JPH05173478A - Navigation device - Google Patents

Abstract

PURPOSE:To provide the navigation device which is very convenient for setting a running route by displaying an appropriate route being advantageous as to a distance and timewise extending from a start point to a target point. CONSTITUTION:A road map is divided into plural sections, traffic frequencies 1, 2 and 3 of three ranks corresponding to the traffic are allocated to each section, and also, to each traffic frequency, a traffic coefficient corresponding to a ratio of a running time required of a prescribed distance is allocated, and data obtained by linking them is stored in a memory. Also, with respect to a CPU combined with a retrieving part, at every combination of seven kinds of the traffic frequencies 1, 2 and 3, for instance, in routes of combination of the traffic frequency 2 shown with a double line and the traffic frequency 3 shown by a thick line, the shortest route as a distance is derived and its real vector quantity is derived, and a function for retrieving the route by which the real vector quantity becomes minimum, obtained by combination of seven kinds, as an appropriate route is imparted.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a navigation device used for running a vehicle.

[0002]

2. Description of the Related Art Generally, when driving a vehicle, a planned travel route from a certain point (starting point) to another point (destination point) is displayed on a map, or the time required to drive the route is calculated. Displaying is an effective means for the driver to change the setting of the traveling route and predict the arrival time.

For this reason, various navigation devices have been proposed in the past, for example, Japanese Patent Laid-Open No. 59-195.
In Japanese Patent Publication No. 790 (Gazette 1), a traveling path from a starting point to a destination is approximated to a straight line of X and Y axis components by sliding a bar code around a road map with a displacement sensor. A method of displaying is described, and Japanese Patent Application Laid-Open No. 64-6861.
Japanese Unexamined Patent Publication No. 5 (Gazette 2) describes a technique of displaying a grid where an intersection is located at the current position of a vehicle by superimposing it on a map based on information from an azimuth sensor and a distance sensor. Further, in Japanese Patent Laid-Open No. 1-141313 (publication 3), a road on a map is divided for each branch point, a required travel time based on attribute information is allocated between the branch points, and these are added to each other to make a planned travel rule. The technique for calculating the time required for the operation is described.

According to these publications, the planned travel route is displayed in a straight line approximation or overlapped with a grid, so that the way from the starting point to the destination point can be visually inspected. Since the time required for the planned traveling route can be grasped, all of them provide effective information to the driver.

[0005]

However, in the navigation device described in (Gazettes 1 and 2), since the linear approximation data and the lattice display are superposed, it is possible to intuitively grasp the distance and the route. Although it is possible to compare the distances between each other, the traffic volume, maximum speed, or the number of intersections are usually different for each road, so even if you could simply grasp the shortest distance route (the route with the shortest distance), When traveling along the route, it does not always arrive early. Further, in the navigation device described in Publication 3, when there are many routes from the starting point to the destination point, the route with the shortest required time is selected when the required time of the planned traveling route can be grasped. Is difficult to do.

Furthermore, even if the route with the shortest required time can be selected, the route may be complicated and easily confused, and even if the route is a detour, it may be sudden. When there is a concern about heavy traffic congestion, it may be difficult to select psychologically, and it may be difficult to say that it is appropriate even if the calculation time is the shortest.

The present invention has been made under such a background, and an object thereof is to provide a navigation device capable of displaying an appropriate route which is relatively short in distance and fast in time. To provide.

[0008]

According to the display request of the shortest route, the road section is converted into, for example, a vector connecting grid points or a vector in which a curved line portion is formed into a polygonal line, and stored in the first memory. However, it is also possible to use a memory in which such a vector is stored in advance.

On the other hand, in the second memory, data in which a road section, a traffic frequency and a traffic coefficient are linked is stored. The traffic volume is, for example, a value obtained by ranking a plurality of levels of traffic volume (degree of congestion), and the traffic volume coefficient is represented by, for example, the ratio of the reciprocal of the average speed based on a certain traffic volume. And if the traffic frequency is "1""2""3"
₃ C ₁ , ₃ C ₂ , and ₃ C ₃ combinations, in this case, for each of the 7 combinations, the real vector relating to the shortest route in terms of distance as described above for each combination. The total value of the quantities is obtained, and the shortest route in terms of distance having the smallest total value is displayed as an appropriate route.

