JP2008058022A - Navigation device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a navigation device which searches precisely for a recommendation route in consideration of traveling speed different for every driver. <P>SOLUTION: When a destination is set, a CPU 41 specifies a mesh or the like including the own car position, determines the minimum slice width and the maximum slice width stored relative to each mesh, then sets the minimum reachable range and the maximum reachable range reachable in each 15 minutes from the present position of a vehicle, and stores them in a RAM 42 (S11-S16). The CPU 41 synthesizes each minimum reachable range and each maximum reachable range by overlapping them, sets an arrival time range at each distance from the own car position, and sets the arrival time range wherein each link is reached from the own car position. Thereafter, the CPU 41 sets a mean value of each link cost 390C of a time zone 380B of each link pertinent to the arrival time range wherein each link is reached as a predicted link cost, and searches for the recommendation route for the destination (S17-S20). <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a navigation device, and more particularly to a navigation device that displays route information on a map of a display device.

  2. Description of the Related Art In recent years, a navigation device is often mounted on a vehicle that provides vehicle travel guidance so that a driver can easily arrive at a desired destination. This navigation device detects the current position of a vehicle with a GPS receiver or the like, acquires map data corresponding to the current position through a recording medium such as a DVD-ROM or HDD, or a network, and displays it on a liquid crystal monitor. It is a device that can. Then, map data including the current position of the vehicle is read from a recording medium or the like, a map image around the current position of the vehicle is drawn based on the map data and displayed on the display device, and the vehicle position mark is overlaid on the map image. You can also see at a glance which point the vehicle is currently driving by scrolling the map image as the vehicle moves, or by fixing the map image to the screen and moving the vehicle position mark I have to.

Here, various navigation devices have been proposed that search for a route that can be reached in the shortest time by performing a route search based on the received traffic information.
For example, the area is divided for each mesh, and the straight line distances to the representative coordinates of each mesh area having each mesh ID from the starting point to the destination are sequentially calculated, and this is used as the reference distance. Then, by dividing the reference distance by the moving speed, the reference moving time to each mesh area is calculated. Then, the reference arrival time to this mesh area is calculated by adding this reference movement time to the departure time. Thereafter, a navigation device configured to obtain traffic data of each link in a time zone including a reference arrival time to the mesh area having the mesh ID and to search for a recommended route connecting the starting point and the destination (For example, refer to Patent Document 1).
Japanese Patent Laying-Open No. 2004-301677 (paragraphs (0009) to (0073), FIGS. 1 to 9)

With the navigation device described in Patent Document 1, it is possible to search for a recommended route with high accuracy using traffic data collected in the past.
However, in general, the driving speed of the driver differs for each driver, and it becomes difficult to accurately estimate the arrival time as the distance increases. Therefore, if the movement time between each mesh area is set uniformly and the arrival time from the departure place to each area is calculated, an error occurs between the arrival time to each area when actually traveling and the calculation is performed. Because traffic information (congestion information, traffic regulation information, etc.) is acquired based on the arrival time to each area, the recommended route using traffic information in a time zone significantly different from the actual arrival time There is a problem of searching.

  Accordingly, the present invention has been made to solve the above-described problems, and provides a navigation device capable of accurately searching for a recommended route in consideration of different driving speeds for each driver. With the goal.

  In order to achieve the above object, a navigation device according to claim 1 is the navigation device (2) mounted on a vehicle, wherein the minimum prediction distance range data and the maximum prediction distance range are predicted for the distance range that the vehicle reaches within a predetermined time. Predicted distance range data storage means (38) for storing data, traffic data storage means (39) for storing traffic data generated based on traffic conditions for each time zone for each link, and the minimum Based on the predicted distance range data, the minimum reachable range setting means (23) for setting the minimum reachable range that the vehicle reaches every predetermined time with the current vehicle position as the center, based on the current time, and the maximum predicted Based on the distance range data, the maximum reachable range for the vehicle to reach every predetermined time with the current vehicle position as the center is set based on the current time. A reach time range predicting means (23) for predicting a reach time range for each distance from the current vehicle position based on the current time based on the minimum reach range and the maximum reach range. 23) and link arrival time range setting means (23) for setting the arrival time range to reach each link based on the position information of each link and the arrival time range predicted by the arrival time range prediction means Based on traffic data corresponding to the arrival time range of each link, predicted traffic data setting means (23) for setting predicted traffic data of each link, and the predicted traffic data set for each link And a search means (23) for searching for a recommended route from the current vehicle position to the destination.

  Further, the navigation device according to claim 2 is the navigation device (2) according to claim 1, wherein the predicted traffic data setting means (23) includes traffic data corresponding to each time zone within the arrival time range. An average value is set as the predicted traffic data.

  According to a third aspect of the present invention, in the navigation device (2) according to the first or second aspect, the minimum predicted distance range data and the maximum predicted distance range data indicate that the vehicle reaches within a predetermined time. It is predicted based on the average value and the standard deviation of each distance range calculated from the statistical data of the distance range.

  A navigation device according to claim 4 is the navigation device (2) according to any one of claims 1 to 3, wherein the predicted distance range data storage means (38) includes the minimum predicted distance range data and the navigation device (2). Maximum predicted distance range data is stored for each map mesh, and the minimum reach range setting means (23) detects a map mesh including the vehicle position, and the minimum prediction corresponding to the detected map mesh. The distance range data is read to set the minimum reachable range, and the maximum reachable range setting means (23) detects the map mesh including the vehicle position, and the maximum predicted distance range corresponding to the detected map mesh Data is read and the maximum reachable range is set.

  The navigation device according to claim 5 is the navigation device (2) according to any one of claims 1 to 4, wherein the minimum predicted distance range data and the maximum predicted distance range data include an area, a season, It is generated for every day of the week, holiday or consecutive holiday.

  In the navigation device according to claim 1 having the above-described configuration, the minimum reachable range in which the vehicle reaches every predetermined time around the current vehicle position is set based on the current predicted time based on the minimum predicted distance range data. The Further, based on the maximum predicted distance range data, a maximum reach range where the vehicle reaches every predetermined time with the current vehicle position as the center is set with reference to the current time. Based on the minimum reachable range and the maximum reachable range, an arrival time range for each distance from the current host vehicle position is predicted based on the current time. Moreover, the arrival time range which reaches | attains each link is set based on each link position information and this estimated arrival time range. Based on the traffic data corresponding to the arrival time range of each link, predicted traffic data for each link is set. Thereafter, a recommended route from the current vehicle position to the destination is searched using the predicted traffic data set for each link.

  Accordingly, the driver can predict the arrival time range for each distance from the current vehicle position based on the minimum reachable range and the maximum reachable range, and set the reachable time range to reach each link based on the predicted reachable range. It is possible to set the arrival time range from the departure place to each link in consideration of different traveling speeds. In addition, by setting the predicted traffic data based on the traffic data corresponding to the arrival time range of each link, it becomes possible to set the predicted traffic data with high accuracy, taking into account the different driving speeds for each driver. In addition, it is possible to accurately predict a road where a traffic jam occurs when the vehicle passes, a road where the traffic jam is eliminated, and the like, and to search for a recommended route accurately.

  In the navigation device according to claim 2, the predicted traffic data setting means sets the average value of the traffic data corresponding to each time zone within the arrival time range as the predicted traffic data. As a result, even if the difference in traffic data corresponding to each time zone within the arrival time range becomes large and discontinuous, this discontinuity can be complemented and the predicted traffic data for the arrival time range of each link Can be set quickly, and the processing time for predicting the arrival time at which the vehicle reaches each link can be shortened.

  In the navigation device according to claim 3, the minimum predicted distance range data and the maximum predicted distance range data are an average value and a standard value of each distance range calculated from statistical data of a distance range that the vehicle reaches within a predetermined time. Because it is predicted based on the deviation, it is possible to accurately set the minimum predicted distance range data and the maximum predicted distance range data based on statistical data, and the arrival time to reach each link from the current vehicle position The range can be calculated accurately.

