JP2007272119A - Planimetrical feature information output device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a planimetrical feature information output device and a planimetrical feature information output method that can output planimetrical feature information showing arrangements and topology of planimetrical features in detail and also suppress small the amount of data required to be stored in an information storage means to output such planimetrical feature information. <P>SOLUTION: The planimetrical feature information output device includes: a planimetrical feature topology information storage means DB1 which stores planimetrical feature topology information Fa for every planimetrical feature kind; a planimetrical feature arrangement information storage means DB2 which stores planimetrical feature kind information Fb on each planimetrical feature and planimetrical feature arrangement information Fc on each planimetrical feature; and a planimetrical feature information generating means 3 of acquiring planimetrical feature kind information Fb and planimetrical feature arrangement information Fc on a planimetrical feature nearby a specified position from the planimetrical feature arrangement information storage means DB2, acquiring planimetrical feature topology information Fa of the planimetrical feature kind from the planimetrical feature topology information storage means DB1 according to the acquired planimetrical feature kind information Fb, and generating and outputting planimetrical feature information F showing the arrangement and topology of the planimetrical feature nearby the specified position on a road surface based upon the planimetrical feature arrangement information Fc and planimetrical feature topology information Fa. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a feature information output device that outputs feature information about a planar feature placed on a road surface in the vicinity of the designated position in accordance with an input of the designated position from a position input means.

  As a technique for managing feature information used for a map database or the like, for example, the following Patent Document 1 discloses a technique related to a feature management apparatus as described below. This device has a configuration in which an input / output unit, a graphic element storage unit, an attribute data storage unit, a memory storing a feature management program, a memory storing a basic program, and a CPU are connected by a bus. Yes. Here, the feature management program is a program that defines and manages the storage method of the graphic elements and attribute data of the features. Each feature is managed by being given a unique management number to distinguish it from other features. That is, the management number (ID code) is input from the input / output unit for each feature, and the graphic element of the feature is input. With these inputs, the graphic element is stored in the graphic element storage unit in association with the management number.

  For example, when the feature is a building, the representative coordinate value of the graphic element is expressed as a flat shape of the graphic element generated by horizontal projection of the digital space data, and is given as the latitude and longitude values of the center of gravity of the flat shape. . In addition, in the graphic element storage unit, the graphic element is associated with the management number given as described above for each feature, and the graphic element uses a point, line, or plane according to the feature definition as a coordinate value. Is memorized. Lines and surfaces are expressed and stored in a vector format based on points, line lengths, and orientations. In the attribute data storage unit, for example, when the feature is a building, the structure, usage, number of floors, and the like are stored in association with the management number of the feature object as the subject attribute representing the characteristic.

JP 2005-164945 A (page 2-5, Fig. 1-2)

  In the above technique, when storing feature information, for each feature to be managed, the coordinate value of the center of gravity of the planar shape of the feature is used as information on the representative position of the feature, and the length and direction of the points, lines, The vector data according to is stored as information representing the planar shape of the feature. When the number of features to be managed becomes enormous, there is a problem that the amount of data stored in the graphic element storage unit has to be very large.

  On the other hand, in order to reduce the amount of data, it may be possible to store the shape of the feature as data that is simplified from the actual shape. However, in order to perform image recognition using the feature information and use the result for vehicle control, route guidance, or the like, it is necessary to create a detailed database of the position and form of the feature. Therefore, depending on the use of the feature information, information representing the shape of the feature cannot be easily simplified.

  The present invention has been made in view of the above-described problems, and an object of the present invention is to output feature information indicating the arrangement and form of the feature in detail and to output such feature information. Therefore, an object of the present invention is to provide a feature information output device and a feature information output method that can reduce the amount of data required to be stored in an information storage means.

  According to the present invention for achieving the above object, a feature that outputs feature information about a planar feature arranged on a road surface in the vicinity of the designated position in accordance with an input of the designated position from the position input means. The characteristic configuration of the information output device is arranged on the road surface with the feature form information storage means for storing the feature form information that defines the form of the feature of one feature type for each feature type. For a plurality of planar features, feature location information storage means for storing feature type information indicating the feature type of each feature and feature location information indicating the position and direction of each feature, and the vicinity of the specified position The feature type information and the feature arrangement information of the feature are acquired from the feature arrangement information storage means, and the feature form information of the feature type is obtained in accordance with the acquired feature type information. Obtained from the morphological information storage means, A feature information generating means for generating and outputting feature information indicating the arrangement and form of the feature in the vicinity of the designated position on the road surface based on the feature arrangement information and the feature form information; is there.

  By the way, for example, a planar feature arranged on a road surface, such as a road marking or a lane marking called a so-called road paint, is often stipulated by laws and regulations. According to this feature configuration, the shape, size, etc. of the features having a common form to the features belonging to the same feature type, such as the features whose shapes, dimensions, etc. are stipulated by laws and regulations, are as follows. The included feature form information is stored in the feature form information storage unit for each feature type. Therefore, the feature form information, which is redundant information for a plurality of features belonging to the same feature type, is stored in common, and the amount of data stored in the information storage unit can be kept small. On the other hand, information such as the position and direction of each feature placed on the road surface is stored in the feature placement information storage unit together with the feature type information as feature placement information. Therefore, by combining the information stored in the feature form information storage means and the information stored in the feature placement information storage means based on the feature type information, the arrangement and form of the features on the road surface can be determined. Detailed feature information can be generated and output.

  Here, it is preferable that the feature form information includes at least contour information indicating a contour shape of the feature of each feature type.

  As a result, information indicating the arrangement of the feature on the road surface and at least the contour shape can be generated and output as the feature information. Therefore, it is possible to output the minimum necessary information for use in image recognition or the like.

In addition, for features having a relatively simple shape such as a crosswalk, a stop line, and a traffic division according to the traveling direction, the feature arrangement information is at least 2 located on the outermost contour line of each feature. It is preferable that the information is constituted by position information of individual representative points. Here, if there are two representative points, the position and direction of the feature are determined, but there are features that require information in the width direction, such as pedestrian crossings and stop lines, so depending on the feature type, 3 In some cases, position information of more than one representative point is provided as feature arrangement information.
Further, for example, for a feature having a relatively complicated shape such as a character indicating a maximum speed or a rotation prohibition, the feature arrangement information includes a rectangular frame that surrounds each feature along the outermost part of its outline. It is preferable that the information is constituted by position information of four representative points located at the corners.

  With this configuration, the feature arrangement information can be information that can specify the position and orientation of each feature on the road. In addition, by defining the feature arrangement information according to certain rules in this way, the feature arrangement information can be made general-purpose information.

  In addition, the configuration information of multiple measurement points that serve as a reference for image recognition of features of each type of feature is defined as the arrangement of multiple measurement points in the overall form of the feature. The measurement point form information storage means stored for each type, and the measurement point form information corresponding to the feature type of the feature near the specified position are acquired from the measurement point form information storage means and the acquired The position information of the measurement point is derived based on the feature information of the measurement point and the feature information generated by the feature information generation means, and the form of a plurality of measurement points for the feature near the specified position It is preferable to further include measurement point information generating means for generating and outputting measurement point information including information and position information.

  If comprised in this way, the shape information and position information of the measurement point used as the reference | standard at the time of the image recognition about each feature can be acquired appropriately. At this time, the position information of the measurement points is derived based on the measurement point form information acquired from the measurement point form information storage unit and the feature information generated by the feature information generation unit. Therefore, it is possible to reduce the amount of information that needs to be stored in the information storage means, compared to the case where position information such as the coordinates of all measurement points is stored.

