WO2018191885A1

WO2018191885A1 - Method, apparatus and computer readable medium for providing optimized locational information

Info

Publication number: WO2018191885A1
Application number: PCT/CN2017/081077
Authority: WO
Inventors: Biyun ZHOU; Markus Seidel
Original assignee: Bayerische Motoren Werke Aktiengesellschaft
Priority date: 2017-04-19
Filing date: 2017-04-19
Publication date: 2018-10-25
Also published as: CN110431377B; CN110431377A

Abstract

Method, apparatus and computer readable medium for providing to a driver an optimized locational information of a destination are disclosed. In an exemplary embodiment, a computer-implemented method of providing to a driver an optimized locational information of a destination comprises: obtaining information indicating a location of the destination and individual wayfinding model of the driver; generating a locational information of the destination optimized individually for the driver, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver; and informing the driver of the optimized locational information.

Description

METHOD, APPARATUS AND COMPUTER READABLE MEDIUM FOR PROVIDING OPTIMIZED LOCATIONAL INFORMATION

TECHNICAL FIELD

The present disclosure relates in general to a field of locational information optimization, and in more particular, to a method, apparatus and computer readable medium for providing to a driver an optimized locational information of a destination.

BACKGROUND

The On-Demand Mobility (ODM) service has been expanding rapidly. When a driver receives a request and provides corresponding service for a user, current ODM system provides to the driver a pick-up spot information automatically obtained by the user’s device or input from the user’s device. Such pick-up spot information may be translated by the ODM system into House Number based location and/or Point of Interest based location. The driver may need to contact the client to further clarify the pick-up spot information one or more times.

SUMMARY

An aspect of the present disclosure relates to providing a method, apparatus and computer readable medium for providing to a driver an optimized locational information of a destination by using an individual wayfinding model of the driver.

In accordance with a first exemplary embodiment of the present disclosure, a computer-implemented method of providing to a driver an optimized locational information of a destination is provided, the method comprising: obtaining information indicating a location of the destination and individual wayfinding model of the driver； generating a locational information of the destination optimized individually for the driver, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver； and informing the driver of the optimized locational information.

In an example of the present embodiment, the optimized locational information may comprise at least one of the followings for expressing the destination: a House Number System based location, a Point of Interest based location, a left-right based information, an orientation based information, a landmark based information, a street-name based information, and a routing information to the destination

In another example of the present embodiment, the method may further comprise: obtaining a current location and a travelling direction of the driver, wherein the optimized locational information is generated on the basis of the current location and the travelling direction of the driver in addition to the information indicating the location of the destination and the individual wayfinding model of the driver.

In another example of the present embodiment, the individual wayfinding model may comprise an environment-centered style or a self-centered style for wayfinding.

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

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

In another example of the present embodiment, the optimized locational information may comprise at least one of a House Number System based location and an orientation based information for expressing the destination, in the case that the individual wayfinding model comprises the environment-centered style； and the optimized locational information may comprise at least one of a Point of Interest based location, a left-right based information, a landmark based information for expressing the destination, in the case that the individual wayfinding model comprises the self-centered style.

In another example of the present embodiment, the individual wayfinding model may comprise a familiarity degree of the driver to the destination.

In another example of the present embodiment, the optimized locational information may comprise at least one of a House Number System based location and a Point of Interest based location, in the case that the familiarity degree of the driver to the destination is equal or higher than a predetermined threshold； and the optimized locational information may comprise at least one of an orientation based information, a left-right based information, a landmark based information for expressing the destination, in the case that the familiarity degree of the driver to the destination is lower than the predetermined threshold.

In another example of the present embodiment, the individual wayfinding model may comprise a structure type of a city the driver lives in.

In another example of the present embodiment, the optimized locational information may comprise at least a landmark based information for expressing the destination, in the case that the structure type of the city is a grid-like type； and the optimized locational information may comprise at least a street-name based information for expressing the destination, in the case that the structure type of the city is an askew type.

In another example of the present embodiment, the optimized locational information may comprise a routing information to the destination, which may comprise one or more landmarks or Points of Interest along a path from the current location to the destination, accompanying with left-right information or orientation information.