  In the navigation device according to the fourth aspect, the minimum predicted distance range data and the maximum predicted distance range data are stored for each map mesh. For this reason, the minimum reachable range setting means and the maximum reachable range setting means only need to read out the minimum predicted distance range data and the maximum predicted distance range data of the map mesh including the own vehicle position. The processing can be reduced compared to detecting a link within a predetermined distance and sequentially searching the minimum predicted distance range data and the maximum predicted distance range data corresponding to the link.

  Furthermore, in the navigation device according to the fifth aspect, the minimum predicted distance range data and the maximum predicted distance range data are generated for each factor such as area, season, day of week, holiday, and consecutive holidays. For this reason, locality, timing factors, and temporal factors can be added to the minimum predicted distance range data and the maximum predicted distance range data, so that the accuracy of the minimum predicted distance range data and the maximum predicted distance range data can be improved. Can do.

  Hereinafter, a navigation device according to the present invention will be described in detail with reference to the drawings based on a first embodiment and a second embodiment in which the navigation system is embodied.

  First, a schematic configuration of the navigation system 1 according to the first embodiment will be described with reference to FIG. FIG. 1 is a block diagram illustrating a navigation system 1 according to the first embodiment.

  As shown in FIG. 1, the navigation system 1 according to the first embodiment includes a navigation device 2, update information for updating map information for the navigation device 2, and slice width data 380 (predicted distance range data described later) 4) and an information distribution center 3 that distributes traffic data 390 (see FIG. 6) and a network 4. The navigation device 2 and the information distribution center 3 are configured to be able to transmit and receive various types of information via the network 4.

In addition, a road traffic information center (VICS: registered trademark) 5 is connected to the network 4, and the navigation device 2 and the information distribution center 3 are connected via the network 4 to a traffic control system of the police, the Japan Highway Public Corporation, or the like. It is configured to be able to receive traffic information such as road traffic congestion information and traffic regulation information created by collecting the above information every predetermined time. The traffic information is, for example, detailed information on road traffic information such as road traffic information related to road traffic jams, traffic regulation information due to road construction, construction work, and the like. In the case of road traffic information, the detailed information includes a VICS link ID (to be described later), the actual length of the traffic, the time required to pass through the traffic, the level of traffic (no traffic / congested / separated traffic, etc.), and traffic Vehicle speed, travel time, traveling direction of traffic jam lane, time when traffic jam is expected to be resolved, etc.In the case of traffic regulation information, the duration of VICS link ID, road construction, building construction, etc., which will be described later, no traffic, one-way alternating traffic, The type of traffic regulation such as lane regulation, the time zone of traffic regulation, etc.
The configuration of the navigation device 2 will be described in detail later with reference to FIG.

  As shown in FIG. 1, the information distribution center 3 includes a server 10, a center-side map information database (center-side map information DB) 14 as a map information recording unit connected to the server 10, and a navigation update history information database (update). History information DB) 15, center side traffic information database (center side traffic information DB) 16, center side communication device 17, slice width database (slice width DB) 18 for storing slice width data 380, and traffic data 390 A traffic database (traffic DB) 19 is stored. In addition, the server 10 is based on requests from the navigation device 2 and the arithmetic unit that controls the entire server 10 and the CPU 11 as the control unit, the RAM 12 that is used as a working memory when the CPU 11 performs various arithmetic processes. Of the map information stored in the navigation device 2, the update information for updating the map information of the predetermined area to the new version of the map information is extracted from the center side map information DB 14 and distributed to the navigation device 2. An internal storage device such as a ROM 13 in which various control programs for performing information update processing and the like are recorded is provided. An MPU or the like can be used instead of the CPU 11.

  In the center-side map information DB 14, update map information 14A, which is map information that is the basis for updating the map information created by the information distribution center 3 and stored in the navigation device 2, is classified for each version. Is remembered. Furthermore, update information for updating part or all of the map information stored in the current navigation device 2 to the update map information 14A is also stored. Here, the version is creation time information for specifying the time when the map information is created, and the time when the map information is created can be specified by referring to the version.

The update map information 14A stored in the center-side map information DB 14 stores various information necessary for route guidance and map display by the navigation device 2, for example, for displaying a map. It consists of map display data, route data 48 (see FIG. 3), and the like.
Here, in particular, the map display data is divided into 4 (length 1/2), 16 (1/4), and 64 (1/8) based on a secondary mesh partitioned by 10 km × 10 km. Each unit is configured so that the data amount of each unit is approximately the same level. The smallest 64 division size unit is about 1.25 km square. Each secondary mesh (hereinafter referred to as “mesh”) partitioned by 10 km × 10 km is assigned a mesh ID 380A (see FIG. 4).

  As shown in FIG. 3, the route data 48 includes a header 48A, intersection data relating to each intersection, node data 48B relating to node points, link data 48C relating to roads (links) which are a kind of facility, and a route search. The link cost 48D, coordinate data 48E indicating the coordinates of nodes and links, version 48F, and the like. Further, the route data 48 includes store data related to POI (Point of Interest) such as a store as a kind of facility, search data for searching for a point, and the like.

The header 48A stores the mesh ID number and the like as an area dividing the whole country.
The node data 48B includes actual road junctions (including intersections, T-junctions, etc.), node point coordinates (positions) set for each road according to a radius of curvature, etc. A node attribute indicating whether the node is a node corresponding to an intersection, a connection link number list which is a list of link IDs which are identification numbers of links connected to the node, a node number of a node adjacent to the node via the link A list of adjacent node numbers as a list, data on the height (altitude) of each node point, and the like are recorded.

  The link data 48C includes, for each road link constituting the road (hereinafter referred to as “link”), the width of the road to which the link belongs, the gradient, the cant, the bank, the road surface state, the number of lanes of the road The data indicating the number of lanes decreasing, the width narrowing, the railroad crossing, etc. for the corner, the curvature radius, the intersection, the T-junction, the corner entrance and exit, etc. In addition to roads such as roads, national roads, prefectural roads, narrow streets, etc., and highway automobile national roads, city expressways, general toll roads, toll roads, etc. Is done. Furthermore, regarding toll roads, data relating to entrance roads (rampways), toll gates (interchanges) and the like of toll roads are recorded.

  The link cost 48D is recorded for data used when searching and displaying a route to a set destination. The link cost 48D is a distance of a link that forms a right or left turn or a road when passing through a node, In order to display the cost data used for calculating the weight of each node (hereinafter referred to as “cost”) determined by the road width, the road type, etc., and the route selected by the route search on the map of the liquid crystal display 25. This is a fixed value.

  Further, as store data, data on POIs such as hotels, hospitals, gas stations, parking lots, and tourist facilities in each region are recorded together with IDs that identify the POIs. The center-side map information DB 14 also records voice output data for outputting predetermined information by the speaker 26 of the navigation device 2.

  Then, the information distribution center 3 uses the latest version of the update map information 14A stored in the center-side map information DB 14 at the timing when the request is received from the navigation device 2, and the navigation device 2 uses the latest version of the update map information 14A. Update the map information stored in. Specifically, in the navigation system 1 according to the first embodiment, when there is a distribution request for the update map information 14A from the navigation device 2, the update information for updating to the newest update map information 14A. Is delivered to the navigation device 2. Here, as the update information transmitted to the navigation device 2, all information including new road information for identifying the new road of the newest update map information 14A of the latest version may be transmitted. Necessary minimum information for updating from the map information stored in the current navigation device to the newest update map information 14A (only information on the update portion including the new road information for identifying the new road) It may be sent.

  On the other hand, in the navigation update history information DB 15, information related to the update history in which the map information stored in the navigation device 2 has been updated so far is stored together with the navigation identification ID that identifies the navigation device 2. As the update history, which version of map information is used for each link data or node data that specifically constitutes map information is stored, and a new one is updated every time the map information of the navigation device 2 is updated. Rewritten to update history.

