CN110431377B

CN110431377B - Method, apparatus and computer readable medium for providing optimized location information

Info

Publication number: CN110431377B
Application number: CN201780088572.1A
Authority: CN
Inventors: 周碧云; M·塞德尔
Original assignee: Bayerische Motoren Werke AG
Current assignee: Bayerische Motoren Werke AG
Priority date: 2017-04-19
Filing date: 2017-04-19
Publication date: 2023-04-28
Anticipated expiration: 2037-04-19
Also published as: WO2018191885A1; CN110431377A

Abstract

The invention discloses a method, apparatus and computer readable medium for providing optimized location information of a destination to a driver. In an exemplary embodiment, a computer-implemented method of providing optimized location information of a destination to a driver includes: obtaining information indicating a location of a destination and a personalized route finding model of a driver; obtaining the current position and the driving direction of a driver; generating destination location information that is personalized optimized for a driver based on information indicative of a location of the destination and a personalized route finding model of the driver, and based on a current location and a driving direction of the driver; and informing the driver of the optimized position information.

Description

Method, apparatus and computer readable medium for providing optimized location information

Technical Field

The present disclosure relates generally to the field of location information optimization, and more particularly to a method, apparatus, and computer readable medium for providing optimized destination location information to a driver.

Background

On Demand Mobile (ODM) services have rapidly expanded. When a driver receives a request and provides a corresponding service to a user, the current ODM system provides the driver with access point information automatically obtained by or input from the user device. Such guest point information may be converted by the ODM system into a house number based location and/or a point of interest based location. The driver may need to contact the client one or more times to further clarify the access point information.

Disclosure of Invention

Aspects of the present disclosure relate to providing methods, apparatus, and computer-readable media for providing optimized destination location information to a driver using a personalized way finding model of the driver.

According to a first exemplary embodiment of the present disclosure, there is provided a computer-implemented method of providing optimized destination location information to a driver, the method comprising: obtaining information indicating a location of a destination and a personalized route finding model of a driver; generating destination location information that is personalized optimized for the driver based on information indicative of the location of the destination and a personalized routing model of the driver; and informing the driver of the optimized destination location information.

In an example of the present embodiment, the optimized destination location information may include at least one of the following information for representing a destination: the method includes the steps of determining a location of the house based on a house numbering system, a location of the point of interest based on left and right information, azimuth based information, landmark based information, street name based information, and route information of the destination.

According to the invention, the method further comprises: the current position and the driving direction of the driver are obtained, wherein optimized destination position information is generated based on the current position and the driving direction of the driver in addition to information indicating the position of the destination and the personalized route finding model of the driver.

In another example of this embodiment, the personalized way finding model may include an environment-centric style or a self-centric style for the way finding.

In another example of the present embodiment, the environment-centered style or the self-centered style for the road finding may be determined by a score of a psychological factor of the driver.

In another example of the present embodiment, the score of the psychological factor may be estimated by considering the driver's cognitive map, sense of direction, gender, cultural background, and/or age.

In another example of the present embodiment, in the case where the personalized way finding model includes an environment-centric style, the optimized location information may include at least one of a house numbering system-based location and an azimuth-based information for representing the destination; and in the case where the personalized way finding model includes a self-centering style, the optimized location information may include at least one of a point-of-interest-based location, left-right-based information, landmark-based information for representing the destination.

In another example of this embodiment, the personalized way finding model may include a driver's familiarity with the destination.

In another example of the present embodiment, in a case where the familiarity of the driver with the destination is equal to or higher than a predetermined threshold, the optimized location information may include at least one of a location based on the house numbering system and a location based on the point of interest; and in the case where the familiarity of the driver with the destination is lower than a predetermined threshold, the optimized location information may include at least one of azimuth-based information, left-right-based information, landmark-based information for representing the destination.

In another example of this embodiment, the personalized way finding model may include the type of structure of the city in which the driver resides.

In another example of the present embodiment, in the case where the structure type of the city is a mesh type, the optimized location information may include at least landmark-based information for representing a destination; and in case that the structure type of the city is a skew type, the optimized location information may include at least street name-based information for representing the destination.