[0010]

FIG. 1 is an explanatory view showing the overall configuration of a navigation device according to an embodiment of the present invention. 1 in FIG.
Is a CPU (central processing unit), 11 and 12 are RAMs,
ROMs 21 to 23 are input / output interfaces,
These are, for example, micro computers installed in vehicles.
Is built into the computer. The CPU 1 performs control such as passing of data and commands between devices connected to the CPU 1 and, in this embodiment, the road of road map data stored in a memory 3 described later. for,
A vectorization processing unit that approximates a vector connecting grid points of XY coordinates, and a shortest distance route from a starting point to a destination point is searched based on the data obtained by this processing unit. It also serves as a search unit for searching for an appropriate route from the starting point to the destination point based on the data obtained in the section and the data in the attribute information memory described later.

The CPU 1 is provided with a road map data memory 3 and an attribute information memory 31 via an interface 21.
A graphic controller 4 (corresponding to the second memory of the present invention) and an amplifier 61 are connected, and an image display unit 5 and an audio display unit 6 are connected to the graphic controller 4 and the amplifier 61, respectively. ing.

In the attribute information memory 31, for example, when an intersection between three or more roads is defined as road sections L1, L2 ... As shown in FIG. 2, each road section and time as shown in FIG. A traffic volume table in which a traffic volume frequency indicating the degree of traffic volume (vehicle congestion) for each band is linked, and a traffic volume coefficient table that correlates the traffic volume frequency and the traffic volume coefficient as shown in FIG. 4, for example. Are stored, and attribute information is given to each vector by these tables. In FIG. 3, T1 to Tn are time zones, numbers are traffic volume frequencies, and K1 to Kn in FIG. 4 are traffic volume coefficients, for example, numerical values in parentheses are assigned. This traffic volume coefficient is, for example, the ratio of the time (reciprocal of the average speed) passing through the same distance to the reference time (reciprocal of the reference average speed). Therefore, the larger the traffic frequency is, the greater the traffic coefficient is and the smaller the average speed of the road section is. In the example of the figure, the road with the traffic frequency of 1 is about half the time compared with the road with the traffic frequency of 3. It means that you can pass by. In this case, the average speed is often different between the expressway and the general road even if the degree of congestion is the same, and the travel time for the same distance differs depending on the number of intersections, etc. Will be different.

Although the traffic volume coefficient may be directly assigned to the traffic volume table shown in FIG. 3, if the tables are separated as in the above-mentioned example, the processing is easy when changing the value of the traffic volume coefficient. .. Further, the traffic volume table shown in FIG. 3 may be provided for each weather condition such as sunny, cloudy, and rain, or may be provided according to the season, month, day of the week, date, etc. You can also prepare a table for a special day or period. Further, instead of setting such a table in advance and storing it in the memory 31, for example, the traffic frequency or traffic coefficient corresponding to each road section is updated in real time based on the information from the satellite. Good. It should be noted that various road sections can be set such as between major intersections and between interchanges.

The image display unit 5 is composed of, for example, a CRT, and has the shortest distance rule searched by the road map cut out from the memory 3 and the search unit (CPU 1 in this example).
It has the role of displaying the route, the shortest route in time, and the appropriate route. In addition, the voice display unit 6 is provided to display the traveling direction of the next intersection by voice so that the user can drive along these routes, if necessary, after the shortest route and an appropriate route are searched. Has been.

Further, in the CPU 1, the setting of the cut-out area of the road map data in the memory 3, the scrolling of the image on the image display unit 5 or the setting of the starting point and the destination point is performed from the driver's seat. The touch panel T is connected via the interface 22, and the distance sensor S1 and the direction sensor S2 for detecting and starting the traveling distance and the direction of the vehicle, respectively, are connected via the interface 23.