  Further, the measurement point state information storage means for storing the measurement point state information indicating the state related to the difficulty of image recognition is further provided for each measurement point of the feature, and the measurement point information generation means includes the measurement point information as the measurement point information. It is preferable that the measurement point state information of each measurement point is also output.

Here, the “measurement point state information” includes, for example, various kinds of information that affects the image recognition of the measurement point, such as blurring and dirt information around each measurement point in the target feature.
According to this configuration, when performing image recognition of the measurement points set on the feature based on the feature information output by the feature information output device, the degree of fading or dirt on the feature is taken into consideration. Thus, image recognition of the measurement point can be performed. Therefore, it is possible to reduce a calculation load by omitting useless image recognition processing, and it is possible to suppress erroneous recognition of the position of the measurement point.

  The image recognition apparatus according to the present invention is characterized in that the image recognition apparatus includes the feature information output apparatus as described above, and acquires image information acquired by an image pickup apparatus mounted on the host vehicle. The feature information output device further comprises: means; own vehicle position information acquisition means for acquiring own vehicle position information indicating the current position of the own vehicle; and image recognition means for performing image recognition on the image information. The feature information of the feature imaged by the imaging device is output with the position indicated by the vehicle position information as the designated position, and the image recognition means uses the outputted feature information, The feature is that image recognition is performed on the feature included in the image information.

  According to this feature configuration, it is possible to satisfactorily perform image recognition of features around the host vehicle using the feature information output from the feature information output device. Further, according to this feature configuration, in order to generate the feature information indicating the arrangement and form of the feature on the road surface, which is necessary for the image recognition, the amount of data stored in the information storage unit is reduced. Can be suppressed.

  A characteristic configuration of the vehicle position recognition device according to the present invention is a vehicle position recognition device including a feature information output device including the measurement point information output means as described above, and is an imaging device mounted on the vehicle. From the image information acquisition means for acquiring the image information captured by the vehicle, the own vehicle position information acquisition means for acquiring the own vehicle position information indicating the current position of the own vehicle, and the measurement point information generation means of the feature information output device Based on the output measurement point information, image recognition means for performing image recognition of the plurality of measurement points for the feature included in the image information, and the measurement points recognized by the image recognition means The vehicle position correction means for correcting the vehicle position information based on at least one of the image recognition results and the measurement point information of the measurement point.

  According to this feature configuration, image recognition is performed on measurement points determined in advance according to the feature type of each feature, so that the recognition accuracy of the distance between the host vehicle and the recognition target feature can be improved. It becomes possible. At this time, since multiple measurement points are set, even if one measurement point cannot be recognized, the distance between the vehicle and the recognition target feature can be recognized if another measurement point can be recognized. Can be recognized, so that the recognition rate can be prevented from decreasing. In addition, since the measurement points set in the recognition target feature are set as the object of image recognition, and more than one measurement point is set in one recognition target feature, the entire feature is unified. Even if it is not included in one piece of image information, image recognition can be performed relatively easily.

[First embodiment]
A first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram schematically showing the overall configuration of the vehicle position recognition device 1 according to the present embodiment. FIG. 2 is an explanatory diagram showing the contents of information stored in each of the feature form database DB1, feature arrangement database DB2, measurement point form database DB3, and measurement point state database DB4 shown in FIG. In the present embodiment, the target feature to be handled for vehicle position recognition is a planar feature such as a so-called road paint arranged on the road surface, particularly a road such as a pedestrian crossing or a stop line. A case where a sign is used will be described as an example.

  Each functional unit of the vehicle position recognition device 1, specifically, a feature information generation unit 3, a measurement point information generation unit 4, a vehicle position calculation unit 5, an image information acquisition unit 6, an image recognition unit 7, a position The relation calculation unit 8 and the vehicle position correction unit 9 have a function unit for performing various processes on input data using a calculation processing device such as a CPU as a core member, or hardware or software (program). Or it is mounted and comprised by both. Each of the databases DB1, DB2, DB3, DB4, and DBm is a recording medium capable of storing information, such as a hard disk drive, a DVD drive equipped with a DVD-ROM, a CD drive equipped with a CD-ROM, etc. And a device having the driving means thereof as a hardware configuration. Hereinafter, the configuration of each unit will be described in detail.

1. Feature form database DB1
First, the configuration of each database will be described with reference to FIG. In the feature form database DB1 as the feature form information storage means, a plurality of feature form information Fa defining the form of a feature of one feature type is stored for each feature type. The feature form information Fa for each feature type is stored in the database in association with the feature type code Cf representing the feature type. The feature form information Fa is information indicating the form of the road marking as the target feature, and is configured to have contour information indicating the contour shape of the feature of each feature type. FIGS. 3A to 3F show examples of contour shapes and positions of representative points R of features of a plurality of feature types. That is, (a) is “pedestrian crossing”, (b) is “stop line”, (c) is “pedestrian crossing notice (with pedestrian crossing)”, (d) is “traffic classification according to traveling direction”, (e) Indicates the contour shape of each road marking of “maximum speed”. A feature type code Cf is set corresponding to each road marking name. The contour shape information as shown in FIGS. 3 (a) to 3 (f) is stored as the feature form information Fa in a state associated with the feature type code Cf indicating each road marking. Naturally, the feature type shown in FIG. 3 is an example of a part of the target feature, and a feature type other than that shown in FIG. 3 can also be the target feature.

  Further, in relation to the contour shape of each feature type feature, information on a portion that is regulated by the law and does not change is stored as the feature form information Fa. That is, the length of the pedestrian crossing and the stop line in the road width direction naturally changes depending on the road width of the arrangement place. For example, in the case of “pedestrian crossing” in FIG. 3A, the width L1 and the line interval L2 of each line are prescribed by law, but the length of each line (width of the pedestrian crossing) L3 and the entire pedestrian crossing. The length L4 varies depending on the location. On the other hand, for example, in the case of “passage by direction of travel” in FIG. 3D, all dimensions of each part are prescribed by law.

2. Feature arrangement database DB2
The feature arrangement database DB2 as the feature arrangement information storage means includes, for a plurality of features, feature type information Fb indicating the feature type of each feature, and feature arrangement information Fc indicating the position and direction of each feature. Is stored. The feature type information Fb and the feature arrangement information Fc for each feature are stored in the database in association with the feature identification code IDf unique to each feature. The feature identification code IDf is a unique code given to each feature without overlapping. The feature type information Fb is information indicating the feature type to which each feature belongs, and here is a feature type code Cf representing the feature type to which each feature belongs.

  The feature arrangement information Fc is information indicating the position and direction of each feature. Here, as shown in FIG. 3, the feature arrangement information Fc is configured by position information of a plurality of representative points R arranged on the outer shape of each feature. In this example, the representative point R is (1) set to be positioned on the outermost part on the contour line of each feature as shown in FIGS. 3 (a) to 3 (d), and (2) FIG. As shown in (e) and (f), the feature may be set to be located at the corner of the rectangular frame Fr surrounding the feature along the outermost part of the outline. In the case of (1), the number of representative points R is appropriately set according to the feature type. For example, as in the case of a “crosswalk” in FIG. 3A, the width L3 and the length L4 are set to four locations in the case of a feature that varies depending on the arrangement location. On the other hand, for example, in the case of the “traveling direction classified by traveling direction” in FIG. 3D, all dimensions of each part are defined by laws and regulations, and if two places are set, the arrangement of the features on the road is determined. The arrangement of the representative points R as in the above (2) is performed on features having a relatively complicated contour shape such as characters and a plurality of combinations.