In another example of this embodiment, the optimized location information may include route information to the destination, which may include one or more landmarks or points of interest along the route from the current location to the destination, accompanied by left-right information or bearing information.

In another example of the present embodiment, the route information may include left and right information in a case where the personalized route finding model includes a self-centering style for route finding, and the route information may include azimuth information in a case where the personalized route finding model includes an environment-centering style for route finding.

In another example of the present embodiment, the personalized way finding model of the driver may be obtained automatically by performing data mining of the driver and/or analyzing a side-writing of the driver.

According to a second exemplary embodiment of the present disclosure, there is provided an apparatus for providing optimized destination location information to a driver, the apparatus including: a unit configured to obtain information indicative of a location of a destination and a personalized route finding model of a driver; a unit configured to obtain a current position and a traveling direction of the driver; a unit configured to generate destination location information personalized optimized for a driver based on location information indicating a destination and information of a personalized route finding model of the driver and based on a current location and a driving direction of the driver; and means for informing the driver of the optimized destination location information.

According to a third exemplary embodiment of the present disclosure, there is provided an apparatus for providing optimized destination location information to a driver, the apparatus including: one or more processors; and one or more memories configured to store a series of computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform any of the methods described above.

According to a fourth exemplary embodiment of the present disclosure, a non-transitory computer-readable medium is provided having instructions stored thereon, which when executed by one or more processors, cause the one or more processors to perform any one of the methods described above.

Drawings

The above and other aspects and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the disclosure. Note that the drawings are not necessarily drawn to scale.

Fig. 1 illustrates a flowchart of a method for providing optimized destination location information to a driver according to an exemplary embodiment of the present disclosure.

Fig. 2 illustrates an exemplary usage scenario showing detailed map information of a driver, a destination, and the like, according to an exemplary embodiment of the present disclosure.

Fig. 3 illustrates a block diagram of an apparatus for providing optimized destination location information to a driver according to an exemplary embodiment of the present disclosure.

Fig. 4 illustrates a general hardware environment in which the present disclosure may be applied, according to an exemplary embodiment of the present disclosure.

Detailed Description

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the exemplary embodiments. It will be apparent, however, to one skilled in the art, that the embodiments may be practiced without some or all of these specific details. In other exemplary embodiments, widely known structures or process steps have not been described in detail in order to avoid unnecessarily obscuring the concepts of the present disclosure.

The term "route finding" as used throughout the specification includes all ways in which a person self-orients in a physical space and navigates between places. Furthermore, due to individual differences, people will have different routing models. Thus, the present disclosure obtains a personalized way finding model for each driver, and provides each driver with different location information based on his/her personalized way finding model, so as to facilitate easy understanding and arrival at a destination.

The term "optimized location information" as used throughout the specification means location information that is personalized optimized for the driver. For example, the optimized location information may be represented in a manner that is preferable and easily understood by the driver, i.e., in accordance with the driver's personalized route-finding model.

The term "house numbering system" as used throughout the specification relates to a system that gives each building in a street or area a unique number, purposely making it easier to locate a particular building, e.g. "road a No. 123".

The term "point of interest (POI)" as used throughout the specification means that someone can find a useful or interesting special place location, which is widely used in navigation systems to represent a special point in a map, such as a hotel, camp, gas station or any other category used in navigation systems. POIs typically specify the latitude and longitude of the POI at a minimum, assuming some map data. Typically includes a name or description for the POI and may also be accompanied by other information such as latitude or telephone numbers.

The term "landmark" as used throughout the specification relates to identifiable natural or artificial features that are easily noticeable and can be used to determine your location or the location of other buildings or features, such as, but not limited to, skyscrapers, shopping malls, parks, tourist attractions, etc.

The term "cognitive map" relates to the type of mental performance that serves an individual to acquire, encode, store, recall, and decode information about their relative location. The cognitive map may also be expressed as "how people might see their location in the world".

The term "a and/or B" as used throughout the specification relates to "a", "B" or "a and B".