An example of vectorization processing of road map data executed by the CPU 1 will be described below.
First, the CPU 1 uses the road map data in the memory 3 to extract the data cut out by operating the touch panel T from the RAM 1
1, the vectorized program in the ROM 12 is read out, and the following processing is performed. That is, as shown in FIG. 5, a predetermined road map data (thick line) is overlapped with grids (thin lines) arranged at the same vertical and horizontal intervals, and the grid interval is set to k, and one grid is displayed as shown in FIG. When the intersection of the road map data and the grid is on the line segment a, it is at the grid point A, when it is on the line segment b, it is at the grid point B, and when it is on the line segment c, it is at the grid point. When the point C is on the line segment d, the intersections are approximated to the grid points D, and the approximated grid points are connected as a vector to obtain vectorized data. However, the midpoint of A and B is A, the midpoint of B and C, or C and D
The middle point is close to C, and the middle points of D and A are close to D. If the approximated grid point is 1, no vector is assigned (invalid). The long two points are valid. It should be noted that for a one-way road, a vector is assigned only in that direction, and a vector is assigned in both directions other than that direction.

FIG. 7 shows an example of conversion for approximating intersections of road map data and a grid to grid points, and FIG. 8 shows vector data obtained by vectorizing the road map data shown in FIG. .. However, in FIG. 8, the vector is shown by a simple straight line for convenience.

Regarding the starting point and the destination point, among the grid points (effective grid points) to which the road data are approximated, the effective grid point closest to the starting point (target point) is set as the starting point (target point). )
Is approximated as. In this approximation processing, for example, square areas each having one side k, 2k, ... nk are sequentially generated until the effective grid point exists around the starting point, the search range is expanded, and all the existing effective grid points are detected. This can be performed by calculating the straight line distance from the starting point and approximating the starting point to the minimum effective grid point among them.

In the above example, the start point and the end point of the vector are in contact with the grid points of the grids which are orthogonal to each other, but not limited to such grid points, the points arranged in a zigzag pattern or the points of the oblique coordinate system. You may make the start point and the end point of the vector contact a set of points arranged in a fixed pattern on a plane such as an array,
You may use the vector which just made the curve part a polygonal line.
However, when the grid points are used as in the above-mentioned example, since a certain point is equidistant from the other four points vertically or horizontally adjacent to each other, a line segment of length 1 is assigned and adjacent to each other in the diagonal direction. Since a line segment having a length of 2 ^1/2 and an angle of 45 degrees can be assigned to the other four points, there is an advantage that the calculation is simplified. Regarding the relationship between the interval between the grid points along the grid and the actual length, for example, a length of 500 m in the city and 1 km in the suburbs is set as the grid interval. The vectorized data as described above may be stored in advance in the attribute information memory 31 such as a ROM shown by a chain line in FIG.
In this case, vectorization processing in the CPU 1 is unnecessary.

Next, the shortest route search executed by the search unit (CPU 1 in this example) based on the vectorized data will be described with reference to FIGS.
The CPU 1 has a function of multiplying each vector amount in the above-mentioned vectorized data by a corresponding traffic amount coefficient in the attribute information memory 31 to obtain an actual vector amount (actual vector amount = vector amount × traffic amount coefficient). Specifically, for example, when the time zone is specified, for each vector obtained by the vectorization processing unit, the traffic volume frequency assigned to the road section to which the vector belongs is picked up from the table shown in FIG.
Further, the corresponding traffic volume coefficient is obtained by referring to the table shown in FIG. 4, and the vector volume is multiplied to obtain the actual vector volume. That is, this real vector corresponds to the required travel time. Therefore, in this search, the shortest route in terms of distance is calculated when the vector quantity simply representing the distance is used, and the shortest route in time is calculated when the quantity of real vector is used.

