WO2017124331A1 - Appareil et procédé de navigation - Google Patents

Appareil et procédé de navigation Download PDF

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Publication number
WO2017124331A1
WO2017124331A1 PCT/CN2016/071472 CN2016071472W WO2017124331A1 WO 2017124331 A1 WO2017124331 A1 WO 2017124331A1 CN 2016071472 W CN2016071472 W CN 2016071472W WO 2017124331 A1 WO2017124331 A1 WO 2017124331A1
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WO
WIPO (PCT)
Prior art keywords
link
route
user
data
familiarity
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PCT/CN2016/071472
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English (en)
Inventor
Jesper Olsen
Jilei Tian
Yang Cao
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Bayerische Motoren Werke Aktiengesellschaft
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Priority to PCT/CN2016/071472 priority Critical patent/WO2017124331A1/fr
Publication of WO2017124331A1 publication Critical patent/WO2017124331A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3484—Personalized, e.g. from learned user behaviour or user-defined profiles

Definitions

  • the present disclosure relates to a navigation apparatus and method.
  • Navigation systems such as satellite navigation systems, are commonly used to determine a general location of a user, to determine a route to a destination location, to navigate to that destination location, or to log a route that was taken by the user.
  • a navigation system uses a global navigation satellite system, such as the Global Positioning System (GPS) , and receives satellite signals broadcasted from multiple satellites.
  • GPS Global Positioning System
  • Navigation systems often include receivers carried by, for example, a user or a vehicle as the user or vehicle moves.
  • These navigation system receivers may receive navigation signals from satellites, wireless fidelity (Wi-Fi) access points (APs) , and inertial sensors; process the navigation signals to obtain navigation data, such as estimated position of the receiver; and provide the navigation data, navigation data log, or navigation instructions based on the navigation data to the user.
  • Navigation instructions may be provided by displaying a navigation route with an approximate receiver (i.e., user or vehicle) location on a map using a map application.
  • the navigation data log can be provided as raw navigation data or navigation data stamped with a differential or an absolute time stamp.
  • GPS navigation systems for vehicles play a vital role in providing direction information and guiding a driver on the route from a start location toward a destination location.
  • GPS navigation systems offer the route planning depending on the real time traffic, and the fastest route or the shortest route may be selected by the GPS navigation systems.
  • One aspect of the disclosure aims to provide a new navigation apparatus and method for providing personalized navigation service.
  • a navigation apparatus is a navigation apparatus comprising:a memory configured to store user’s travel profile data including at least link familiarity data of links, the link familiarity data for each link indicating familiarity for the user with regard to the link; and a processor configured to generate a set of candidate routes in response to a request for navigation, compute a route score for each candidate route based at least on the user’s travel profile data and output navigation information based on the route score, wherein each candidate route comprises one or more links.
  • the link familiarity data of a certain link is based on the number of times the user has visited the certain link in history.
  • the user’s travel profile data further includes node familiarity data of a node, the node familiarity data indicating familiarity for the user with regard to the node, wherein the node connects two or more links.
  • the node familiarity data is calculated as a posterior probability from a prior link to a posterior link connected by the node.
  • the navigation apparatus further comprises an interface for receiving the request for navigation from the user, and presenting the navigation information to the user.
  • the interface does not provide voice navigation prompt for a link when the link familiarity data of the link is greater than or equal to a threshold.
  • the navigation apparatus further comprises a location acquiring device for providing location data indicating a current location of the apparatus.
  • the processor is further configured to update the driver’s travel profile data based on the driver’s travel history recorded during the navigation.
  • the navigation information which is output includes the route with the minimum route score.
  • the route score is a cost traversing through the route, and a value of the route score is in negative correlation with a value of the link familiarity data.
  • it is switchable between a first mode in which the user prefers a familiar route and a second mode in which the user prefers an unfamiliar route in response to the user’s input.
  • a value of the route score in the first mode is in negative correlation with a value of the link familiar data, and in the second mode a value of the route score is in positive correlation with a value of the link familiar data.
  • the route score for each candidate route is computed based further on at least one of a length of the link, an estimated time of arrival, a traffic status, a distance to a desired place, and popularity of a place.