In another example of the present embodiment, the routing information may comprise the left-right information in the case that the individual wayfinding model comprises a self-centered style for wayfinding, and the routing information may comprise the orientation information in the case that the individual wayfinding model comprises an environment-centered style for wayfinding.

In another example of the present embodiment, the individual wayfinding model of the driver may be obtained automatically by performing data mining for the driver and/or analyzing a profile of the driver.

In accordance with a second exemplary embodiment of the present disclosure, an apparatus for providing to a driver an optimized locational information of a destination is provided, the apparatus comprising: a unit configured for obtaining information indicating a location of the destination and individual wayfinding model of the driver； a unit configured for generating a locational information of the destination optimized individually for the driver, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver； and a unit configured for informing the driver of the optimized locational information.

In accordance with a third exemplary embodiment of the present disclosure, an apparatus for providing to a driver an optimized locational information of a destination is provided, 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 any one of the above-mentioned methods.

In accordance with a fourth exemplary embodiment of the present disclosure, a non-transitory computer readable medium is provided, having instructions stored thereon that, when executed by one or more processors, causing the one or more processors to perform any one of the above-mentioned methods.

BRIEF DESCRIPTION OF THE 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 which illustrate, by way of example, the principles of the present disclosure. Note that the drawings are not necessarily drawn to scale.

Fig. 1 illustrates a flow chart of a method for providing to a driver an optimized locational information of a destination in accordance with an exemplary embodiment of the present disclosure.

Fig. 2 illustrates an exemplary usage scenario showing detailed map information for the driver, the destination, and the like, in accordance with an exemplary embodiment of the present disclosure.

Fig. 3 illustrates a block diagram of an apparatus for providing to a driver an optimized locational information of a destination in accordance with an exemplary embodiment of the present disclosure.

Fig. 4 illustrates a general hardware environment wherein the present disclosure is applicable in accordance with an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

The term “wayfinding” used throughout the specification encompasses all of the ways in which persons orient themselves in physical space and navigate from place to place. And, due to the individual difference, the persons will have different wayfinding models. Thus, the present disclosure obtains an individual wayfinding model for each driver, and provides a differentiated locational information for each driver based on his/her individual wayfinding model, so as to facilitate to easily understand and arrive at the destination.

The term “optimized locational information” used throughout the specification means a locational information optimized individually for the driver. For example, the optimized locational information may be expressed in a preferred and easily understanding way to the driver, i.e., comply with the individual wayfinding model of the driver.

The term “House Number System” used throughout the specification refers to a system of giving a unique number to each building in a street or area, with the intention of making it easier to locate a particular building, e.g., “No. 123, Road A” .

The term “point of interest (POI) ” used throughout the specification means a specific point location that someone may find useful or interesting, which is widely used in navigation systems to represent a specific point in the map, e.g., hotels, campsites, fuel stations or any other categories used in the navigation systems. A POI generally specifies, at minimum, the latitude and longitude of the POI, assuming a certain map datum. A name or description for the POI is usually included, and other information such as altitude or a telephone number may also be attached.

The term “landmark” used throughout the specification refers to recognizable natural or artificial feature that is easily noticed and can be used to judge your position or the position of other buildings or features, for example but not limited to, skyscrapers, shopping malls, parks, tourist attractions, etc.

The term “cognitive map” refers to a type of mental representation which serves individuals to acquire, code, store, recall, and decode information about the relative locations of themselves. The cognitive map can also be described as “how peoples may see their locations in the world” .

The term “A and/or B” used throughout the specification refers to “A” , “B” , or “A and B” .

Referring first to Fig. 1, there illustrates a flow chart of a method 100 for providing to a driver an optimized locational information of a destination in accordance with an exemplary embodiment of the present disclosure. The steps of the method 100 presented below are intended to be illustrative. In some embodiments, method may be accomplished with one or more additional steps not described, and/or without one or more of the steps discussed. Additionally, the order in which the steps of method are illustrated in FIG. 1 and described as below is not intended to be limiting. In some embodiments, method may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit 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 executing some or all of the steps of method in response to instructions stored electronically on an electronic storage medium. The one or more processing modules may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the steps of method.

