Abstract: A vehicle location recognition apparatus includes a link determination unit which accurately determines a link traveled by a vehicle from among a plurality of links meeting at a narrow-angle branch point, on the basis of vehicle location information and road information, travel history information representing a route traveled by the vehicle at the branch point. The vehicle location recognition apparatus includes a history information generation unit for generating travel history information for the determined link. The travel history information is stored in a learning database. On the basis of the travel history information, a learning unit generates learned priority information representing the priorities of the respective links meeting at a branch point and used in the determination by the link determination unit.

Abstract: Systems, methods, and programs capture an image of a periphery of a vehicle. The systems, methods, and programs, store predetermined patterns representing conditions of road markings formed on a road surface, detect a road marking on the basis of the captured image, and compare the detected road marking to the predetermined patterns. If the detected road marking matches one of the predetermined patterns, the systems, methods, and programs execute a predetermined action associated with the matching predetermined pattern.

Abstract: A feature extraction method includes: the step of grouping a cluster of features, in which an internal of the respective features is less than or equal to a predetermined grouping interval, to form a feature group, for a plurality of features of which feature information including at least information of a position and a feature type is included in a predetermined feature information storage unit; the step of excluding the feature, not suitable for use in an image recognition process of the feature with respect to image information, from the cluster of the features within the feature group; and the step of extracting a part or all of one or more of the features within the feature group remaining as a result of the exclusion step as a target feature suitable for the use in the image recognition process.

Abstract: A lane determining device includes: a feature information obtaining unit for obtaining feature information F of a target feature existing in each lane in the traveling direction of a vehicle 30, based on vehicle position information; an image recognizing unit for conducting image recognition processing of a feature type of the target feature in a vehicle lane, with respect to image information; and a lane accuracy determining unit for determining a vehicle lane accuracy for the respective lanes based on a type of a recognized feature fa shown in an image recognition result, feature types Ft1 to Ft4 of the target feature of the respective lanes shown in the feature information F, in accordance with a level of probability for each of the feature types Ft1 to Ft4 of the target feature of the respective lanes is recognized as the type of the recognized feature fa.

Abstract: Vehicle behavior learning apparatuses, methods, and programs store pieces of feature information including position information and attribute information of a plurality of target features. The apparatuses, methods, and programs obtain vehicle position information that shows a current position of a vehicle, obtaining image information of surroundings of the vehicle, and obtain one of the stored pieces of feature information corresponding to the surroundings of the vehicle based on the vehicle position information. The apparatuses, methods, and programs perform an image recognition for recognizing a target feature contained in the image information that corresponds to the obtained piece of feature information, detect a behavior of the vehicle that is performed within a predetermined range from a position of the recognized target feature, and store, based on the vehicle position information, the detected behavior in correspondence with information of a position in which the detected behavior was detected.

Abstract: Vehicle-position-recognition apparatuses, methods, and programs acquire current position information indicating a current position of a vehicle and acquire road information indicating at least one road based on a connection relationship between plural links. The apparatuses, methods, and programs determine a link where the vehicle travels based on the current position information and the road information and generate travel-history information indicating a route that the vehicle travels at a point where a link is branched based on the determined link. The apparatuses, methods, and programs acquire an image of an area surrounding the vehicle, recognize a recognition target in the acquired image, and store the generated travel-history information in the memory in association with information indicating the recognition of the recognition target.

Abstract: Image information apparatuses, methods, and programs acquire image information of a plurality of frames of images sensed at predetermined regular time intervals. The apparatuses, methods, and programs detect a leading and trailing end of a lane mark included in a detection area defined in each frame of the image information. The apparatuses, methods, and programs detect a detected distance from one of the leading and trailing end of the lane mark to the other one as a detected distance based on the speed of the vehicle and the number of frames between a frame in which one of the leading end and the trailing end of the lane mark is detected and a frame in which the other one of the leading end and the trailing end of the lane mark is detected and determine a lane mark type of the lane mark on the basis of the detected distance.

Abstract: A feature extraction method includes: the step of grouping a cluster of features, in which an internal of the respective features is less than or equal to a predetermined grouping interval, to form a feature group, for a plurality of features of which feature information including at least information of a position and a feature type is included in a predetermined feature information storage unit; the step of excluding the feature, not suitable for use in an image recognition process of the feature with respect to image information, from the cluster of the features within the feature group; and the step of extracting a part or all of one or more of the features within the feature group remaining as a result of the exclusion step as a target feature suitable for the use in the image recognition process.