Further, in this example, the search unit, for each combination of the traffic frequency shown in FIG. 3 in the attribute information memory 31, has the shortest route in terms of distance only by the vector of the traffic frequency included in the combination. And the shortest rule in terms of distance.
It has a function of obtaining the total value of the actual vector amount for each route and searching the shortest route in terms of distance that minimizes this total value as an appropriate route. Then, the search unit causes these routes, that is, the shortest route in terms of distance and the shortest route in terms of time.
The above-mentioned appropriate route that takes into consideration the combination of traffic flow and traffic frequency is sequentially obtained. An example of a method for searching for the shortest route will be described below. However, if the search is simply performed using vector quantities, the shortest route in terms of distance is obtained, and if a similar search is performed using actual vector quantities, it is time-consuming. The shortest route is required. In this search, the effective lattice points (lattice points in contact with the vector) are expanded by lattice points having three or more vectors (this is called "intersecting lattice point"). Therefore, the following (a), Preprocessing of (b) and (c) is performed. The numbers given in FIG. 8 and FIG. 9 are the numbers of intersection grid points, ▲ is the starting point, and ■ is the destination point.

(A) For the vector from the intersection grid point,
A relative vector quantity indicating the next crossing grid point position and the total vector quantity up to that point is added. This processing is also performed for all points, treating the starting point and the destination point as intersection grid points.

Taking the intersection grid point (11) in FIG. 9 (hereinafter, the numbers in parentheses indicate the intersection grid points) as an example, the next intersection grid point position of the vector a1 is (8). , The relative vector amount up to that point is (a1 + a2). Further, in the vector b1, (10) is the target intersection grid point.

(B) In the search, a plurality of routes are developed from the starting point on the intersection grid points. At a certain search route at that time, the total vector amount from the starting point to each intersection grid point is calculated. The absolute vector amount shown is added to all intersection grid points, and the initial value is + ∞ (maximum value). However, the initial value of the starting point is 0. For example, in FIG. 8, a search rule
If the starting point is expanded to (20) → (21) ..., the absolute vector amount of (20) is the total vector amount from the starting point to (20), and the absolute vector amount of (21) is the starting point. It is the total vector amount from the point to (21) via (20).

The reason for adding this absolute vector amount is to stop the expansion if it has already been expanded with a smaller absolute vector amount at the time of expansion to the next intersection lattice point. For example, when a certain search route expands from the starting point → (20) → (21) → (18) → (17), it does not make sense to expand it to (20) next time. Starting point in a search route → (20) →
When the absolute vector amount of (20) when expanded to (21) ... is Z, the starting point is expanded to (22) in another route and the absolute vector amount of (22) + ( It is meaningless to expand to (20) if the relative vector amount to 20) is Z or more.

(C) When expanding from one crossing grid point to the next crossing grid point, calculate [relative vector amount to the next crossing grid point + straight line distance from the next crossing grid point to the target point]. , The one with the smallest value is given priority. However,
The direction of deployment is invalid and does not go back. For example, if it is expanded in the order of → (18) → (11) in FIG. 8, the priority of the next expanded intersection lattice point is (10), (8) is the same first rank, and the next is ( 13)
Becomes

To summarize the above items (a) to (c), the above-described preprocessing is performed by calculating the total amount of vectors arranged along the route from the starting point to the absolute vector for each intersection grid point. In addition to adding as a quantity, the total amount of vectors arranged along the route from one intersection grid point to the next intersection grid point is added as a relative vector quantity, and the intersection grid point is expanded along the route without reversing. If the absolute vector amount already added to the intersection grid point at the tip is smaller than the absolute vector amount currently obtained when branching, the already developed route is used, and conversely, if it is large, the current route is used. It is intended to reduce the waste of deployment by making the best use of, and to prioritize the deployment further from the one having a smaller straight line distance to the destination point.