As shown in Fig. 1, at step 110, in an embodiment, information indicating a location of the destination and individual wayfinding model of the driver may be obtained.

As an example, the destination may be a pick-up spot for the passenger, when the driver is providing the ODM service through an ODM service platform. In this case, the passenger inputs his/her position or the intended pick-up spot manually at his/her electronic device, or confirms his/her position automatically input by e.g. GPS function of the electronic device. Then, the service platform translates the received pick-up spot information into the standard House Number System based location (e.g., “No. 123, Road A” ) and/or Point of Interest based location (e.g., “X restaurant” ) as widely used in various map datum, as shown in e.g. Fig. 2 described later, which can be deemed as the information indicating the location of the destination. Alternatively, the service platform can directly forward the input pick-up spot information, as the information indicating the location of the destination, to the driver-end device (e.g. the driver’s portable electronic device, the central console of the driven car, or the like) , and then the driver-end device can optionally translate the received pick-up spot information into the standard House Number System based location and/or Point of Interest based location.

It should be noted that the destination is not limited to the pick-up spot as discussed above, but can also be any location to which the driver is going. For example, in some cases, the driver may input information indicating the location of his/her destination at his/her electronic device (e.g. smart phone or the like) or at the central console of the in-vehicle system. In addition, the information indicating the location of the destination is not limited to the above-described position information input by the user, standard House Number System based location, or Point of Interest based location, but can be any information as long as it can specify the location of the destination.

As explained above, the individual wayfinding model of the driver may indicate how the driver orients himself/herself in physical space and navigates from place to place. From the individual wayfinding model, it can be derived what kind of locational information would be preferred to the driver, i.e., easily understood to the driver. With the preferred locational information, which will be described later, the driver would reach the destination without effort. In some cases, the preferred locational information may comprise additional information for clarifying the destination in the preferred way to the driver, which may greatly help the driver especially when he/she is not familiar with the destination. In the case that the driver is providing the ODM service, with the preferred locational information, the driver may pick up the passenger easily and quickly. Thus, the pick-up process may be improved.

In some embodiments, the individual wayfinding model may describe the wayfinding style of the driver from one or more of various aspects which comprises the psychological factor, the individual factor, the environmental factor and the like.

In an example, from the psychological aspect, the driver may be classified into two styles, i.e., an environment-centered style and a self-centered style for wayfinding. As the environment-centered style, orientation information (e.g. North, South, West, East, and the like) is preferred to the driver. On the other hand, as the self-centered style, the driver prefers to orient himself/herself relying on left-right information (e.g., on the left/right side of someplace, turning left/right, and the like) . In this case, if the driver has a poor sense of direction, providing more landmarks near the destination or along the route to the destination may be more helpful to the driver. In addition, in some examples, whether as the environment-centered style or as the self-centered style, in the case of a female driver, providing more landmarks near the destination or along the route to the destination may be more preferred to the driver.

In an example, whether the driver is the environment-centered style or the self-centered style for wayfinding may be extracted from a profile of the driver stored in the ODM service platform, social platform or other database. Alternatively or additionally, whether the driver is the environment-centered style or the self-centered style for wayfinding may be determined by a score of psychological factor of the driver. For example, the higher score of the psychological factor may indicate the environment-centered style, and the lower score of the psychological factor may indicate the self-centered style. In some examples, the score of psychological factor may be estimated by considering cognitive map, sense of direction, gender, culture background and/or age of the driver. Those factors can be extracted from the profile of the driver in the ODM service platform, social platform or other database, and/or can be determined by data-mining messages in the social platform, history records in the ODM service platform, and the like. It should be understood that, the invention is not limited to the above examples, and can use any technique to obtain the desired information. In one example, the higher score of the cognitive map may indicate the environment-centered style, and the lower score of the cognitive map may indicate the self-centered style. The cognitive map may solely determine the environment-centered style or the self-centered style for wayfinding, or may be combined with other factors. Generally, the strong sense of direction (i.e., the higher score of sense of direction) means the environment-centered style, and the poor sense of direction (i.e., the lower score of sense of direction) means the self-centered style. If the cognitive map or the sense of direction may be difficult to be determined or may be ambiguous (e.g., its score is in the middle range) , the gender, age, culture background and/or the like of the driver will be additionally considered into the score of the psychological factor of the driver. These factors may have their respective weights according to e.g. the experience or practical application. As to the gender, generally, women are more likely to be self-centered and rely on more landmarks, and men are more likely to be environment-centered and prefer the orientation information. As to the culture background, generally, the driver in the northern China (like Beijing driver) may be more likely to be environment-centered and prefer the orientation information, and the driver in the southern China may be more likely to be self-centered and prefer the orientation information.