  • one or more weights are determined based on the link familiarity data of the one or more links, and the route score is obtained by applying the one or more weights to one or more values computed based on at least one of a length of the link, an estimated time of arrival, a traffic status, a distance to a desired place, and popularity of a place.
  • a navigation method is a navigation method comprising: obtaining user’s travel profile data including at least link familiarity data of links, the link familiarity data for each link indicating familiarity for the user with regard to the link; and generating a set of candidate routes in response to a request for navigation, computing a route score for each candidate route based at least on the user’s travel profile data and outputting navigation information based on the route score, wherein each candidate route comprises one or more links.
  • a navigation apparatus is a navigation apparatus comprising: a module for storing user’s travel profile data including at least link familiarity data of links, the link familiarity data for each link indicating familiarity for the user with regard to the link; and a module for generating a set of candidate routes in response to a request for navigation, computing a route score for each candidate route based at least on the user’s travel profile data and outputting navigation information based on the route score, wherein each candidate route comprises one or more links.
  • a machine-readable storage medium is a machine-readable storage medium encoded with instructions, that when executed by one or more processors, cause the processor to carry out a process for generating navigation information, the process comprising: obtaining user’s travel profile data including at least link familiarity data of links, the link familiarity data for each link indicating familiarity for the user with regard to the link; and generating a set of candidate routes in response to a request for navigation, computing a route score for each candidate route based at least on the user’s travel profile data and outputting navigation information based on the route score, wherein each candidate route comprises one or more links.
  • FIG. 1 is a simplified schematic diagram illustrating a hardware example of a computing device that can work in accordance with an implementation of the present disclosure
  • FIG. 4 illustrates an exemplary example of a map including links and nodes according to an exemplary implementation of the present disclosure
  • FIG. 5 illustrates an exemplary example of a navigation apparatus according to an exemplary implementation of the present disclosure.
  • the computing device 2000 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.
  • a navigation apparatus to be described in the following may be wholly or at least partially implemented by the computing device 2000 or a similar device or system.
  • the computing device 2000 may comprise elements that are connected with or in communication with a bus 2002, possibly via one or more interfaces.
  • the computing device 2000 may comprise the bus 2002, and one or more processors 2004, one or more input devices 2006 and one or more output devices 2008.
  • the one or more processors 2004 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 2006 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 touchscreen, a microphone and/or a remote control.
  • the output devices 2008 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 2000 may also comprise or be connected with non-transitory storage devices 2010 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.
  • non-transitory storage devices 2010 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
  • the non-transitory storage devices 2010 may be detachable from an interface.
  • the non-transitory storage devices 2010 may have data/instructions/code for implementing the methods and steps which are described.
  • the computing device 2000 may also comprise a communication device 2012.
  • the communication device 2012 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 deviFH, 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.
  • the computing device 2000 (and therefore the navigation apparatus) may be used in a vehicle as an on-vehicle device, so it may be connected to an external device which may be, for example, at least one of 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 2000 may, for example, receive location data and sensor data indicating the traveling situation of the vehicle.
  • the computing device 2000 When the computing device 2000 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.
  • non-transitory storage devices 2010 may have map information and software elements so that the processor 2004 may perform route guidance processing.
  • the output device 2006 may comprise a display for displaying the map, the location mark of the vehicle and also images indicating the traveling situation of the vehicle.
  • the output device 2006 may also comprise a speaker or interface with an ear phone for audio guidance.
  • the bus 2002 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 anon-vehicle device, the bus 2002 may also include a Controller Area Network (CAN) bus or other architectures designed for application on an automobile.
  • ISA Industry Standard Architecture
  • MCA Micro Channel Architecture
  • EISA Enhanced ISA
  • VESA Video Electronics Standards Association
  • PCI Peripheral Component Interconnect
  • CAN Controller Area Network
  • the computing device 2000 may also comprise a working memory 2014, which may be any kind of working memory that may store instructions and/or data useful for the working of the processor 2004, and may comprise but is not limited to a random access memory and/or a read-only memory device.