  The center-side traffic information DB 16 stores current traffic information 16A, which is information on traffic congestion on the current road, which is created by collecting traffic information received from the road traffic information center (VICS: registered trademark) 5. Yes. The center-side traffic information DB 16 stores statistical traffic information 16B, which is statistical traffic information related to traffic jams and the like created in the past. This statistical traffic information 16B is the event schedule information such as the scheduled location and the scheduled date and time of events such as festivals, parades, fireworks festivals, etc., for example, roads around stations and large commercial facilities at specific times every day except weekends. Statistical traffic jam information and traffic jam prediction information such as the occurrence of traffic jams or the occurrence of traffic jams during summer holidays may be included on roads around the beach. Further, in the center side traffic information DB 16, every predetermined time in the future (for example, about every 30 minutes from the current time, about every 1 hour) for each traffic jam created based on the current traffic information 16 A and the statistical traffic information 16 B. , Approximately every 2 hours, etc.) of predicted traffic information 16C, such as traffic jam prediction information.

  Then, at the timing when the information distribution center 3 receives a request from the navigation device 2, the information distribution center 3 is connected between the intersections based on the current traffic information 16A, statistical traffic information 16B, and predicted traffic information 16C stored in the center side traffic information DB 16. Select and distribute traffic information.

  The traffic information received from the road traffic information center (VICS: registered trademark) 5 includes a VICS link ID together with information such as type information, position, distance of a traffic jam section, and traffic jam degree. The VICS link ID is an identification number assigned to a VICS link as a travel guide link that is standardized by dividing a road into predetermined intersections. The traffic information also includes information such as the coordinates of the start point and end point in each VICS link, the distance from the start point to the end point, and the like.

Here, the road (link) and the VICS link stored in the center side map information DB 14 are not the same (generally, the road (link) is subdivided more than the VICS link). . Therefore, each road (link) has a conversion table (contrast table) between the road link ID and the VICS link ID given as an identification number, and the corresponding road link ID is specified based on the VICS link ID. Be able to. Therefore, when the navigation apparatus 2 has a conversion table, when a VICS link ID is received from the information distribution center 3 or the road traffic information center (VICS: registered trademark) 5, it is based on the VICS link ID. It is possible to specify a road section where traffic information such as traffic jam information should be displayed.
However, if the navigation device 2 does not have a conversion table, the road section cannot be specified based on the VICS link ID. Therefore, this conversion table is also stored in the center side traffic information DB 16. Thereby, VICS link ID can be converted into road link ID currently used in the navigation apparatus 2, and traffic information can be transmitted.

Further, the information distribution center 3 distributes the slice width data 380 stored in the slice width DB 18 at a timing when a distribution request is received from the navigation device 2.
Here, the slice width data 380 will be described with reference to FIGS.
FIG. 4 is an explanatory diagram for explaining the data structure of the slice width data 380 stored in the slice width DB 18. FIG. 5 is a diagram for explaining the minimum slice width and the maximum slice width.
As shown in FIG. 4, the slice width data 380 includes a mesh ID 380A, a season 380B, a day of the week 380C, and a slice width 380D as a distance range. As described above, the mesh ID 380A is an ID assigned to each mesh that divides the whole country into 10 km squares. The subordinate data of the mesh ID 380A is the season 380B. In the season 380B, the slice width data 380 is divided into spring, summer, autumn, winter, and long holidays. Further, the lower day of the week 380C includes each day of the week and holidays.

In addition, the slice width 380D is stored for each mesh, and the minimum slice as the minimum predicted distance range data in which the minimum value of the distance range in which the vehicle can travel is predicted for 15 minutes as the predetermined time in the mesh. The width and the maximum slice width as the maximum predicted distance range data in which the maximum value of the distance range in which the vehicle can travel in 15 minutes as the predetermined time is 24 hours for each 15-minute time zone. Is stored.
That is, the minimum slice width and the maximum slice width of each slice width 380D are predicted based on factors such as mesh ID 380A (regional characteristics), season 380B, day of the week 380C, and time zone, and further, a different traveling speed for each driver. In the first embodiment, the information distribution center 3 predicts the data based on the VICS signal and data statistically processed based on the probe information collected from each vehicle.

  Specifically, as shown in FIG. 5, first, from the VICS signal corresponding to each link included in the mesh and the probe information from the navigation device 2 mounted on each vehicle, all traveling on each link in the mesh. The average distance L and the standard deviation σ are calculated from the statistical data of each travel distance that arrives within a predetermined time (15 minutes) obtained by multiplying each vehicle speed by a predetermined time (15 minutes). calculate. Then, the value obtained by subtracting the standard deviation σ from the average distance L is set as the minimum value, that is, the minimum reach distance range in which a vehicle having a low traveling speed reaches a predetermined time (15 minutes), and the minimum slice width of the mesh The slice width 380D corresponding to the mesh is stored. Further, a value obtained by adding the standard deviation σ to the average distance L is set as a maximum value, that is, a maximum reach distance range in which a vehicle having a high traveling speed reaches a predetermined time (15 minutes), and a maximum slice width of the mesh, The slice width 380D corresponding to this mesh is stored.

In the first embodiment, the value obtained by subtracting the standard deviation σ from the average distance L is set as the minimum value, and the value obtained by adding the standard deviation σ to the average distance L is set as the maximum value. However, the present invention is not limited to this. For example, a value obtained by subtracting a distance twice the standard deviation σ from the average distance L may be the minimum value, and a value obtained by adding the distance twice the standard deviation σ to the average distance L may be the maximum value. Alternatively, a value obtained by subtracting a distance three times the standard deviation σ from the average distance L may be a minimum value, and a value obtained by adding a distance three times the standard deviation σ to the average distance L may be a maximum value.
In addition, when there are a plurality of peak values in the statistical data of each mileage that reaches within a predetermined time (within 15 minutes), the minimum peak value of each peak value is set as the minimum value, and each peak value The maximum peak value may be set as the maximum value.

In addition, the information distribution center 3 distributes the traffic data 390 stored in the traffic DB 19 at a timing when a request is made from the navigation device 2.
Here, the traffic data 390 will be described with reference to FIG.
FIG. 6 is an explanatory diagram for explaining the data structure of the traffic data 390 stored in the traffic DB 19.
As shown in FIG. 6, the traffic data 390 is generated for each mesh ID 380A, for example, and has a link cost 390C for the link ID 390A of each link for each time zone 390B. The time zone 390B is set every 15 minutes and has the same division as the time zone (eg, “0:00” to “0:14”) set in the slice width data 380.
The link cost 390C is data indicating the average travel time required to pass through the link in the time zone 390B, and is, for example, “3 (min)”. That is, the link cost 48D of the route data 48 is a cost that does not include the time zone 390B based on the link length, the road width, and the like. The link cost 390C of the traffic data 390 is a cost reflecting the traffic situation of each time zone 390B, and the route data is obtained by dividing each link cost 390C by a reference link cost (eg, “2 min”). This is data for generating a new link cost LC by adding 48 link costs 48D.

  The information distribution center 3 may be operated by any one of an individual, a company, an organization, a local government, a government-related organization, or the like, and may be operated by a road traffic information center (VICS: registered trademark) 5.

  The network 4 includes, for example, a LAN (Local Area Network), a WAN (Wide Area Network), an intranet, a mobile phone line network, a telephone line network, a public communication line network, a dedicated communication line network, a communication line network such as the Internet, etc. Can be used. A communication system using CS broadcasting, BS broadcasting, terrestrial digital television broadcasting, FM multiplex broadcasting, or the like by a broadcasting satellite can also be used. Furthermore, a communication system such as a non-stop automatic fee payment system (ETC) or a narrow area communication system (DSRC) used in an intelligent road traffic system (ITS) can also be used.