In an example, from the individual aspect, the individual wayfinding model may define a familiarity degree of the driver to the destination. The familiarity degree may be determined from the profile of the driver, and/or may be determined by analyzing the history records in the ODM service platform and/or messages in the social platform. If the driver lives near the destination, the familiarity degree may be high. If the history records show most of the driver’s trips are in the vicinity of the destination, and/or the messages/texts in the social platform show many activities of the drivers occur near the destination, the familiarity degree may be high. It should be understood that, the invention is not limited to the above examples, and can use any possible technique to obtain the familiarity degree of the driver to the destination.

In another example, from the environmental aspect, the individual wayfinding model may define a structure type of a city the driver lives in. The city structure may affect the driver’s preference for expressing and understanding the address. In some implementations, the city structure may comprise a grid-like type and an askew type. For example, as Beijing city has a grid-like city structure, the Beijing driver may prefer the orientation information and the landmark information. As Shanghai city has an askew city structure, the Shanghai driver may prefer to express the location with street names.

It should be noted that the individual wayfinding model is not limited to those above-described aspects, and can cover any possible factor/style as long as it can indicate the driver’s preference for expressing and understanding the address of the destination.

At the step 110, in an example, the individual wayfinding model of the driver may be obtained automatically by performing data mining for the driver. For example, the data mining may be performed on messages of the driver posted in the social platform, history records in the ODM service platform, and the like, so as to obtain various aspects of the individual wayfinding model. Alternatively or additionally, the individual wayfinding model of the driver may be obtained automatically by analyzing a profile of the driver. The profile may be established in the ODM service platform and/or the associated social platform. In some cases, the profile may comprise many attributes of the driver, describing the cognitive map, the sense of direction, the gender, the culture background, the age, the home address, the company address, and/or the like. For example, the profile may be established by a questionnaire to the driver.

Next, at step 120 of Fig. 1, in an embodiment, a locational information of the destination optimized individually for the driver may be generated, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver.

In an example, the optimized locational information may comprise at least one of the followings for expressing the destination: a House Number System based location, a Point of Interest based location, a left-right based information, an orientation based information, a landmark based information, a street-name based information, and a routing information to the destination.

In the case of generating the routing information to the destination or some other information regarding the route or the like, a current location and a travelling direction of the driver may also be obtained. Then, the optimized locational information may be generated on the basis of the current location and the travelling direction of the driver in addition to the information indicating the location of the destination and the individual wayfinding model of the driver.

In some implementations, as discussed above, the individual wayfinding model may indicate whether the driver is the environment-centered style or the self-centered style for wayfinding. In the case that the individual wayfinding model comprises the environment-centered style, the optimized locational information may comprise at least one of a House Number System based location and an orientation based information for expressing the destination. On the other hand, in the case that the individual wayfinding model comprises the self-centered style, the optimized locational information may comprise at least one of a Point of Interest based location, a left-right based information, a landmark based information for expressing the destination. The House Number System based location may indicate the House Number of the destination, like “No. 123, Road A” . The Point of Interest based location may indicate the specific location name of the destination, like “X restaurant” . The orientation based information may comprise an orientation information in combination with a specific location (a POI or landmark) and/or a distance to the destination. For example, the orientation based information may be expressed as “north of the Exhibition Hall” , “200m north of the driver” , “100m west of the A building” , or the like. The left-right based information may comprise a left-right information in combination with a specific location (aPOI or landmark) and/or a distance to the destination. For example, the left-right based information may be expressed as “left-hand side of the driver” , “right side of the Central Park” , “50m left side of the Central Park” , or the like. The landmark based information may indicate one or more landmarks near the destination or along the route to the destination, for example, may be expressed as “opposite to the Exhibition Hall” , “go straight 100m and pass the Shopping Mall” , “turn right after passing the Shopping Mall” , or the like. Please be note that, the invention is not limited to the above examples, and can use other kinds of information for clearly expressing the destination.

