the specified function of the described component (for example, a functional equivalent), even if it is not structurally equivalent of the disclosed structure which performs the function illustrated in this document are typical aspects of the claimed of the invention. In this sense, you should also take into account that the invention includes a system, and that the same machine-readable medium, having Mashinostroenie instructions for performing actions and/or events of the various methods declared the invention.

In addition, although the specific characteristic of the invention may have been disclosed relative to only one of several implementations, this feature can be combined with one or more signs of other implementations that can be demanding and preferably for any given or particular application. Moreover, to the extent that the terms "includes" and "including" and their variants are used in either the detailed description or the claims, these terms should be interpreted inclusive way, similarly, the term "contains".

1. The system, which contributes to the determination of one or more destination user, containing
interface component that accepts input data, and
evaluation of destination that a probabilistic way predicts one or more destinations for the movement based on at least one of a priori data and probability based at least in part on the received input data,
the probability associated with the set of possible destinations, which provide information about the target location, and the likelihood is the probability of effective the Toms movement and/or the possibility of time travel,
the probability of time travel based at least in part on the estimated time to the destination and at the last time of movement, and
the likelihood of effective motion refers to the calculated efficiency of movement associated with the set of possible destinations, and the calculated efficiency based on the ratio of how much time you will spend driving to the eventual destination, if the user follows an effective route to how much time actually passed.

2. The system according to claim 1, in which the a priori data are a priori data about personal destinations related to the previous set of destinations of the user.

3. The system according to claim 1, in which the a priori data are a priori data about the properties area to the probability that the cell is a destination based on the property location in the cell.

4. The system according to claim 1, in which the evaluation of the destination uses the Bayes rule to probabilistic way to predict one or more destinations.

5. The system according to claim 1, additionally containing the aggregation component that allows selection of at least one of the a priori data about personal destinations, a priori information about the property is estaste, the probability of efficient movement and probability of time travel for the Association to probabilistic way to predict one or more destinations.

6. The system according to claim 1, additionally containing the content component that provides information associated with one or more of the predicted destinations, and the content component provides at least one of the warnings about traffic, construction, security issues ahead of the displayed ads, directions, advice on the route and updates.

7. The system according to claim 6, which additionally contains the configuration component that configures the relevant information provided by the feature based content associated with the user preferences.

8. The system according to claim 1, in which the evaluation of the destination predicting one or more destinations during movement using location data in real time.

9. The system according to claim 1, further containing the component grid, which generates probabilistic grid associated with a map of a geographical location, which is a component of the evaluation of the destination, to predict one or more destinations.

10. The system according to claim 1, additionally containing a component of planning is of the route, which generates the routes likely to be used in the movement to possible destinations, and determines a time estimate associated with the routes.

11. The system according to claim 1, in which the evaluation of the destination combines the analysis of open-world and closed analysis in the prediction location, and the rating component of the destination performs a prediction about the likelihood that the driver will visit not previously observed location, and spatial relationships new locations, specified a priori by the observation locations.

12. Way that contributes to the determination of the destination user, comprising stages, which
form a probabilistic grid associated with geographic locations,
evaluate data associated with movement to determine one or more of a priori data and probability, and
predict one or more destinations related to movement, using a coordinate grid by probabilistic merging of one or more of a priori data and probability.

13. The method according to item 12, optionally containing phase, which selects one or more of a priori data and probability to combine probabilistic way.

14. The method according to item 12, additionally containing the stage at which the predicted one or more destinations in the course of movement.

15. The method according to item 12, optionally containing phase, which provide relevant information associated with one or more predicted destinations.

16. The method according to item 12, optionally containing phase, which form the a priori data based on one or more of a set of previous destinations of the user and the probability that the cell is a destination on the basis of the properties location in the cell.

17. The method according to item 12, optionally containing phase in which the probability of a form based on one or more changes in time before arrival in the possible destination and elapsed time of movement.

18. The system, which contributes to the determination of one or more destination user, containing
means receiving input data associated with at least one or more a priori data and the one or more probabilities, and
means of probabilistic predictions of one or more destinations based on a combination of at least one or more a priori data and the one or more probabilities,
these one or more probabilities associated with the set of possible destinations, which provide information about the target location, and mentioned one or more probabilities are the probability of effective the Pro-motion and/or the possibility of time travel,
the probability of time travel based at least in part on the estimated time to the destination and at the last time of movement, and
the likelihood of effective motion refers to the calculated efficiency of movement associated with the set of possible destinations, and the calculated efficiency based on the ratio of how much time you will spend driving to the eventual destination, if the user follows an effective route to how much time actually passed.