  The position information of the representative point R constituting the feature arrangement information Fc is information indicating a position on the map that can be expressed by the latitude and longitude of each representative point R. As shown in FIG. 1, in this example, the feature arrangement database DB2 is configured as a part of the map database DBm. The position information of the representative point R is information indicating the position on the map stored in the map database DBm. Therefore, the configuration of the map database DBm will be described below.

3. Map database DBm
The map database DBm as map information storage means is a database storing map information E. FIG. 4 is an explanatory diagram showing the contents of the map information E stored in the map database DBm. As shown in this figure, the map database DBm according to the present embodiment stores a road network layer X1, a road shape layer X2, and a feature layer X3 as map information E. In this example, the feature layer X3 constitutes the above-described feature arrangement database DB2.

The road network layer X1 is a layer indicating connection information between roads. Specifically, it has information on a large number of nodes N having position information on a map expressed by latitude and longitude, and information on a large number of links L constituting the road by connecting the two nodes N. It is configured. Each link L includes information such as the type of road (type of highway, toll road, national road, prefectural road, etc.) and link length as link information.
The road shape layer X2 is stored in association with the road network layer X1 and indicates the shape of the road. Specifically, information on a number of road shape complementary points Q that are located between two nodes N (on the link L) and have position information on a map expressed by latitude and longitude, and each road shape complementary point Information of road width W in Q.

  The feature layer X3 is a layer that is stored in association with the road network layer X1 and the road shape layer X2, and indicates information on various features provided on the road and in the vicinity of the road. Here, with respect to the target feature, feature type information Fb and feature arrangement information Fc are stored in association with the feature identification code IDf (see FIG. 2). The feature arrangement information Fc is position information indicating the position of the representative point R of each feature on the map, as shown in FIG. Here, the position information of each representative point R is expressed by an offset amount from the position information of the node N or the road shape complementing point Q stored in the road network layer X1 and the road shape layer X2. Yes. The feature layer X3 also stores information on road paint other than road markings such as lane markings other than the target feature, solid objects such as signs and traffic lights, and the like. Based on the feature arrangement information Fc indicating the position information of the representative point R of each feature, the position information of the measurement point P of each feature is calculated and derived in the measurement point information generating unit 4 described later.

4). Measurement point form database DB3
As shown in FIG. 2, in the measurement point form database DB3 as the measurement point form information storage means, the form of each of a plurality of measurement points P (see FIGS. 5 and 6) for each feature type feature is stored. The specified measurement point form information Ma is stored. Here, a plurality of measurement points P are set for each feature type feature, and a measurement point number Mn (= not overlapping with each of the plurality of measurement points P set for the feature type feature. 1, 2, ...) are set. And the measurement point number Mn of the measurement points P at a plurality of locations set in the feature type in association with the feature type code Cf, and the measurement point form information Ma for the measurement points P of each measurement point number Mn. Stored.

  The measurement point P is a specific position set on the target feature. This measurement point P is a target of image recognition in the image recognition unit 7 described later, and serves as a reference in the calculation of the positional relationship between the host vehicle C and the target feature in the positional relationship calculation unit 8. And the measurement point form information Ma is prescribed | regulated as arrangement | positioning of the several measurement point P in the whole form of the terrestrial feature. FIGS. 5 and 6 (a) to 6 (f) show examples of the arrangement of the measurement points P for a plurality of feature types. That is, (a) is “pedestrian crossing”, (b) is “stop line”, (c) is “pedestrian crossing notice (with pedestrian crossing)”, (d) is “traffic classification according to traveling direction”, (e) Indicates the arrangement of the measurement points P (that is, the measurement point form information Ma) for each feature type of “maximum speed”. The numbers in parentheses in FIGS. 5 and 6 indicate the measurement point numbers Mn of the respective measurement points P. As shown in these drawings, at least two measurement points P are set for a feature of one feature type. Then, each measurement point P is mainly on a contour line substantially orthogonal to the traveling direction d of the host vehicle C, or when the contour shape has a corner, depending on the contour shape of the feature of each feature type. Is set on. Further, the measurement point P is not limited to a literal point, and is also set as a line extending the position of the measurement point P in a direction substantially orthogonal to the traveling direction d of the host vehicle C. This is because in this example, the position of the host vehicle C in the direction of travel d is corrected by the host vehicle position correction unit 9 described later, and therefore the position in the direction orthogonal to the direction of travel d is not a problem.

  In this example, the measurement point number Mn of each measurement point P represents the priority of each measurement point P. That is, here, the value of the measurement point number Mn is the priority of the measurement point P. And as shown to (a)-(f) of FIG.5 and FIG.6, the measurement point number Mn is the most in the advancing direction d of the own vehicle C regarding the several measurement points P about one target feature. The measurement point numbers Mn are set in order from the measurement point P located in front. Therefore, with respect to a plurality of measurement points P for one target feature, the priority order is 1, 2, 3,... In order from the measurement point P located at the forefront in the traveling direction d of the host vehicle C. That is, the priority of the measurement point P located in front of the traveling direction d of the host vehicle C is set higher than the priority of the measurement point P located behind in the traveling direction d. By setting the priority of each measurement point P in this manner, the measurement point P recognized with a high degree of confidence is given priority over the measurement point P recognized with a low degree of confidence.

  Here, the confidence level is a scale representing the certainty that at least the image of the feature included in the image information G is the target feature at that time, and is determined by the confidence level determination unit 16 described later. The The degree of confidence increases as the number of frames of the image information G captured in a state where the feature can be recognized as the vehicle C progresses. In particular, as shown in FIGS. 7D to 7F, for example, when the entire target feature does not fit in the image information G of one frame, by using the image information G of a plurality of frames, The certainty that the image of the feature included in the image information G is the target feature at that time is increased. Therefore, by setting the priority of the measurement point P positioned in front of the traveling direction d of the host vehicle C, the measurement point P recognized with a high degree of confidence is given higher priority. Thereby, it is possible to suppress the erroneous recognition of the measurement point P in the image recognition unit 7. In particular, when the entire target feature does not fit in one frame of image information G, such as when performing image recognition of a large target feature in the traveling direction d of the host vehicle C, erroneous recognition of the measurement point P Can be effectively suppressed. For the target feature that is small in the traveling direction d of the host vehicle C as shown in the “stop line” in FIG. 5B, the priority of the measurement point P located behind the traveling direction d is set high. Also good.

  Next, specific examples of the arrangement of the measurement points P for the features of each feature type shown in FIGS. 5 and 6A to 6F will be described in order. In these drawings, the measurement point P is indicated by a thick broken line. In the case of the “pedestrian crossing” in FIG. 5A, the measurement point P is a straight line connecting the front and rear short sides of each of a plurality of parallel strip-shaped contour lines that are long in the traveling direction d of the host vehicle C. Two places are set. In the case of the “stop line” in FIG. 5B, the measurement points P are set at two straight lines arranged on the front and rear long sides orthogonal to the traveling direction d of the host vehicle C. In the case of “pedestrian crossing notice (with pedestrian crossing)” in FIG. 5C, the measurement point P is the frontmost corner of the traveling direction d of the vehicle C in each of the rhombuses on the outer peripheral side and the inner peripheral side. Are set at two points. In the case of the “direction of travel by traveling direction” in FIG. 6D, the measurement point P is one point in the forefront corner of the arrow indicating the straight traveling direction and the vehicle C at the neck of the arrow. It is set to two places in total, one straight line arranged on a line orthogonal to the traveling direction d. In the case of “prohibit turning” in FIG. 6E, the measurement point P is the frontmost apex in the traveling direction d of the host vehicle C on each of the outer peripheral side and the inner peripheral side of the curved portion of the U-shaped arrow. It is set to a total of three locations: two linear locations extending in the tangential direction that passes through and one linear location arranged on a line perpendicular to the traveling direction d of the vehicle C at the neck of the arrow. In the case of “maximum speed” in FIG. 6F, the measurement point P is the frontmost apex in the traveling direction d of the host vehicle C on each of the outer peripheral side and the inner peripheral side of the curved portion of “0”. It is set at two linear locations extending in the tangential direction.