In other implementations, as discussed above, alternatively or additionally, the individual wayfinding model may indicate the familiarity degree of the driver to the destination. In an example, the optimized locational information may comprise at least one of a House Number System based location and a Point of Interest based location, in the case that the familiarity degree of the driver to the destination is high, i.e., is equal or higher than a predetermined threshold. On the other hand, the optimized locational information may comprise at least one of an orientation based information, a left-right based information, a landmark based information for expressing the destination, in the case that the familiarity degree of the driver to the destination is lower than the predetermined threshold.

In other implementations, as discussed above, alternatively or additionally, the individual wayfinding model may indicate the structure type of the city the driver lives in. In some examples, the optimized locational information may comprise at least a landmark based information for expressing the destination, in the case that the structure type of the city is a grid-like type. And, the optimized locational information may comprise at least a street-name based information for expressing the destination, in the case that the structure type of the city is an askew type. The street-name based information may express the destination with at least two street names, which means the destination locates near the intersection of these streets. The street-name based information may be, for example, “Road A-Road B” . In some cases, in addition to the street-name based information, there may also provide other information for clearly defining the destination, e.g., the House Number System based location, the Point of Interest based location, the left-right based information, the orientation based information, the landmark based information or the like.

In another example, the optimized locational information may comprise a routing information to the destination, which may comprise one or more landmarks or Points of Interest along a path from the current location to the destination, optionally accompanying with left-right information or orientation information. In some implementations, the routing information may comprise the left-right information in the case that the individual wayfinding model comprises a self-centered style for wayfinding, and the routing information may comprise the orientation information in the case that the individual wayfinding model comprises an environment-centered style for wayfinding.

In order to facilitate to understand the present invention thoroughly, now a specific example to which the present invention is applied is described in details with reference to Fig. 2. Fig. 2 illustrates an exemplary usage scenario showing detailed map information for the current location of the driver, the destination, the landmarks near the destination and along the path from the driver to the destination, and the like, in accordance with an exemplary embodiment of the present disclosure.

In Fig. 2, the schematic car represents the driver 201, and the pentagram represents the destination. As shown in Fig. 2, the House Number of the destination is “No. 123, Road A” , and the Point of Interest information of the destination is “X restaurant” . The horizontal (west-east) road is Road A, and the vertical (south-north) road is Road B. The driver 201 is travelling from south to north.

If the individual wayfinding model indicates the driver 201 is the environment-centered style, the optimized locational information for the destination may comprise the House Number System based location “No. 123, Road A” , and/or the orientation based information, like “200m north of the driver” , etc. If the individual wayfinding model further indicates the driver 201 is very familiar with the destination, the optimized locational information can only comprise the House Number System based location “No. 123, Road A” . If the individual wayfinding model further indicates the familiarity degree of the driver 201 is neutral, i.e., in the middle range, the optimized locational information may comprise the House Number “No. 123, Road A” with the orientation based information like “200m north of the driver” , “northeast of the driver” , etc. If the driver 201 is not familiar with the destination, the optimized locational information may additionally comprise one or more landmarks in combination with optionally the orientation, e.g., “opposite to the Exhibition Hall” , “north of the Exhibition Hall” , “east of the Central Park” , “100m north of the Shopping Mall” , and the like. If the driver 201 lives in Shanghai city, the optimized locational information may alternatively or additionally comprise the street-name based information, e.g., “Road A-Road B” .

On the other hand, if the individual wayfinding model indicates the driver 201 is the self-centered style, the optimized locational information for the destination may comprise the Point of Interest based location “X restaurant” , and/or the left-right based information (such as “left-hand side of the driver” ) , and/or the landmark based information (such as “opposite to the Exhibition Hall” , “right side of the Central Park” ) , etc. If the individual wayfinding model further indicates the driver 201 is very familiar with the destination, the optimized locational information can only comprise the Point of Interest based location “X restaurant” . If the individual wayfinding model further indicates the familiarity degree of the driver 201 is neutral, i.e., in the middle range, the optimized locational information may comprise the Point of Interest based location “X restaurant” with the left-right based information like “left-hand side of the driver” , etc. If the driver 201 is not familiar with the destination, the optimized locational information may additionally comprise one or more landmarks in combination with optionally the left-right information, e.g., “opposite to the Exhibition Hall” , “right side of the Central Park” , and the like.