  Next, a schematic configuration of the navigation device 2 configuring the navigation system 1 according to the first embodiment will be described with reference to FIG. FIG. 2 is a block diagram illustrating the navigation device 2 according to the first embodiment.

  As shown in FIG. 2, the navigation device 2 according to the first embodiment is based on a current location detection processing unit 21 that detects the current position of the host vehicle, a data recording unit 22 that records various data, and input information. The navigation control unit 23 that performs various arithmetic processes, the operation unit 24 that receives operations from the operator, the liquid crystal display 25 that displays information such as a map to the operator, and voice guidance related to route guidance are output. And a communication device 27 that communicates with the road traffic information center (VICS: registered trademark) 5, the information distribution center 3, and the like. The navigation control unit 23 is connected to a vehicle speed sensor 28 that detects the traveling speed of the host vehicle.

  The components constituting the navigation device 2 will be described below. The current location detection processing unit 21 includes a GPS 31, a geomagnetic sensor 32, a distance sensor 33, a steering sensor 34, a gyro sensor 35 as an azimuth detection unit, and an altimeter (not shown). And the like, and it is possible to detect the current position, direction, distance to a target (for example, an intersection), and the like.

  Specifically, the GPS 31 detects the current location and current time of the vehicle on the earth by receiving radio waves generated by artificial satellites, and the geomagnetic sensor 32 measures the direction of the vehicle by measuring the geomagnetism. The distance sensor 33 detects a distance between predetermined positions on the road. Here, as the distance sensor 33, for example, the rotational speed of a wheel (not shown) of the vehicle is measured, a sensor that detects the distance based on the measured rotational speed, the acceleration is measured, and the measured acceleration is 2 A sensor that integrates the times and detects the distance can be used.

  The steering sensor 34 detects the rudder angle of the host vehicle. Here, as the steering sensor 34, for example, an optical rotation sensor attached to a rotating portion of a steering wheel (not shown), a rotation resistance sensor, an angle sensor attached to a wheel, or the like is used.

  And the gyro sensor 35 detects the turning angle of the own vehicle. Here, as the gyro sensor 35, for example, a gas rate gyro, a vibration gyro, or the like is used. Further, by integrating the turning angle detected by the gyro sensor 35, the vehicle direction can be detected.

  The data recording unit 22 includes an external storage device and a hard disk (not shown) as a storage medium, a navigation side traffic information database (navigation side traffic information DB) 36, a navigation side map information database (navigation) stored in the hard disk. Side map information DB) 37, slice width database (slice width DB) 38, traffic database (traffic DB) 39, a recording program (not shown) that is a driver for reading predetermined programs and the like and writing predetermined data to the hard disk ). In the first embodiment, a hard disk is used as the external storage device and the storage medium of the data recording unit 22, but a magnetic disk such as a flexible disk can be used as the external storage device in addition to the hard disk. Also, a memory card, magnetic tape, magnetic drum, CD, MD, DVD, optical disk, MO, IC card, optical card, etc. can be used as an external storage device.

  Here, in the navigation side traffic information DB 36, the current road composed of the actual length of the traffic jam received from the road traffic information center (VICS: registered trademark) 5, the cause of the traffic jam, the time when the traffic jam is expected to be resolved, and the like. The current traffic information 36A created from traffic information such as road traffic information on traffic jams, traffic regulation information due to road construction, building construction, etc. is stored. The navigation-side traffic information DB 36 stores statistical traffic information 36B, which is statistical traffic information relating to traffic jams and the like created in the past. This statistical traffic information 36B is the event schedule information such as the scheduled location and the scheduled date and time of events such as festivals, parades, fireworks festivals, etc., for example, roads around stations and large commercial facilities at specific times every day except weekends. Statistical traffic jam information and traffic jam prediction information such as the occurrence of traffic jams or the occurrence of traffic jams during summer holidays may be included on roads around the beach. Further, in the navigation side traffic information DB 36, every predetermined time in the future (for example, about every 30 minutes from the current time, about every hour) for each traffic jam created based on the current traffic information 36A and the statistical traffic information 36B. , Approximately every 2 hours, etc.), predicted traffic information 36BC is stored.

The navigation side map information DB 37 stores navigation map information 37 </ b> A that is used for travel guidance and route search of the navigation device 2 and is updated by the information distribution center 3. Here, the navigation map information 37A includes various information such as map display data and route data 48 (see FIG. 3) necessary for route guidance and map display, similar to the update map information 14A. As shown in FIG. 3, the route data 48 is data for each region that divides the whole country into areas, and includes a header 48A, intersection data for each intersection, node data 48B for node points, and roads that are a kind of facility ( Link data 48C related to a link), link cost 48D for searching for a route, coordinate data 48E indicating coordinates of nodes and links, version 48F, and the like.
Since details of each data have already been described, details thereof are omitted here.
The contents of the navigation-side map information DB 37 are updated by downloading update information distributed from the information distribution center 3 via the communication device 27.

The slice width DB 38 stores the above-described slice width data 380 (see FIG. 4) to be updated by the information distribution center 3. The traffic DB 19 stores the above-described traffic data 390 (see FIG. 6) to be updated by the information distribution center 3.
Each content of the slice width data 380 stored in the slice width DB 38 and the traffic data 390 stored in the traffic DB 39 downloads update information distributed from the information distribution center 3 via the communication device 27. Updated by.

  As shown in FIG. 2, the navigation control unit 23 constituting the navigation device 2 is a computing device that performs overall control of the navigation device 2, a CPU 41 as a control device, and working when the CPU 41 performs various types of computation processing. In addition to being used as a memory, the RAM 42 for storing route data when a route is searched, traffic information received from the information distribution center 3 and the like, a control program, a minimum arrival area and a maximum arrival area described later An internal storage device such as a ROM 43 storing a route search processing program (see FIG. 7) for searching for a guidance route from the vehicle position to the destination, a flash memory 44 storing a program read from the ROM 43, A timer 45 for measuring time is provided. As the RAM 42, ROM 43, flash memory 44, etc., a semiconductor memory, a magnetic core or the like is used. As the arithmetic device and the control device, an MPU or the like can be used instead of the CPU 41.

  In the first embodiment, various programs are stored in the ROM 43, and various data are stored in the data recording unit 22. However, the programs, data, and the like are stored in the same external storage device, memory card, and the like. It is also possible to read a program, data, etc. from the above and write it into the flash memory 44. Further, the program, data, etc. can be updated by exchanging a memory card or the like.

  Furthermore, the navigation control unit 23 is electrically connected to peripheral devices (actuators) of the operation unit 24, the liquid crystal display 25, the speaker 26, and the communication device 27.

  The operation unit 24 is operated when correcting the current location at the start of traveling, inputting a departure point as a guidance start point and a destination as a guidance end point, or when searching for information about facilities, etc. And a plurality of operation switches. Then, the navigation control unit 23 performs control to execute various corresponding operations based on switch signals output by pressing the switches. As the operation unit 24, a keyboard, a mouse, a barcode reader, a remote control device for remote operation, a joystick, a light pen, a stylus pen, or the like can be used. Furthermore, it can also be configured by a touch panel provided on the front surface of the liquid crystal display 25.

  In addition to a route guidance screen on which a map based on the navigation map information 37A is displayed and traffic information on each link is displayed on the liquid crystal display 25, operation guidance, operation menu, key guidance, from the current location to the destination Guide route, guidance information along the guide route, traffic information, news, weather forecast, time, mail, TV program, etc. are displayed. Instead of the liquid crystal display 25, it is also possible to use a CRT display, a plasma display, or the like, or a hologram device that projects a hologram on the windshield of a vehicle.

  In addition, the speaker 26 outputs voice guidance or the like that guides traveling along the guidance route based on an instruction from the navigation control unit 23. Here, examples of the voice guidance to be guided include “200m ahead, turn right at XX intersection”, “Now, the next national road XX is congested”. Note that as the sound output from the speaker 26, in addition to the synthesized sound, various sound effects and various guidance information recorded in advance on a tape, a memory or the like can be output.

