JPH0562687B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a vehicle navigator device that guides and displays the current position of a vehicle on a course of travel from a starting point to a destination point.

(Prior Art) Conventionally, this type of vehicle navigator device is known, for example, as disclosed in Japanese Patent Application Laid-Open No. 56-74798.

In this conventional device, for example, when a departure point A and a destination point B are set while looking at a road map image (road network image) displayed on a display as shown in FIG. 9, the points are stored in the memory. The system searches for the optimal course from map data and displays the vehicle's course as indicated by a broken line on the road network on the display.
When the vehicle starts traveling, the current position of the vehicle as it travels is calculated from the direction of travel of the vehicle using the magnetic azimuth sensor and the amount of travel of the vehicle determined by the distance sensor, and the current position of the vehicle that changes from moment to moment on the displayed course is displayed. I am trying to display the guide.

By the way, Moore's method, which finds the shortest distance between multiple points, is known as a method for searching the vehicle course from road network data, for example. In this case, the optimal course is determined by executing a course search using all road network data included in the target area, that is, intersection position data.

(Problems to be Solved by the Invention) However, in such a conventional course search, as the distance to the destination point increases, the number of intersection data to be searched for the course increases, resulting in an increase in calculation time and Since memory capacity continues to increase at an accelerating pace, long-distance course searches have been difficult in practice.

(Means for Solving the Problems) The present invention has been made in view of these conventional problems, and allows for long-distance course searches without significantly increasing calculation time or data capacity. To provide a vehicle navigator device that allows easy course search.

In order to achieve this purpose, in the present invention, identification information indicating a hierarchy level according to the degree of importance, such as national roads, prefectural roads, and city roads, is added to the road network data, and when searching for a course, the top A rough search for a course is performed based on road network data at a lower level, and then connecting routes, etc. of the rough search course are interpolated by searching lower level road network data.

(Embodiment) FIG. 1 is a block diagram showing the basic configuration of the present invention.

First, to explain the configuration, 1 is a position calculation means that calculates the current position of the vehicle, and is equipped with a magnetic azimuth sensor and a distance sensor, and calculates the vehicle's traveling direction and movement based on the direction of the earth's magnetism detected by the magnetic azimuth sensor. The vehicle position on the map coordinates is calculated based on the travel distance of the vehicle measured by the quantity sensor. 2 is a map data storage means, which stores road network data for each district, for example, data indicating the position coordinates of intersections, and each road network data has information indicating the importance of the road, for example, rank A, rank B. Identification information of rank and C rank are added. The hierarchy levels A to C are predetermined depending on the degree of usage, such as national highways, prefectural roads, and city roads. Further, even if the national highway is the same, a higher rank level is set for important intersections such as branch points with other national highways and urban areas.
Furthermore, for roads in urban areas where the distance between intersections is long, at least one of the intersections within a range of 50 km in diameter, for example, is assigned the highest A rank.

A specific example of the road network data to which identification information of a hierarchical level determined according to the degree of importance is added, which is stored in the map data storage means 2, will be explained with reference to FIGS. 2 and 3.

FIG. 2 shows an example of a road map for obtaining road network data, and position data of each intersection No. 1 to No. 8 is stored as the road network data.
Also, depending on the importance of each intersection, Nos. 2 and 6 are ranked A, Nos. 1, 4, 7, and 8 are ranked B, and Nos. 3 and 5 are ranked C.
It is assumed that it is positioned in the rank.

FIG. 3 shows an example of road network data on the road map in FIG. 2. The first data area stores position data corresponding to intersections No. 1 to 8, followed by each intersection The route to the next intersection at points 8 to 8 is stored as data in meters, followed by the next intersection number to be the branch destination. Taking intersection No. 2 as an example, this next No. branches into intersection Nos. 1, 3, and 4, so Nos. 1, 3, and 4 are the next intersection No. Stored. The last data area stores identification information of ranks A, B, and C indicating the rank level determined for each intersection.

Referring again to FIG. 1, reference numeral 3 denotes input means, which is used to set data on the starting point and destination point, and further includes a start button and the like to start the course guide.

4 is a course search means which reads road network data of the target area specified by the input means 3 from the map data storage means 2, and selects the optimal vehicle based on the data of the starting point and destination point set by the input means 3. Search for progression courses. This course search method 4
As shown in FIG. 3, the course selection in , first of all, is based on the fact that the road network data from the map data storage means 2 has ranks A to C indicating the degree of demand for the road network data. Top rank A
A rough search for the course is performed using only the road network data with A final course interpolation search is performed using C-rank road network data, which is a lower level, and a vehicle course is determined.