In some cases, the optimized locational information may alternatively or additionally comprise a routing information to the destination. If the individual wayfinding model indicates the driver 201 is the environment-centered style, the routing is expressed by mainly using the orientation information, for example, “go north until the crossroad, and turn east” . If the individual wayfinding model indicates the driver 201 is the self-centered style, the routing is expressed by mainly using the left-right information, for example, “go straight 100m and pass the Shopping Mall, then turn right” . If the individual wayfinding model indicates the driver is not familiar with the destination, the routing information and/or other locational information may comprise more landmarks.

Please be noted that the above examples regarding Fig. 2 are merely illustrative, and would not be deemed as any limit to the present invention.

Next, at step 130 of Fig. 1, in an embodiment, the driver may be informed of the optimized locational information as have been generated above. In some cases, the optimized locational information may be shown to the driver through a display on his/her electronic device or the console of the car. In other cases, the driver may be informed of the optimized locational information in the form of audio signal. In some cases, the optimized locational information may be transmitted to the electronic device or the console of the car via wireless or wired network.

With the generated optimized locational information, the driver may easily understand and clarify where the destination is. In the case of providing the ODM service, such optimized locational information will improve the pick-up process between the driver and the passenger. That is, it will reduce the burdens on the driver and the passenger, reduce the waiting time of the passenger and the like.

Optionally, the generated optimized locational information can also be transmitted to and be confirmed by the user’s electronic device, for example, the smart phone or the like.

Fig. 3 illustrates a block diagram of an apparatus 300 for providing to a driver an optimized locational information of a destination in accordance with an exemplary embodiment of the present disclosure. The blocks of the apparatus 300 may be implemented by hardware, software, firmware, or any combination thereof to carry out the principles of the present disclosure. It is understood by those skilled in the art 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. Therefore, the description herein may support any possible combination or separation or further definition of the blocks described herein.

Referring to Fig. 3, the apparatus 300 for providing to the driver the optimized locational information of the destination may comprise: obtaining unit 301, generating unit 302, and informing unit 303.

The obtaining unit 301 is configured for obtaining information indicating a location of the destination and individual wayfinding model of the driver.

The generating unit 302 is configured for generating a locational information of the destination optimized individually for the driver, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver.

The informing unit 303 is configured for informing the driver of the optimized locational information.

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

Please note that, the respective units in the apparatus 300 can be configured to perform the respective operations as discussed above in the method 100 of Fig. 1, and thus their details are omitted here.

As can be easily understood by those skilled in the art, the types, numbers, and locations of the above units are not limited to the illustrated embodiment, and other types, numbers, and locations may be also used according to the actual requirements. For instance, the apparatus 300 can include other unit (e.g. a navigation unit) .

Fig. 4 illustrates a general hardware environment 400 wherein the present disclosure is applicable in accordance with an exemplary embodiment of the present disclosure.

With reference to FIG. 4, a computing device 400, which is an example of the hardware device that may be applied to the aspects of the present disclosure, will now be described. The computing device 400 may be any machine configured to perform processing and/or calculations, may be but is not limited to a work station, a server, a desktop computer, a laptop computer, a tablet computer, a personal data assistant, a smart phone, an on-vehicle computer or any combination thereof. The aforementioned apparatus 300 may be wholly or at least partially implemented by the computing device 400 or a similar device or system.

