WO2010113552A1 - Serveur de génération de route expert et dispositif de navigation - Google Patents

Serveur de génération de route expert et dispositif de navigation Download PDF

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Publication number
WO2010113552A1
WO2010113552A1 PCT/JP2010/052372 JP2010052372W WO2010113552A1 WO 2010113552 A1 WO2010113552 A1 WO 2010113552A1 JP 2010052372 W JP2010052372 W JP 2010052372W WO 2010113552 A1 WO2010113552 A1 WO 2010113552A1
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WO
WIPO (PCT)
Prior art keywords
route
data
expert
return
vehicle
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Application number
PCT/JP2010/052372
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English (en)
Japanese (ja)
Inventor
大石一樹
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日立ソフトウエアエンジニアリング株式会社
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Publication of WO2010113552A1 publication Critical patent/WO2010113552A1/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
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
    • G09B29/003—Maps
    • G09B29/006—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes
    • G09B29/007—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes using computer methods
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
    • G09B29/10—Map spot or coordinate position indicators; Map reading aids

Definitions

  • the present invention relates to an expert route generation server and a navigation device.
  • a general navigation apparatus has a route search function for calculating a recommended route (hereinafter referred to as “route”) to a destination set by a user and displaying the calculated route on a screen.
  • the route calculation algorithm in the route search function uses the static road link cost in the road data stored in the map database and the weighting cost set for each road type as a basis, and real-time such as VICS and probe data.
  • a dynamic route link cost reflecting the actual traffic situation data stored in traffic information data and past statistical traffic information data is added, and a recommended route that is optimized by a network calculation algorithm such as the Dijkstra method is calculated. ing.
  • the route quality indicating how close to the optimum route desired by the user continues to improve.
  • the route output from the navigation device is still a problem because a route deviating from the optimum route requested by the user may still be output.
  • documents described in Patent Document 1 and Patent Document 2 below are known. Both documents are techniques for automatically tuning a weighting cost set for each road type to be considered at the time of network calculation according to the user by a learning function.
  • the road type frequently used by the user such as comparing the road type before and after the departure, when the user deviates from the initial route calculated by the navigation device, etc.
  • the above learning technique is merely a technology that is closed and evolved inside the navigation device used by the user.
  • the present invention seeks to obtain expert route data that can be shared by a plurality of users, and to provide a technique that can be used effectively.
  • the present invention relates to a car navigation device to be mounted on a vehicle, a navigation device that saves when the own vehicle travels and stores route deviation / return data when the vehicle deviates from the initial route and then returns, and Or, collect the travel locus data and route departure / return data from multiple navigation devices, extract and analyze the travel locus data from the route departure point to the route return point, and then use the recommended route calculated by the navigation device first Is a system that obtains the optimum route suitable for actual road conditions as expert route data.
  • the present invention collects and analyzes the travel locus data information and the route deviation / return data inside the navigation device used by each user by an external expert route data generation system, thereby enabling a plurality of uses. By generating a cycle of generating useful expert route data that can be shared by a user, distributing it to users of the navigation device, and using it when searching for a route, the route quality can be continuously improved for the entire user.
  • a departure is made from a recommended route (hereinafter referred to as “initial route”) that is output first after a new destination is set, and then the initial route is restored
  • a recommended route hereinafter referred to as “initial route”
  • Each departure position and departure date / time, route departure / return data including the return position and return date / time, and travel locus data were acquired from at least one navigation device of at least one vehicle, and the recovery means From the travel locus data, search and extraction means for searching and extracting data of a vehicle that has traveled in a section from the route departure point to the route return point, and from the route departure point to the return point based on the extracted travel data Means for analyzing and obtaining the optimum route of the user, and the result of the analysis is generated as expert route data and distributed to the navigation device.
  • Expert route data generation server characterized in that it comprises means, is provided.
  • the analysis means groups duplicate data sets for a large number of the route departure / return data, and then counts the number of duplicates.
  • the data set with a large number of duplicates is the start / end point of the expert route. It is preferable to analyze as follows.
  • the analysis means uses a route that can be excluded from the expert route because a data set in which the date and time and the number of duplicates change temporarily is likely due to temporary closure. It is good to do so.
  • the present invention provides a map database storing road data in digital format, means for displaying an arbitrary map on the screen using the map database, data acquired from a sensor unit, and the map database.
  • Means for receiving the expert route data from the expert route data generation server With such a configuration, it is possible to analyze the optimum route from the route departure point to the return point, generate a useful route pattern that can be shared by a plurality of users, and provide it to the user.
  • FIG. 2A is a diagram showing the configuration of map data, route data, traffic information data, and travel locus data road data
  • FIG. 2B is an exemplary diagram showing the relationship between date and frequency. It is. It is a figure which shows the example of 1 structure of route deviation / restoration data. It is a functional block diagram which shows the example of 1 structure of the system containing the expert route production