  The communication device 27 is a communication unit that communicates with the information distribution center 3, and transmits / receives updated map information of the latest version, slice width data 380, traffic data 390, and the like to / from the information distribution center 3. In addition to the information distribution center 3, the communication device 27 includes congestion information, regulation information, parking lot information, traffic accident information, and service area congestion transmitted from the road traffic information center (VICS: registered trademark) 5 or the like. The traffic information consisting of each information is received.

Next, in the navigation system 1 having the above-described configuration, the CPU 41 of the navigation device 2 sets a minimum reachable area, a maximum reachable area, and the like, and shows route search processing for searching for a guide route from the vehicle position to the destination. This will be described with reference to FIGS. FIG. 7 is a flowchart illustrating a route search process for searching for a guide route from the vehicle position to the destination, which is executed by the navigation device 2 according to the first embodiment.
7 is stored in the ROM 43 provided in the navigation control unit 23 of the navigation device 2, and is executed by the CPU 41 at regular intervals (for example, every 10 msec to 100 msec).

As shown in FIG. 7, first, in step (hereinafter abbreviated as “S”) 11, the CPU 41 performs an input operation of the operation unit 24 such as a touch panel or an operation switch in accordance with a route search processing program stored in the ROM 43. A determination process for determining whether or not a destination has been set is executed. And when the destination is not set (S11: NO), CPU41 complete | finishes the said process.
On the other hand, if it is determined that the destination has been input (S11: YES), the CPU 41 temporarily stores the coordinates of the destination in the RAM 42, and then proceeds to the processing of S12.
Subsequently, in S <b> 12, the CPU 41 detects the current position of the own vehicle (hereinafter referred to as “own vehicle position”) by the current position detection processing unit 21. Then, the CPU 41 reads out map data including the current position of the vehicle from the navigation map information 37A, detects a mesh including the vehicle position based on the map data, and acquires a mesh ID 380A of the mesh.

In S13, the CPU 41 reads the time data of the timer 45, acquires the current date and time, the current time, and identifies the current season 380B, day 380C, and time zone factor.
In S14, when the CPU 41 identifies the mesh ID 380A, the season 380B, the day of the week 380C, and the time zone factor, the CPU 41 reads the slice width 380D of the slice width data 380 associated with the mesh ID 380A, and based on the slice width 380D, The minimum slice width and the maximum slice width are determined and stored in the RAM 42.

For example, when the season 380B is “spring”, the day 380C is “Monday”, and the current time is a time included in 9:00 to 9:14, the CPU 41 includes the slice width data 380 shown in FIG. , The slice width 380D of “9:00 to 9:14” is read, and the minimum slice width and the maximum slice width are determined based on the slice width 380D.
For example, when the minimum slice width of the slice width 380D is “4 km” and the maximum slice width is “5 km”, the minimum reach distance range (minimum prediction range) in which the vehicle having a low traveling speed reaches 15 minutes in this mesh. The distance range data) is 4 (km), and the maximum reachable distance range (maximum predicted distance range data) in which a vehicle having a high traveling speed reaches 15 minutes is 5 (km).

Next, in S15, the CPU 41 reads the minimum slice width from the RAM 42 again, and sets the minimum arrival area (minimum arrival range) that the vehicle reaches every predetermined time (every 15 minutes) from the current vehicle position as the current time. Is set as a reference and stored in the RAM.
Here, an example of the minimum reachable range that the CPU 41 sets with the current time as a reference when the minimum slice width is “4 km” will be described with reference to FIGS. 8 and 9.
FIG. 8 is an explanatory diagram illustrating an example of the minimum reachable range set with the minimum slice width “4 km” on the outer side in the radial direction around the current vehicle position. FIG. 9 is a diagram showing an example of the relationship between the distance from the current vehicle position of each minimum reach range set with the minimum slice width “4 km” and the reach time range from the current time.

As shown in FIG. 8, the CPU 41 reads the minimum slice width “4 km” from the RAM 42, and has a circular shape with a radius “4.0 km” from the own vehicle position 50 around the current (9:00) own vehicle position 50. Is set and stored in the RAM. The first minimum reachable range 51 is a minimum reachable range in which the vehicle reaches within “9:00 to 9:14”.
Subsequently, the CPU 41 sets a second second minimum reachable range 52 that is separated from the circumference of the first minimum reachable range 51 by the minimum slice width “4 km” and stores the second minimum reachable range 52 in the RAM 42. The second minimum reachable range 52 is a minimum reachable range in which the vehicle reaches within “9:15 to 9:29”.

Further, the CPU 41 sets a third third minimum reachable range 53 that is separated from the circumference of the second minimum reachable range 52 by the minimum slice width “4 km” and stores it in the RAM 42. The third minimum reachable range 53 is a minimum reachable range in which the vehicle reaches within “9:30 to 9:44”.
Then, the CPU 41 sets a fourth fourth minimum reachable range 54 that is separated from the circumference of the third minimum reachable range 53 by the minimum slice width “4 km” and stores it in the RAM 42. The fourth minimum reachable range 54 is a minimum reachable range in which the vehicle reaches within “9:45 to 9:59”.

  Furthermore, as shown in FIG. 9, the CPU 41 determines that the vehicle reaches the fifth minimum reach range 55 to 8th in which the vehicle reaches within “10:00 to 10:14” to “10:45 to 10:59”. The minimum reachable range 58 is set and stored in the RAM 42.

Subsequently, in S16, the maximum slice width is read again from the RAM 42, and the maximum arrival area (maximum reach range) that the vehicle reaches every predetermined time (every 15 minutes) from the current vehicle position is used as a reference. Set and store in RAM 42.
Here, an example of the maximum reachable range that the CPU 41 sets with the current time as a reference when the maximum slice width is “5 km” will be described with reference to FIGS. 10 and 11.
FIG. 10 is an explanatory diagram illustrating an example of the maximum reachable range set with the maximum slice width “5 km” on the outer side in the radial direction around the current vehicle position. FIG. 11 is a diagram showing an example of the relationship between the distance from the current vehicle position of each maximum reach range set with the maximum slice width “5 km” and the reach time range from the current time.

As shown in FIG. 10, the CPU 41 reads the maximum slice width “5 km” from the RAM 42, and is a circle having a radius “5.0 km” from the own vehicle position 50 around the current (9:00) own vehicle position 50. Is set and stored in the RAM. The first maximum reachable range 61 is a maximum reachable range in which the vehicle reaches within “9:00 to 9:14”.
Subsequently, the CPU 41 sets a second second maximum reachable range 62 that is separated from the circumference of the first maximum reachable range 61 by the maximum slice width “5 km” and stores it in the RAM 42. The second maximum reachable range 62 is a maximum reachable range in which the vehicle reaches within “9:15 to 9:29”.

In addition, the CPU 41 sets a third third maximum reachable range 63 that is separated from the circumference of the second maximum reachable range 62 by the maximum slice width “5 km”, and stores it in the RAM 42. The third maximum reachable range 63 is a maximum reachable range in which the vehicle reaches within “9:30 to 9:44”.
Further, as shown in FIG. 11, the CPU 41 determines whether the vehicle reaches within the range of “9:45 to 9:59” to “10:30 to 10:44”. The maximum reachable range 67 is set and stored in the RAM 42.

In S17, the CPU 41 reads out the minimum reachable range set in S15 and the maximum reachable range set in S16 from the RAM 42, superimposes them, and synthesizes them for each distance from the current vehicle position ( For example, it is about every 500 m to 1 km.) The arrival time range is set based on the current time and stored in the RAM 42.
Here, the first minimum reachable range 51 to the eighth minimum reachable range 58 shown in FIG. 9 and the first maximum reachable range 61 to the seventh maximum reachable range 67 shown in FIG. FIG. 12 shows an example of the arrival time range for each distance from the own vehicle position (for example, about every 500 m to 1 km).