Referring initially to fig. 1, a flow chart of a method 100 for providing optimized destination location information to a driver is shown in accordance with an exemplary embodiment of the present disclosure. The steps of method 100 described below are intended to be exemplary. In some embodiments, the method may be implemented with one or more additional steps not described and/or without one or more steps described. Additionally, the order of method steps as illustrated in fig. 1 and described below is not intended to be limiting. In some embodiments, the methods may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, digital circuitry designed to process information, analog circuitry designed to process information, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more modules that perform some or all of the method steps in response to instructions stored electronically on an electronic storage medium. The one or more processing modules may include one or more devices configured by hardware, firmware, and/or software to be specifically designed to perform one or more method steps.

As shown in fig. 1, in step 110, in an embodiment, information indicating a location of a destination and a personalized route-finding model of a driver may be obtained.

As an example, when the driver provides the ODM service through the ODM service platform, the destination may be a passenger's access point. In this case, the passenger manually inputs his/her location or a desired access point at his/her electronic device, or automatically confirms his/her location through, for example, the GPS function of the electronic device. Then, the service platform converts the received access point information into a standard house numbering system-based location (e.g., "road a No. 123") and/or a point-of-interest-based location (e.g., "X restaurant"), as widely used in the respective map data, which may be regarded as information indicating the location of the destination, as shown in fig. 2 described later. Alternatively, the service platform may forward the entered guest point information directly to a driver-side device (e.g., a driver's portable electronic device, a central console driving a car, etc.) as information indicating the location of the destination, and the driver-side device may then selectively convert the received guest point information to a standard house numbering system based location and/or a point of interest based location.

It should be noted that the destination is not limited to the access point described above, but may be any location where the driver will go. For example, in some cases, the driver may enter information indicating the location of his/her destination at his/her electronic device (e.g., smart phone, etc.) or at a central console of the in-vehicle system. Further, the information indicating the location of the destination is not limited to the above-described location information input by the user, the standard house numbering system-based location, or the point-of-interest-based location, but may be any information as long as it can indicate the location of the destination.

As described above, the personalized way finding model of the driver may instruct the driver how to orient himself in the physical space and how to navigate between places. From the personalized way finding model it can be deduced what type of location information will be preferred for the driver, i.e. easily understood by the driver. With the preferable position information, which will be described later, the driver will arrive at the destination without difficulty. In some cases, the preferred location information may include additional information for clarifying the destination in a manner preferred by the driver, which may greatly assist the driver, especially when he/she is unfamiliar with the destination. In the case where the driver provides the ODM service, the driver can easily and quickly pick up passengers using the preferred location information. Accordingly, the guest receiving method can be improved.

In some embodiments, the personalized way-finding model may describe the way-finding style of the driver from one or more of the various aspects, including psychological factors, personalized factors, environmental factors, and the like.

In an example, from a psychological aspect, the driver can be divided into two styles, namely an environment-centric style and a self-centric style for road finding. As an environment-centric style, azimuth information (e.g., north, south, west, east, etc.) is preferable to the driver. On the other hand, as a self-centering style, the driver preference determines an orientation to himself/herself (e.g., left/right side in a place, left/right turn, etc.) depending on left/right information. In this case, providing more landmarks near or along the route to the destination may be more helpful to the driver if the driver is less directional. Furthermore, in some examples of whether the environment is a center style or a self-centering style, in the case of a female driver, providing more landmarks near or along the route to the destination may be more preferable for the driver.

In an example, whether the driver is ambient or self-centric for the route may be extracted from driver-side writes stored in an ODM service platform, social platform, or other database. Alternatively or additionally, whether the driver is ambient or self-centering for the route may be determined from the score of the psychological factor of the driver. For example, a higher score for a psychological factor may indicate an environmental centric style and a lower score for a psychological factor may indicate a self centric style. In some examples, the score of the psychological factor may be estimated by considering the driver's cognitive map, direction sense, gender, cultural background, and/or age. These factors may be extracted from driver side writes in the ODM service platform, social platform, or other database, and/or may be determined by data mining messages in the social platform, histories in the ODM service platform, and so forth. It should be appreciated that the present invention is not limited to the above examples and that any technique may be used to obtain the desired information. In one example, a higher score of a cognitive map may indicate an environment-centric style and a lower score of a cognitive map may indicate a self-centric style. The cognitive map may determine a center-on style or a self-centering style for the routed environment alone or may be combined with other factors. In general, strong sense of direction (i.e., a higher sense of direction score) means that the environment is a center style, and poor sense of direction (i.e., a lower sense of direction score) means that the environment is a self-center style. If the cognitive map or direction sense is difficult to determine or ambiguous (e.g., its score is in the middle range), the driver's gender, age, cultural background, and/or driver preference will additionally be considered in the score of the driver's psychological factors. These factors may have their own weights depending on, for example, experience or practical application. Regarding gender, in general, females are more likely to be self-centering and rely more on landmarks, while males are more likely to be environmental centering and prefer bearing information. Regarding cultural backgrounds, in general, north-china drivers (e.g., beijing drivers) may be more likely to be environmental-centric and prefer bearing information, while south-china drivers may be more likely to be self-centric and prefer bearing information.