  Information on the arrangement of the measurement points P for the features of each feature type as described above is the measurement point form information Ma. Each of these measurement points P is set at a position where it is easy to recognize an edge by image recognition from the traveling vehicle C, and the distance between the vehicle C and the measurement point P is relatively easy to recognize. . Of the measurement points P set as described above, each measurement point P set in a straight line may be set to any one point such as the center on the line. Each measurement point P set in a dot shape is preferably set in a straight line shape substantially orthogonal to the traveling direction d.

5). Measurement point state database DB4
As shown in FIG. 2, the measurement point state database DB4 as the measurement point state information storage means stores measurement point state information Mb indicating a state relating to difficulty in image recognition for each measurement point P of a plurality of features. Has been. According to the information stored in the measurement point form database DB3 as described above, a plurality of measurement points P are set for each feature according to the feature type, and each measurement point P has a measurement point number Mn (= 1, 2, ...) are set. And the measurement point number Mn of the measurement points P at a plurality of locations set in the feature in association with the feature identification code IDf unique to each feature, and the measurement points for the measurement points P of each measurement point number Mn Status information Mb is stored. The feature identification code IDf is a code common to the feature arrangement database DB2.

  The measurement point state information Mb is information indicating a state relating to difficulty in image recognition by the image recognition unit 7 described later. Therefore, the measurement point state information Mb includes various types of information that affects the image recognition of the measurement point P, such as information on blurring and dirt around each measurement point P in the target feature. In this example, the measurement point state information Mb is “OK” indicating that image recognition is possible based on the result of determining in advance the difficulty of image recognition in consideration of blurring and dirt around each measurement point P. Alternatively, the information is represented by one of “NG” indicating that image recognition is difficult. By using the measurement point state information Mb, the image recognition unit 7 can perform image recognition of the measurement point P in consideration of blurring of the target feature, the degree of dirt, and the like. Therefore, it is possible to reduce a calculation load by omitting useless image recognition processing, and it is possible to suppress erroneous recognition of the position of the measurement point P.

6). Feature information generator 3
The feature information generation unit 3 functions as a feature information generation unit that generates the feature information F and a feature information acquisition unit that acquires the feature information F. That is, the feature information generation unit 3 uses the vehicle position information S acquired from the vehicle position calculation unit 5 as a designated position, and uses the feature arrangement information Fc and the feature type information Fb of the feature near the position as the feature. Obtained from the placement database DB2. Further, the feature information generation unit 3 acquires the feature form information Fa of the feature type from the feature form database DB1 in accordance with the acquired feature type information Fb. Then, based on the feature arrangement information Fc and the feature form information Fa, feature information F indicating the arrangement and form on the road surface of the feature in the vicinity of the position indicated by the vehicle position information S is generated. get. The feature information F generated by the feature information generation unit 3 is output to the measurement point information generation unit 4 and the image recognition unit 7 as shown in FIG.

  Here, the feature information generation unit 3 generates and acquires feature information F about the target feature imaged by the imaging device 14 based on the own vehicle position information S. In this example, the feature information generation unit 3 exists in the traveling direction d of the host vehicle C, and the feature for one target feature that is to be imaged by the imaging device 14 as the host vehicle C travels. The information F is generated and acquired in advance before being imaged by the imaging device 14. Specifically, the feature information generation unit 3 is present within a predetermined distance in the traveling direction d of the host vehicle C based on the host vehicle position information S, and is captured by the imaging device 14 as the host vehicle C travels. One target feature to be processed is extracted from the feature arrangement database DB2 based on the feature arrangement information Fc. Here, the predetermined distance from the own vehicle C can be set to 50 [m], 100 [m], etc., for example. Further, the feature type information Fb and the feature arrangement information Fc for the extracted target feature are acquired from the feature arrangement database DB2. Next, the feature information generation unit 3 acquires the feature form information Fa of the feature type from the feature form database DB1 in accordance with the acquired feature type information Fb. In this example, the same feature identification code IDf of the extracted target feature is combined with the feature form information Fa, the feature type information Fb, and the feature arrangement information Fc to obtain one feature information F. Generate. Here, the feature arrangement information Fc is position information on the map of the plurality of representative points R as described above. Then, the feature form information Fa included in the feature information F is obtained by changing the dimensions of the portions not defined by laws and regulations, thereby arranging the contour shape of the target feature on the map of the plurality of representative points R. The contour information is adjusted to be deformed according to the contour information.

7). Measurement point information generator 4
The measurement point information generation unit 4 functions as a measurement point information generation unit that generates measurement point information and a measurement point information acquisition unit that acquires measurement point information. In other words, the measurement point information generation unit 4 forms the form of the measurement points P at a plurality of locations of the target feature indicated by the feature information F based on the feature information F output from the feature information generation unit 3. Measurement point information M including information and position information is generated and acquired. Therefore, the measurement point information generation unit 4 first acquires the measurement point form information Ma corresponding to the feature type indicated by the feature type information Fb included in the feature information F from the measurement point form database DB3. In addition, the measurement point information generation unit 4 derives position information of the measurement point P based on the acquired measurement point form information Ma and the feature information F. And the measurement point information generation part 4 produces | generates and acquires the measurement point information M containing the form information and position information of the measurement point P of the several location of the target feature shown by the feature information F. FIG. The measurement point information M generated by the measurement point information generation unit 4 is output to the image recognition unit 7 and the vehicle position correction unit 9 as shown in FIG.

  The position information of the measurement point P constituting the measurement point information M is derived based on the measurement point form information Ma acquired from the measurement point form database DB3 and the feature information F output from the feature information generation unit 3. . Specifically, the position on the map of a plurality of representative points R of the target feature related to the feature information F based on the feature form information Fa and the feature arrangement information Fc included in the feature information F, In addition, the contour shape of the target feature that is deformed and adjusted in accordance with the arrangement of the plurality of representative points R on the map is shown. Moreover, the measurement point form information Ma is defined as the arrangement of the measurement points P at a plurality of places in the entire form of the feature. Accordingly, by combining these pieces of information and deriving the positions of the plurality of measurement points P indicated in the measurement point form information Ma with reference to the arrangement of the target features on the map related to the feature information F, the map Position information indicating the position of each measurement point P above is acquired. That is, the contour of the target feature on the map is calculated from the position of the representative point R indicated by the feature arrangement information Fc on the target feature on the map and the contour shape of the target feature indicated by the feature form information Fa. An arrangement of shapes is derived. Moreover, the measurement point form information Ma of the target feature indicates the arrangement of the measurement points P at a plurality of locations in the contour shape representing the entire form of the target feature. Therefore, by combining these pieces of information, it is possible to calculate and derive position information indicating the arrangement of the measurement points P on the map via the information on both contour shapes. As a result of the calculation, the position of the measurement point P may coincide with the position of the representative point R, or may coincide with a line connecting the representative points R. On the other hand, the form information of the measurement point P constituting the measurement point information M can be the same as the content of the measurement point form information Ma acquired from the measurement point form database DB3.

