Patent Abstract:
Systems, methods, and programs for determining whether a vehicle is off-road or on-road, store map data, determine the vehicle&#39;s current position; and obtain information about an area around the current position of the vehicle from the stored map data. The systems, methods, and programs obtain information related to a road-side zone of a road from the obtained information about the area around the current position of the vehicle, and determine that the road-side zone is crossed. The systems, methods, and programs may make an off-road determination if the determined current position of the vehicle was on-road before the crossing of the road-side zone and make an on-road determination if the current position of the vehicle was off-road before the crossing of the road-side zone.

Full Description:
INCORPORATION BY REFERENCE 
   The disclosure of Japanese Patent Application No. 2004-373081 filed on Dec. 24, 2004 including the specification, drawings and abstract is incorporated herein by reference in its entirety. 
   BACKGROUND 
   1. Related Technical Fields 
   Related technical fields include systems, methods, and programs that determine whether a vehicle is off-road or on-road. Related technical fields include navigation systems. 
   2. Related Art 
   Conventional navigation systems, such as that disclosed in Japanese Patent Application Publication No. JP-A-10-307037, determine that a vehicle is no longer on the road, i.e., is off-road. By determining that the vehicle is off-road, the system may prevent erroneous map matching. For example, when the vehicle is always assumed to be on-road, the current position is assumed to being on a closest road that is recorded as map data, even when the vehicle is actually far from any road recorded in the map data. 
   Conventional navigation systems, such as that disclosed in Japanese Patent Application Publication No. JP-A-2002-357431, create and store driving route data in response to a command to store an off-road driving route and can read and display the off-road running path at a later date. 
   SUMMARY 
   The above systems attempt to prevent erroneous map matching by determining an angle error of road directional data. The angle error indicates a measured error between the road direction at the current position and the distance and direction the vehicle has moved. The system then determines whether the vehicle is off-road depending on whether the absolute value of the angle error is greater than a predetermined angle error. Accordingly, the systems cannot make an off-road determination unless the vehicle has traveled a predetermined distance. As a result, not only is the system unable to make an off-road determination rapidly, but it is also unable to accurately determine the position off-road. 
   Also, in the above systems, if the system makes the off-road determination based on a user setting, operation is troublesome and complicated and deviation may occur depending on the setting. 
   In view of at least one or more of the foregoing problems, it is beneficial to enable an off-road determination and an on-road determination to be made both accurately and rapidly without delay. 
   Various exemplary implantations of the broad principles described herein provide systems, methods, and/or programs for determining whether a vehicle is off-road or on-road, that may store map data, determine the vehicle&#39;s current position; and obtain information about an area around the current position of the vehicle from the stored map data. The systems, methods, and/or programs may obtain information related to a road-side zone of a road from the obtained information about the area around the current position of the vehicle, and determine that the road-side zone is crossed. The systems, methods, and/or programs may make an off-road determination if the determined current position of the vehicle was on-road before the crossing of the road-side zone and make an on-road determination if the current position of the vehicle was off-road before the crossing of the road-side zone. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Exemplary implementations will now be described with reference to the accompanying drawings, wherein: 
       FIG. 1  is a view of an exemplary vehicle current position information management system; 
       FIG. 2  is a view of an exemplary structure of a macro-matching processing portion; 
       FIG. 3  is a view of an example structure of a dead-reckoning navigation processing portion; 
       FIG. 4  shows an exemplary database; 
       FIG. 5  shows an exemplary micro-matching method; 
       FIG. 6  shows an exemplary micro-matching method; 
       FIG. 7  shows an example of various features and paint; 
       FIG. 8  is a view explaining the determination of the lane position, in-lane position, and crossing state; 
       FIG. 9  shows an exemplary determination of the lane position, in-lane position, and crossing state using a calculated path; 
       FIG. 10  shows exemplary determination of the lane position, in-lane position, and crossing state using an optical beacon; 
       FIGS. 11A and 11B  show a general outline of an off-road/on-road determination based on the road-side zone determination; 
       FIG. 12  shows an exemplary off-road/on-road determination method based on a determination of a road-side zone with a white line; and 
       FIG. 13  shows an exemplary off-road/on-road determination method based on a determination of a road-side zone without a white line. 