In an example, from a personalized aspect, the personalized routing model may define a driver's familiarity with the destination. Familiarity may be determined from driver side-writing and/or may be determined by analyzing histories in the ODM service platform and/or messages in the social platform. Familiarity may be high if the driver is resident near the destination. Familiarity may be high if the history record shows that most of the driver's itineraries are near the destination, and/or the messages/text in the social platform show that many of the driver's activities are present near the destination. It should be appreciated that the present invention is not limited to the above examples and that any possible technique may be used to obtain a driver's familiarity with the destination.

In another example, from an environmental aspect, the personalized way finding model may define the type of structure of the city in which the driver resides. The city structure may influence the driver's preferences for representing and understanding the address. In some implementations, the urban structure can include a grid type and a skew type. For example, since beijing city has a grid-like city structure, a beijing driver may prefer azimuth information and landmark information. Because Shanghai city has a skewed city structure, shanghai drivers may prefer to represent locations with street names.

It should be noted that the personalized way-finding model is not limited to those aspects described above, and may cover any possible factors/styles, as long as it may indicate the driver's preferences for representing and understanding the destination address.

In step 110, in an example, a personalized way finding model of the driver may be automatically obtained by performing data mining of the driver. For example, data mining may be performed on messages posted by drivers in a social platform, histories in an ODM service platform, and so forth, in order to obtain aspects of a personalized way-finding model. Alternatively or additionally, the personalized way finding model of the driver may be obtained automatically by analyzing the driver's side-writing. The side notes may be established in the ODM service platform and/or an associated social platform. In some cases, the cursive script may include a number of attributes of the driver describing a cognitive map, a sense of direction, gender, cultural background, age, home address, company address, and so forth. For example, a side-write may be established by a questionnaire for the driver.

Next, in step 120 of fig. 1, destination location information optimized for driver personalization may be generated based on information indicative of the location of the destination and the driver's personalized route finding model, in an embodiment.

In an example, the optimized location information may include at least one of the following information for representing the destination: the method includes the steps of determining a location of the house based on a house numbering system, a location of the point of interest based on left and right information, azimuth based information, landmark based information, street name based information, and route information of the destination.

In the case of generating route information to a destination or some other information about a route or the like, the current position and the traveling direction of the driver may also be obtained. Then, optimized location information may be generated based on the current location and driving direction of the driver in addition to information indicating the location of the destination and the personalized route finding model of the driver.

In some embodiments, as described above, the personalized route-finding model may indicate whether the driver is ambient or self-centric for the route-finding. In the case where the personalized way finding model includes an environment-centric style, the optimized location information may include at least one of a house numbering system-based location and location-based information for representing the destination. On the other hand, in the case where the personalized way finding model includes a self-centering style, the optimized location information may include at least one of a point-of-interest-based location, left-right-based information, landmark-based information for representing the destination. The house number of the destination, e.g. "road a No. 123", may be indicated based on the location of the house number system. A special location name of the destination, such as an "X restaurant," may be indicated based on the location of the point of interest. The location-based information may include location information along with a particular location (POI or landmark) and/or distance to the destination. For example, the orientation-based information may be represented as "north of the exhibition hall", "north of the driver 200m", "west of the a building 100m", and so on. The left-right based information may include the left-right information along with a particular location (POI or landmark) and/or distance from the destination. For example, the information based on the left and right may be expressed as "driver left hand side", "center park right side", "center park left side 50m", or the like. The landmark-based information may indicate one or more landmarks near or along the route to the destination, e.g., may be represented as "opposite the exhibition hall", "going straight 100m and through a shopping mall", "turning right after passing through the shopping mall", and so forth. Note that the present invention is not limited to the above examples, and other types of information may be used to clearly represent a destination.