As shown in FIG. 12, in the first reachable range 71 having a radius of “4.0 km” from the own vehicle position 50 around the current (9:00) own vehicle position 50, the CPU 41 Is set to “9:00 to 9:14” and stored in the RAM 42.
Subsequently, in the second reachable range 72 that is separated from the circumference of the first reachable range 71 by a distance “1 km”, that is, in the ring-shaped second reachable range 72 having a radius “4 km to 5 km” from the vehicle position 50, The CPU 41 sets the arrival time range for the vehicle to reach from “9:00 to 9:29” and stores it in the RAM 42.

Further, in the third reachable range 73 that is a distance “3 km” away from the circumference of the second reachable range 72, that is, in the ring-shaped third reachable range 73 having a radius “5 km to 8 km” from the vehicle position 50, the CPU 41. Sets the arrival time range for the vehicle to reach from “9:15 to 9:29” and stores it in the RAM 42.
Further, in the fourth reachable range 74 that is a distance “2 km” away from the circumference of the third reachable range 73, that is, in the ring-shaped fourth reachable range 74 having a radius “8 km to 10 km” from the own vehicle position 50, the CPU 41. Sets the arrival time range for the vehicle to reach from “9:15 to 9:44” and stores it in the RAM 42.

Then, in the fifth reachable range 75 that is a distance “2 km” away from the circumference of the fourth reachable range 74, that is, in the ring-shaped fifth reachable range 75 having a radius “10 km to 12 km” from the own vehicle position 50, the CPU 41. Sets the arrival time range for the vehicle to reach from “9:30 to 9:44” and stores it in the RAM 42.
Further, the CPU 41 sets the arrival time ranges in which the vehicle reaches the sixth reachable range 76 to the thirteenth reachable range 83 from “9:30 to 9:59” to “10:30 to 10:59”, respectively. Are sequentially set and stored in the RAM 42.

  Next, in S18, the CPU 41 determines the current vehicle position based on the arrival time range for each distance from the current vehicle position stored in the RAM 42 in S17 (for example, approximately every 500 m to 1 km). The distance from the vehicle position to each link is calculated with the center as the center, and the arrival time range for reaching each link is set.

  Subsequently, in S19, the CPU 41 sequentially reads again the arrival time range for each link stored in the RAM 42 in S18 from the RAM 42, and obtains each link cost 390C of the time zone 380B of each link corresponding to this arrival time range. Read from the traffic data 390 stored in the traffic DB 39. Then, an average value of the link costs 390C of each read link is calculated, and the calculated average value is set as a predicted link cost (predicted traffic data) of each link and stored in the RAM 42.

Here, an example of the arrival time range and the predicted link cost (predicted traffic data) set for each link N, N + 1, N + 2 shown in FIG. 6 will be described based on FIG.
For example, as shown in FIG. 12, when the straight line distance from the current vehicle position 50 to the link N is “11 km”, that is, the coordinate position of the link N exists in the fifth reach 75. In this case, the CPU 41 sets the arrival time range for reaching the link N to “9:30 to 9:44” and stores it in the RAM 42.
Then, the CPU 41 reads the arrival time range “9:30 to 9:44” of the link N from the RAM 42 again, and the link N corresponding to this arrival time range “9:30 to 9:44” is read. “2min”, which is the link cost 390C of the time zone 380B “9:30 to 9:44”, is read from the traffic data 390, and this “2min” is set as the predicted link cost (predicted traffic data) of the link N. Store in the RAM 42.

Further, when the straight line distance from the current vehicle position 50 to the link N + 1 is “14 km”, that is, when the coordinate position of the link N + 1 exists in the sixth reachable range 76, the CPU 41 The arrival time range for reaching this link N + 1 is set to “9:30 to 9:59” and stored in the RAM 42.
Then, the CPU 41 reads again the arrival time range “9:30 to 9:59” of the link N + 1 from the RAM 42 and the link N + 1 corresponding to this arrival time range “9:30 to 9:59”. The link costs 390C “15min” and “16min” for the time zone 380B “9:30 to 9:44” and “9:45 to 9:59” are read from the traffic data 390, and this “15min ”And“ 16 min ”are calculated, and the average value“ 15.5 min ”is set as the predicted link cost (predicted traffic data) of the link N + 1 and stored in the RAM 42.

Furthermore, when the straight distance from the current vehicle position 50 to the link N + 2 is “24.5 km”, that is, when the coordinate position of the link N + 2 is in the tenth reach 80, The CPU 41 sets the arrival time range for reaching the link N + 2 to “10:00 to 10:44” and stores it in the RAM 42.
Then, the CPU 41 reads the arrival time range “10:00 to 10:44” of the link N + 2 from the RAM 42 again, and the link N + 2 corresponding to this arrival time range “10:00 to 10:44” is read. “15 min” and “15 min” which are link costs 390C of the time zone 380B “10:00 to 10:14”, “10:15 to 10:29”, and “10:30 to 10:44” “17 min” and “18 min” are read from the traffic data 390, and an average value “16.7 min” of “15 min”, “17 min” and “18 min” is calculated, and this average value “16.7 min” is calculated for link N + 2. The predicted link cost (predicted traffic data) is set and stored in the RAM 42.

  Subsequently, in S20, the CPU 41 searches for a recommended route from the current vehicle position to the destination by the Dijkstra method or the like based on the predicted link cost (predicted traffic data) set for each link, and the RAM 42 After the storage, the process is terminated.

  As described above in detail, in the navigation device 2 according to the first embodiment, when the destination is set, the CPU 41 specifies a mesh or the like including the vehicle position, and stores the minimum slice width stored for each mesh. After determining the maximum slice width, a minimum reachable range (minimum reachable area) and a maximum reachable range (maximum reachable area) that are reached every 15 minutes from the current position of the vehicle are set and stored in the RAM 42 (S11 to S16). Then, the CPU 41 reads out each minimum reachable range and each maximum reachable range from the RAM 42, superimposes and combines them, and arrives at each distance from the current vehicle position (for example, approximately every 500 m to 1 km). The range is set based on the current time and stored in the RAM 42 (S17). Subsequently, the CPU 41 calculates the distance from the vehicle position to each link based on the arrival time range for each distance from the current vehicle position, and sets the arrival time range to reach each link ( S18). And CPU41 sets the average value of each link cost 390C of the time zone 380 of each link applicable to the arrival time range which reaches | attains each link as a predicted link cost (predicted traffic data) of each said link, and sets to each link. Based on the set predicted link cost (predicted traffic data), a recommended route from the current vehicle position to the destination is searched by the Dijkstra method or the like (S19 to S20).

  Therefore, the CPU 41 of the navigation device 2 combines the minimum reachable range and the maximum reachable range set by the minimum slice width and the maximum slice width stored for each mesh and combines them from the current vehicle position for each distance. By setting the arrival time range to reach each link based on the predicted arrival time range, the arrival time range from the departure point to each link is set in consideration of different driving speeds for each driver. It becomes possible.

  Further, since the CPU 41 sets the average value of the link costs 390C of the time zones 380B of the links corresponding to the arrival time ranges of the links as the predicted link costs, the links corresponding to the time zones 380B in the arrival time ranges Even if the difference in cost 390C is large and discontinuous, this discontinuity can be complemented, and the predicted link cost can be quickly set for the arrival time range of each link. It is possible to reduce the processing time for predicting the arrival time at which the signal arrives. In addition, by setting the predicted link cost based on the link cost 390C of the time zone 380B of each link corresponding to the arrival time range of each link, it is possible to predict with high accuracy in consideration of the different traveling speed for each driver. It becomes possible to set a link cost, and it is possible to accurately predict a recommended route by accurately predicting a road where traffic congestion occurs, a road where traffic congestion is eliminated, and the like.