  Moreover, in this example, the measurement point information generation unit 4 is based on the feature identification code IDf of the feature information F output from the feature information generation unit 3, and the measurement point state information Mb of the same feature identification code IDf. Is extracted from the measurement point state database DB4 and acquired. Therefore, the measurement point information generation unit 4 also functions as a measurement point state information acquisition unit that acquires measurement point state information. As described above, the measurement point state information Mb is information indicating a state regarding the difficulty of image recognition for each of the plurality of measurement points P of the target feature. And the measurement point information generation part 4 produces | generates one measurement point information M combining the position information and form information of said measurement point P, and measurement point state information Mb with this feature identification code IDf.

  As described above, the feature form database DB1, the feature arrangement database DB2, and the feature information generation unit 3, or a configuration in which the measurement point form database DB3, the measurement point state database DB4, and the measurement point information generation unit 4 are added thereto. Is the feature information output device 2 that generates and outputs the feature information F (and the measurement point information M).

8). Own vehicle position calculation unit 5
The host vehicle position calculation unit 5 is a host vehicle position information acquisition unit that acquires host vehicle position information S indicating the current position of the host vehicle C, and a position input unit that inputs a specified position to the feature information generation unit 3. Function. Here, the vehicle position calculation unit 5 is connected to the GPS receiver 11, the direction sensor 12, and the distance sensor 13. Here, the GPS receiver 11 is a device that receives a GPS signal from a GPS (Global Positioning System) satellite. This GPS signal is normally received every second and is output to the vehicle position calculation unit 5. The own vehicle position calculation unit 5 can analyze the signal from the GPS satellite received by the GPS receiver 11 and acquire information such as the position (latitude and longitude) of the own vehicle, the traveling direction, and the moving speed. The direction sensor 12 is a sensor that detects the traveling direction of the host vehicle C or a change in the traveling direction. The azimuth sensor 12 includes, for example, a gyro sensor, a geomagnetic sensor, an optical rotation sensor attached to the rotating part of the handle, a rotary resistance volume, an angle sensor attached to the wheel part, and the like. Then, the direction sensor 5 outputs the detection result to the vehicle position calculation unit 5. The distance sensor 13 is a sensor that detects the moving distance and vehicle speed of the host vehicle C. The distance sensor 13 includes, for example, a vehicle speed pulse sensor that outputs a pulse signal every time a drive shaft or wheel of the vehicle rotates by a certain amount, a yaw / G sensor that detects acceleration of the host vehicle C, and 2 detected accelerations. It consists of a circuit that integrates times. Then, the distance sensor 13 outputs the detection result to the own vehicle position calculation unit 5.

  And the own vehicle position calculating part 5 performs the calculation which pinpoints the present position of the own vehicle C by a well-known method based on the output from these GPS receiver 11, the direction sensor 12, and the distance sensor 13. FIG. In addition, the host vehicle position calculation unit 5 acquires map information E around the host vehicle position from the map database DBm, and performs known map matching based on the map information E to indicate the current position of the host vehicle C in the map information E. Correction on the road is also performed. Then, the own vehicle position calculation unit 5 outputs information on the current position of the own vehicle C as the calculation result to the feature information generation unit 3 and the own vehicle position correction unit 9 as own vehicle position information S. At this time, the vehicle position information S is represented as position information represented by latitude and longitude, for example.

  Note that the accuracy of the vehicle position information S calculated by the vehicle position calculation unit 5 is greatly affected by the accuracy of the GPS signals received by the direction sensor 12, the vehicle speed sensor 13, and the GPS receiver 11. For this reason, the own vehicle position information may include an error of about several [m] to several tens [m].

9. Image information acquisition unit 6
The image information acquisition unit 6 functions as an image information acquisition unit that acquires image information G captured by the imaging device 14 mounted on the host vehicle C. The imaging device 14 includes an imaging element such as a CCD sensor or a CMOS sensor, and a lens that constitutes an optical system for guiding light to the imaging element. The imaging device 14 is mounted toward the front or rear of the host vehicle C, and is provided so that at least the road surface of the road on which the host vehicle C travels is photographed. As such an imaging device 14, an in-vehicle camera that is already provided for capturing images of the front and rear of the host vehicle C is preferably used.

  The image information acquisition unit 6 captures analog imaging information captured by the imaging device 14 at a predetermined time interval, converts it into image information G of a digital signal, and stores it in the image memory 15. The time interval for capturing the image information G at this time can be set to, for example, about 10 to 50 ms. Thereby, the image information acquisition part 6 can acquire the image information G of the several frame which imaged the road surface where the own vehicle C is drive | working substantially continuously. The image information G once stored in the image memory 15 is output to the image recognition unit 7.

10. Image recognition unit 7
The image recognition unit 7 functions as an image recognition unit that performs image recognition of a plurality of measurement points P for a target feature included in the image information G. The image recognition unit 7 performs image recognition of the measurement point P mainly based on the form information included in the measurement point information M. In this example, as described above, the feature information generation unit 3 exists in the traveling direction d of the host vehicle C, and is one target feature that is captured by the imaging device 14 as the host vehicle C travels. The feature information F for is generated and acquired in advance. Therefore, the image recognition unit 7 applies the feature information to the image information G of a plurality of frames acquired almost continuously by the image information acquisition unit 6 after the feature information F is output from the feature information generation unit 3. Image recognition of a plurality of measurement points P of the target feature indicated by the information F is performed. Specifically, the image recognition unit 7 first extracts the contour shape of the target feature indicated by the feature information F by binarization processing, edge extraction processing, or the like on the image information G. Thereafter, based on the form information included in the measurement point information M for the target feature output from the measurement point information generation unit 4, a plurality of measurement points P included in the image information G are obtained by pattern recognition processing. Perform image recognition.

  At this time, in this example, the image recognition unit 7 performs image recognition on the measurement points P other than the measurement points P for which image recognition is difficult based on the measurement point state information Mb included in the measurement point information M. That is, as described above, in this example, the measurement point state information Mb can be used for image recognition based on the result of determining in advance the difficulty of image recognition in consideration of blurring and dirt around each measurement point P. The information is represented by either “OK” indicating that the image is recognized or “NG” indicating that the image recognition is difficult. Therefore, the image recognition unit 7 performs image recognition only on the measurement point P whose measurement point state information Mb is “OK” among the plurality of measurement points P of the target feature. As a result, it is possible to omit the image recognition processing of the measurement point P that is in a state where image recognition is difficult due to blurring, dirt, or the like. It is possible to suppress erroneous recognition.

  The image recognition unit 7 includes a confidence level determination unit 16 as a confidence level determination unit. The confidence level determination unit 16 represents the certainty that at least the image of the feature included in the image information G is the image of the target feature indicated by the feature information F output from the feature information generation unit 3. Determine confidence. In this example, the degree of confidence is a scale that also represents the recognition rate by the image recognition unit 7 of a plurality of measurement points P set for the target feature. Then, when the confidence level determined by the confidence level determination unit 16 is equal to or greater than a predetermined threshold, the positional relationship calculation unit 8 and the vehicle position correction unit 9 described later recognize the image of the measurement point P for the target feature. The process which correct | amends the own vehicle position information S using a result is performed. In this example, the degree of confidence is the certainty that the image of the feature included in the image information G is the target feature at that time, and the measurement points P of a plurality of locations set for the target feature. It is a scale representing the image recognition rate. The degree of confidence increases as the number of frames of the image information G captured in a state where the target feature is image recognizable as the host vehicle C progresses, and the image of the measurement point P of the target feature is thereby increased. It increases as it is recognized.