  • FIG. 1 is a diagram showing a configuration example of a navigation apparatus according to an embodiment of the present invention.
  • the navigation apparatus A includes a control unit 1 including a microprocessor (CPU) 1a, a memory 1b such as DRAM, SRAM, etc., and instructions from a user.
  • Data from an input unit 2 for receiving information input, a sensor 3 composed of a vehicle speed sensor, a gyroscope, a GPS receiver, and the like, and a storage medium such as a CD-ROM, DVD-ROM, or HDD storing map data Drive device 4 for reading out data, CD-ROM, DVD-ROM, HDD medium 5 mounted on the drive device 4, and data stored in the navigation device are transmitted to the outside or from the expert route data generation system
  • An interface for receiving provided optimum route data which is a communication unit 6 such as a mobile phone
  • An external storage medium 7 such as a USB memory
  • a display device 8 for displaying a map, a vehicle position, and route information on a screen
  • a receiving unit 9 for receiving traffic information data such as VICS and probe data; have.
  • map data stored in a CD-ROM or DVD-ROM medium mounted on the drive device 4 will be described.
  • the map data is generally stored for each grid-like range called a mesh that is divided in parallel with the latitude and longitude.
  • Each mesh has a unique mesh number, and the map data in the mesh is divided into links at the intersection. That is, the link connects the intersections.
  • a link is formed by a plurality of nodes to represent the shape of the road.
  • Each link has a unique link number in the mesh and a static link cost such as a link length and travel time, and these data are used when searching for a route.
  • the searched route is output as route data.
  • Traffic information data such as VICS and probe data is acquired via the communication unit 6. It has a dynamic link cost such as a link length and travel time in a form corresponding to the link, and this is also used when executing a route search.
  • route calculation data is recorded as part of the map data.
  • This route calculation data represents an evaluation standard used for evaluating each route during route search, and includes at least a link cost, an intersection cost, and a weighting coefficient.
  • the link cost represents the time required for the vehicle to pass through the road, and is set for each road link.
  • the intersection cost represents the time required for the vehicle to pass through the intersection, and is set according to the traveling direction of the vehicle at the intersection.
  • the intersection cost is set to a common value for each intersection on the map.
  • the weighting coefficient is a coefficient for performing weighting reflecting the user's selectivity toward the road type, and is set according to the road type.
  • the navigation device calculates the respective route evaluation values for a plurality of routes based on the above evaluation criteria included in the route calculation data. At this time, for all road links included in each route, a value obtained by multiplying the link cost by a weighting coefficient corresponding to the road type is added, and further, an intersection cost corresponding to the traveling direction for all intersections included in each route. Is added, the route evaluation value for each route is calculated. The route with the smallest route evaluation value is set as the recommended route.
  • the travel locus data is always obtained and stored in the memory 1b. Further, when returning to the initial route after deviating from the route, the deviation data deviating from the initial route and the return data returning thereafter are acquired as route deviation / return data and stored as a pair. At this time, the coordinates of the departure and return of the route, the date and time, the mesh number and the link number of the road link immediately before and immediately after that are recorded. And those data are regularly uploaded to the expert route data generation system mentioned later.
  • FIG. 2 (a) is a diagram showing a typical configuration example of map data, route data, traffic information data, and travel locus data.
  • these data D1 are collected for each mesh from # 0 to #m, and each data for each mesh is, for example, from link # 0 to #n. It consists of link data.
  • Each link data is composed of, for example, link data # 0, a link length, a link cost (required time), and the number of nodes.
  • the number of nodes is composed of node information from the node # 0 coordinates to the node #p coordinates. ing.
  • FIG. 3 is a diagram showing an example of route departure / return data, which is one feature of the technique according to the present embodiment.
  • the route departure / return data D2 is composed of route departure / return data # 0 to route departure / return data #m, and these data are obtained for each route departure / return event as described above.
  • the data is stored and stored.
  • the route departure / return data is composed of a pair of departure information and return information, and is further added with travel locus data shown in FIG.
  • the departure information includes departure point coordinates indicating the coordinates of the departure point, departure date and time, entry link mesh # and link #, exit link mesh # and link #.
  • the return information includes return point coordinates indicating the coordinates of the return point, return date and time, mesh # and link # of the incoming link, mesh # and link # of the outgoing link. As the number of runs increases, the number of route departure / return data increases.
  • FIG. 4 is a functional block diagram showing a configuration example of the expert route data generation system according to the present embodiment.
  • the expert route data generation system includes the navigation device A shown in FIG. 1 and the expert route generation server (center server) B shown in FIG.
  • the expert route generation server (center server) B includes a processing device 10 including a memory such as a microprocessor and a DRAM, an input unit 11 for receiving input of instructions and information from the server center device, a DVD, an HDD, and the like.
  • FIG. 1 shows a drive device (in this case, a DVD drive is used as an example) 12 for reading / writing collected data and analysis data to / from the storage medium, a DVD medium mounted on the drive device 12, and an HDD medium 13.
  • a communication unit 14 for receiving data from the navigation device via the Internet and transmitting (distributing) analysis data, an external storage medium 16 such as a USB memory, and a display device for displaying various data on a screen ( Display) 18.
  • a CPU 10a and a memory 10b for storing various processing programs and the like are provided.
  • FIG. 5 is a flowchart showing a flow of processing for creating expert route data in the expert route data generation system.