  • working memory 2014 may be any kind of working memory that may store instructions and/or data useful for the working of the processor 2004, 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 2014, including but are not limited to an operating system 2016, one or more application programs 2018, drivers and/or other data and codes.
  • FIG. 2 is a flowchart showing the navigation method in accordance with an exemplary implementation of the present disclosure.
  • the navigation method shown in FIG. 2 may be performed by a navigation apparatus which is or includes the computing device 2000 as shown in FIG. 1.
  • the flow starts at step S201, wherein user’s travel profile data is obtained.
  • the user’s travel profile can be learnt, for example, from the user’s travel history.
  • the user’s travel profile data may be stored in the storage device 2010, and in operation, the memory 2014 may obtain the user’s travel profile data from the storage device 2010. Alternatively, the user’s travel profile data may also be obtained from a network via the communication device 2012.
  • the non-transitory storage devices 2010 may also store map information and software elements so that the processor 2004 may perform route processing.
  • the map information or software elements can also be stored in other storage devices or be provided via the bus 2002 or other interfaces.
  • the user’s travel profile data at least includes link familiarity data.
  • the storage devices 2010 may also store at least a map including a plurality of links, and each link is described by using a link ID. It is possible to store the links with GPS coordinates of their starts and ends of the respective road segments.
  • FIG. 3 is a schematic view showing an exemplary example of a map including links (link (1) - link (6) ) according to an exemplary implementation of the present disclosure. It can be seen from FIG. 3 that link (1) , for example, may be a road segment between road intersection 1 and road intersection 2, and link (3) , for example, may be a road segment between a road intersection 1 and a point where a traffic constraint changes (e.g. a speed change point 1) .
  • a map may also store other attributes of the links (e.g. public facilities such as restaurants, gas stations, or the like) .
  • the link familiarity data for each link indicates familiarity for the user with regard to the link.
  • the link familiarity data can be relative values and may have numerical ranges as desired. For example, the more familiar the user is with a certain link (road segment) , the larger the value of the link familiarity data will be.
  • the link familiarity data of a link may be defined based at least on the times the user has traversed through the link. Only as examples, the link familiarity data of a link may be defined as the number of times the user has traversed through the link or a value obtained by dividing the number of times the user has visited the link by the sum of visits to all links in history. It is also possible that the link familiarity data of a link may be based further on the times the user has traversed through adjacent or connected links.
  • the link familiarity data of a link can be set to a non-zero value.
  • the link familiarity data of a non-visited link may be set to a small positive floor value, may be set based on the distance to home (e.g., where L is a positive predetermined value and distance (link,home) represents the distance between the link and the user’s home. The closer the link is to the user’s home, the larger the value of link familiarity data is) , or may be set based on the link familiarity data of the closest link which has been visited and the distance thereto.
  • the distance may be a distance normalized by a predetermined distance.
  • the times for which links (1) - (6) have been visited may, for example, be 12, 20, 8, 8, 24 and 28. If the link familiarity data of a certain link is defined as a value obtained by dividing the number of times the user has visited the certain link by the sum of visits to all links in history, the link familiarity data for links (1) - (6) may be calculated as 0.12, 0.20, 0.08, 0.08, 0.24 and 0.28.
  • Step S203 a set of candidate routes are generated in response to a request for navigation.
  • Each of the generated candidate routes includes one or more links.
  • Step S203 may be performed by the processor 2004 by executing program codes corresponding to step S203 which are loaded from the storage device 2010 into the working memory 2014.
  • the request for navigation may, for example, comprise a start location and a destination location, and each of the generated candidate routes is a connected sequence of links from the start location toward the destination location.
  • the navigation apparatus may comprise a location acquiring device such as a GPS receiver or the like configured to provide location data indicating a current location of the navigation apparatus.
  • the request for navigation includes only the destination location, and the start location is generated by the location acquiring device.
  • the request for navigation may only include an intended activity (for example, dining, gas filling, or the like) .
  • the destination may be automatically generated according to the intended activity (for example, the closest restaurant, the closest gas station, or the like) and no specific destination location is necessary to be input by the user.