In other embodiments, as described above, the personalized route model may alternatively or additionally indicate a driver's familiarity with the destination. In an example, in case the driver's familiarity with the destination is high, i.e. equal to or higher than a predetermined threshold, the optimized location information may include at least one of a location based on the house numbering system and a location based on the point of interest. On the other hand, in the case where the driver's familiarity with the destination is lower than the predetermined threshold, the optimized location information may include at least one of azimuth-based information, left-right-based information, landmark-based information for representing the destination.

In other embodiments, as described above, the personalized route model may alternatively or additionally indicate the type of structure of the city in which the driver resides. In some examples, where the structural type of the city is a grid-like type, the optimized location information may include at least landmark-based information for representing the destination. And, in case that the structure type of the city is a skew type, the optimized location information may include at least street name-based information for representing the destination. The street name based information may represent the destination with at least two street names, which means that the destination is located near the intersection of these streets. The information based on the street name may be, for example, "road a-road B". In some cases, other information may be provided in addition to the street name-based information for clearly defining the destination, such as house number system-based location, point of interest-based location, left-right-based information, bearing-based information, landmark-based information, and so forth.

In another example, the optimized location information may include route information to the destination, which may include one or more landmarks or points of interest along a path from the current location to the destination, optionally accompanied by side-to-side information or bearing information. In some implementations, the routing information may include left and right information in the case where the personalized way-finding model includes a self-centric style for way-finding, and the routing information may include azimuth information in the case where the personalized way-finding model includes an environment-centric style for way-finding.

In order to facilitate a thorough understanding of the present invention, a specific example to which the present invention applies will now be described in detail with reference to fig. 2. Fig. 2 illustrates an exemplary usage scenario showing detailed map information of a current location of a driver, a destination, and a landmark near the destination and along a path from the driver to the destination according to an exemplary embodiment of the present disclosure.

In fig. 2, a schematic car represents a driver 201, and a pentagram represents a destination. As shown in fig. 2, the house number of the destination is "road a No. 123", and the point-of-interest information of the destination is "X restaurant". The horizontal (west-east) road is road a and the vertical (north-south) road is road B. The driver 201 drives from south to north.

If the personalized way finding model indicates that the driver 201 is in an environment-centric style, the optimized destination location information may include location based house numbering system "road a No. 123", and/or location based information, such as "driver north 200m", etc. If the personalized way finding model further indicates that the driver 201 is very familiar with the destination, the optimized location information may include only the location based on house numbering system "road a No. 123". If the personalized way finding model further indicates that the familiarity of the driver 201 is moderate, i.e., in the mid-range, the optimized location information may include house number "road a No. 123" as well as location-based information, such as "driver north 200m", "driver north east", etc. If driver 201 is unfamiliar with the destination, the optimized location information may additionally include one or more landmarks along with optional orientations, such as "opposite to the exhibition hall," "north of the exhibition hall," "east of the central park," "north of the shopping mall 100m," and so forth. If the driver 201 resides in Shanghai city, the optimized location information may alternatively or additionally include information based on street names, such as "road A-road B".

On the other hand, if the personalized way finding model indicates that the driver 201 is self-centering, the optimized destination location information may include a location "X restaurant" based on the point of interest, and/or left-right based information (such as "driver left-hand side"), and/or landmark based information (such as "opposite the exhibition hall", "center park right side"), and the like. If the personalized way finding model further indicates that the driver 201 is very familiar with the destination, the optimized location information may include only the location "X restaurant" based on the point of interest. If the personalized way finding model further indicates that the familiarity of the driver 201 is moderate, i.e., in the middle range, the optimized location information may include a location "X restaurant" based on the point of interest, and information based on left and right, such as "driver left hand side," etc. If the driver 201 is unfamiliar with the destination, the optimized location information may additionally include one or more landmarks along with optional left-right information, such as "opposite the exhibition hall," "right side of the central park," and so forth.

