Aisin Aw Co., Ltd.

Toyota Jidosha Kabushiki Kaisha

Hitachi, Ltd.

Osamu Aisaka

Masaki Nakamura

Tomoaki Ishikawa

Motoki Kanba

Koichi Nakao

Kiyokazu Okada

Takeshi Shima

Motohiro Nakamura

Masahiro Kiyohara

Kota Irie

Naoki Kourogi

Kota c/o Hitachi Ltd Irie

Naoki c/o Hitachi Ltd Kourogi

Masahiro c/o Hitachi Ltd Kiyohara

Takeshi c/o Hitachi Ltd Shima

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.

Vehicle position recognition device and vehicle position recognition program

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.

Road marking recognition system

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.

Feature extraction method, and image recognition method and feature database creation method using the same

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, 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 Ftto Ftof 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 Ftto Ftof the target feature of the respective lanes is recognized as the type of the recognized feature fa.

Lane determining device, lane determining method and navigation apparatus using the same

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. The apparatuses, methods, and programs extract, based the detected behavior being stored a plurality of times at the same position, the detected behavior as a learned behavior, output learned behavior information including attribute information of the detected behavior and the position information of the detected behavior, each of which are kept in correspondence with the piece of feature information of the recognized target feature.

Vehicle behavior learning apparatuses, methods, and programs

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. The apparatuses, methods, and programs generate learning-priority information indicating a priority of each link branched out at the branch point based on the stored travel-history information and the stored recognition information, the generated learning-priority information usable to determine on which of the links branched out at the branch point that the vehicle is traveling.

Vehicle-position-recognition apparatuses, methods, and programs

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.

Growing community of inventors

Okazaki, Japan

Osamu Aisaka