5 is a display control means, which displays the road network image of the designated area read out by the course search means 4.
The image is displayed on a display means 6 using a CRT, liquid crystal, display, etc., and the vehicle course searched by the course search means 4 is displayed superimposed on the road network image. Further, when the course guide is started, since the position calculation means 1 provides the vehicle position which changes from time to time, this vehicle position is displayed as a bright spot on the display means 6.

FIG. 4 is a flowchart showing the course search processing operation according to the embodiment of FIG.

First, when searching for a course, when a target area is specified using the input means 3, the course search means 4 reads the road network data of the specified area from the map data storage means 2, and under the control of the display control means 5. The road network image is then displayed on the display means 6. When the data of the starting point and the destination point are set by the input means 3 as shown in block 7 in the display state of this road network image, the process proceeds to determination block 8, where it is checked whether the destination is a long distance or not. At this time, if the set course is a long distance, for example over 100 km, blocks 9, 10,
The course search process shown in step 11 is executed.

Figure 5 shows the state of course search using A rank data in block 7 of Figure 4.
Only road network data that provides a road network, ie, intersection locations, are extracted and shown, with double circles indicating A rank, white circles indicating B rank, and black dots indicating C rank. Therefore, when searching for courses using A rank data, which is the upper level in block 9,
For example, if the A-rank intersection A1 is set as the departure point 14 and the intersection A5 is set as the destination point 15, the vehicle course will be indicated by a broken line connecting the A-rank intersections that exist between the departure point 14 and the destination point 15. A rough search is performed.

When the course rough search shown in Fig. 5 is completed,
The process moves to course interpolation using the B rank data shown in block 10.

Figure 6 shows the interpolation process for a roughly searched course using B rank data (intersections marked with white circles). For example, B-rank intersection data B1 included in a circle 16 whose diameter is intersections A1 and A2,
Select B2 and B3 and use this B rank data B1~
When searching for the shortest distance course between B3,
A course that passes through intersection B2 is searched as shown by the dashed line. Course interpolation using the B rank data is similarly performed for the remaining intersections A2 to A5 as shown by the dashed line.

Note that if the circle 16 whose diameter is the distance between intersections A1 and A2 does not include B-rank intersection data, course interpolation using B-rank data is not performed.

When the course interpolation using the B rank data shown in FIG. 6 is completed, the C
Course interpolation is performed using rank data.

FIG. 7 shows course interpolation using this C rank data. For example, between intersection data A1 and intersection B2 that give starting point 14,
Set a circle 17 whose diameter is the path of both intersections,
Using C-rank intersection data C1 and C2 included in this circle 17, a solid line course passing through the intersection C1, which is the shortest course, is interpolated. Such interpolation using the C-rank data is performed in the same way for the vehicle course obtained by course interpolation using the remaining B-rank data, and the final vehicle course from the departure point 14 to the destination point 15 is shown by a solid line. A search is performed.

In this way, when the vehicle course is determined by the course search using sequentially the A to C rank data shown in blocks 9 to 11, the process proceeds to block 12, where the display means 6 is displayed under the control of the display control means 5. 7, the vehicle course shown by the solid line in FIG. 7 is displayed superimposed on the road network image.

On the other hand, if the destination is a short distance,
Since the amount of road network data that exists between the destination and the destination is relatively small, proceed from determination block 8 to block 13 to perform a course search using all the intersection data that exists between the departure point and the destination. Let's do it.

In this way, in the course search of the present invention, when the number of road network data is enormous, such as for long distances, the course selection is performed from the upper level to the lower level for each rank data indicating the rank level. In the past, because this was done sequentially, the number of road network data ranked in the higher ranks did not increase significantly even if the distance became longer, and when a rough course search using the higher level road network data was completed, the higher level road network data Since course interpolation is performed for each lower-level road network data between This can significantly reduce the calculation time for course search and the storage capacity of road network data required for course search.

In the flowchart of Fig. 4, the course search is divided into long distance and short distance, but in another embodiment, the course search is divided into three stages: long distance, medium distance, and short distance, and the search for long distance A rank, B rank, C
A course search is performed in order of rank, and for medium distances, a course search is performed using A rank and B rank data as the same level data, and then a course search is performed using the lower level C rank data, and for short distances, the previous data is used. You can also search for courses using . Furthermore, in the course interpolation shown in Figs. 6 and 7, lower-ranked road network data to be interpolated between higher-ranked intersections was selected based on road network data included in a circular area. The area from which road network data is selected is not limited to a circular shape, but can be any suitable shape such as a rectangle or a polygon.