The computing device 400 may comprise elements that are connected with or in communication with a bus 402, possibly via one or more interfaces. For example, the computing device 400 may comprise the bus 402, and 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 kinds of processors, and may comprise but are not limited to one or more general-purpose processors and/or one or more special-purpose processors (such as special processing chips) . The input devices 406 may be any kinds of devices that can input information to the computing device, and may comprise but are not limited to a mouse, a keyboard, a touch screen, a microphone and/or a remote control. The output devices 408 may be any kinds of devices that can present information, and may comprise but are not limited to display, a speaker, a video/audio output terminal, a vibrator and/or a printer. The computing device 400 may also comprise or be connected with non-transitory storage devices 410 which may be any storage devices that are non-transitory and can implement data stores, and may comprise but are not limited to a disk drive, an optical storage device, a solid-state storage, a floppy disk, a flexible disk, hard disk, a magnetic tape or any other magnetic medium, a compact disc or any other optical medium, a ROM (Read Only Memory) , a RAM (Random Access Memory) , a cache memory 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 devices 410 may be detachable from an interface. The non-transitory storage devices 410 may have data/instructions/code for implementing the methods and steps which are described above. The computing device 400 may also comprise a communication device 412. The communication device 412 may be any kinds of device or system that can enable communication with external apparatuses and/or with a network, and may comprise but are not limited to a modem, a network card, an infrared communication device, a wireless communication device and/or a chipset such as a Bluetooth^TM device, 1302.11 device, WiFi device, WiMax device, cellular communication facilities and/or the like.

When the computing device 400 is used as an on-vehicle device, it may also be connected to external device, for example, a GPS receiver, sensors for sensing different environmental data such as an acceleration sensor, a wheel speed sensor, a gyroscope and so on. In this way, the computing device 400 may, for example, receive location data and sensor data indicating the travelling situation of the vehicle. When the computing device 400 is used as an on-vehicle device, it may also be connected to other facilities (such as an engine system, a wiper, an anti-lock Braking System or the like) for controlling the traveling and operation of the vehicle.

In addition, the non-transitory storage device 410 may have map information and software elements so that the processor 404 may perform route guidance processing. In addition, the output device 406 may comprise a display for displaying the map, the location mark of the vehicle and also images indicating the travelling situation of the vehicle. The output device 406 may also comprise a speaker or interface with an ear phone for audio guidance.

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

The computing device 400 may also comprise a working memory 414, which may be any kind of working memory that may store instructions and/or data useful for the working of the processor 404, and may comprise but is not limited to a random access memory and/or a read-only memory device.

Software elements may be located in the working memory 414, including but are not limited to an operating system 416, one or more application programs 418, drivers and/or other data and codes. Instructions for performing the methods and steps described in the above may be comprised in the one or more application programs 418, and the units of the aforementioned apparatus 300 may be implemented by the processor 404 reading and executing the instructions of the one or more application programs 418. More specifically, the obtaining unit 301 of the aforementioned apparatus 300 may, for example, be implemented by the processor 404 when executing an application 418 having instructions to perform the step 110 of Fig. 1. In addition, the generating unit 302 of the aforementioned apparatus 300 may, for example, be implemented by the processor 404 when executing an application 418 having instructions to perform the step 120 of Fig. 1. Other units of the aforementioned apparatus 300 may also, for example, be implemented by the processor 404 when executing an application 418 having instructions to perform one or more of the aforementioned respective steps. The executable codes or source codes of the instructions of the software elements may be stored in a non-transitory computer-readable storage medium, such as the storage device (s) 410 described above, and may be read into the working memory 414 possibly with compilation and/or installation. The executable codes or source codes of the instructions of the software elements may also be downloaded from a remote location.

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

Those skilled in the art may clearly know from the above embodiments that the present disclosure may be implemented by software with necessary hardware, or by hardware, firmware and the like. Based on such understanding, the embodiments of the present disclosure may be embodied in part in a software form. The computer software may be stored in a readable storage medium such as a floppy disk, a hard disk, an optical disk or a flash memory of the computer. The computer software comprises a series of instructions to make the computer (e.g., a personal computer, a service station or a network terminal) execute the method or a part thereof according to respective embodiment of the present disclosure.

It should also be appreciated that variations may be made in accordance with specific requirements. For example, customized hardware might also be used, and/or particular elements might be implemented in hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. Further, connection to other computing devices such as network input/output devices may be employed. For example, some or all of the disclosed methods and apparatuses may be implemented by programming hardware (for example, a programmable logic circuitry including field-programmable gate arrays (FPGA) and/or programmable logic arrays (PLA) ) with an assembler language or a hardware programming language (such as VERILOG, VHDL, C++) by using the logic and algorithm according to the present disclosure.