   

   DETAILED DESCRIPTION OF EMBODIMENTS 
     FIG. 1  shows an exemplary vehicle current position information management system. The vehicle current position information management system may physically, functionally, and or conceptually include, for example, a micro-matching processing portion  1 , a macro-matching processing portion  2 , a dead-reckoning navigation processing portion  3 , a current position managing portion  4 , a vehicle control unit  5 , a vehicle information processing unit  6 , a database  7 , an image recognition device  8 , a driver input information managing unit  9 , a position checking and correcting portion  11 , a feature determining portion  12 , a micro-matching results portion  13 , and a lane determining portion  14 . 
   The dead-reckoning navigation processing portion  3  may obtain a calculated path by calculating the direction and distance of a host vehicle from various sensor data, including, for example, vehicle speed data, G (acceleration) data, gyro data, and/or GPS data, The dead-reckoning navigation processing portion  3  may also calculate the current host vehicle position. The dead-reckoning navigation processing portion  3  then may sends the calculated information to, for example, the current position managing portion  4 . The host vehicle position obtained in this manner may not match a road in the map data because the calculated path is obtained directly by using sensor data and has not been matched with the map data. 
   The macro-matching processing portion  2  may use a road map in the database  7  and a conventional calculated path obtained by the dead-reckoning navigation processing portion  3  as a base, and may more accurately estimate which road the host vehicle is traveling on using, for example, database information and/or new device information, in addition to the map matching routine. The macro-matching processing portion  2  may manage, as macro information, information such as, for example, whether the vehicle is on the road or off the road, road type, area information, confidence level (i.e., degree of updatedness, reliability, accuracy, and degree of certainty regarding the information viewed from the time of update), matching road, coordinates, and/or whether the vehicle is on the route or off the route, and may send that macro information to the current position managing portion  4 . 
   The micro-matching processing portion  1  may manage the detailed position of the host vehicle in a small area. The micro-matching processing portion  1  may mainly perform feature determination based on image recognition, as well as perform lane determination based on, for example, calculated information, optical beacon information, driver input information, and/or image recognition. The micro-matching processing portion  1  may perform a position check using the results of the lane determination and feature determination, may performs correction of the current position according to macro information, and may creates and/or manage, as micro information, the in-lane position (i.e., the position of the vehicle in the lane), host lane position (i.e., the position of the lane, with respect to the road, in which the vehicle is traveling), and/or the total number of lanes from the micro-matching results. The micro-matching processing portion  1  may then send the micro information to, for example, the current position managing portion  4 . 
   The feature information may include information about various structures relating to the road, such as, for example, stoplights, overpasses, road signs, streetlights, poles, electrical poles, guard rails, road shoulders, sidewalk steps, medians, manholes in the road, and/or paint (i.e., such as that of center lines, vehicle lanes, left/right turns and proceeding straight ahead, stop lines, bicycle crossings, crosswalks). Because the feature information may have feature types, feature positions, their update times, and/or the reliability of the information itself as the confidence level (i.e., degree of updatedness, reliability, accuracy, and degree of certainty regarding the information viewed from the time of update), if a feature is recognized as a result of image recognition, the current position can be corrected with high accuracy based on the position of that feature. 
   The current position managing portion  4  may manage micro information obtained by the micro-matching processing portion  1 , macro information obtained by the macro-matching processing portion  2 , and calculated information obtained by the dead-reckoning navigation processing portion  3  and may send that information to the micro-matching processing portion  1  and the macro-matching processing portion  2  as appropriate. The current position managing portion  4  may also create current position information from the macro information and micro information and may send it, for example, to the vehicle control unit  5  and the vehicle information processing unit  6 . 
   The vehicle control unit  5  may perform vehicle control such as, for example, speed control and/or brake control when cornering based on the current position information obtained by the current position managing portion  4 . The vehicle information processing unit  6  may include, for example, a navigation system, VICS®, and/or other application system that may display a route by showing, for example, characteristic objects and/or intersections up to the destination based on, for example, current position information obtained by the current position managing portion  4 . The database  7  may be stored in a memory, and may store data relating to, for example, the confidence level, the positions and types of features of each road, and various road data. 