  • the set of candidate route may be determined by exhausting all possibilities, i.e., starting from the given node and iterating over all potential paths until reaching the destination node, such as breadth-first and depth-first search.
  • the set of candidate route may also be determined by strategically eliminate impossible path, such as A-star and Dijkstra's algorithm.
  • a set of candidate routes including three candidate routes may be generated as follows:
  • Step S205 a route score is computed for each candidate route based at least on the user’s travel profile data.
  • Step S205 may be performed by the processor 2004 by executing program codes corresponding to step S205 which are loaded from the storage device 2010 into the working memory 2014.
  • the route score is a score computed for each route.
  • the route score for a certain route is based at least on the user’s travel profile data, which includes at least the link familiarity data of the links included in the certain route.
  • the route score may be designed so that a higher score is preferred (which may be called “positive score” ) , or may be designed so that a lower score is preferred (usually called “cost” ) .
  • the route score for a certain route may be simply calculated by taking an opposite of the average value of all link familiarity data of the links included in the certain route. In this case, a minimum route score may be preferred if the user prefers a more familiar route, or a maximum route score may be preferred if the user prefers a less familiar route.
  • the route score of each of the candidate route may be calculated for example as follows.
  • Step S207 navigation information is output based on the route score.
  • Step S207 may be performed by the processor 2004 by executing program codes corresponding to step S207 which are loaded from the storage device 2010 into the working memory 2014.
  • the ways for outputting the navigation information is not limited to any specific way.
  • the navigation information which is output includes the route with the minimum route score if the route score is in negative correlation with the user’s preference (in this case, the route score may also be referred to as a route cost) .
  • the optimal route which is the route with a maximum route score if the route score is in positive correlation with the user’s preference.
  • the processor 2004 may find out an optimal route which means a route that may be preferred by the user, and output the optimal route. It is not necessary to output only one optimal route.
  • the user may select one route according to his preferred route score data.
  • the navigation information which is output includes the route with the minimum route score and the route score is in negative correlation with the user’s preference
  • Dijkstra’s algorithm, A-star algorithm or the like can be used to obtain the route with the minimum route score.
  • the present application provides a new idea in which the user’s travel profile data (user’s travel history data) is considered during navigation. Therefore, the user may drive in a route which he feels comfortable.
  • every user may have his own regular pattern of traveling. For example, a user may be particularly familiar with some links but does not like some other links. Generally speaking, driving in familiar areas is more relaxing, more multi-task-flexible, and/or safer. In addition, a user may particularly like some links because there are user’s favorable places in the midway of the links and therefore these links will have higher familiarity data.
  • the route score for each candidate route may be computed not only based on the link familiarity data of the links included in the candidate route, but also based further on at least one of lengths of the links, an estimated time of arrival, a traffic status, a distance to a desired place during the midway, and popularity of a place during the midway. These further factors may be considered in combination with the link familiarity data in any manner.
  • one or more weights may be determined based on the link familiarity data of the one or more links, and the route score may be obtained by applying the one or more weights to one or more values computed based on at least one of a length of the link, an estimated time of arrival, a traffic status, a distance to a desired place, and popularity of a place.
  • the route score of each candidate route may be computed as a route cost for traversing through the candidate route.
  • a candidate with a lower cost will be preferred by the user.
  • a value of the route score may be in negative correlation with a value of the link familiarity data. In other words, if the user prefers a more familiar route, then the higher the link familiarity data of a link is, the lower the route score for traversing through a candidate route including the link will be.
  • a preliminary route score of traversing a candidate route may be computed according to any known method, and a weight calculated based on the familiarities of the links included in the candidate route may be applied to the preliminary route score to obtain the final route score.
  • the route score (route cost) of each of the candidate routes will be calculated as:
  • the candidate route (3) has the lowest preliminary score (estimated time to be spent) , after the familiarities of the links are taken into consideration, the candidate route (3) turns out to have the lowest score because user is more familiar with the links included in the candidate route (3) . If an optimal route is to be recommended in step S207, it is possible to output the candidate route (3) which has the minimum route score, which reflects a best balance between the time to be spent and the familiarity of the route.