Abstract: In a case where it is determined that a road surface marking exists within a specified range from a vehicle 2 (YES at S2), the road surface marking is recognized based on an image that has been acquired by a rear camera 3 (S3), an on-road distance from the vehicle 2 to a control object that is associated with the recognized road surface marking is computed (S5 to S7), and in a case where it has been determined that the on-road distance to the control object has become a specified distance (YES at S8), driving guidance and the vehicle control are performed in accordance with the type of the associated control object (S11).

Abstract: A lane determining device includes: a feature information obtaining unit for obtaining feature information F of a target feature existing in each lane in the traveling direction of a vehicle 30, based on vehicle position information; an image recognizing unit for conducting image recognition processing of a feature type of the target feature in a vehicle lane, with respect to image information; and a lane accuracy determining unit for determining a vehicle lane accuracy for the respective lanes based on a type of a recognized feature fa shown in an image recognition result, feature types Ft1 to Ft4 of the target feature of the respective lanes shown in the feature information F, in accordance with a level of probability for each of the feature types Ft1 to Ft4 of the target feature of the respective lanes is recognized as the type of the recognized feature fa.

Abstract: Image information apparatuses, methods, and programs acquire image information of a plurality of frames of images sensed at predetermined regular time intervals. The apparatuses, methods, and programs detect a leading and trailing end of a lane mark included in a detection area defined in each frame of the image information. The apparatuses, methods, and programs detect a detected distance from one of the leading and trailing end of the lane mark to the other one as a detected distance based on the speed of the vehicle and the number of frames between a frame in which one of the leading end and the trailing end of the lane mark is detected and a frame in which the other one of the leading end and the trailing end of the lane mark is detected and determine a lane mark type of the lane mark on the basis of the detected distance.

Abstract: Systems, methods, and programs capture an image of a periphery of a vehicle. The systems, methods, and programs, store predetermined patterns representing conditions of road markings formed on a road surface, detect a road marking on the basis of the captured image, and compare the detected road marking to the predetermined patterns. If the detected road marking matches one of the predetermined patterns, the systems, methods, and programs execute a predetermined action associated with the matching predetermined pattern.

Abstract: Vehicle behavior learning apparatuses, methods, and programs acquire image information of an area around a vehicle and perform image recognition of a particular feature included in the image information. The apparatuses, methods, and programs detect a behavior of the vehicle and acquire relation information indicating the relationship between the detected behavior of the and the recognized particular feature before the recognition of the behavior. The apparatuses, methods, and programs store detected behavior information including behavior property information indicating a property of the detected behavior of the vehicle and the acquired relation information associated with the behavior in a memory and produce learned behavior information indicating a result of learning of the behavior related to the particular feature on the basis of the stored detected behavior information.

Abstract: Vehicle-position-recognition apparatuses, methods, and programs acquire current position information indicating a current position of a vehicle and acquire road information indicating at least one road based on a connection relationship between plural links. The apparatuses, methods, and programs determine a link where the vehicle travels based on the current position information and the road information and generate travel-history information indicating a route that the vehicle travels at a point where a link is branched based on the determined link. The apparatuses, methods, and programs acquire an image of an area surrounding the vehicle, recognize a recognition target in the acquired image, and store the generated travel-history information in the memory in association with information indicating the recognition of the recognition target.

Abstract: A vehicle location recognition apparatus includes a link determination unit which accurately determines a link traveled by a vehicle from among a plurality of links meeting at a narrow-angle branch point, on the basis of vehicle location information and road information, travel history information representing a route traveled by the vehicle at the branch point. The vehicle location recognition apparatus includes a history information generation unit for generating travel history information for the determined link. The travel history information is stored in a learning database. On the basis of the travel history information, a learning unit generates learned priority information representing the priorities of the respective links meeting at a branch point and used in the determination by the link determination unit.

Abstract: Vehicle behavior learning apparatuses, methods, and programs store pieces of feature information including position information and attribute information of a plurality of target features. The apparatuses, methods, and programs obtain vehicle position information that shows a current position of a vehicle, obtaining image information of surroundings of the vehicle, and obtain one of the stored pieces of feature information corresponding to the surroundings of the vehicle based on the vehicle position information. The apparatuses, methods, and programs perform an image recognition for recognizing a target feature contained in the image information that corresponds to the obtained piece of feature information, detect a behavior of the vehicle that is performed within a predetermined range from a position of the recognized target feature, and store, based on the vehicle position information, the detected behavior in correspondence with information of a position in which the detected behavior was detected.