   The image recognition device  8 , for example, may scan images in the direction of travel of the vehicle with a camera, may recognize paint information on the road, and may send, for example, the recognized number of lanes, host lane position, in-lane position, number of increased/decreased lanes, direction of increased/decreased lanes, road shoulder information, crossing state, paint information, and/or confidence level to the micro-matching processing portion  1  as an event. Moreover, the image recognition device  8  may perform recognition processing of features designated in accordance with a demand from the micro-matching processing portion  1  and may send, for example, the recognition results, feature types, feature positions, and/or confidence level to the micro-matching processing portion  1 . 
   The driver input information managing portion  9  may detect, for example, with a steering angle sensor, a steering angle following an operation of a steering wheel by a driver, as well as detect left-right turn commands from a direction indicator. The driver input information managing portion  9  may send the steering information and turn signal information to the micro-matching processing portion  1  as an event. 
   Exemplary structures of the micro-matching processing portion  1 , the macro-matching processing portion  2 , and the dead-reckoning navigation processing portion  3  will be described with reference to  FIGS. 2 and 3 .  FIG. 2  shows an exemplary structure of the macro-matching processing portion and  FIG. 3  shows an exemplary structure of the dead-reckoning navigation processing portion. 
   As shown in  FIG. 1 , the micro-matching processing portion  1  may include, for example, a position checking and correcting portion  11 , a feature determining portion  12 , a micro-matching results portion  13 , and a lane determining portion  14 . The feature determining portion  12  may, for example, search the database  7  for a feature based on the current position according to macro information, request image recognition of that feature from the image recognition device  8  according to feature type, feature position, and confidence level, and specify the distance to the feature, for example, based on the confidence level, feature position, feature type, and recognition results obtained from the image recognition device  8 . The lane determining portion  14  may, for example, specify the in-lane position and lane position of the host vehicle based on, for example,  1 ) an event of the recognized number of lanes, position of the host lane within those lanes, in-lane position (i.e., whether the vehicle is toward the right or left in the lane), number of increased/decreased lanes, direction of increased/decreased lanes, road shoulder information (e.g., the existence or absence thereof), crossing state (e.g. whether the vehicle is crossing the lane/white line), paint information (e.g. straight ahead, left/right turns, crosswalks, bicycle crossings and the like), and/or confidence level from the image recognition device  8 , and/or  2 ) an event of the steering information and turn signal information from the driver input information managing portion  9 , the calculated information of the current position managing portion  4 , and/or the optical beacon information of the vehicle information processing unit  6 . The micro-matching processing portion  1  may send those determination results to the position checking and correcting portion  11  and the micro-matching results portion  13 . 
   The position checking and correcting portion  11  may, for example, check the position of the host vehicle using the feature recognition information of the feature determining portion  12  obtained by the feature determination, and further, the current position according to macro information, and the in-lane position and the lane position of the lane determining portion  14  obtained by the lane determination. If the positions based on one or more of the checked information do not match up, the position checking and correcting portion  11  may then correct the current position according to the macro information to the current position calculated based on the feature recognition information. The micro-matching results portion  13  may send the micro information, e.g., the total number of lanes, the lane position, in-lane position and confidence level of the lane determining portion  14  obtained by the lane determination, to the current position managing portion  4 . 
   For example, when the recognition information of a manhole is obtained as a feature, for example, the position of the manhole and the distance to it may be specified from the recognition data. As a result, if the current position according to macro information and the current position of the vehicle in the direction of travel obtained from that distance do not match up, the current position according to macro information can be corrected. Also, if the current position according to macro information and current position of the host vehicle do not match up due to the position of the manhole being toward the left, right, or center, the current position according to macro information can be corrected also in the direction of the road width, not the direction of travel. 
   In the same way, according to lane determination, for example, when traveling on a two-lane road, if the host vehicle lane position is near the shoulder of the road and the in-lane position moves from the center of the lane toward the right and then the vehicle changes to the lane on the center line side, the current position according to macro information can be corrected if the current position of the host vehicle and the current position according to macro information do not match up. Also, if the number of lanes changes, e.g., if a right turn lane newly appears on the right side or if the number of lanes decreases from three to two or from two to one, the current position according to macro information can be corrected by performing a match determination of that position. 