Although aspects of the present disclosures have been described by far with reference to the drawings, the methods and apparatuses described above are merely exemplary examples, and the scope of the present invention is not limited by these aspects, but is only defined by the appended claims and equivalents thereof. Various elements may be omitted or may be substituted by equivalent elements. In addition, the steps may be performed in an order different from what is described in the present disclosures. Furthermore, various elements may be combined in various manners. What is also important is that as the technology evolves, many of the elements described may be substituted by equivalent elements which emerge after the present disclosure.

Claims

A computer-implemented method of providing to a driver an optimized locational information of a destination, the method comprising:

obtaining information indicating a location of the destination and individual wayfinding model of the driver；

generating a locational information of the destination optimized individually for the driver, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver； and

informing the driver of the optimized locational information.
The method of claim 1, wherein the optimized locational information comprises at least one of the followings for expressing the destination:

a House Number System based location, a Point of Interest based location, a left-right based information, an orientation based information, a landmark based information, a street-name based information, and a routing information to the destination.
The method of claim 1, further comprising:

obtaining a current location and a travelling direction of the driver,

wherein the optimized locational information is generated on the basis of the current location and the travelling direction of the driver in addition to the information indicating the location of the destination and the individual wayfinding model of the driver.
The method of claim 1, wherein the individual wayfinding model comprises an environment-centered style or a self-centered style for wayfinding.
The method of claim 4, wherein the environment-centered style or the self-centered style for wayfinding is determined by a score of psychological factor of the driver.
The method of claim 5, wherein the score of psychological factor is estimated by considering cognitive map, sense of direction, gender, culture background and/or age of the driver.
The method of any one of claims 4-6, wherein

the optimized locational information comprises at least one of a House Number System based location and an orientation based information for expressing the destination, in the case that the individual wayfinding model comprises the environment-centered style； and

the optimized locational information comprises at least one of a Point of Interest based location, a left-right based information, a landmark based information for expressing the destination, in the case that the individual wayfinding model comprises the self-centered style.
The method of claim 1, wherein the individual wayfinding model comprises a familiarity degree of the driver to the destination.
The method of claim 8, wherein

the optimized locational information comprises at least one of a House Number System based location and a Point of Interest based location, in the case that the familiarity degree of the driver to the destination is equal or higher than a predetermined threshold； and

the optimized locational information comprises at least one of an orientation based information, a left-right based information, a landmark based information for expressing the destination, in the case that the familiarity degree of the driver to the destination is lower than the predetermined threshold.
The method of claim 1, wherein the individual wayfinding model comprises a structure type of a city the driver lives in.
The method of claim 10, wherein

the optimized locational information comprises at least a landmark based information for expressing the destination, in the case that the structure type of the city is a grid-like type； and

the optimized locational information comprises at least a street-name based information for expressing the destination, in the case that the structure type of the city is an askew type.
The method of claim 3, wherein

the optimized locational information comprises a routing information to the destination, which comprises one or more landmarks or Points of Interest along a path from the current location to the destination, accompanying with left-right information or orientation information.
The method of claim 12, wherein

the routing information comprises the left-right information in the case that the individual wayfinding model comprises a self-centered style for wayfinding, and

the routing information comprises the orientation information in the case that the individual wayfinding model comprises an environment-centered style for wayfinding.
The method of claim 1, wherein the individual wayfinding model of the driver is obtained automatically by performing data mining for the driver and/or analyzing a profile of the driver.
An apparatus for providing to a driver an optimized locational information of a destination, the apparatus comprising:

a unit configured for obtaining information indicating a location of the destination and individual wayfinding model of the driver；

a unit configured for generating a locational information of the destination optimized individually for the driver, on the basis of the information indicating the location of the destination and the individual wayfinding model of the driver； and

a unit configured for informing the driver of the optimized locational information.
An apparatus for providing to a driver an optimized locational information of a destination, 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 one of claims 1-14.
A non-transitory computer readable medium having instructions stored thereon that, when executed by one or more processors, causing the one or more processors to perform the method of any one of claims 1-14.