  • a plurality of weights may be determined based on each of the link familiarity data of the links included in a certain candidate route, and each of the weights is applied to one value determined according to one further factor of a link.
  • the total route score for a candidate route is calculated by summing up all the score of the links included in the candidate route.
  • the total route score of the candidate route may, for example, be represented as
  • score route (route (n) ) ⁇ i score link (link (i) ) ...expression (1)
  • score routs (route (n) ) is the total route score of the n-th candidate route
  • score link (link (i) ) is a link score of the i-th link denoted by link (i)
  • i represents the index of the links included in the route.
  • the score of link (i) can be defined as
  • f p may be a personalized cost function for link (i) .
  • time (link (i) ) is an estimated time to be spent on the i-th link
  • e is the natural logarithm
  • familiarity (link (i) ) is the link familiarity data for the i-th link
  • e - ⁇ *familiarity (link (i) ) is a weight determined based on the link familiarity data for the i-th link
  • ⁇ is the weight coefficient to control the strength of the weighting by the link familiarity data and therefore to control the balance between the familiarity factor and other factors.
  • f p (link (i) ) can also be defined similarly as:
  • length (link (i) ) is the length of the i-th link.
  • f p (link (i) ) can also be defined similarly as the following so that both times and lengths may be considered in the route score:
  • each link is considered not only in terms of time to be spent and/or length, but also in terms of whether the user is familiar with the link. In this way, the balance between the user’s preference on other factors (for example, a shorter driving time) , and the user’s preference on a more familiar route may be achieved.
  • the user’s travel profile includes link familiarity data of links included in a route
  • a route score is computed based at least on the link familiarity data
  • the user’s travel profile data may further include node familiarity data of a node.
  • FIG. 4 illustrates an exemplary example of a map including links and nodes according to an exemplary implementation of the present disclosure.
  • a node herein means a point which connects two or more links.
  • a node may be a road intersection at which links (road sections) intersect.
  • a node may also be a point where a traffic constraint changes (e.g. a speed change point) .
  • the node familiarity data indicate familiarity for the user with regard to the node.
  • the node familiarity data may be calculated as, for example, a posterior probability from a prior link to a posterior link connected by the node.
  • each node has a personal transition probability from one link to another link.
  • the node familiarity data can be learnt from the user’s travel history and calculated as, for example but are not limited to, a posterior probability from a prior link (link (a) ) to a posterior link (link (b) ) connected by the node.
  • the posterior probability represents the transition possibility when the user chooses which link the user prefers to traverse through at the node.
  • the total route score of the candidate route may include a term of a node score calculated based on the node familiarity data of the nodes of the candidate route and a term of a link score calculated based on the link familiarity data of the links of the candidate route.
  • the term of a node score and the term of a link score may be summed or may be multiplied with each other, or may be combined in other ways.
  • the total route score of the candidate route can be represented as
  • score route (route (n) ) ⁇ i [score link (link (i) ) +w*score node (node (j) ) ] ,
  • the transition score of the node (j) can be defined as
  • link (k) and link (i) are connected by node (j)
  • link (k) ) represents the transition probability from link (k) to link (i) of the user according to the user’s travel history.
  • the route score may be designed also in negative correlation with the node familiarity data.
  • score link (link (i) ) may be defined as,for example but is not limited to
  • f n (x) can be defined as, for example but is not limited to
  • step S205 a specific example of determining the route score in step S205 will be described in detail. It is to be noted that the specific example is only exemplary and does not intend to constitute any limitation.
  • score route (route (n) ) ⁇ i [score link (link (i) ) +w*score node (node (j) ) ] ,
  • link score for the i-th link (link (i) ) may be calculated according to the following expression:
  • f p (link (i) ) is the personalized cost for link (i) and may be calculated according to expression (3) , (4) or (5)
  • length (link (i) ) is the length of link (i) (which may be stored in the storage device 2010 in advance)
  • time (link (i) ) is the estimated traversing time of link (i)
  • f o (other factors) is a cost associated with other attributes such as whether there is a toll in the link (i) , the safety of link (i) , etc
  • parameters ⁇ 1 ⁇ 4 can be set according to the navigation scheme as the user selects.