  • the expert route data generation system collects the travel locus data and route departure / return data sent from time to time or periodically from the navigation device shown in FIG. 1 and stores them in a recordable DVD or HDD 12.
  • the data collection target of FIG. The data for many vehicles, especially commercial vehicles such as taxis and trucks, is likely to have been made by the driver with a good understanding of the route and correct decisions regarding detours and return. It is preferable to rank by the driver of the car and increase the weight of the data from the skilled person's car. By acquiring a large number of vehicle data, statistical processing is automatically performed, and there is an advantage that the accuracy of expert route data is improved.
  • step 100 the travel locus data D1 and the route deviation / return data D2 (see FIG. 3) stored in the recordable DVD or the HDD 12 are read.
  • step 101 with respect to the route departure / return data pattern, overlapping data sets are grouped, and the number of duplicates is counted. For a data set with a large number of overlapping cases, this means that many users (that is, automobiles each having a navigation device) feel a sense of discomfort in the initial route and are likely to be avoided by the same travel locus.
  • a data set with a large number of duplicates is a taxi driver who is familiar with the traffic characteristics of the area, such as normal “passing places” such as “crossing without opening” and “right turn intersection with long traffic lights”. It is highly likely that this is the start / end point of the “expert route” chosen to avoid the problem.
  • the date and time (particularly time) and the frequency (number of duplicates) rise suddenly temporarily as in P1, and the frequency increases on the next day or the next week. If not, it is estimated that there is a high possibility of temporary closure, for example. Such a case can be said to be a route that can be excluded from the “expert route”. In this way, whether there is regularity such as a specific time period, day of the week, or a specific day in January, or whether it can be expert data depending on whether it is a temporary (sudden) increase in frequency ( The former can be estimated because it has regularity).
  • step 102 for the route departure / return data pattern having a large number of overlapping cases, the travel route data actually traveled in the section of the route traveled without deviating (hereinafter referred to as “travel track data group A”). And a vehicle that deviates and travels on a route other than the first route (hereinafter referred to as “running locus data group B”).
  • step 103 the number of duplicates is counted after grouping duplicates in the traveling locus data group B that has deviated and traveled other than the initial route.
  • a large number of overlapping cases means that the candidate is an expert route candidate, and the travel locus data itself is highly likely to be data indicating a more suitable route (route).
  • step 104 when the cost of the expert route candidate is less than the cost of the travel locus data group A, the average link cost (travel time) is recognized and output as “expert route”.
  • the non-volatile memory stores various link cost tables such as a recommendation cost table, a toll road priority cost table, and a distance priority cost table.
  • the above-described link cost table is a list of distance correction coefficients (additional magnifications) for weighting the link cost set for each link, and each has a different tendency.
  • each link cost table includes, for each road type, a distance correction coefficient “easy to pass” for making it easy to pass through that road type, and a distance correction coefficient “to make it difficult to pass” for that road type.
  • the recommended cost table is set such that a highway and a toll road are prioritized among highways, toll roads, or general roads, and a difference in a distance correction coefficient between road types is set small.
  • the toll road priority cost table gives priority to expressways and toll roads among expressways, toll roads or general roads, and is set to have a large difference in the distance correction coefficient between the expressways and toll roads and general roads. Yes.
  • the same distance correction coefficients are set for highways, toll roads, and ordinary roads.
  • FIG. 6 is a diagram showing a configuration example of expert route data.
  • the expert route data D3 is generated and updated periodically.
  • the expert route data D3 periodically updated on the navigation device side shown in FIG. 1 can be downloaded as appropriate and used in the route search function on the navigation device side.
  • the expert route data D3 is composed of expert route data # 1 to #m.
  • Each expert route data (for example, # 1) includes an application day of the week, an application time zone, an average link cost (required time), Route data (FIG. 2A).
  • Route data (FIG. 2A).
  • expert route data is identified by application day of the week and application time zone to distinguish between sudden special cases and cases that occur regularly. This is because it depends on the application time zone.
  • expert route data where the departure point and return point are both on the initial route, and the current date and time correspond to the applicable date and time, is applied. Search for.
  • the link cost (required time) of the section from the departure point to the return point is compared between the initial route and the expert route, and if the expert data has a smaller link cost (required time), the section portion
  • the travel time can be shortened by replacing the presented route with expert data.
  • FIG. 7 is a diagram showing an example of the expert route according to the present embodiment.
  • the route from A′-B (2 minutes) to B ′ (3 minutes) is changed to the expert route 1A′-B ′ (4 minutes).
  • the time required by replacing the route C′-C (3 minutes) -C ′′ (3 minutes) with the expert route 2C′-C ′′ (4 minutes) Cost can be reduced. Therefore, as shown in the right figure, the route data can be changed to A-A′-B′-C′-C ′′ -D (R2).
  • This expert route becomes a new initial presentation route in a large number of navigation devices, and new travel locus data is always acquired and stored in the memory 1b.
  • the expert route data that can be used to reduce the travel time generated by each of the users of a large number of navigation devices is not limited to the user. It is possible to construct a system that can be used effectively by users. Thereby, smoother movement to the destination is possible.
  • a DRGS Dynamic Route Guidance System
  • CO 2 emissions can be reduced by reducing traffic congestion.
  • the present invention can be used for a car navigation apparatus.

