JP4370565B2 - Vehicle navigation device - Google Patents

Description

  The present invention relates to a vehicle navigation device that improves position detection accuracy.

  The vehicle travel distance is obtained by counting pulses (vehicle speed pulses) generated according to the rotation of the tire and multiplying this counted value by a coefficient (distance coefficient) for converting the number of pulses into a distance. The current position is being detected. However, since the vehicle navigation system does not know what type of tire is installed, it calculates and learns the distance count for each vehicle using the map data and past travel data, and the detection accuracy of the current position is increased. I try to raise it. The learning of the distance coefficient is usually performed by detecting that the vehicle has traveled a certain distance (straight road) on the map data using GPS positioning data and obtaining the number of vehicle speed pulses during this period. In order to further improve the detection accuracy, the altitude of the vehicle is also detected using an inclination angle sensor.

By the way, if the tire diameter or pressure changes during high-speed running, an error occurs only with the learned distance coefficient, and the current position detection accuracy deteriorates. For this reason, there has been proposed a method for correcting the travel distance by measuring a change in the diameter of the tire (Patent Document 1), but it is not necessarily sufficient for improving the current position accuracy. Further, even when the vehicle travels in an area where a level difference occurs, an error occurs only with the learned distance coefficient, and the current position accuracy deteriorates. For example, on a slope with a steep slope, the error between the distance on the two-dimensional map and the actual distance increases, and the vehicle position detection accuracy deteriorates. On the other hand, although what has detected and correct | amended the height of a vehicle using an inclination-angle sensor is proposed (patent document 2), it is not necessarily enough for the improvement of the present position accuracy.
Japanese Patent Laid-Open No. 10-239092 JP-A-10-253352

The present invention is intended to solve the above-described problems, and an object thereof is to maintain and improve the current position detection accuracy of a vehicle.
Therefore, the present invention has a distance coefficient learning means for learning a distance coefficient of a vehicle speed pulse with respect to a travel distance of the vehicle, a control means for controlling the distance coefficient learning means, and a learning stop factor for stopping learning of the distance coefficient. Determination means for determining whether or not, the control means registers an area determined to have a learning stop factor as a learning stop area, and stops distance coefficient learning when traveling in the learning stop area It is characterized by that.

  According to the present invention, it is possible to prevent the distance coefficient learned by traveling at a predetermined distance from being corrected by a subsequent traveling in a situation that is not suitable for learning, thereby preventing the position detection accuracy from being lowered, and maintaining and improving the current position detection accuracy. It becomes possible.

Hereinafter, embodiments will be described.
FIG. 1 is a conceptual diagram illustrating an example of an embodiment of a vehicle navigation device.
The navigation control unit 1 is a control unit that executes each process of navigation. In this embodiment, the navigation control unit 1 includes a distance coefficient learning unit 2, a current position detection unit 3, and a control unit 4, and includes an information transmission / reception device 10, a GPS (Global Positioning System). ) The data of the map data 20 stored in the information storage means 14 while taking in the information from the position detection device (hereinafter referred to as GPS) 11, the vehicle speed sensor 12 that generates a pulse corresponding to the rotation of the tire, and the inclination angle sensor 13. The distance coefficient learning process and the current position detection process are performed with reference to the above, and the control unit 4 controls these processes. The information storage means 14 stores altitude polygon data 21, travel data 22, distance coefficient 23, and other various data 24 necessary for navigation, in addition to map data 20 including road data to which a learning stop attribute is assigned. Yes.

  The distance coefficient learning unit 2 is controlled by the control unit 4 and travels 100 m using, for example, GPS 11 data on a straight road acquired from the map data 20 stored in the information storage unit 14. A coefficient for converting the acquired number of pulses into a distance (distance coefficient) is calculated. The distance coefficient is learned by performing such processing at any time during vehicle travel, the distance coefficient is substantially stabilized by learning by traveling about 10 km, and the accuracy is improved by further learning, and the current position detecting means 3 Detects the current position from the learned distance coefficient and the vehicle speed pulse obtained from the vehicle speed sensor 12.

FIG. 2 is a diagram showing a processing flow for stopping learning of the distance coefficient.
In the present embodiment, the distance coefficient is learned by traveling the vehicle (step S1), and it is determined whether the vehicle has learned the distance coefficient by traveling a predetermined distance, for example, 10 km or more (step S2). After the distance coefficient is stabilized, the control unit 4 determines whether the driving is in a situation that is not suitable for learning (there is a learning stop factor) (step S3). The coefficient learning means is controlled to stop distance coefficient learning. In this process, after the distance coefficient is stabilized, if the learning is further continued in a driving state that is not suitable for learning, the distance coefficient is corrected by inaccurate data to cause an error, and the position detection accuracy is lowered. Because.

  As a learning stop factor, for example, when traveling in an area that can be recognized as having a height difference from the elevation polygon data 21, an area in which the inclination angle sensor 13 detects that the vehicle is inclined at a predetermined angle (for example, 10 °) or more. If the driver receives information on weather conditions and road conditions that may cause a slip from the information transmitting / receiving device 10 and the driver inputs the information to the navigation device, a learning stop attribute is given to the road data. For example, when traveling in a certain area, it may be traveling in an area determined not to be learned in past traveling. Of course, when traveling in an area where there is no cause for learning stoppage, the distance coefficient is learned at any time to further improve the position detection accuracy.