   As shown in  FIG. 2 , the macro-matching processing portion  2  may include, for example, a macro-matching results portion  21 , a micro position correction reflecting portion  22 , a road determining portion  23 , and/or a macro shape comparing portion  24 . The macro shape comparing portion  24  may perform map matching by comparing the calculated path in the calculated information managed by the current position managing portion  4  with the map road shape based on the road information and confidence level of the database  7 . The road determining portion  23  may determine whether the current position is on-road or off-road, and perform a road determination at the current position. The micro position correction reflecting portion  22  may reflect the correction information of the current position from the micro-matching processing portion  1  of the macro information in the current position according to the macro shape comparing portion  24  and the current position according to the road determining portion  23 . The macro-matching results portion  21  may send, as macro information, the coordinates, road type, area information, on-road/off-road, matching road, on-route/off-route, and/or confidence level to the current position managing portion  4  following a road determination by the road determining portion  23 . 
   As shown in  FIG. 3 , the dead-reckoning navigation processing portion  3  may include, for example, a dead-reckoning navigation results portion  31 , a calculated path creating portion  32 , a learning portion  33 , and a correcting portion  34 . The dead-reckoning navigation processing portion  3  may scan the various information from a vehicle sensor  51 , a G sensor  52 , a gyro  53 , and GPS  54 , and may create a calculated path. The dead-reckoning navigation processing portion  3  may and sends the calculated path, together with the various sensor information, to the current position managing portion  4  as calculated information. The learning portion  33  may learn the coefficient and sensitivity relating to each sensor. The correcting portion  34  may correct errors and the like of the sensors. The calculated path creating portion  32  may create a calculated path of the vehicle from the various sensor data. The dead-reckoning navigation results portion  31  may send the created calculated path of the dead-reckoning navigation results and the various sensor information to the current position managing portion  4  as calculated information. 
     FIG. 4  is a view illustrating an exemplary database structure. A guidance road data file may be stored in the database. As shown in  FIG. 4 , the guidance road data file includes road data (A), including, for example, data for the road number, length, road attribute data, the size and address of shape data, and/or the size and address of guidance data for each of n number of roads of a route searched by a route search, and is obtained by a route search and stored as data necessary for performing route guidance. 
   The guidance road data file includes shape data (B), including, for example, coordinate data made up of east longitude and north latitude for each of m number of nodes when each road is divided into a plurality of nodes (plots). The guidance road data file includes guidance data (C), including for example, data for names of intersections (or branching points), caution data, road name data, sizes and addresses of road name voice data, sizes and addresses of destination data, and/or sizes and addresses of feature data. 
   Of these, the destination data, for example, may include destination road numbers, destination names, sizes and addresses of destination name voice data and destination direction data, and/or travel guidance data. Of the destination data, the destination direction data may be data that indicates information regarding cancellation (i.e., not using destination direction data), unnecessity (i.e., no guidance), advance straight ahead, to the right, at an angle to the right, return to the right, to the left, at an angle to the left, and/or return to the left. 
   The guidance road data file includes feature data (D), including, for example, feature number, feature type, feature position, and/or size and address of feature recognition data for each of k number of features on each road. The guidance road data file includes feature recognition data (E), that is data necessary for recognition by each feature, e.g., shape, size, height, color, position in which it appears in the image, and/or the like. 
   The road number may be set differently for each road between branching points depending on the direction (outbound route, return route). The road attribute data may be road guidance assistance information data that indicates elevated road and underground road information such as whether a road is elevated, is next to an elevated road, is an underground road, and/or is next to an underground road, and/or information about the number of vehicle lanes. The road name data may be data for information about expressways, urban expressways, toll roads, public highways (national highways, prefectural highways, other), and/or information indicating through lanes and access roads of expressways, urban expressways, and/or toll roads. The road name data may include road type data as well as type internal numbers that are individual number data for each type of road. 
     FIG. 5  is a view illustrating an exemplary micro-matching method using feature determination. The exemplary method may be implemented, for example, by one or more components of the above-described vehicle current position information management system. However, even though the exemplary structure of the above-described vehicle current position information management system may be referenced in the description, it should be appreciated that the referenced structure is exemplary and the exemplary method need not be limited by any of the above-described exemplary structure. 
   As shown in  FIG. 5 , for example, when the current position according to macro information is obtained (step S 11 ), the database is searched from the current position and the feature recognition data is obtained (step S 12 ). Then it is determined whether there is a feature to be recognized (step S 13 ). If there is no feature to be recognized (step S 13 =no), the process returns to step S 11 . If there is a feature to be recognized (step S 13 =yes), image recognition of the feature is requested, for example, by the image recognition device  8  (step S 14 ). 