In some cases, the optimized location information may alternatively or additionally include route information to the destination. If the personalized route finding model indicates that the driver 201 is in an environment-centric style, the route is represented primarily by using the azimuth information, e.g. "go straight north to intersection, then go east". If the personalized way finding model indicates that the driver 201 is self-centering, the route is represented mainly by using left and right information, e.g. "go straight 100m and go through shopping mall, then turn right". If the personalized route-finding model indicates that the driver is unfamiliar with the destination, the route information and/or other location information may include more landmarks.

Note that the above example with respect to fig. 2 is illustrative only and should not be construed as limiting the invention in any way.

Next, in step 130 of fig. 1, in an embodiment, the optimized location information generated as described above may be notified to the driver. In some cases, the optimized location information may be displayed to the driver through a display on his/her electronic device or a console of the car. In other cases, the optimized location information may be communicated to the driver in the form of an audio signal. In some cases, the optimized location information may be sent to the electronic device or console of the car via a wireless or wired network.

With the resulting optimized location information, the driver can easily understand and understand where the destination is. In the case of ODM services, such optimized location information would improve the pickup process between the driver and the passenger. That is, it will reduce the burden on the driver and passengers, reduce passenger waiting time, and the like.

Optionally, the generated optimized location information may also be sent to and confirmed by the user's electronic device, such as a smart phone or the like.

Fig. 3 illustrates a block diagram of an apparatus 300 for providing optimized destination location information to a driver according to an exemplary embodiment of the present disclosure. The blocks of device 300 may be implemented by hardware, software, firmware, or any combination thereof to perform the principles of the present disclosure. Those skilled in the art will appreciate that the blocks described in fig. 3 may be combined or separated into sub-blocks to implement the principles of the present disclosure as described above. Thus, the present description may support any possible combination, separation, or further definition of the blocks described herein.

Referring to fig. 3, an apparatus 300 for providing optimized destination location information to a driver may include: an obtaining unit 301, a generating unit 302, and a notifying unit 303.

The obtaining unit 301 is configured to obtain information indicating the location of the destination and a personalized route finding model of the driver.

The generating unit 302 is configured for generating destination location information optimized for driver personalization based on information indicative of the location of the destination and the driver personalized route model.

The notification unit 303 is configured to notify the driver of the optimized position information.

In an example of this embodiment, the apparatus 300 may be a processor, microprocessor, or the like, and may be provided on the vehicle side, for example in an application on the driver-side device, or at a central console of the in-vehicle system. Alternatively, the apparatus 300 may be provided remotely and may be accessed by a driver-side device via various networks or the like. Alternatively, the device 300 may be provided on or integrated with a server for supporting ODM services between passengers and drivers.

Note that the various elements in the apparatus 300 may be configured to perform the various operations described above in the method 100 of fig. 1, and thus details thereof are omitted herein.

As will be readily appreciated by those skilled in the art, the types, numbers and locations of the above units are not limited to the illustrated embodiments, and other types, numbers and locations may be used as desired. For example, the device 300 may include other units (e.g., a navigation unit).

Fig. 4 illustrates a general hardware environment 400 in which the present disclosure is applicable, according to an example embodiment of the present disclosure.

With reference to fig. 4, a computing device 400, which is an example of a hardware device that may be suitable for use with aspects of the present disclosure, will now be described. Computing device 400 may be any machine configured to perform processes and/or calculations and may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal data assistant, a smart phone, an in-vehicle computer, or any combination thereof. The foregoing apparatus 300 may be implemented, in whole or at least in part, by a computing device 400 or similar device or system.