  • ⁇ 1 , ⁇ 3 and ⁇ 4 may be set to a value smaller than 1 (0 for example) , and ⁇ 2 may be set to 1.
  • ⁇ 1 , ⁇ 2 and ⁇ 4 may be set to a value smaller than 1 (0 for example)
  • ⁇ 3 may be set to 1.
  • ⁇ 2 , ⁇ 3 and ⁇ 4 may be set to a value smaller than 1 (0 for example)
  • ⁇ 1 may be set to 1.
  • time (link (i) ) may be a predetermined empirical value for traveling through link (i) which is stored in the storage device 2010 in advance.
  • time (link (i) ) can be defined as follows.
  • traffic (link (i) ) represents the predicted or real-time traffic condition of the link (i) . If the traffic condition for link (i) is very good, traffic (link (i)) is set to a lower limit such as 1. If there is a serious traffic jam for link (i) , traffic (link (i) ) is set to an upper limit such as 10. For other traffic conditions, link (i) may be set between 1 and 10.
  • type (link (i) ) represents the parameter for the road type of the link (i) , such as city road, highway, country road, ramp or the like. As an example, type (link (i) ) may be set to1 for high way, and may be set to a larger value for a city road. The values of type (link (i)) may be determined according to the historical data.
  • f 0 (other attributes) can be defined as follows:
  • toll (link (i) ) is set to 1 if there is a toll in the midway of the link (i) , and is set to 0 if there is no toll in the midway of the link (i) .
  • unsafery (link (i) ) is set to 1 if there link (i) is unsafe, and is set to 0 if link (i) is safe.
  • transition score node (node (j) ) in expression (6) can be defined as
  • link (k) and link (i) are connected by node (j)
  • link (k) ) represents the personal transition probability the user traverses link (k) and turns to link (i) at node (j)
  • f n is the personalized cost and may be calculated according to expression (9)
  • f a measures the cost due to angle change of two links
  • f b measures the cost of other attribute such as traffic light signal
  • link (k) ) indicates the angle formed between link (i) and link (k) , and may for example be 90°, 180° or 270°
  • ⁇ 1, ⁇ 2, ⁇ 3 are coefficient which can be set according to the navigation scheme as the user selects.
  • f n (x) , f a (x) , f b (x) can be defined but are not limited to as
  • dur red represents the average duration the red light is on
  • dur green represent the average duration the green light is on
  • f b represents the average time cost for the vehicle to wait from link (k) to link (i) due to the red light.
  • it is switchable between a first mode (standard mode) in which the user prefers a familiar route and a second mode (challenge mode) in which the user prefers an unfamiliar route in response to the user’s input.
  • the switching may be made by user inputs via input devices 2006 to change the mode of the navigation to the second mode (challenge mode) , in which unfamiliar route will obtain a relatively low cost and thus will be recommended to the user.
  • the route score is a route cost traversing the candidate route, and a route with a minimum route score is preferred by the user.
  • a value of the route score is in negative correlation with a value of the link familiar data so that a higher familiar data will result in a lower route score and therefore will be preferred by the user.
  • a value of the route score is in positive correlation with a value of the link familiar data so that a lower familiar data will result in a lower route score and therefore will be preferred by the user.
  • all other steps are the same between the first mode and the second mode except for that the negative and the positive correlations may be switched by inverting the sign of the link familiarity data and the node familiarity data.
  • expression (3) may be changed to the following form:
  • the navigation apparatus tends to recommend the route with lower familiarity.
  • all other steps are the same between the first mode and the second mode except for that the negative and the positive correlations may be switched by inverting the value of the link familiarity data and the node familiarity data.
  • the exponent in expression (3) may be inverted and expression (3) may be changed to the following form:
  • exponent in expression (9) may be inverted and expression (9) may be changed to the following form:
  • the navigation apparatus tends to recommend the route with lower familiarity.
  • the navigation apparatus may, for example, further comprise an interface for receiving the request for navigation from the user, and presenting the navigation information to the user.
  • the interface may be implemented with the input device 2006 and the output device 2008.
  • a navigation voice prompt may, for example, be automatically turned on and off depending on user’s need.