A program for executing such preprocessing is stored in the ROM 12 in advance, and the processing shown in FIGS. 10, 11A and 11B is performed by the search program in the ROM 12. The flowcharts shown in FIGS. 10, 11A, and 11B will be described with reference to the vectorized data shown in FIG. First of all, to give an overview,
The starting point is stored as the first intersection grid point to be expanded. Next, from the first crossing grid point (starting point), determine the next crossing grid point according to the priority order of (c) above, until the current absolute vector amount (starting point is 0) + next crossing grid point Is smaller than the relative vector amount (initial value is ∞) and smaller than the absolute vector amount of the destination point (initial value is ∞), the intersection lattice point is stored as the second intersection lattice point and the absolute vector amount is updated. On the contrary, if it is larger, the expansion to the intersection grid point is stopped, the intersection grid point having the next highest priority is determined, and the same processing is performed. If one intersection grid point is expanded and the intersection grid point is the target point, the current expansion route is stored as the shortest route.

When the next expansion at a certain crossing grid point is completed, if the crossing grid point is the first crossing grid point (starting point) to be expanded, the search is finished, and if not, the expansion is carried out. Returning to the previous intersection point that has been performed, the same processing as above is performed. Therefore, the shortest route is obtained when the development is started from the starting point and finally returned to the starting point.

If such expansion is followed, a large number of branches will occur, and the processing amount will increase, so when the expansions of the branches collide with each other, the expansion with the larger absolute vector amount of the intersection grid point is changed. By incorporating a comparison process for canceling thereafter, the processing amount is significantly reduced, and the processing amount can be further reduced by performing a plurality of search routes as shown in FIG.

Next, the flowcharts of FIGS. 10, 11A and 11B will be described in correspondence with the outline of the search based on FIG. 8 described above. 12 and 13 are image diagrams of the first area and the second area, respectively.
R and p in (r, p) indicate the root number and the intersection grid point number counted from the starting point in the root, respectively. Therefore, the second intersection grid point from the starting point is K (1, 2). ), And if the expansion is completed up to K (1, 2), the pointer of root 1, that is, P (1) becomes number 2, and this value is shown in FIG.
The pointer number for the search route 1 in the second area shown in FIG.

Next, the data of the first memory will be described with reference to FIG. When the number of search routes is set to 2 (n = 2), first, at 101 in FIG. 10, to the first intersection lattice points K (1,1) and K (2,1) of the routes 1 and 2. The position (coordinate position) of the starting point is registered, and the pointers P of the routes 1 and 2 are registered at 102.
(1) and P (2) are set to 1. (The starting point is the first intersection grid point). Then, the search route 1 is selected by 103 and the pointer of the route 1 is obtained by 104. In this case, the first (starting point) is obtained, and the process proceeds to 105 to execute the search process of FIG. In this search process, as shown in FIG. 11, it is confirmed by 201 that the search has been completed for all the vectors branched from the starting point. In this case, however, since no search has been performed, at 202, the branch destination crosses. Among the lattice points, the intersecting lattice point closest to the target point
0) is picked up, and the searched flag is set to the vector from the starting point toward (20) by 203, and then 2
At 04, the absolute vector amount a from the starting point to (20) is obtained along the route 1.

Then, for (20), the absolute vector amount already obtained in 205 is compared with a.
Since + ∞ is initially assigned, the process proceeds to 207 via 206, and the absolute vector amount is updated as the current absolute vector amount with respect to (20). Then, after clearing the flag that has been searched, proceed to 213 via 209,
The position of the intersecting grid point (20) is registered in K (1, 2), 1 is added to the pointer (p), and then Exit is executed via 214.
And ends the process.

Then, the process proceeds to 106 in FIG. 10, and secondly, the pointer P (1) of the route 1 in the area is updated as No. 2, and the process moves to 108 via 107, and the next search route is executed by 108 to 110. -Go to G. In the search route 2, (22) having the second highest priority after (20) is targeted for expansion at 202, and the position of (22) is registered in K (2, 2) at 213 through the same steps. Then, the pointer P (2) of the route 2 is set to 2 at 106 via 214 and after updating, the pointer P (2) is moved to 108 via 107. That is, in this case, since the routes 1 and 2 are performed, the process from 104 is performed again for the route 1.