  Further, the minimum slice width and the maximum slice width are the average distance L and the standard deviation σ of the statistical data of each travel distance that arrives within a predetermined time (within 15 minutes) of all the vehicles traveling on each link in the mesh. Therefore, the minimum slice width and maximum slice width can be accurately set based on statistical data, and the arrival time range to reach each link from the current vehicle position can be accurately set. It is possible to calculate.

Further, when determining the minimum slice width and the maximum slice width, the CPU 41 only needs to read out the minimum slice width and the maximum slice width stored for each map mesh. The processing can be reduced compared to detecting a link within the distance and sequentially searching the minimum slice width and the maximum slice width corresponding to the link.
Further, the minimum slice width and the maximum slice width are generated for each factor such as the area, season, day of the week, holiday, and consecutive holidays. For this reason, regional characteristics, timing factors, and temporal factors can be added to the minimum slice width and the maximum slice width, so that the accuracy of the minimum slice width and the maximum slice width can be improved.

Next, in the navigation system 100 according to the second embodiment, the route search process for searching the route from the current vehicle position to the destination executed by the CPU 41 of the navigation device 2 and the current executed by the CPU 11 of the information distribution center 3. A route search process for searching for a recommended route from the vehicle position to the destination and delivering it to the navigation device 2 will be described with reference to FIG. FIG. 13 is a flowchart illustrating a route search process executed by the navigation device 2 of the navigation system 100 according to the second embodiment and a route search process executed by the information distribution center 3.
In the following description, the same reference numerals as those of the navigation system 1 according to the first embodiment in FIGS. 1 to 12 indicate the same or corresponding parts as those of the navigation system 1 according to the first embodiment.

The schematic configuration of the navigation system 100 according to the second embodiment is substantially the same as that of the navigation system 1 according to the first embodiment. Various control processes are also substantially the same as those in the navigation system 1 according to the first embodiment.
However, as shown in FIG. 13, the navigation system 100 according to the second embodiment is different from the navigation system 1 according to the first embodiment in that the information distribution center 3 executes processes corresponding to S12 to S20. Yes.

  First, the “route search process” executed by the CPU 41 of the navigation device 2 will be described with reference to FIG. Note that the programs shown in the flowcharts of S101 to S103 in FIG. 13 are stored in the RAM 42 and ROM 43 provided in the navigation device 2, and are executed by the CPU 41.

As shown in FIG. 13, in S101, the CPU 41 determines whether or not a destination is set by an input operation of the operation unit 24 such as a touch panel or an operation switch in accordance with a route search processing program stored in the ROM 43. Execute the judgment process. And when the destination is not set (S101: NO), CPU41 complete | finishes the said process.
On the other hand, if it is determined that the destination has been input (S101: YES), the CPU 41 temporarily stores the coordinates of the destination in the RAM 42, and then proceeds to the processing of S102.
Subsequently, in S102, the CPU 41 transmits, to the information distribution center 3, a request command for requesting a route search, current vehicle position data, destination coordinate data, navigation map information 37A version information, and the like. To do.

  Thereafter, in S103, the CPU 41 receives route information such as a guide route (recommended route) from the information distribution center 3, stores the route information of the guide route in the RAM 42, and ends the processing.

Next, the “route search process” executed by the CPU 11 of the information distribution center 3 will be described with reference to FIG. Note that the programs shown in the flowcharts of S201 to S210 in FIG. 13 are stored in the RAM 12 and ROM 13 provided in the information distribution center 3, and are executed by the CPU 11.
First, in S201, the CPU 11 makes a request command for requesting a route search transmitted from the navigation device 2 in S102, as well as various information such as vehicle position data, destination coordinate data, and navigation map information 37A version information. Are stored in the RAM 12. And CPU11 performs the process corresponded to said S12 based on the update map information 14A corresponding to the version information of the navigation map information 37A stored in the center side map information DB14.

In S202 to S209, the CPU 11 updates map information 14A corresponding to the version information of the navigation map information 37A stored in the center-side map information DB 14, slice width data 380 stored in the slice width DB 18, Based on the traffic data 390 stored in the traffic DB 19, the processes of S13 to S20 are executed.
Subsequently, in S210, the CPU 11 ends the process after transmitting the route information and the update information of the searched recommended route to the navigation device 2.

  As described above, in the navigation system 100 according to the second embodiment, the CPU 11 of the information distribution center 3 specifies a mesh or the like including the vehicle position, and uses the minimum slice width and the maximum slice width stored for each mesh. After the determination, a minimum reachable range (minimum reachable area) and a maximum reachable range (maximum reachable area) that are reached every 15 minutes from the current position of the vehicle are set and stored in the RAM 12 (S201 to S205). Then, the CPU 11 reads out each minimum reachable range and each maximum reachable range from the RAM 12, superimposes them, and synthesizes them, and arrives at each distance from the current vehicle position (for example, approximately every 500 m to 1 km). The range is set based on the current time and stored in the RAM 12 (S206). Subsequently, the CPU 11 calculates the distance from the vehicle position to each link based on the arrival time range for each distance from the current vehicle position, and sets the arrival time range to reach each link ( S207). And CPU11 sets the average value of each link cost 390C of the time zone 380B of each link corresponding to the arrival time range which reaches | attains each link as a predicted link cost (predicted traffic data) of each said link, and sets to each link. Based on the set predicted link cost (predicted traffic data), a recommended route from the current vehicle position to the destination is searched by the Dijkstra method or the like and distributed to the navigation device 2 (S208 to S210).

  Therefore, the CPU 11 of the information distribution center 3 combines the minimum reachable range and the maximum reachable range that are set by the minimum slice width and the maximum slice width stored for each mesh, and synthesizes the distance from the current vehicle position. Estimate the arrival time range for each link and set the arrival time range to reach each link based on it, and set the arrival time range from the departure point to each link in consideration of different driving speeds for each driver It becomes possible to do.

  Further, since the CPU 11 sets the average value of the link costs 390C of the time zones 380B of the links corresponding to the arrival time ranges of the links as the predicted link costs, the links corresponding to the time zones 380B within the arrival time ranges. Even if the difference in cost 390C is large and discontinuous, this discontinuity can be complemented, and the predicted link cost can be quickly set for the arrival time range of each link. It is possible to shorten the processing time for predicting the arrival time at which the signal arrives. In addition, by setting the predicted link cost based on each link cost 390C of the time zone 380 of each link corresponding to the arrival time range of each link, taking into account different traveling speeds for each driver, high accuracy It is possible to set a predicted link cost, and it is possible to accurately predict a recommended route by accurately predicting a road where traffic congestion occurs, a road where traffic congestion is eliminated, and the like. In addition, since the information distribution center 3 searches for a route from the vehicle position to the destination, the processing load on the navigation device 2 can be reduced.

  Further, the minimum slice width and the maximum slice width are the average distance L and the standard deviation σ of the statistical data of each travel distance that arrives within a predetermined time (within 15 minutes) of all the vehicles traveling on each link in the mesh. Therefore, the minimum slice width and maximum slice width can be accurately set based on statistical data, and the arrival time range to reach each link from the current vehicle position can be accurately set. It is possible to calculate.

Further, when determining the minimum slice width and the maximum slice width, the CPU 11 only needs to read out the minimum slice width and the maximum slice width stored for each map mesh. The processing can be reduced compared to detecting a link within the distance and sequentially searching the minimum slice width and the maximum slice width corresponding to the link.
Further, the minimum slice width and the maximum slice width are generated for each factor such as the area, season, day of the week, holiday, and consecutive holidays. For this reason, regional characteristics, timing factors, and temporal factors can be added to the minimum slice width and the maximum slice width, so that the accuracy of the minimum slice width and the maximum slice width can be improved.

  In addition, this invention is not limited to the said Example 1 and Example 2, Of course, various improvement and deformation | transformation are possible within the range which does not deviate from the summary of this invention. For example, the following may be used.