  Specifically, the degree of confidence is image recognition of the contour shape of a characteristic shape part that can determine the feature type of the feature regardless of whether or not the entire feature is captured within one frame. It becomes higher according to the number of frames of possible image information G. In addition, the confidence level is determined based on the measurement point information M output from the measurement point information generation unit 4 when the feature is the target feature. The number of measurement points P that can be recognized among the measurement points P increases as the number of measurement points P increases. In this example, the confidence level determination unit 16 has a characteristic shape portion for each feature type regarding the certainty that the feature image included in the image information G is an image of the target feature. Parameters indicating the certainty of the target feature according to whether or not the contour shape of the image can be recognized and the number of frames of the image information G capable of such image recognition are defined for each feature type. Determined according to the table (not shown). Then, the confidence level determination unit 16 determines the confidence level based on the parameters and the image recognition rates of the plurality of measurement points P set for the target feature. Here, as an example, the degree of confidence is represented by a value of 0 to 100 [%], and the process of correcting the vehicle position information S using the image recognition result of the measurement point P for the target feature is executed. For example, the confidence threshold is set to 70%. Note that an appropriate value can be arbitrarily set as the threshold value of the confidence level according to the situation.

  Then, next, the specific example of the change of the confidence level in each position of the own vehicle C with respect to the target feature and whether or not the image recognition can be executed will be described with reference to FIG. FIG. 7 is a diagram illustrating an example of the image information G at each position with respect to the target feature of the host vehicle C when the road marking of “travel classification by traveling direction” is the target feature. (A) and (d), (b) and (e), (c) and (f) in FIG. 7 correspond to each other, and (d) to (f) correspond to (a) to (c). An example of image information G at each position of the host vehicle C shown is shown. In this example, a back camera that images the road surface behind the host vehicle C is used as the imaging device 14. The image information G at the position of the vehicle shown in FIG. 7A hardly includes the characteristic shape portion of the target feature as shown in FIG. Further, each frame of the image information G so far does not include a characteristic shape portion of the target feature. Furthermore, the image of the measurement point P set for the target feature is not included, and the image of the measurement point P cannot be recognized. Therefore, the confidence level is in a low state (for example, 30 [%]) below the threshold value. The image information G at the vehicle position shown in FIG. 7B includes many characteristic shape portions of the target feature as shown in FIG. In addition, a considerable number of frames of the image information G so far include characteristic portions of the target feature. Further, one image of the measurement point P set for the target feature is included, and image recognition of the measurement point P is performed. Here, the image recognition of the measurement point P having the measurement point number Mn set to “2” (priority order “2”) set at the neck of the arrow indicating the straight direction of the measurement point P is performed. Therefore, the confidence level is higher than the threshold (for example, 80 [%]). The image information G at the vehicle position shown in FIG. 7C includes the entire target feature including the characteristic shape portion of the target feature as shown in FIG. In addition, a considerable number of frames of the image information G so far include characteristic portions of the target feature. Furthermore, an image of another measurement point P set for the target feature is included, and image recognition of this measurement point P is performed. Here, the image recognition of the measurement point P with the measurement point number Mn set to the foremost corner of the arrow indicating the straight direction of the measurement point P is “1” (priority is “1”) is performed. Therefore, the degree of confidence is the highest (for example, 100 [%]).

  The image recognizing unit 7 recognizes an image of one measurement point P from among a plurality of frames of image information G including the measurement point P and selects the frame most suitable for image recognition of the measurement point P. This is performed using the image information G. Usually, the image recognition unit 7 performs image recognition using image information G of a frame in which the measurement point P is captured at a position closest to the host vehicle C.

11. Positional relation calculation unit 8
The positional relationship calculation unit 8 calculates the positional relationship between the host vehicle C and the measurement point P based on at least one image recognition result among the measurement points P recognized by the image recognition unit 7. Further, the positional relationship calculation unit 8 includes a measurement point selection unit 17 as selection means for selecting at least one of one or more measurement points P recognized by the image recognition unit 7. Here, the measurement point selection unit 17 selects the measurement points P recognized by the image recognition unit 7 based on the priorities set in advance for each of the plurality of measurement points P for one target feature. One measurement point P with the highest priority is selected. In this example, the priority of each measurement point P is determined by the priority of the measurement point P indicated by the value of the measurement point number Mn as described above. Therefore, for example, as shown in FIG. 7, when both the measurement point P with the priority “1” and the measurement point P with the priority “2” are recognized by the image recognition unit 7, the measurement point selection unit 17 selects the measurement point P with the priority “1”.

  Then, the positional relationship calculation unit 8 determines the own vehicle C and the selected measurement point P based on the image recognition result of the measurement point P selected by the measurement point selection unit 17 (hereinafter simply referred to as “selected measurement point P”). Is calculated. That is, the positional relationship between each position in the image constituting one frame of the image information G and the own vehicle C is determined based on the mounting position of the imaging device 14 on the own vehicle C and the imaging direction. Therefore, the positional relationship calculation unit 8 determines the image information related to the frame based on the arrangement of the selected measurement point P in one frame of the image information G as the image recognition result of the selected measurement point P by the image recognition unit 7. The positional relationship between the host vehicle C and the selected measurement point P when G is imaged is derived. Further, the distance sensor 13 or the like derives the moving distance of the host vehicle C from the time of capturing the image information G related to the frame to the current time point. Based on these, the positional relationship calculation unit 8 calculates and derives the current positional relationship between the host vehicle C and the selected measurement point P. The calculation process by the positional relationship calculation unit 8 is performed when the determination result of the confidence level by the confidence level determination unit 16 is a predetermined threshold value or more (for example, 70% or more).

12 Own vehicle position correction unit 9
The own vehicle position correction unit 9 uses the positional relationship between the current own vehicle C and the selected measurement point P as the calculation result by the positional relationship calculation unit 8 and the position information of the selected measurement point P included in the measurement point information M. Based on this, the vehicle position information S is corrected. That is, the positional relationship between the current host vehicle C and the selected measurement point P is determined from the calculation result by the positional relationship calculation unit 8. Further, the position information included in the measurement point information M for the selected measurement point P indicates an accurate position (latitude and longitude) of the selected measurement point P on the map. Therefore, based on these pieces of information, the detailed position of the host vehicle C can be calculated and derived based on the exact position of the selected measurement point P on the map. The host vehicle position correction unit 9 corrects the current host vehicle position information S output from the host vehicle position calculation unit 5 based on the calculation result of the detailed position of the host vehicle C. Thereby, the position of the own vehicle C can be specified with high accuracy. The corrected host vehicle position information S ′ output from the host vehicle position correcting unit 9 is the travel of the host vehicle C such as the host vehicle position display and travel guidance in the navigation device, or the steering and acceleration / deceleration by the vehicle control device. Used for control.

  In this example, the positional relationship calculation unit 8 and the vehicle position correction unit 9 are based on at least one image recognition result among the measurement points P recognized by the image recognition unit 7 and the position information of the measurement points P. Thus, the vehicle position correction means 10 that corrects the vehicle position information S functions.