By repeating the above processing,
Start point 1 → (20) → (21) → (18) → (1
1) → (8), Route 2 is the starting point → (22) → (23)
Assuming that the development has proceeded to → (24) → (19) → (10), first, in the root 1, the sixth intersection lattice point (8) is obtained at 104, and the destination point is picked up at 202. Two
Go from 03 to 209 to 210 K (1,1) ~ K
(1, 6) → Register the route up to the destination point as the shortest route at the present time. Next, in Route 2, the sixth intersection grid point (10) is obtained at 104 and the target point is picked up at 202, but at 205, the absolute vector amount with the next intersection grid point (target point in this case) is obtained. And the absolute vector amount a obtained in 204 is larger, the process returns to 201.
Then, in step 202, (9) is picked up next, but in the comparison processing in step 206, the process returns to step 201. Subsequently, (11) is picked up at 202, but also returns to 201 at 205, and the process proceeds to 212 via 211 due to the completion of all the expansions at the intersection grid point (10), and the intersection grid point (19) immediately before is intersected. ) Return to. Pointer P of root 2 at this time
(2) becomes 5.

When the above processing is advanced, the object of expansion returns to the starting point, the search is completed, and the route where the absolute vector amount of the destination point becomes the minimum value, that is, the shortest route is obtained. If multiple routes are set as search routes, 1
After completion of one search route, pointer P of each route
(1), P (2), ... P (n) is searched for a minimum value other than 1 and the crossing grid point position of the searched route is transferred to the route for which the search is completed. -This is preferable because it further improves the performance. The flow chart in this case is shown in FIG.

In the above search, if the vector quantity simply representing the length is used, the shortest route in terms of distance can be obtained.
Also, the shortest route in time can be obtained by substituting the real vector quantity.

Next, returning to FIG. 1, the overall operation of the embodiment of the present invention will be described. First, the driver or passenger operates the touch panel T to operate the road map data in the memory 3.
The area including the starting point and the destination point is cut out from the computer and stored in the graphic controller 4, the starting point and the destination point are set by scrolling, and then the touch panel T is used to set the starting time point and the weather point. After inputting information corresponding to external factors, the CPU 1 requests the root display.
Give to. In response to this instruction, the CPU 1 first vectorizes the cut road map data as described above, and stores the vectorized data in the RAM 11 (which corresponds to the memory 1 of the present invention in this example). After storing, search processing is performed as described above. In this case, first, the vector amount obtained from the vectorized data is used as it is to perform the above-mentioned search, thereby finding the shortest route in distance, and thereafter, as described above in detail, each vector amount and the attribute information memory 31 are stored. The actual vector quantity is obtained by multiplying with the corresponding traffic volume coefficient of and the similar search is performed using this actual vector quantity.
This determines the shortest route in time.

Further, an appropriate route is obtained by the search processing, and the manner of the determination will be described with reference to FIGS. 15 to 24. For convenience of explanation, if the traffic volume frequency and the traffic volume coefficient are set as shown in the table shown in FIG. 15, there are seven combinations of traffic volume frequencies as shown in the table shown in FIG. If traffic volume frequencies are assigned to the vector groups as shown in FIG. 17, the shortest distance-wise rule is obtained only by the traffic volume frequencies for each combination of traffic volumes shown in FIG.
Then, the total value of the actual vector amount of the route is calculated. When there are a plurality of shortest distance routes in a certain combination, the total value of the actual vector amounts is calculated for each. 18 to 24 show distance-wise shortest routes corresponding to the above seven combinations with respect to the vectorized data shown in FIG. 17, respectively.

Here, the total value of the real vector amount of the shortest route in each distance is calculated as follows.

(Regarding the shortest route in the case of traffic volume 1) As shown in FIG. 18, there is one route.

1 (vector volume) × 1 (traffic volume coefficient) × 23 (number of vectors) = 23 2 ^1/2 × 1 × 5 = 7.1 total real vector volume = 30.1 (distance of traffic frequency 2) The shortest route) Figure 19
As shown in, there is no shortest route in terms of distance. (Regarding the shortest route with a traffic frequency of 3)
As shown in, there is no shortest route in terms of distance. (Regarding the shortest route in terms of distance with traffic frequencies 1 and 2) As shown in FIG. 21, there is one shortest route in terms of distance.