   Operation of he method then waits until a recognition result is obtained from the image recognition device  8  (step S 15 ) and, if so (step S 15 =yes), the current position obtained from the feature recognition information is checked against the current position according to the macro information (step S 16 ). If the current position obtained from the feature recognition information matches the current position according to the macro information (step S 17 =yes), the process returns to step S 11 . If the current position according to the macro information does not match (step S 17 =no), it is corrected based on the current position obtained from the feature recognition information (step S 18 ). 
     FIG. 6  is a view illustrating an exemplary micro-matching method using lane determination. The exemplary method may be implemented, for example, by one or more components of the above-described vehicle current position information management system. However, even though the exemplary structure of the above-described vehicle current position information management system may be referenced in the description, it should be appreciated that the referenced structure is exemplary and the exemplary method need not be limited by any of the above-described exemplary structure. 
   As shown in  FIG. 6 , for example, when an event is input from the driver input information managing portion  9  and an event is input from the image recognition device  8  (step S 21 =yes), the lane position and in-lane position are specified from the image recognition results and the driver input information (step S 22 ). The total number of lanes, lane position, in-lane position, and confidence level of the micro-matching results are then output as micro information (step S 23 ). Next, the lane position and in-lane position are checked against the current position according to macro information (step S 24 ) and it is determined whether the lane position and in-lane position match the current position according to macro information (step S 25 ). If the lane position and in-lane position match the current position according to macro information (step S 25 =yes), operation of the method returns to step S 21 . If it does not match (step S 25 =no), the current position according to macro information is corrected based on the lane position and in-lane position (step S 26 ). 
     FIG. 7  shows an example of various features and paint and  FIG. 8  is a view explaining the determination of the lane position, in-lane position, and crossing state. The various features and paint may include, for example, a manhole (a), lanes (b and c), a median or center line (d), a stop line (e), a sidewalk step (f), a road sign (g), and a traffic signal (h). These features may be recognized, for example, from the shapes of the features, and the current position can be obtained from the recognized positions. Thus, the recognized positions of features and/or paint can be recognized by the position on a grid when the image is divided by a grid indicated with dotted lines, or specified by the field angle of the features and/or paint to be targeted. Further, the lane position, in-lane position, and/or crossing state can be determined from the position of the bottom point of the lane marking (white line) a, the center line b, and the road shoulder c on the image, as shown in  FIG. 8 . 
     FIG. 9  shows an exemplary determination of the lane position, in-lane position, and crossing state using a calculated path. Even if the image recognition device  8  is unable to be used, the calculated path and optical beacon can still be used to determine the lane position, in-lane position, and crossing state. In a case where the calculated path is used, as shown in  FIG. 9 , for example, by monitoring the calculated information (i.e., the path or the amount of left-right movement) with the current position managing portion  4 , e.g., by adding up the amount of movement in the width direction of the lane and comparing it to the lane width, a determination of a lane change may be made if the amount of movement is equal to the lane width, and at half-way a determination of a crossing state may be made. A correction may also be made to compensate for the in-lane position being toward the left or toward the right. 
     FIG. 10  shows exemplary determination of the lane position, in-lane position, and crossing state using an optical beacon. The information related to the lane is included in the optical beacon, so the optical beacon shown in  FIG. 10  can be used irrespective of whether or not there is a camera and image recognition device. Moreover, with image recognition there are also cases in which the total number of lanes is unable to be identified, so optical beacon information is given priority. Also, the final lane determination result is determined by combining the current determined lane position and the optical beacon information. If the information does not match up, the confidence level may be lowered, for example. 
   As discussed above, the off-road determination/on-road determination may be made, for example, by using the determination of the host lane position, the in-lane position, lane change, and/or the like.  FIGS. 11A and 11B  show a general outline of an off-road/on-road determination based on the road-side zone determination. When there is a white line in a road-side zone, as shown in  FIG. 11A , for example, an off-road determination may be made if it has been determined that the vehicle has moved from a road to an off-road area following detection of a white line crossing by image recognition by a camera. Conversely, an on-road determination may be made if it has been determined that the vehicle has moved from an off-road area to a road following detection of a white line crossing by image recognition by a camera. 
