Nissan Motor Company Limited

Renault S.a.s.

Chikao Tsuchiya

Yasuhisa Hayakawa

Osamu Fukata

Shinya Tanaka

Yasuhito Sano

Takuya Nanri

Hiroyuki Furushou

Masanori Furuya

Ichiro Yamaguchi

Hiroyuki Takano

Daisuke Oiki

Yukinori Nishida

Hirotoshi Ueda

Shoichi Takei

A travel control device carries out a travel control for a vehicle based on detected lane boundary lines. A first prescribed position, an absolute vehicle position and an absolute vehicle azimuth angle are stored while changing from a state where the lane boundary lines can be detected to a state where the lane boundary lines cannot be detected. A second prescribed position is stored while changing from a state in which the lane boundary lines cannot be detected to a state in which the lane boundary lines can be detected. The host vehicle is controlled to travel along a travel path connecting the first prescribed position and the second prescribed position where a current absolute position and a current absolute azimuth angle of the host vehicle do not deviate by a prescribed value or more from the stored absolute position and the stored absolute azimuth angle.

Travel control method and travel control device

A self-position estimation method includes: detecting a relative position between a target present in surroundings of a moving object and the moving object; storing a position where the relative position is moved by the moved amount of the moving object, as target position data; selecting the target position data on the basis of reliability of the relative position of the target position data with respect to the moving object; and comparing the selected target position data with map information including the position information on the target present on a road or around the road, thereby estimating a self-position which is a current position of the moving object.

Self-position estimation method and self-position estimation device

A self-position estimation method includes: detecting a relative position of each target existing around a moving body relative to the moving body; estimating a movement amount of the moving body; correcting the relative position on a basis of the movement amount of the moving body and accumulating the corrected relative position as target position data; detecting a slope amount of a traveling road of the moving body; selecting, from among the accumulated target position data, the target position data of one or more targets in one or more sections having a slope amount less than a threshold value; and collating the selected target position data with map information indicating positions of the targets on a two-dimensional map to estimate a present position of the moving body.

A self-position estimation method includes: detecting a relative position of a target existing around a moving body relative to the moving body; estimating a movement amount of the moving body; correcting the relative position on a basis of the movement amount of the moving body and accumulating the corrected relative position as target position data; detecting a behavior change amount of the moving body; selecting, from among the accumulated target position data, the target position data of the relative position detected during a period in which the behavior change amount is less than a threshold value; and collating the selected target position data with map information indicating a position on a map of the target to estimate a present position of the moving body.

A travel lane detection method overlaps a plurality of travel lane boundary point groups parallel to each other, extracted according to continuity of a plurality of travel lane characteristic points detected by a target detection sensor installed in a vehicle, estimates a travel lane outline according to the travel lane characteristic points included in the overlapped travel lane boundary point groups, and determines travel lane boundaries based on lateral positions of the travel lane boundary point groups parallel to each other and the estimated travel lane outline.

Travel lane detection method and travel lane detection device

A three-dimensional object detection deviceincludes a cameraand a calculator. The calculatorperforms viewpoint conversion processing on an image captured by the camerato create a bird's eye view image, calculates, for each of a plurality of positions along a vertical imaginary line extending in a vertical direction in an actual space, a luminance difference between two pixels near the position, and detects a three-dimensional object on the basis of continuities of the calculated luminance differences of the respective positions.

Three-dimensional object detection device and three-dimensional object detection method

A self-position estimation device includes: a target position detection unit mounted in a vehicle and configured to detect a relative position of a target present around the vehicle and the vehicle; a target position accumulation unit configured to move the detected relative position by a movement amount of the vehicle and to accumulate it as target position data; a curve start position estimation unit configured to estimate a curve start position of a travel path on which the vehicle travels; and a self-position estimation unit configured to perform comparison in which at least the target position data present around the vehicle and the target position data present between a current position of the vehicle and the estimated curve start position, of the accumulated target position data, is compared with the target position information included in the map information to estimate a self-position which is the current position of the vehicle.

Self-position estimation device and self-position estimation method

A travel lane detection method detects travel lane boundaries according to a plurality of travel lane characteristic points detected by a target detection sensor installed in a vehicle. When a lane change of the vehicle is detected, the method determines continuity of the travel lane characteristic points detected before the lane change is completed with respect to the travel lane characteristic points detected after the lane change is completed while taking account of a lane change amount so as to detect the travel lane boundaries according to the continuity of the travel lane characteristic points.