1 (vector amount) × 1 (traffic amount coefficient) × 7 (number of vectors) = 7 2 ^1/2 × 1 × 5 = 7.1 1 × 1.5 × 10 = 15 total actual vector amount = 29 .1 (Regarding the shortest route in terms of distance of traffic frequency 1 and 3) There is one shortest route in terms of distance as shown in FIG.

1 (vector amount) × 1 (traffic volume coefficient) × 9 (vector number) = 9 2 ^1/2 × 1 × 3 = 4.2 1 × 2 × 8 = 16 2 ^1/2 × 2 × 3 = 8.5 Total of real vector amount = 37.7 (about the shortest route in terms of distance of traffic volume 2 and 3) Fig. 23
As shown in, the shortest route in terms of distance is four ways. Route A → C 1 (vector amount) × 1.5 (traffic coefficient) × 5 (number of vectors) = 7.5 1 × 2 × 12 = 24 2 ^1/2 × 2 × 3 = 8.5 actual vector amount Total = 40 Route A → D 1 (vector amount) × 1.5 (traffic coefficient) × 3 (number of vectors) = 4.5 1 × 2 × 14 = 28 2 ^1/2 × 2 × 3 = 8.5 Total real vector amount = 41 Route B → C 1 (vector amount) × 1.5 (traffic amount coefficient) × 9 (number of vectors) = 13.5 1 × 2 × 8 = 16 2 ^1/2 × 2 × 3 = 8.5 Total actual vector amount = 38 routes B → D 1 (vector amount) × 1.5 (traffic coefficient) × 7 (number of vectors) = 10.5 1 × 2 × 10 = 20 2 ^1/2 × 2 × 3 = 8.5 Total real vector amount = 39 (about the shortest route of traffic volume 1, 2, 3)
As shown in FIG. 24, there are two shortest routes in terms of distance. Route A → B 1 (vector amount) x 1 (traffic volume coefficient) x 3 (number of vectors) = 3 2 ^1/2 x 1 x 3 = 4.2 1 x 1.5 x 3 = 4.5 1 x 2 × 5 = 10 2 ^1/2 × 2 × 3 = 8.5 Total real vector amount = 30.2 Route A → C 1 (vector amount) × 1 (traffic coefficient) × 3 (number of vectors) = 3 2 ^{1 / 2} x1 x3 = 4.2 1 x1.5 x1 = 1.5 1 x2 ^x7 = 14 2 ^1/2 x2 x3 = 8.5 Total real vector amount = 31.2 Therefore In the above example, the total value of the actual vector quantities by the combination of the traffic volume frequencies 1 and 2 is 29.1, which is the minimum value, so the route shown in FIG. 21 is an appropriate route.

After such a retrieval process is performed, the graphic controller 4 selects the shortest route in terms of distance from the road map data displayed on the image display unit 5, for example.
Color display, blinking display, etc. are performed for the shortest route in time and the appropriate route. Further, a voice display for detecting the position of the vehicle by the azimuth sensor S1 and the distance sensor S2 and guiding the traveling direction of the intersection and the like may be performed together with the screen display, for example. The shortest route in terms of distance and the shortest route in terms of time do not necessarily need to be searched and displayed. However, if these are displayed together with an appropriate route, the degree of freedom in selecting a route is expanded. Therefore, it is preferable.

The appropriate route obtained in the above embodiment is a route which is psychologically matched, that is, a route which the driver can select without difficulty because the distance is relatively short and the time is fast. On the other hand, the shortest route in terms of distance does not take into account the traffic volume, so the required time may be considerably long in some cases, and the shortest route in terms of time is short, but Depending on the situation, the route may become a detour or a complicated route, and it may be difficult to select psychologically.