Furthermore, in the flowchart shown in Figure 4, the course search was performed without distinguishing between expressways and local roads, but when using expressways, the course search can be performed using only entrances and exits, as shown in Figure 4. Block 9-1
The course search according to the hierarchy level shown in 1 only needs to be performed for general roads other than expressways.

FIG. 8 is a block diagram showing a specific embodiment of the present invention.

In FIG. 8, the position calculation means 1 is first composed of a position calculation CPU 16, a magnetic azimuth sensor 17, and a movement amount sensor 18.
Based on the unit travel distance of the vehicle detected by the moving layer sensor 18, the position calculation CPU 16 calculates the vehicle position with the starting point as the origin.

The map data storage means 2 in FIG. 1 is a ROM
This is realized by a card 19, and by setting the ROM card 19 in the card reader 20, it is possible to read road network data to which hierarchical level identification information has been added. The input means 3 also includes a shift key 3a for moving a cursor to set a starting point and a destination point, a set key 3b for setting a destination point and a starting point at the cursor set position by the shift key 3a, and a set key 3b for starting a course guide. A start button 3c is provided.

Next, the course search means 4 in the embodiment shown in FIG. 1 is realized by a map calculation CPU 21 and a RAM 22. The map calculation CPU 21 reads the road network data of the area specified by the input means 3 from the card reader 20 in which the RAM card 19 is set.
RAM22 is exceptional. Furthermore, when the data of the departure point and destination point are given from the input means 3,
A course search is performed from a higher level to a lower level based on the hierarchical level identification information added to the road network data stored in the RAM 22, and the searched vehicle course is written in the RAM 22 as well. Furthermore, vehicle position data calculated by the position calculation CPU 16 is written into the RAM 22 when route guidance is started.

The display control means 5 is realized by a display CPU 24 operated by the clock pulse of the second clock 23,
The display CPU 24 is connected to the RAM 22 on the map calculation CPU 21 side via the I/O port 25.
Based on the data stored in the RAM 22, a display device 26 serving as a display means displays a road network image, a vehicle course, and the current location of the vehicle. For this display control, the display CPU 24
The output stage includes a first clock 26, a frequency divider 27, and a P
-S converters 28a to 28c, controller 29,
data selector 30, bus separator 31,
A display control circuit consisting of RAM 32 is provided.

(Effects of the Invention) As described above, according to the present invention, identification information indicating a rank level according to the degree of importance, such as national highway, prefectural road, or city road, is added to the road network data, When searching for a course, a rough search for the course is performed based on the upper-level road network data, and then the connecting routes of the rough-searched course are interpolated by searching the lower-level road network data, making it possible to search for long-distance courses. However, since the course search is performed sequentially from the upper level to the lower level, the number of road network data used at each hierarchical level is limited, and as a result, even when searching for a long distance course, the calculation time decreases. Since the time is short and the amount of data used for course search is small, data storage capacity can be significantly reduced.

[Brief explanation of the drawing]

Figure 1 is a block diagram showing the basic configuration of the present invention, Figure 2 is an explanatory diagram of a road map with hierarchical levels added, Figure 3 is an explanatory diagram showing an example of road network data, and Figure 4 is an explanatory diagram showing an example of road network data. A flowchart showing the course search operation according to the present invention. Figures 5, 6, and 7 are explanatory diagrams showing the progress of route search according to the present invention. Figure 8 is a block diagram showing a specific embodiment of the present invention. , FIG. 9 is an explanatory diagram of conventional route search. 1: Position calculation means, 2: Map data storage means,
3: input means, 4: course search means, 5: display control means, 6: display means, 17: magnetic direction sensor,
18: Movement amount sensor, 16: CPU for position calculation,
19: ROM card, 20: Card reader, 2
1: CPU for map calculation, 22, 32: RAM, 2
4: Display CPU, 23: Second clock, 25:
I/O port, 40: Display device, 26:
First clock, 27: Frequency divider, 28a-28c:
P-S converter, 29: controller, 30: data selector, 31: bus separator.

Claims (1)

[Scope of Claims] 1. A vehicle traveling course is determined based on road network data from the map data storage means, current position data from the position calculation means, and starting point and destination point data from the input means, and is displayed as a guide on the display means. In a vehicle navigator device configured to enable navigation from a departure point to a destination point using upper-level road network data for arterial roads among identification information indicating road rank levels added to the road network data. The present invention is characterized by comprising a course retrieval means that performs a rough search for the course up to and after the rough search, uses lower-level road network data to correct connecting roads, etc. of the rough search course to calculate a vehicle traveling course. Vehicle navigator device.