A driving assistance device includes a camera, a moving object detector, a candidate reflection region detector, a predetermined position brightness detector and a cause determiner. The moving object detector and the candidate reflection region detector set a detection region for detecting, from image data captured by the camera, an existence of an adjacent vehicle in an adjacent lane. The predetermined position brightness detector detects a candidate light projecting object which projects light with brightness equal to or higher than a predetermined threshold value. The cause determiner determines whether or not there exists a candidate light projecting object which indicates the existence of the adjacent vehicle in the detection region.

Driving assistance device and method of detecting vehicle adjacent thereto

A moving-body-detecting device is provided with an image capture unit, an image conversion unit, a three-dimensional-object-detecting unit, a turn detecting unit, and a control unit. The image capture unit captures images of the rear side of a vehicle. The image conversion unit performs viewpoint conversion of the captured images into bird's-eye view images. The three-dimensional-object-detecting unit generates differential waveform information based on a differential image of bird's-eye view images taken as different points in time, and detects three-dimensional objects based on the differential waveform information. The control unit suppresses detection of the three-dimensional object based on the differential waveform information, in cases in which turning of the vehicle has been detected as compared with cases in which the vehicle is not turning.

Moving body detection device and moving body detection method

A three-dimensional object detection device basically includes a three-dimensional object detection unit, a natural object assessment unit and a control unit. The three-dimensional object detection unit detects three-dimensional objects based on image information of a rear of a vehicle from a camera. The natural object assessment unit assesses that a detected three-dimensional object is a natural object, such as a plant or snow, based on an irregularity evaluation value calculated based on a first pixel number of first pixels representing a first predetermined differential in the differential image containing the detected three-dimensional object and a second pixel number of second pixels corresponding to the three-dimensional object and representing a second predetermined differential greater than the first predetermined differential.

Three-dimensional object detection device

A driving assistance device is provided with a turning state detection unit, an imaging unit, a solid object detection unit and a detection region modification unit. When the turning state detection unit detects that a host vehicle is in a turning state, the detection region modification unit alters a position of a detection region with respect to the host vehicle, or alters a shape or an area of the detection region based on the turning state of the host vehicle. For example, the detection region modification unit sets a shorter region length of the detection region as the turning radius of the host vehicle becomes smaller. Hereby, the region closest to the host vehicle is set, to a limited extent, as the detection regions.

Detection region modification for driving assistance apparatus and driving assistance method

A solid object detection device detects solid objects in the periphery of a vehicle. A camera captures images including detection regions set in adjacent traffic lanes to the rear of the vehicle. A solid object assessment unit assesses whether or not a solid object is present in the detection regions. A lateral position detection unit detects a distance between the vehicle position and a dividing line that divides traffic lanes. A region setting unit enlarges the detection region on the side of the dividing line by a greater amount correspondingly with respect to an increase in the distance to the dividing line. A traffic lane change detection unit detects a traffic lane change made by the vehicle. Upon detecting a traffic lane change by the vehicle, a smaller enlarged amount is used when enlarging the size of the predetermined region outward in the vehicle-width direction.

Object detector and object detection method

A periodic stationary object detection system extracts a feature point of a three-dimensional object from image data on a predetermined region of a bird's eye view image for each of multiple sub regions included in the predetermined region, calculates waveform data corresponding to a distribution of the feature points in the predetermined region on the bird's eye view image, and judges whether or not the three-dimensional object having the extracted feature point is a periodic stationary object candidate on the basis of whether or not peak information of the waveform data is equal to or larger than a predetermined threshold value.

Periodic stationary object detection system and periodic stationary object detection method

A travel distance detection device includes: an image-capturing unit configured to capture an image; an alignment unit configured to align images in position of bird's-eye view image, the images captured at different times by the image-capturing unit; and a solid object detection unit configured to detect a solid object based on difference image data between data of the images captured at the different times and aligned by the alignment unit. The solid object detection unit counts the number of pixels which each express a certain difference on the difference image data and which are arranged in a direction in which the solid object is inclined when the image of the predetermined region is converted into the bird's-eye view image, then produces a frequency distribution of the counted pixels to generate a difference waveform, and calculates a travel distance of the solid object based on time variation of the difference waveform.

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Yokohama, Japan

Chikao Tsuchiya