  (1) In the first embodiment and the second embodiment, the circular first minimum reachable range 51 to the eighth minimum reachable range 58 and the first maximum reachable range 61 to the seventh maximum reachable range 67 are set. It is not necessary to be an ellipse, and it may be an ellipse or a deformed circle partially protruding. For example, if there is a road whose number of lanes is extremely large compared to the surrounding roads, a deformed circle that protrudes the part corresponding to the road is set, or an ellipse that makes the road correspond to the long axis part of the ellipse A shape area may be set.

  (2) In the first embodiment, the navigation device 2 receives the slice width data 380 and the traffic data 390 from the information distribution center 3 and stores and updates them in the slice width DB 38 and the traffic DB 39, respectively. Alternatively, the slice width data 380 and the traffic data 390 stored in a DVD-ROM or the like may be read and stored in the slice width DB 38 and the traffic DB 39 for updating.

  (3) In the first embodiment, in step 20, the CPU 41 determines from the current own vehicle position to the destination by the Dijkstra method based on the predicted link cost (predicted traffic data) set in each link in step 19. The vehicle speed at each link is calculated from the link length of each link on the recommended route and the predicted link cost, and the degree of congestion at each link included in the route to the destination (for example, the vehicle speed is Predicting “no traffic jam” when the vehicle speed is 20 km / h or more, “congestion” when the vehicle speed is 10 km / h or more and less than 20 km / h, “congestion” when the vehicle speed is less than 10 km / h, etc.) You may make it extract traffic congestion links, such as "congestion" degree. Then, the CPU 41 reads out map data including the current position of the vehicle from the navigation map information 37A and displays it on the liquid crystal display 25, and also displays a traffic jam link with a traffic jam degree of “traffic jam” or the like blinking red on the map image, You may make it display so that identification by a red arrow etc. is possible. Further, the CPU 41 may simultaneously display traffic jam section information indicating a traffic jam section, a time required for passing, and the like. As a result, the driver can accurately predict the traffic jam on the recommended route, and can easily determine a detour route or the like that bypasses the traffic jam.

  (4) In the second embodiment, the CPU 11 determines from the current own vehicle position to the destination by the Dijkstra method based on the predicted link cost (predicted traffic data) set for each link in step 208 in step 209. The vehicle speed at each link is calculated from the link length of each link on the recommended route and the predicted link cost, and the degree of congestion at each link included in the route to the destination (for example, the vehicle speed is Predicting “no traffic jam” when the vehicle speed is 20 km / h or more, “congestion” when the vehicle speed is 10 km / h or more and less than 20 km / h, “congestion” when the vehicle speed is less than 10 km / h, etc.) You may make it extract traffic congestion links, such as "congestion" degree. In step 210, the CPU 11 may transmit the route information of the searched recommended route and the extracted traffic jam link information to the navigation device 2.

  On the other hand, in step 103, the CPU 41 receives route information such as a guidance route (recommended route) and traffic jam link information from the information distribution center 3, and sends the route information of the guidance route and traffic jam link information to the RAM. Remember. Then, the CPU 41 reads out map data including the current position of the vehicle from the navigation map information 37A and displays it on the liquid crystal display 25, and based on the received information on the traffic jam link, the traffic jam is a traffic jam such as “Junk traffic”. The link may be displayed on the map image so as to be identifiable with a red blinking display or a red arrow. Further, the CPU 41 may simultaneously display the traffic jam section information indicating the traffic jam section and the time required for passing. Thus, the driver can accurately predict the traffic jam on the recommended route, and can easily determine a detour route or the like that bypasses this traffic jam. Moreover, since the information distribution center 3 searches for a route from the vehicle position to the destination and extracts a traffic jam link, the processing load on the navigation device 2 can be further reduced.

1 is a block diagram illustrating a navigation system according to Embodiment 1. FIG. It is the block diagram which showed the navigation apparatus of the navigation system. It is explanatory drawing of the data structure of path | route data. It is explanatory drawing of the data structure of slice width data. It is a figure explaining the minimum slice width and the maximum slice width. It is explanatory drawing of the data structure of traffic data. It is a flowchart which shows the route search process which searches the guidance route from the own vehicle position to the destination which the navigation apparatus which concerns on Example 1 performs. It is explanatory drawing explaining an example of the minimum reach | attainment area each set by the minimum slice width | variety "4 km" on the radial direction outer side centering on the present own vehicle position. It is a figure which shows an example of the relationship between the distance from the own vehicle position of each minimum arrival area set with the minimum slice width "4 km", and the arrival time range from the present time. It is explanatory drawing explaining an example of the maximum reach | attainment area each set by the largest slice width | variety "5 km" on the radial direction outer side centering on the present own vehicle position. It is a figure which shows an example of the relationship between the distance from the own vehicle position of each largest arrival area set by the maximum slice width "5km", and the arrival time range from the present time. It is a figure which shows an example of the relationship between the distance from the present own vehicle position which synthesize | combined each minimum arrival area shown in FIG. 9 and FIG. 11, and each maximum arrival area, and the arrival time range from the present time. 10 is a flowchart illustrating a route search process executed by a navigation device of a navigation system according to a second embodiment and a route search process executed by an information distribution center.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1,100 Navigation system 2 Navigation apparatus 3 Information distribution center 4 Network 10 Server 11, 41 CPU
12, 42 RAM
13, 43 ROM
14 Center side map information DB
17 Center side communication device 18, 38 Slice width DB
19, 39 Traffic DB
23 navigation control unit 27 communication device 37 navigation side map information DB
50 Own vehicle position 51-58 1st minimum reachable range-8th minimum reachable range 61-67 1st maximum reachable range-7th maximum reachable range 71-83 1st reachable range-13th reachable range 380 Slice width data 380A Mesh ID
380B Season 380C Day of the week 380D Slice width 390 Traffic data 390A Link ID
390B Time zone 390C Link cost

Claims (5)

In a navigation device mounted on a vehicle,
Predicted distance range data storage means for storing the minimum predicted distance range data and the maximum predicted distance range data for predicting the distance range that the vehicle will reach within a predetermined time;
Traffic data storage means for storing traffic data generated based on traffic conditions for each time zone for each link;
Based on the minimum predicted distance range data, minimum reach range setting means for setting a minimum reach range that the vehicle reaches every predetermined time with the current host vehicle position as the center, with reference to the current time;
Based on the maximum predicted distance range data, maximum reach range setting means for setting a maximum reach range that the vehicle reaches every predetermined time with the current vehicle position as a center, with reference to the current time;
An arrival time range prediction means for predicting an arrival time range for each distance from the current vehicle position based on the minimum arrival range and the maximum arrival range;
Link arrival time range setting means for setting an arrival time range to reach each link based on position information of each link and the arrival time range predicted by the arrival time range prediction means;
Predicted traffic data setting means for setting predicted traffic data for each link based on traffic data corresponding to the arrival time range of each link;
Search means for searching for a recommended route from the current vehicle position to the destination using the predicted traffic data set for each link;
A navigation device characterized by comprising:   The navigation device according to claim 1, wherein the predicted traffic data setting unit sets an average value of traffic data corresponding to each time zone within the range between the arrival times as the predicted traffic data.   The minimum predicted distance range data and the maximum predicted distance range data are predicted based on an average value and a standard deviation of each distance range calculated from statistical data of a distance range where the vehicle reaches within a predetermined time. The navigation device according to claim 1 or 2, wherein The predicted distance range data storage means stores the minimum predicted distance range data and the maximum predicted distance range data for each map mesh,
The minimum reach range setting unit detects a map mesh including the vehicle position, reads the minimum predicted distance range data corresponding to the detected map mesh, sets a minimum reach range,
The maximum reachable range setting means detects a map mesh including the vehicle position, reads the maximum predicted distance range data corresponding to the detected map mesh, and sets the maximum reachable range. The navigation device according to any one of claims 1 to 3.   5. The navigation according to claim 1, wherein the minimum predicted distance range data and the maximum predicted distance range data are generated for each zone, season, day of the week, holiday, or consecutive holiday period. apparatus.

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