13. Next, a specific example of the vehicle position recognition method executed in the vehicle position recognition device 1 according to the present embodiment will be described with reference to the flowchart shown in FIG. First, the vehicle position calculation unit 5 acquires the vehicle position information S (step # 01). Next, in the feature information generation unit 3, based on the own vehicle position information S acquired in step # 01, the target feature existing within a predetermined distance in the traveling direction d of the own vehicle C is selected as the feature arrangement information Fc. Is extracted from the feature arrangement database DB2 based on (step # 02). When the target feature does not exist within a predetermined distance in the traveling direction d of the host vehicle C (step # 03: NO), the process returns to step # 01, and new host vehicle position information S is acquired. On the other hand, when the target feature exists within a predetermined distance in the traveling direction d of the host vehicle C (step # 03: YES), the feature information generation unit 3 obtains the feature information F about the target feature. Generate and obtain (step # 04). Next, the measurement point information generation unit 4 generates and acquires measurement point information M for a plurality of measurement points P of the target feature indicated by the feature information F acquired in step # 04 (step #). 05).

  Next, the image information acquisition unit 6 acquires the image information G captured by the imaging device 14 (step # 06). The image information G acquired here is sent to the image recognition unit 7. Then, the image recognition unit 7 performs image recognition of a plurality of measurement points P for the target feature included in the image information G (step # 07). Thereafter, in the confidence level determination unit 16, the certainty that the image is the image of the target feature indicated by the feature information F acquired in step # 04, and the measurement points P of a plurality of locations set for the target feature. The degree of confidence representing the image recognition rate is determined (step # 08). If this confidence level is less than the predetermined threshold (step # 09: NO), the process returns to step # 06. That is, at that time, the process does not proceed to the subsequent step # 10, and the image information G of the next frame is further acquired.

  On the other hand, when the degree of confidence is equal to or greater than a predetermined threshold (step # 09: YES), next, in the measurement point selection unit 17, one or more measurement points P recognized by the image recognition unit 7 are included. The one measurement point P with the highest priority is selected from (Step # 10). Then, the positional relationship calculation unit 8 calculates the positional relationship between the host vehicle C and the selected measurement point P based on the image recognition result of step # 09 of the selected measurement point P selected in step # 10 (step # 11). Next, the current vehicle position information S is acquired in the own vehicle position calculation unit 5 (step # 12). And in the own vehicle position correction | amendment part 9, the positional relationship of the present own vehicle C and the selection measurement point P as a calculation result by step # 11, and the selection measurement contained in the measurement point information M acquired at step # 05 Based on the position information of the point P, the current vehicle position information S acquired in step # 12 is corrected (step # 13), and the corrected vehicle position information S ′ is output. Above, the process which concerns on the own vehicle position recognition method is complete | finished.

[Second Embodiment]
Next, a second embodiment of the present invention will be described. FIG. 9 is a block diagram illustrating a schematic configuration of a device that serves as both the navigation device 30 and the vehicle control device 31 according to the present embodiment. As shown in this figure, this apparatus includes a map information acquisition unit 21 that acquires map information E from a map database DBm as map information storage means in addition to the vehicle position recognition apparatus 1 according to the first embodiment. And a traveling control unit 22 for performing traveling control of the host vehicle C, and a navigation computing unit 23 for performing various computing processes for route guidance of the host vehicle C. Furthermore, a monitor 24 as display means capable of displaying arbitrary information and a speaker 25 as sound output means capable of outputting arbitrary sound information are provided.

  The map information acquisition unit 21 obtains the map information E of the surrounding area including the traveling direction d of the host vehicle C based on the host vehicle position information S output from the host vehicle position calculation unit 5 of the host vehicle position recognition device 1. Obtain from DBm. The map information E around the host vehicle C acquired by the map information acquisition unit 21 is output to the travel control unit 22 and the navigation calculation unit 23.

  In addition, the vehicle position recognition device 1 outputs the vehicle position information S ′ corrected by the vehicle position correction unit 9 to the travel control unit 22 and the navigation calculation unit 23. The travel control unit 22 is based on the map information E in the traveling direction d of the host vehicle C output from the map information acquisition unit 21 and the corrected host vehicle position information S ′ output from the host vehicle position recognition device 1. Thus, calculation processing for traveling control of the host vehicle C is performed, and a control command for each part of the vehicle is output. Specifically, the traveling control unit 22 performs, for example, lane keeping, collision, etc. based on road shapes included in the map information E in the traveling direction d of the host vehicle C, or information on various features such as stop lines and traffic lights. Travel control of the host vehicle C, such as steering and acceleration / deceleration for preventing accidents and the like, is performed.

  The navigation calculation unit 23 is based on the map information E around the host vehicle C output from the map information acquisition unit 21 and the corrected host vehicle position information S ′ output from the host vehicle position recognition device 1. Then, various calculation processes for route guidance of the host vehicle C are performed, and a control command for one or both of the monitor 24 and the speaker 25 is output. Specifically, the navigation calculation unit 23 indicates the detailed vehicle position based on the corrected vehicle position information S ′ on the map image generated based on the map information E around the vehicle C. The images are superimposed and displayed on the monitor 24. Further, based on the road shape and the like included in the map information E of the traveling direction d of the host vehicle C and the corrected host vehicle position information S ′, the driver for the route guidance at an accurate timing is sent to the driver. The notification is performed using one or both of the monitor 24 and the speaker 25.

[Other Embodiments]
(1) In the above-described embodiment, the feature form information Fa stored in the feature form database DB1 includes contour information indicating the contour shape of the feature and information related to a location that is prescribed by law and whose dimensions do not change. The case where it was included was explained. However, the information stored as the feature form information Fa is not limited to these. Therefore, for example, it is suitable as a configuration including color information, pattern information, and the like of features of each feature type.

(2) In the above embodiment, the case where the feature arrangement information Fc stored in the feature arrangement database DB2 is configured by the position information of a plurality of representative points R arranged on the outer shape of each feature has been described. . However, the configuration of the feature arrangement information Fc indicating the position and direction of each feature is not limited to this. That is, any information that can represent the position and direction of each feature may be used. For example, it is one of the preferred embodiments that the information includes the position information of the center of gravity of each feature and the unit vector indicating the direction. .

(3) In the above embodiment, the feature information generation unit 3 precedes the feature information F about the target feature existing in the traveling direction d of the host vehicle C before the imaging device 14 captures the feature information. The case where the image recognition of the measurement point P is performed using the feature information F and the measurement point information M generated based on the feature information F has been described as an example. However, the timing for acquiring the feature information F and the measurement point information M is not limited to this. That is, for example, at the same time that the image information G of each frame is acquired, the feature information F and the measurement point information M about the target feature included in the imaging region related to the image information G are generated and acquired. In another preferred embodiment, the image recognition of the measurement point P with respect to the image information G is performed using them.

(4) In the above embodiment, as an example, the priority of a plurality of measurement points P for one target feature is determined by the measurement point number Mn as a priority order determined in advance for each feature type. explained. However, the method for determining the priority of each measurement point P is not limited to this. Therefore, for example, according to the state of image recognition, the traveling state of the host vehicle C, etc., the priority is calculated each time so that the priority of the measurement point with good conditions is increased, and the priority is determined. This is also a preferred embodiment.