  FIG. 3 is a plan view for explaining an example of elevation polygon data. In this example, each small section having coordinates (latitude and longitude) at the four corners is a section of several tens of meters (for example, 30 m), and FIG. 3A shows an elevation difference (in the area) for each small section ( Difference between maximum altitude and minimum altitude) ΔH1, ΔH2..., FIG. 3B shows the average altitude H1, H2 in the area for each subdivision and elevation data at the center position of the subdivision An example is shown.

  When traveling in an area where elevation polygon data shown in FIG. 3 (a) is set, there is a difference in height, so the error between the distance on the two-dimensional map and the actual distance becomes large. The vehicle position detection accuracy detected at 1 will deteriorate. Therefore, after traveling over a predetermined distance and stabilizing the distance coefficient, learning of the distance coefficient is stopped when traveling in an area in which such elevation polygon data is set. In the area where the elevation polygon data shown in FIG. 3B is set, if it can be recognized that there is an elevation difference of a predetermined value or more from the difference in average elevation of each area, it is determined that the situation is not suitable for learning. Then, learning of the distance coefficient is stopped.

FIG. 4 is a diagram for explaining learning attributes of road data.
The road data of this embodiment has a distance coefficient learning attribute in addition to the road number, road attribute, shape data, and the like. The road number is set for each direction between the junctions (outbound and return roads), and the road attributes are data indicating the type of road (highway, national road, prefectural road, etc.), elevated, underpass, width, number of lanes, etc. is there. The shape data is composed of a plurality of node rows, and each node is composed of coordinate data, elevation, node attributes (attributes indicating intersection nodes, simple nodes, road end nodes), and the like, and links are connected to the nodes. The distance coefficient learning attribute is a flag ("1": distance coefficient learning non-target road, "0": distance coefficient learning target road) indicating whether the distance coefficient learning target road or non-target road, for example, a mountain area It is attached in advance as a road that is not suitable for learning because of its undulations. The distance coefficient learning attribute may be any method such as for each road number, for each link, or for node data.

FIG. 5 is a diagram showing a processing flow for utilizing past travel data.
In the present embodiment, if there is an error between the current position calculated using the distance coefficient when traveling and the current position calculated based on GPS positioning data after sufficiently learning the distance coefficient, Is recognized and registered as “the region where the positional deviation has occurred”, and it is determined that the distance coefficient should not be learned the next time the vehicle travels in that region, the learning is stopped.

  The distance coefficient is learned by traveling the vehicle (step S11), it is determined whether the vehicle has learned the distance coefficient by traveling more than a predetermined distance (step S12), and the distance coefficient is stabilized after traveling for a predetermined distance. By means 4, it is determined whether or not the vehicle is traveling in an area where there has been a positional shift when traveling in the past (step S13). In the case of traveling in an area where there is a positional deviation, learning of the distance coefficient is stopped (step S16). If it is not registered as an area where there has been a position shift during past travel, it is determined whether there is an error between the current position calculated by the distance coefficient in the current travel and the current position calculated based on GPS positioning data ( Step S14). If there is an error, for example, the positional deviation data is registered as an area where the positional deviation is in units of districts such as large letters and small letters levels (step S15), and distance coefficient learning is stopped.

  In the above-described embodiment, after learning a factor for stopping the learning by determining the learning factor after the distance coefficient is stabilized after traveling a predetermined distance, the learning factor for the learning is canceled before the distance factor is stabilized. The presence or absence of learning may be controlled. In this case, the distance coefficient learning is canceled while traveling in a region that includes a learning stop factor, so it takes time for the distance coefficient to stabilize, but accurate learning can be performed from the start of distance coefficient learning. It becomes.

  According to the present invention, an error can be prevented from occurring in a sufficiently learned distance coefficient, and the current position detection accuracy can be maintained and improved. Therefore, the industrial utility value is extremely large.

It is a conceptual diagram explaining the example of this Embodiment. It is a figure which shows the processing flow which stops learning of a distance coefficient. It is a figure explaining the example of elevation polygon data. It is a figure explaining the learning attribute of road data. It is a figure which shows the processing flow using past driving | running | working data.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Navigation control part, 2 ... Distance learning means, 3 ... Current position detection means, 4 ... Control means, 10 ... Information transmission / reception apparatus, 11 ... GPS, 11 ... Vehicle speed sensor, 13 ... Inclination angle sensor, 14 ... Information storage means .

Claims (3)

Distance coefficient learning means for learning a distance coefficient of a vehicle speed pulse with respect to a travel distance of the vehicle;
And control means for controlling the distance coefficient learning means,
A determination means for determining whether or not there is a learning stop factor for stopping learning of the distance coefficient ,
The vehicle navigation apparatus, wherein the control means registers an area determined to have a learning stop factor as a learning stop area, and stops distance coefficient learning when traveling in the learning stop area . The determination means determines that there is a learning stop factor when an error occurs between a current position calculated using a distance coefficient when the vehicle travels and a current position calculated based on GPS positioning data, 2. The vehicle navigation apparatus according to claim 1, wherein the control unit registers an area determined to have a learning stop factor by the determining unit as a learning stop area. The control means, when learning by the distance coefficient learning means has reached a predetermined distance or more, registers an area determined to have a learning stop factor by the determination means as a learning stop area. Item 3. The vehicle navigation device according to Item 1 or 2.

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