Computing device 400 may include elements that may be connected to or in communication with bus 402 via one or more interfaces. For example, computing device 400 may include a bus 402, one or more processors 404, one or more input devices 406, and one or more output devices 408. The one or more processors 404 may be any type of processor and may include, but are not limited to, one or more general purpose processors and/or one or more special purpose processors (such as special purpose processing chips). Input device 406 may be any type of device that can input information to a computing device and may include, but is not limited to, a mouse, a keyboard, a touch screen, a microphone, and/or a remote control. Output device 408 may be any type of device that can present information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Computing device 400 may also include or be coupled to non-transitory storage 410, which may be any storage device that is non-transitory and may implement data storage, and may include, but is not limited to, magnetic disk drives, optical storage devices, solid state storage, floppy disks, flexible disks, hard disks, magnetic tape, or any other magnetic medium, optical diskOr any other optical medium, ROM (read only memory), RAM (random access memory), cache and/or any other memory chip or cartridge, and/or any other medium from which a computer may read data, instructions, and/or code. The non-transitory storage 410 may be detachable from the interface. The non-transitory storage 410 may have data/instructions/code for implementing the methods and steps described above. Computing device 400 may also include a communication device 412. The communication device 412 may be any type of device or system that may enable communication with external equipment and/or with a network and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication device, and/or a chipset such as Bluetooth ^TM Devices, 1302.11 devices, wiFi devices, wiMax devices, cellular communication facilities, and the like.

When the computing device 400 is used as an in-vehicle device, it may also be connected to an external device, for example, a GPS receiver, a sensor for sensing different environmental data such as an acceleration sensor, a wheel speed sensor, a gyroscope, or the like. In this way, the computing device 400 may, for example, receive location data as well as sensor data indicative of the driving conditions of the vehicle. When the computing device 400 is used as an in-vehicle device, it may also be connected to other facilities (such as an engine system, a wiper, an anti-lock brake system, etc.) for controlling the running and operation of the vehicle.

Further, the non-transitory storage device 410 may have map information and software elements so that the processor 404 may perform route guidance processing. Further, the output device 406 may include a display for displaying a map, a position marker of the vehicle, and an image indicating a running condition of the vehicle. The output device 406 may also include a speaker or interface with headphones for audio guidance.

Bus 402 can include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. In particular, for in-vehicle devices, bus 402 may also include a Controller Area Network (CAN) bus or other architecture designed for application on an automobile.

Computing device 400 may also include a working memory 414, which may be any type of working memory that may store instructions and/or data usable for the operation of processor 404, and may include, but is not limited to, random access memory and/or read-only memory devices.

The software elements may reside in a working memory 414 including, but not limited to, an operating system 416, one or more application programs 418, drivers, and/or other data and code. Instructions for performing the methods and steps described above may be included in one or more applications 418, and the elements of the apparatus 300 described above may be implemented by the processor 404 reading and executing the instructions of the one or more applications 418. More specifically, the obtaining unit 301 of the foregoing apparatus 300 may be implemented, for example, by the processor 404 when executing the application 418 having instructions to perform step 110 of fig. 1. Furthermore, the generation unit 302 of the apparatus 300 described above may be implemented, for example, by the processor 404 when executing the application 418 having instructions to perform step 120 of fig. 1. Other elements of the foregoing apparatus 300 may also be implemented, for example, by the processor 404 when executing an application 418 having instructions to perform one or more of the various steps described above. Executable code or source code of instructions of the software elements may be stored in a non-transitory computer readable storage medium, such as the storage device 410 described above, and may be read into the working memory 414, possibly with compilation and/or installation. Executable code or source code for the instructions of the software elements may also be downloaded from a remote location.

It should be further appreciated that the components of computing device 400 may be distributed across a network. For example, some processes may be performed using one processor while other processes may be performed by another processor that is remote from the one processor. Other components of computing system 400 may also be similarly distributed. Accordingly, computing device 400 may be interpreted as a distributed computing system that performs processing at multiple locations.

It will be apparent to those skilled in the art from the foregoing embodiments that the present disclosure may be implemented by software having necessary hardware, or by hardware, firmware, or the like. Based on this understanding, embodiments of the present disclosure may be partially embodied in software. The computer software may be stored in a readable storage medium such as a flexible disk, hard disk, optical disk, or flash memory of a computer. The computer software includes a series of instructions for causing a computer (e.g., a personal computer, a service station, or a network terminal) to perform a method, or a portion thereof, according to various embodiments of the present disclosure.

It will also be appreciated that changes may be made according to particular needs. For example, custom hardware may be used, and/or particular elements may be implemented in hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. Further, connections to other computing devices, such as network input/output devices, may be employed. For example, some or all of the disclosed methods and apparatus may be implemented by programming hardware, e.g., programmable logic circuitry including Field Programmable Gate Arrays (FPGAs) and/or Programmable Logic Arrays (PLAs), in an assembler language or in a hardware programming language, such as VERILOG, VHDL, C ++, using logic and algorithms in accordance with the present disclosure.