 

Same patents:

FIELD: physics, measurement.

SUBSTANCE: device of information provision enables relevant confirmation of information content which facilitates movement of moving object and is represented by image display unit, even in conditions of vibration affecting image display unit at a level not lower than given value. Equipment includes image display unit mounted in vehicle and allowing display of information facilitating movement of vehicle, vibration sensor detecting vibration equal or exceeding specified level applied to image display unit, and transmitting detection output signal, and operation control unit modifying display mode for information presenting image display by image display unit into information including data content which can be recognised if detection output signal of vibration sensor indicates than image display unit is affected by vibration equal or exceeding specified level for time period longer or equal to specified period.

EFFECT: device of information provision enabling relevant confirmation of information content, facilitating movement of moving object.

8 cl, 6 dwg

FIELD: procedure of traffic control of vehicles.

SUBSTANCE: the method consists in forming of the sequence of the junction and chord of the terminal on the basis of data of the route search, selection of the terminal junction and chord separation by comparison of the sequence of the junction and chord with the terminal map, reproduction of the data on route control on a complicated roads crossing with the use of the selected terminal junction and chord separation, coordination with the map and tracing of the route at driving on the basis of the reproduced data and submitting of the information on control of the traced route to the user. The vehicle navigation instrument for route control on a complicated roads crossing includes a means for forming of the sequence of the junction and chord of the terminal by comparison of the sequence and of the junction and chord with the terminal map, the means for reproduction of the data on route control on a complicated roads crossing with the use of the selected junction and chord, the means for coordination with the map and tracing of the route at driving on the basis of the reproduced data and submitting of the information on control of the traced route to the user. The vehicle navigation system includes a server for transmission of the required route data from the remote source of geographic information and information on road traffic, the means for obtaining of the required route data from the server, the means for obtaining of information on the present location of the vehicle from the GPS satellite, means for selection of information on vehicle driving with the use of the obtained information on location of the vehicle, means for memorizing and storage of the geographic information, means of route search for search of the geographic information stored in the memorizing means, and the control means for reproduction of the data on route control on a complicated roads crossing with the use of the route data obtained from the server and means of the route search, for realization and means of the route search, for realization of route control and submitting of information on route.

EFFECT: enhanced precision in route control by reconfiguration of the presentation of the complicated roads crossing that cannot be represented by one junction on a digital navigation map.

18 cl, 10 dwg

FIELD: physics.

SUBSTANCE: route guidance system includes: a unit for detecting current location; processing apparatus for compiling a list of strips which a list of strips (Ls1) taking into account connection between strips for groups of strips (from Lk1 to Lk3) in road junctions in the road list displaying area; processing apparatus for determining the visualisation region which determines whether the number of strips in the list of strips (Ls1) is greater than the number of strips in the display unit; and apparatus for processing and controlling the display region, which selects predetermined strips in a list of strips (Ls1) and displays selected strips only. Strips which may not be displayed can be deleted.

EFFECT: possibility of displaying a guide map on strips which takes into account connections between the strips, thereby preventing deterioration of visibility of the guide map.

4 cl, 21 dwg

FIELD: physics, navigation.

SUBSTANCE: invention relates to a vehicle navigation system. The navigation system includes a vehicle, an information display (40) fitted in the vehicle, a portable GPS unit (10) and an interface (30) for transmitting data between the portable GPS unit and the information display fitted in the vehicle. The information display (40) is mounted on the vehicle and is visible to the driver. The portable GPS unit (10) includes a GPS sensor for determining location of the GPS unit and a portable information display (20). The portable GPS unit (10) is fitted in a positioning unit in the vehicle such that the portable information display is visible to the driver. Data from the portable GPS unit (10) can be displayed on the information display fitted in the vehicle. In the first version, the portable GPS unit (10) includes a central processing unit (15) for storing several locations. The information display (40) fitted in the vehicle and the portable information display (20) display different information on location of the GPS unit relative the stored locations. An input device (50) is designed for transmitting a signal from the portable GPS unit (10) through the data transmission interface (30). The input device (50) is fitted as an alternative solution on the information display (40) fitted in the vehicle or is fitted such that the driver can operate it without taking hands off vehicle control elements. The input device (50) is designed for transmitting a signal to the portable GPS unit (10) for storing the location of the GPS unit in the central processing unit (15). In the second version, the information display (40) fitted in the vehicle displays data from the portable GPS unit (10) when receiving data from the data transmission interface (30) and displays data from a sensor fitted in the vehicle when the data transmission interface (30) and the portable GPS unit (10) are interrupted.