  • a voice navigation prompt may be unnecessary when the user is driving on a familiar road segment.
  • the output device 2008 may not provide voice navigation prompt for a link whose link familiarity data is greater than or equal to a threshold and thus the navigation apparatus may cause less distraction to the user.
  • the processor may, for example, be further configured to update the driver’s travel profile data based on the driver’s travel history recorded during the navigation. For example, after the current travel, the link familiarity data for all the links included in the actually traveled route may be updated. More specifically, the link familiarity data for the actually traveled links will be increased. In addition, the node familiarity data for all the nodes which connect the actually traveled links may be updated. More specifically, the node familiarity data for the nodes with the actually made transitions between the links may be increased. In this way, the driver’s travel profile data may be evolved as the user travels, and the user may experienced the personalized navigation according to the driver’s travel profile data up to date.
  • the navigation apparatus 500 comprise module 510 and 520.
  • the module 510 stores user’s travel profile data including at least link familiarity data of links.
  • the link familiarity data for each link indicates familiarity for the user with regard to the link.
  • the module 520 generates a set of candidate routes in response to a request for navigation, computes a route score for each candidate route based at least on the user’s travel profile data and outputs navigation information based on the route score, wherein each candidate route comprises one or more links.
  • Another embodiment of the present invention provides a navigation machine-readable storage medium encoded with instructions.
  • the instructions When executed by one or more processors, the instructions cause the processor to carry out a process for generating navigation information, the process comprising: obtaining user’s travel profile data including at least link familiarity data of links, the link familiarity data for each link indicating familiarity for the user with regard to the link; and generating a set of candidate routes in response to a request for navigation, computing a route score for each candidate route based at least on the user’s travel profile data and outputting navigation information based on the route score, wherein each candidate route comprises one or more links.
  • Instructions for performing the methods and steps described in the above may be comprised in the one or more application programs 2018, and the modules510 and 520 of the aforementioned navigation apparatus 500 may be implemented by the processor 2004 reading and executing the instructions of the one or more application programs 2018. More specifically, the module510 of the aforementioned navigation apparatus 500 may, for example, be implemented by the processor 2004 when executing an application 2018 having instructions to perform the step S201. In addition, the module 520 of the aforementioned navigation apparatus 500 may, for example, be implemented by the processor 2004 when executing an application 2018 having instructions to perform the step S203, S205 and S207. Other modules of the aforementioned navigation apparatus may also, for example, be implemented by the processor 2004 when executing an application 2018 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) 2010 described above, and may be read into the working memory 2014 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.
  • navigation apparatus for example, the navigation apparatus 500, or the navigation system implemented by the computing device 2000
  • components and/or modules of navigation apparatus 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 2000 may also be similarly distributed.
  • navigation apparatus for example, the navigation apparatus 500, or the navigation system implemented by the computing device 2000
  • software e.g., executable instructions encoded on one or more computer-readable mediums
  • hardware e.g., gate level logic or one or more ASICs
  • firmware e.g., one or more microcontrollers with I/O capability and embedded routines for carrying out the functionality described herein

Abstract

L'invention concerne un appareil de navigation (500), comprenant : une mémoire configurée pour stocker des données de profil de déplacement d'utilisateur comprenant au moins des données de connaissance de liaison associées à des liaisons (1, 2, 3, 4, 5, 6), les données de connaissance de liaison pour chaque liaison (1, 2, 3, 4, 5, 6) indiquant la mesure dans laquelle l'utilisateur connaît la liaison (1, 2, 3, 4, 5, 6) ; et un processeur (2004) configuré pour générer un ensemble d'itinéraires candidats en réaction à une demande de navigation, calculer un score d'itinéraire pour chaque itinéraire candidat sur la base au moins des données de profil de déplacement de l'utilisateur et délivrer des informations de navigation sur la base du score d'itinéraire, chaque itinéraire candidat comprenant une ou plusieurs liaisons (1, 2, 3, 4, 5, 6).
PCT/CN2016/071472 2016-01-20 2016-01-20 Appareil et procédé de navigation WO2017124331A1 (fr)

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