In the above-described embodiment, only one-way vector is assigned to the one-way road, but it is also possible to assign vectors in both directions without incorporating one-way elements. When a route that flows backward in one direction is included, the next shortest route is sequentially searched, and when the backward flow route disappears, that route may be set as the shortest route.

The method of searching for the shortest route is not limited to the above method.

[0049]

According to the present invention, the required travel time for a certain distance is divided into a plurality of frequencies, and the traffic volume frequency corresponding to the congestion level of vehicles is defined. In order to find a route, the shortest route among those shortest routes is selected as an appropriate route, which is advantageous in terms of distance and time, and psychologically. It can be provided to drivers and passengers as a comfortable route and is effective as a navigation device.

[Brief description of drawings]

FIG. 1 is an explanatory diagram showing an overall configuration of an embodiment of the present invention.

FIG. 2 is an explanatory diagram showing an example of a road section.

FIG. 3 is an explanatory diagram showing an example of a traffic volume table.

FIG. 4 is an explanatory diagram showing an example of a traffic volume coefficient table.

FIG. 5 is an explanatory diagram in which road map data is superimposed on a grid.

FIG. 6 is an explanatory diagram illustrating a condition for displaying a road in contact with a grid point.

FIG. 7 is an explanatory diagram exemplifying vectorization of roads.

FIG. 8 is an explanatory diagram showing vectorized data.

9 is an explanatory diagram showing a part of the vectorized data of FIG.

FIG. 10 is a flowchart showing an example of processing by a search unit.
It is

FIG. 11A is a flowchart showing an example of processing by a search unit.

FIG. 11B is a flowchart showing an example of processing by the search unit.

FIG. 12 is an image diagram of data in the first memory.

FIG. 13 is an image diagram of data in the second memory.

FIG. 14 is a flowchart showing another example of a part of the processing of the search unit.

FIG. 15 is a table in which traffic frequency and traffic coefficient are associated with each other.
It is an explanatory view showing a bull.

FIG. 16 is an explanatory diagram showing a table defining a combination of traffic volume frequencies.

FIG. 17 is an explanatory diagram showing vectorized data in association with traffic volume frequencies.

FIG. 18 is an explanatory diagram showing a distance-wise shortest route with a traffic volume frequency of 1;

FIG. 19 is an explanatory diagram showing a shortest route in terms of distance with a traffic volume frequency of 2;

FIG. 20 is an explanatory diagram showing a distance-wise shortest route with a traffic volume of 3;

FIG. 21 is an explanatory view showing a shortest route in terms of distance with traffic volume frequencies of 1 and 2.

FIG. 22 is an explanatory diagram showing a shortest route in terms of distance with traffic volume frequencies 1 and 3.

FIG. 23 is an explanatory diagram showing a shortest route in terms of distance when the traffic volume is 2 or 3;

FIG. 24 is an explanatory diagram showing a distance-wise shortest route of traffic volume frequencies 1, 2, and 3.

[Explanation of symbols]

 1 CPU 21-23 Interface 3 Road map data memory 31 Attribute information memory 5 Image display section 6 Voice display section

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Shinichi Miyagawa 2-3-1, Nishishinjuku, Shinjuku-ku, Tokyo Tokyo Electron Limited

Claims (1)

[Claims] 1. A first memory in which data of a vector group obtained by linearly approximating a road map is stored, and a required travel time for a certain distance is divided into a plurality of frequencies, which are used as traffic frequency frequencies. A second memory in which a coefficient is assigned to the traffic volume, the road map is divided into a plurality of sections, and each section and the traffic volume are stored in association with each other, and the respective memories of the first memory and the second memory are stored. -For each combination of traffic frequency with reference to the data, find the shortest route in distance from the starting point to the destination point only by the vector of traffic frequency included in the combination, For a route, a search unit that obtains the total value of the actual vector amounts obtained based on the vector amount and the coefficient, and searches the distance-wise shortest route that minimizes the total value as an appropriate route. , A table displaying the appropriate routes obtained by this search unit A navigation device comprising a display section.