EFFECT: easy vehicle control.

31 cl, 6 dwg

FIELD: physics; navigation.

SUBSTANCE: invention relates to navigation equipment of vehicles. The proposed navigation device can display directions on a display, receive a video signal from a camera and display a combination of the image from the camera and directions on the display. The device, which is a portable navigation device, includes a built-in camera. The device can provide an option from the menu which enables the user to regulate relative position of the displayed image from the camera with respect to the directions.

EFFECT: using the proposed device, instructions which can be quickly and easily interpreted are displayed for the user.

15 cl, 12 dwg

FIELD: physics; measurement.

SUBSTANCE: invention relates to portable navigation systems particularly for installation in an automobile. The portable personal navigation device is programmed with possibility of linking any function, related to a basic set of functions, with a non-overlapping input sensory area, which is sufficiently large for reliable activation by touching with a finger. The invention is based on understanding that, a set of basic functions can be identified, and can then be reliably selected/activated by touching the input sensory area with a finger, where the input sensory area is sufficiently large for reliable activation. This is especially preferable for a navigation device installed in an automobile, in which the basic functions are those functions which are likely to be activated by the driver when driving the automobile.

EFFECT: design of a portable navigation device with a non-overlapping input sensory area, which is sufficiently large for reliable activation by touching with a finger.

18 cl, 4 dwg

FIELD: physics, measurement.

SUBSTANCE: device of information provision enables relevant confirmation of information content which facilitates movement of moving object and is represented by image display unit, even in conditions of vibration affecting image display unit at a level not lower than given value. Equipment includes image display unit mounted in vehicle and allowing display of information facilitating movement of vehicle, vibration sensor detecting vibration equal or exceeding specified level applied to image display unit, and transmitting detection output signal, and operation control unit modifying display mode for information presenting image display by image display unit into information including data content which can be recognised if detection output signal of vibration sensor indicates than image display unit is affected by vibration equal or exceeding specified level for time period longer or equal to specified period.

EFFECT: device of information provision enabling relevant confirmation of information content, facilitating movement of moving object.

8 cl, 6 dwg

FIELD: physics, measurement.

SUBSTANCE: device of information provision enables relevant confirmation of information content which facilitates movement of moving object and is represented by image display unit, even in conditions of vibration affecting image display unit at a level not lower than given value. Equipment includes image display unit mounted in vehicle and allowing display of information facilitating movement of vehicle, vibration sensor detecting vibration equal or exceeding specified level applied to image display unit, and transmitting detection output signal, and operation control unit modifying display mode for information presenting image display by image display unit into information including data content which can be recognised if detection output signal of vibration sensor indicates than image display unit is affected by vibration equal or exceeding specified level for time period longer or equal to specified period.

EFFECT: device of information provision enabling relevant confirmation of information content, facilitating movement of moving object.

8 cl, 6 dwg

FIELD: physics; measurement.

SUBSTANCE: invention relates to portable navigation systems particularly for installation in an automobile. The portable personal navigation device is programmed with possibility of linking any function, related to a basic set of functions, with a non-overlapping input sensory area, which is sufficiently large for reliable activation by touching with a finger. The invention is based on understanding that, a set of basic functions can be identified, and can then be reliably selected/activated by touching the input sensory area with a finger, where the input sensory area is sufficiently large for reliable activation. This is especially preferable for a navigation device installed in an automobile, in which the basic functions are those functions which are likely to be activated by the driver when driving the automobile.

EFFECT: design of a portable navigation device with a non-overlapping input sensory area, which is sufficiently large for reliable activation by touching with a finger.

18 cl, 4 dwg

FIELD: physics; navigation.

SUBSTANCE: invention relates to navigation equipment of vehicles. The proposed navigation device can display directions on a display, receive a video signal from a camera and display a combination of the image from the camera and directions on the display. The device, which is a portable navigation device, includes a built-in camera. The device can provide an option from the menu which enables the user to regulate relative position of the displayed image from the camera with respect to the directions.

EFFECT: using the proposed device, instructions which can be quickly and easily interpreted are displayed for the user.