Although various aspects of the present disclosure have been described thus far with reference to the accompanying drawings, the methods and apparatus described above are merely illustrative examples, and the scope of the present invention is not limited to these aspects, but only by the appended claims and their equivalents. Various elements may be omitted or equivalent elements may be substituted. Furthermore, the steps may be performed in a different order than described in the present disclosure. Moreover, the various elements may be combined in various ways. It is also important that as technology advances, many of the elements described can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A computer-implemented method of providing optimized destination location information to a driver, the method comprising:

obtaining information indicating a location of a destination and a personalized route finding model of a driver;

obtaining the current position and the driving direction of a driver;

generating destination location information that is personalized optimized for the driver based on the location indicative of the destination and information of a personalized route finding model of the driver, and based on the current location and driving direction of the driver; and

the driver is informed of the optimized destination location information.

2. The method of claim 1, wherein the optimized destination location information includes at least one of the following information for representing a destination:

the method may include the steps of determining a location of the house number system, a location of the point of interest, left and right information, azimuth information, landmark information, street name information, and route information of the destination.

3. The method of claim 1, wherein the personalized way finding model comprises an environment-centric style or a self-centric style for way finding.

4. A method according to claim 3, wherein the context for routing is determined to be a centre style or a self-centre style by a score of a psychological factor of the driver.

5. The method of claim 4, wherein the score of the psychological factor is estimated by considering a driver's cognitive map, sense of direction, gender, cultural background, and/or age.

6. The method according to any one of claims 3 to 5, wherein,

in the case where the personalized way finding model includes the environment-centric style, the optimized destination location information includes at least one of a house numbering system-based location and location-based information for representing a destination; and

in the case where the personalized way finding model includes the self-centering style, the optimized destination location information includes at least one of a point-of-interest-based location, left-right-based information, landmark-based information for representing a destination.

7. The method of claim 1, wherein the personalized way finding model includes a driver's familiarity with a destination.

8. The method of claim 7, wherein,

in the case where the driver's familiarity with the destination is equal to or higher than a predetermined threshold, the optimized destination location information includes at least one of a house-numbering-system-based location and a point-of-interest-based location; and

in the case where the driver's familiarity with the destination is lower than a predetermined threshold, the optimized destination location information includes at least one of azimuth-based information, left-right-based information, landmark-based information for representing the destination.

9. The method of claim 1, wherein the personalized way finding model comprises a structural type of a city in which the driver resides.

10. The method of claim 9, wherein,

in case the structural type of the city is a mesh type, the optimized destination location information includes at least landmark-based information for representing a destination; and

in case the structure type of the city is a skew type, the optimized destination location information includes at least street name-based information for representing a destination.

11. The method of claim 1, wherein,

the optimized destination location information includes route information to the destination including one or more landmarks or points of interest along a path from the current location to the destination, along with left-right information or bearing information.

12. The method of claim 11, wherein,

in the case where the personalized way finding model includes a self-centering style for way finding, the route information includes the left and right information, and

in the case where the personalized way finding model includes an environment-centric style for way finding, the route information includes the location information.

13. The method according to claim 1, wherein the personalized way finding model of the driver is obtained automatically by performing data mining of the driver and/or analyzing a sideview of the driver.

14. An apparatus for providing optimized destination location information to a driver, the apparatus comprising:

a unit configured to obtain information indicative of a location of a destination and a personalized route finding model of a driver;

a unit configured to obtain a current position and a traveling direction of the driver;

a unit configured to generate destination location information personalized optimized for a driver based on the location indicative of the destination and information of a personalized route finding model of the driver and based on a current location and a driving direction of the driver; and

a unit configured to inform a driver of the optimized destination location information.

15. An apparatus for providing optimized destination location information to a driver, the apparatus comprising:

one or more processors; and

one or more memories configured to store a series of computer-executable instructions;

wherein the series of computer-executable instructions, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1 to 13.

16. A non-transitory computer-readable medium having instructions stored thereon, which when executed by one or more processors, cause the one or more processors to perform the method of any of claims 1 to 13.