(5) In the above embodiment, the case has been described where the position information of the measurement point P constituting the measurement point information M is calculated and derived based on the measurement point form information Ma and the feature information F. However, the method for acquiring the position information of the measurement point P is not limited to this. That is, for example, it is also a preferred embodiment of the present invention to further include a measurement point arrangement database and to store the position information of each measurement point P of a plurality of features. In this case, the measurement point number Mn of a plurality of measurement points P set for the feature and the position of each measurement point number Mn with respect to the measurement point P in association with the feature identification code IDf unique to each feature. It is preferable that the information is stored.

(6) In the above embodiment, the measurement point selection unit 17 selects one measurement point P from among the measurement points P recognized by the image recognition unit 7 and uses the information of the measurement point P in detail. The case where the vehicle position is calculated by correcting the vehicle position information S has been described. However, the configuration is such that two or more measurement points P are selected by the measurement point selection unit 17 and the detailed vehicle position is calculated using the information of the plurality of measurement points P to correct the vehicle position information S. Is also one of the preferred embodiments of the present invention. In this case, for example, a detailed own vehicle position is calculated for each of a plurality of measurement points P in the same manner as in the above embodiment, and an average of the plurality of detailed own vehicle positions is set as a final detailed own vehicle position. Is preferred.

(7) In the above embodiment, the confidence level determination unit 16 determines the confidence level that represents the certainty that at least the image of the feature included in the image information G is the target feature at that time, The example in which the correction of the vehicle position information S is executed using the image recognition result of the measurement point P for the feature when the degree of confidence is equal to or greater than a predetermined threshold has been described. However, it is naturally possible to employ a configuration in which the self-vehicle position information S is corrected by using the image recognition result of the measurement point P regardless of the confidence level without including the confidence level determination unit 16.

(8) Without the positional relationship calculation unit 8 and the vehicle position correction unit 9 in the first embodiment, the image recognition unit 7 uses the feature information F output from the feature information generation unit 3, An image recognition apparatus that performs image recognition on a feature included in the image information G is also a preferred embodiment of the present invention. Furthermore, an image recognition apparatus that performs image recognition of the measurement point P for the feature included in the image information G using the measurement point information M output from the measurement point information generation unit 4 is also suitable for the present invention. This is one of the embodiments.

  According to the present invention, according to the input of the designated position, the feature information about the planar features arranged on the road surface in the vicinity of the designated position can be output. For example, a navigation device, a vehicle control device, etc. It is possible to realize a feature information output device that outputs feature information that can be used for vehicle position recognition and the like.

The block diagram which shows typically the whole structure of the own vehicle position recognition apparatus which concerns on 1st embodiment of this invention. Explanatory drawing which shows the content of the information stored in each database shown by FIG. The figure which shows the example of the outline shape of the feature of several feature types, and the position of a representative point Explanatory diagram showing the contents of the map information stored in the map database The figure which shows the example of arrangement | positioning of the measurement point about several feature types (1) Fig. 2 shows an example of the arrangement of measurement points for a plurality of feature types. The figure which shows the example of the image information in each position with respect to the target feature of the own vehicle The flowchart which shows the own vehicle position recognition method which concerns on 1st embodiment of this invention. The block diagram which shows schematic structure of the apparatus which serves as both the navigation apparatus 30 and the vehicle control apparatus 31 which concern on 2nd embodiment of this invention.

Explanation of symbols

1: Vehicle position recognition device 2: Feature information output device 3: Feature information generation unit (feature information generation means)
4: Measurement point information generation unit (measurement point information generation means)
5: Own vehicle position calculation unit (own vehicle position information acquisition means, position input means)
6: Image information acquisition unit (image information acquisition means)
7: Image recognition unit (image recognition means)
8: Position relationship calculation unit 9: Own vehicle position correction unit 10: own vehicle position correction means 14: imaging device 16: confidence level determination unit 17: measurement point selection unit 24: monitor 25: speaker 30: navigation device 31: vehicle control Device DB1: Feature form database (feature form information storage means)
DB2: feature arrangement database (feature arrangement information storage means)
DB3: Measurement point form database (measurement point form information storage means)
DB4: Measurement point state database (measurement point state information storage means)
DBm: map database E: map information F: feature information M: measurement point information S: own vehicle position information S ′: corrected vehicle position information G: image information R: representative point C: own vehicle d: own vehicle Traveling direction Fa: feature form information Fb: feature type information Fc: feature arrangement information Ma: measurement point form information Mb: measurement point state information

Claims (8)

According to the input of the designated position from the position input means, a feature information output device that outputs feature information about a planar feature placed on the road surface near the designated position
Feature form information storage means for storing, for each feature type, feature form information that defines the form of a feature of one feature type;
Feature arrangement information storage that stores feature type information indicating the feature type of each feature and feature arrangement information indicating the position and direction of each feature for a plurality of planar features arranged on the road surface Means,
The feature type information of the feature near the designated position and the feature arrangement information are acquired from the feature arrangement information storage means, and the feature form information of the feature type is acquired according to the acquired feature type information. From the feature form information storage means, and based on the feature arrangement information and the feature form information, feature information indicating the arrangement and form of the feature near the designated position on the road surface is obtained. Feature information generating means for generating and outputting;
A feature information output device comprising:   The feature information output device according to claim 1, wherein the feature form information includes at least contour information indicating a contour shape of a feature of each feature type.   The feature information output device according to claim 1, wherein the feature arrangement information is configured by position information of at least two representative points located on the outermost part on the contour line of each feature.   The ground according to claim 1 or 2, wherein the feature arrangement information includes position information of four representative points located at corners of a rectangular frame that surrounds each feature along the outermost part of its outline. Item information output device. For each feature type, the configuration information of the measurement points at multiple locations that serve as a reference for image recognition of features of each feature type is defined as the arrangement of the measurement points at multiple locations in the overall feature configuration. Measuring point form information storing means stored in
The measurement point configuration information corresponding to the feature type of the feature in the vicinity of the specified position is acquired from the measurement point configuration information storage means, and the acquired measurement point configuration information and the feature information generation means Based on the generated feature information, position information of the measurement point is derived, and measurement point information including form information and position information of a plurality of measurement points for the feature in the vicinity of the specified position is generated. Measuring point information generating means for outputting;
The feature information output device according to any one of claims 1 to 4, further comprising: For each measurement point of the feature, further comprising measurement point state information storage means for storing measurement point state information indicating a state relating to difficulty of image recognition,
The feature information output device according to claim 5, wherein the measurement point information generation unit also outputs measurement point state information of each measurement point as the measurement point information. An image recognition device comprising the feature information output device according to any one of claims 1 to 6,
Image information acquisition means for acquiring image information captured by an imaging device mounted on the host vehicle, host vehicle position information acquisition means for acquiring host vehicle position information indicating the current position of the host vehicle, and an image about the image information Image recognition means for performing recognition,
The feature information output device outputs the feature information of the feature imaged by the imaging device with the position indicated in the vehicle position information as the designated position,
The image recognition device is an image recognition device that performs image recognition on the feature included in the image information using the output feature information. A vehicle position recognition device comprising the feature information output device according to claim 5,
Image information acquisition means for acquiring image information picked up by an imaging device mounted on the own vehicle, own vehicle position information acquisition means for acquiring own vehicle position information indicating the current position of the own vehicle, and the feature information output device Image recognition means for performing image recognition of the plurality of measurement points on the feature included in the image information based on the measurement point information output from the measurement point information generation means, and the image recognition Self-vehicle position correcting means for correcting the self-vehicle position information based on at least one image recognition result among the measurement points recognized by the means and the measurement point information of the measurement point. Car position recognition device.

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