15 cl, 12 dwg

FIELD: physics, navigation.

SUBSTANCE: invention relates to a vehicle navigation system. The navigation system includes a vehicle, an information display (40) fitted in the vehicle, a portable GPS unit (10) and an interface (30) for transmitting data between the portable GPS unit and the information display fitted in the vehicle. The information display (40) is mounted on the vehicle and is visible to the driver. The portable GPS unit (10) includes a GPS sensor for determining location of the GPS unit and a portable information display (20). The portable GPS unit (10) is fitted in a positioning unit in the vehicle such that the portable information display is visible to the driver. Data from the portable GPS unit (10) can be displayed on the information display fitted in the vehicle. In the first version, the portable GPS unit (10) includes a central processing unit (15) for storing several locations. The information display (40) fitted in the vehicle and the portable information display (20) display different information on location of the GPS unit relative the stored locations. An input device (50) is designed for transmitting a signal from the portable GPS unit (10) through the data transmission interface (30). The input device (50) is fitted as an alternative solution on the information display (40) fitted in the vehicle or is fitted such that the driver can operate it without taking hands off vehicle control elements. The input device (50) is designed for transmitting a signal to the portable GPS unit (10) for storing the location of the GPS unit in the central processing unit (15). In the second version, the information display (40) fitted in the vehicle displays data from the portable GPS unit (10) when receiving data from the data transmission interface (30) and displays data from a sensor fitted in the vehicle when the data transmission interface (30) and the portable GPS unit (10) are interrupted.

EFFECT: easy vehicle control.

31 cl, 6 dwg

FIELD: physics.

SUBSTANCE: route guidance system includes: a unit for detecting current location; processing apparatus for compiling a list of strips which a list of strips (Ls1) taking into account connection between strips for groups of strips (from Lk1 to Lk3) in road junctions in the road list displaying area; processing apparatus for determining the visualisation region which determines whether the number of strips in the list of strips (Ls1) is greater than the number of strips in the display unit; and apparatus for processing and controlling the display region, which selects predetermined strips in a list of strips (Ls1) and displays selected strips only. Strips which may not be displayed can be deleted.

EFFECT: possibility of displaying a guide map on strips which takes into account connections between the strips, thereby preventing deterioration of visibility of the guide map.

4 cl, 21 dwg

FIELD: physics.

SUBSTANCE: destinations of a trip are based on at least one of a prior and a likelihood based at least in part on the received input data. The destination estimator component can use one or more of a personal destinations prior, time of day and day of week, a ground cover prior, driving efficiency associated with possible locations, and a trip time likelihood to probabilistically predict the destination. In addition, data gathered from a population about the likelihood of visiting previously unvisited locations and the spatial configuration of such locations may be used to enhance the predictions of destinations and routes. The group of inventions make easier probabilistic prediction of destinations.

EFFECT: output of distributions of probabilities on destinations and routes of a user from observations on content and partial trajectories.

FIELD: instrument making.

SUBSTANCE: there introduced are adaptive modules and connections between them, which allow combining current data on road traffic, weather and time with information on driving habits of particular driver. This information is used during profile formation of particular driver. This driver profile is used for adaptation of navigation instructions. Submission of adaptive instructions to a particular driver can contribute to safer road traffic.

EFFECT: enlarging functional capabilities.

19 cl, 6 dwg

FIELD: information technology.

SUBSTANCE: navigation device has apparatus for digital processing of sounds and audible transmission thereof, memory which stores multiple data in form of text pointers and pre-recorded sounds, apparatus for transmitting data between the processor of the device and memory, an operating system for controlling processing and flow of data between the processor and memory, and determining whether said sounds are reproduced in an audible manner through repeated determination of physical conditions comparable with reference values built into the memory, so that satisfaction of the condition causes the device to generate a sound through the pre-recorded sounds stored on the device, or a sound which is digitally presented by a text to speech (TTS) program component by transmitting a text point to it, which corresponds to an event, a combination of the above said, wherein when determining the event which requires reproduction of sound by the TTS program component, the operating system invokes a set of options selected or marked by the device user during its configuration in order to determine the extent to which this event can be audibly indicated.

EFFECT: possibility of audible indication during enroute navigation of user-predefined information.

14 cl, 6 dwg

FIELD: instrument making.