     FIG. 12  shows an exemplary off-road/on-road determination method based on a determination of a road-side zone with a white line. The exemplary method may be implemented, for example, by one or more components of the above-described vehicle current position information management system. However, even though the exemplary structure of the above-described vehicle current position information management system may be referenced in the description, it should be appreciated that the referenced structure is exemplary and the exemplary method need not be limited by any of the above-described exemplary structure. 
   Accordingly, in this case, as shown in  FIG. 12 , information about the area around the current position of the vehicle may be obtained from map data, and white line information relating to the road-side zone of the road may be obtained from that information about the area around the current position. A white line recognition result may then be obtained by image recognition with a camera (step S 31 ), recognition of white line crossing may be performed (step S 32 =yes), and a determination may be made as to whether the vehicle has crossed the road-side zone of the road by this white line crossing detection (step S 33 ). If crossing of the road-side zone of the road has been determined by the white line crossing detection (step S 33 =yes), and further, depending on whether the vehicle was on-road (step S 34 =yes), a switch to off-road may be made (step S 35 ). If the vehicle was on-road (step S 34 =yes), a switch to on-road is made (step S 36 ). 
   Further, when there is no white line in the road-side zone as shown in  FIG. 11B , lane departure may be determined using the determination of the host lane position, in-lane position, lane change, and/or the like described above. An off-road determination may be made when lane departure has been determined, and an on-road determination may be made based on the calculated path when the vehicle is determined to be off-road. 
     FIG. 13  shows an exemplary off-road/on-road determination method based on a determination of a road-side zone without a white line. The exemplary method may be implemented, for example, by one or more components of the above-described vehicle current position information management system. However, even though the exemplary structure of the above-described vehicle current position information management system may be referenced in the description, it should be appreciated that the referenced structure is exemplary and the exemplary method need not be limited by any of the above-described exemplary structure. 
   In this case, as shown in  FIG. 13 , a determination is first made as to whether the vehicle is on-road (step S 41 ). If the vehicle is determined not to be on-road (step S 41  =no), then a calculated path is obtained (step S 42 ). It is then determined whether the vehicle has crossed the road-side zone (step S 43 ). If it is determined that the vehicle has crossed the road-side zone (step S 43 =yes), a switch to on-road is made (step S 44 ). However, if it is determined that the vehicle is on-road (step  41 =yes), then a calculated path and lane recognition results are obtained (step S 45 ). It is then determined whether the vehicle has departed from the lane and whether the vehicle has crossed the road-side zone (steps S 46  and S 47 ). If it is determined that the vehicle has departed from the lane and crossed the road-side zone (steps S 46  and S 47 =yes), a switch is made to off-road (step S 48 ). 
   While various features have been described in conjunction with the examples outlined above, various alternatives, modifications, variations, and/or improvements of those features and/or examples may be possible. Accordingly, the examples, as set forth above, are intended to be illustrative. Various changes may be made without departing from the broad spirit and scope of the underlying principles. 
   For example, in the examples described above, the on-road determination/off-road determination may be made based on white line crossing detection and lane departure and the like. Alternatively, however, the G at a step of a sidewalk may be detected or the steering angle may be detected and the determination may be made according to an off-road area, on-road area, and/or branch or the like. Also, in order to increase the determination accuracy, two or more determinations from among the on-road/off-road determination by white line crossing detection, off-road determination by lane departure, on-road determination based on a calculated path, on-road/off-road determination according to steering or a turn signal, and/or on-road/off-road determination according to sidewalk step detection may be used. 
   Furthermore, although the vehicle current position information management system as being composed of a micro-matching processing portion  1 , a macro-matching processing portion  2 , a dead-reckoning navigation processing portion  3 , a current position managing portion  4 , a vehicle control unit  5 , a vehicle information processing unit  6 , an image recognition device  8 , a driver input information managing unit  9 , a position checking and correcting portion  11 , a feature determining portion  12 , a micro-matching results portion  13 , and a lane determining portion  14 , one or more of the components may be further divided and/or combined as necessary. For example, each component may be implemented using a controller, such as, for example, a CPU or by a program stored in a storage medium.

Technology Classification (CPC): 6