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Abstract

La présente invention concerne un dispositif de navigation automobile monté dans un véhicule, le dispositif de navigation stockant les données de trajectoire de déplacement concernant le véhicule et stockant les données d'écart de route/de retour sur route lorsque le véhicule s'écarte d'une route initiale et y retourne. L'invention concerne également un système collectant les données de trajectoire de déplacement et les données d'écart de route/de retour sur route provenant d'un seul ou de plusieurs dispositifs de navigation, extrayant et analysant les données de trajectoire de déplacement d'un point d'écart de route à un point de retour sur route, et trouvant, comme données de route expert, une route optimale s'ajustant à la situation de route réelle mieux qu'une route recommandée calculée d'abord par le dispositif de navigation. Ainsi, on propose une technique pour trouver et utiliser efficacement les données de route expert que plusieurs utilisateurs peuvent partager.
PCT/JP2010/052372 2009-03-31 2010-02-17 Serveur de génération de route expert et dispositif de navigation WO2010113552A1 (fr)

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JP2009-088176 2009-03-31
JP2009088176A JP2010237178A (ja) 2009-03-31 2009-03-31 エキスパートルート生成サーバ及びナビゲーション装置

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112601929A (zh) * 2018-08-31 2021-04-02 本田技研工业株式会社 路线评价装置

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5584107B2 (ja) * 2010-12-21 2014-09-03 アイシン・エィ・ダブリュ株式会社 ナビゲーション装置、ナビゲーション方法、およびプログラム
GB201318049D0 (en) * 2013-10-11 2013-11-27 Tomtom Int Bv Apparatus and methods of displaying navigation instructions
JP6692740B2 (ja) * 2016-12-20 2020-05-13 ヤフー株式会社 選択装置、選択方法及び選択プログラム

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JP2000088590A (ja) * 1998-09-11 2000-03-31 Toyota Motor Corp ナビゲーション装置
JP2001124578A (ja) * 1999-10-29 2001-05-11 Denso Corp ナビゲーション装置
JP2007003194A (ja) * 2005-06-21 2007-01-11 Matsushita Electric Ind Co Ltd 経路推定装置

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JP4528528B2 (ja) * 2003-01-10 2010-08-18 株式会社日立製作所 ナビサーバ，ナビゲーションの表示方法
JP4680882B2 (ja) * 2006-12-25 2011-05-11 株式会社日立製作所 交通情報処理装置及び交通情報表示装置

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JP2000088590A (ja) * 1998-09-11 2000-03-31 Toyota Motor Corp ナビゲーション装置
JP2001124578A (ja) * 1999-10-29 2001-05-11 Denso Corp ナビゲーション装置
JP2007003194A (ja) * 2005-06-21 2007-01-11 Matsushita Electric Ind Co Ltd 経路推定装置

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112601929A (zh) * 2018-08-31 2021-04-02 本田技研工业株式会社 路线评价装置

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