SUBSTANCE: colour pattern and screen content of a navigation device monitor are assessed and generated. At the same time it is defined at least for one specified condition of the surrounding lighting, monitoring and evaluation of a signal that specifies conditions of the surrounding lighting, whether display settings are used for current conditions of the surrounding lighting, and, if required, changes are made to display settings, so that they correspond to current conditions of the surrounding lighting.

EFFECT: expansion of functional capabilities.

34 cl, 9 dwg

FIELD: instrument making.

SUBSTANCE: signals of interruption of audiopresentation are received in one of the versions of the method's implementation. At that, interruption command is executed on the basis of commands supplied immediately from navigation device in response to interruption signals reception. When this operation is being performed, audiorepresentation interruption state is maintained. Therefore, there is the possibility of resetting the state of audiorepresentation process after each interruption command supplied immediately from navigation device.

EFFECT: enlarging functional capabilities.

12 cl

FIELD: procedure of traffic control of vehicles.

SUBSTANCE: the method consists in forming of the sequence of the junction and chord of the terminal on the basis of data of the route search, selection of the terminal junction and chord separation by comparison of the sequence of the junction and chord with the terminal map, reproduction of the data on route control on a complicated roads crossing with the use of the selected terminal junction and chord separation, coordination with the map and tracing of the route at driving on the basis of the reproduced data and submitting of the information on control of the traced route to the user. The vehicle navigation instrument for route control on a complicated roads crossing includes a means for forming of the sequence of the junction and chord of the terminal by comparison of the sequence and of the junction and chord with the terminal map, the means for reproduction of the data on route control on a complicated roads crossing with the use of the selected junction and chord, the means for coordination with the map and tracing of the route at driving on the basis of the reproduced data and submitting of the information on control of the traced route to the user. The vehicle navigation system includes a server for transmission of the required route data from the remote source of geographic information and information on road traffic, the means for obtaining of the required route data from the server, the means for obtaining of information on the present location of the vehicle from the GPS satellite, means for selection of information on vehicle driving with the use of the obtained information on location of the vehicle, means for memorizing and storage of the geographic information, means of route search for search of the geographic information stored in the memorizing means, and the control means for reproduction of the data on route control on a complicated roads crossing with the use of the route data obtained from the server and means of the route search, for realization and means of the route search, for realization of route control and submitting of information on route.

EFFECT: enhanced precision in route control by reconfiguration of the presentation of the complicated roads crossing that cannot be represented by one junction on a digital navigation map.

18 cl, 10 dwg

FIELD: physics, measurement.

SUBSTANCE: device of information provision enables relevant confirmation of information content which facilitates movement of moving object and is represented by image display unit, even in conditions of vibration affecting image display unit at a level not lower than given value. Equipment includes image display unit mounted in vehicle and allowing display of information facilitating movement of vehicle, vibration sensor detecting vibration equal or exceeding specified level applied to image display unit, and transmitting detection output signal, and operation control unit modifying display mode for information presenting image display by image display unit into information including data content which can be recognised if detection output signal of vibration sensor indicates than image display unit is affected by vibration equal or exceeding specified level for time period longer or equal to specified period.

EFFECT: device of information provision enabling relevant confirmation of information content, facilitating movement of moving object.

8 cl, 6 dwg

FIELD: physics.

SUBSTANCE: destinations of a trip are based on at least one of a prior and a likelihood based at least in part on the received input data. The destination estimator component can use one or more of a personal destinations prior, time of day and day of week, a ground cover prior, driving efficiency associated with possible locations, and a trip time likelihood to probabilistically predict the destination. In addition, data gathered from a population about the likelihood of visiting previously unvisited locations and the spatial configuration of such locations may be used to enhance the predictions of destinations and routes. The group of inventions make easier probabilistic prediction of destinations.

EFFECT: output of distributions of probabilities on destinations and routes of a user from observations on content and partial trajectories.

FIELD: transport.

SUBSTANCE: geographical zone of coverage with possible fixed route sections of present costs is combined with higher time-dependent costs. Hence, used of individual portable navigator can go on planning the route, in fact, to whatever location in this country, covered by stored map data base. Where possible, the user can take use of traffic data with time-dependent costs so that to allow for multivehicle pile-up influence with whatever time predictability by means of automatic background process. The user can simply move in direction proposed by navigator.

EFFECT: existence of time-dependent costs data on particular route section is determined before route computation algorithm decides on using particular data type available for all route section from modern digital data bases.

54 cl, 3 dwg

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