Denso Corporation

Toyota Jidosha Kabushiki Kaisha

Denso Corpora Tion

Nippon Soken, Inc.

Hirotake Ishigami

Naohide Uchida

Ken Tanabe

Noriaki Shirai

Hiroaki Ito

Masaki Kuwahara

Toshihiko Terada

Naoki Kawasaki

Shunsuke Suzuki

Syunya Kumano

Tetsuya Takafuji

Masayuki Imanishi

Masakazu Nishijima

Toshiyuki Matsubara

Yoshinao Takemae

Satoshi Osanai

Fumio Sugaya

A road surface detecting apparatus comprises a camera sensorfor taking images of a region in front of a vehicle, and an electronic control unit (ECU) configured to: calculate, based on a taken image, parallax information including a parallax of each pixel composing the taken image; extract a pixel corresponding to a road surface from the taken image; acquire, based on information of the extracted pixel, information-indicating-a-road-surface; calculate, based on the information-indicating-a-road-surface, a road surface gradient formula by approximating a gradient of the road surface by a quadratic function; determine whether or not an absolute value of a coefficient of a quadratic term of this formula is greater than a coefficient threshold; and invalidate the information-indicating-a-road-surface when the absolute value is determined to be greater than the threshold.

Road surface detecting apparatus

A target object estimating apparatus comprises parallax information calculating means for calculating parallax information including a parallax between corresponding pixels of taken images taken at a same computation timing, road surface region detecting means for detecting a road surface region in the taken image based on the parallax information, road surface region determining means for determining whether or not each pixel in the taken image corresponds to the road surface region, transition information calculating means for calculating transition information including a change amount of a position of each pixel, using temporally sequential taken images, and estimated value calculating means for estimating a position and a speed of a target object by calculating, based on the parallax/transition information, estimated values of the position and the speed of each pixel. The estimated value calculating means does not calculate the estimated values for a pixel determined to correspond to the road surface region.

Target object estimating apparatus

A vicinity supervising device includes an image capturing unit that captures a plurality of images in which an object is simultaneously captured from different locations; a matching processing unit that calculates a parallax point and a reliability point, the parallax point being the number of pixel regions from which a parallax is calculated among a plurality of pixel regions divided by the plurality of images, and the reliability point being the number of pixel regions having high recognition reliability among the plurality of pixel regions; and an evaluation unit that evaluates, based on the parallax point and the reliability point, an imaging environment where the plurality of images are captured.

Vicinity supervising device and method for supervising vicinity of vehicle

An apparatus comprises three-dimensional object detecting means for detecting a three-dimensional object in a taken image, temporarily determining means for calculating, based on parallax information, a lower end height from a road surface to a lower end of the object, for temporarily determining that the object is a ground-contacting object when the lower end height is less than or equal to a threshold, and for temporarily determining that the object is a floating object when the lower end height exceeds the threshold, and finally determining means for finally determining whether the object is a ground-contacting object or a floating object. The finally determining means finally determines that the object is a ground-contacting object at the current computation timing when the object is determined to be a ground-contacting object in at least one of the temporary determination at the current computation timing and the final determination at the previous computation timing.

Three-dimensional object ground-contact determining apparatus

An apparatus for supervising a vicinity of a vehicle includes: an image capturing unit that captures a plurality of images in which an object is simultaneously captured from different locations; a parallax calculation unit that performs a sub pixel estimation based on the plurality of images to calculate a parallax value; a parallax offset calculation unit that calculates a parallax offset value based on the parallax value under a condition where a decimal part S of the parallax value is within a predetermined range; and a parallax correction unit that corrects the parallax value using the parallax offset value.

Vehicle vicinity supervising device and vicinity supervising method

In an object detection device, a distance image generation part generates a distance image based of a pair of brightness images transmitted from a stereo camera. A road surface estimation part estimates a road surface based on the generated distance image. An object detection part groups together positions having a distance value located on/over the estimated road surface and satisfying a predetermined relationship, and detects an object based on the grouped positions. An abnormality detection part detecting occurrence of image abnormality in the brightness image captured during a current detection period. The road surface estimation part estimates the road surface at the current detection period based on the road surface estimated at the previous detection period when the abnormality detection part detects the occurrence of image abnormality in the brightness image captured during the current detection period.

Object detection device

A parallax calculating apparatus comprises first parallax calculating means for calculating a first parallax on a basis of a reference image for all pixels in the reference image using SGM method, second parallax calculating means for calculating a second parallax on a basis of the reference image for at least a part of pixels among pixels in the reference image using BM method, and final parallax calculating means for calculating a final parallax of each of pixels in the reference image based on the first and second parallaxes. The final parallax calculating means calculates the first parallax as the final parallax for a pixel for which only the first parallax has been calculated and calculates a parallax where a decimal part of the first parallax is replaced with that of the second parallax as the final parallax for a pixel for which the first and second parallaxes have been calculated.

Parallax calculating apparatus

A road surface information acquisition apparatus a lane line recognizer configured to, based on a road surface image of a carpool lane forward of a vehicle acquired by a camera, recognize lane lines of the carpool lane. The apparatus further includes a determiner configured to, based on a lane partition line recognized by the lane line recognizer, determine a start point and an end point of the entrance and exit, and a lane-width calculator configured to, based on the lane lines recognized by the lane line recognizer, calculate a lane width of the carpool lane. The apparatus further includes a lane-width corrector configured to, based on determinations made by the determiner, correct the lane width calculated by the lane-width calculator. Based on a broken line as the lane partition line, the determiner determines the start point and the end point of the entrance and exit of the carpool lane.

Road surface information acquisition apparatus for entrance/exit lane

In an object detection apparatus, a range image generator, based on distance information indicative of distances from a given measurement point to objects in real space, generates a range image indicative of a distribution of distance information of objects located around the measurement point. A subgroup generator horizontally divides the range image into a number of column regions of prescribed width, and for each column region, generates one or more subgroups that extend continuously in a vertical direction of the range image and fall within a given range of distance information. A continuity determiner determines, for each of the subgroups, whether or not there is distance continuity between the subgroup and its horizontally adjacent subgroup in the range image. A merger merges together horizontally continuous-in-distance subgroups. An object detector detects an object in each of regions of the range image corresponding to the respective merged groups.

Object detection apparatus method

An object detection apparatus includes a camera and an ECU. The camera is mounted to a vehicle and captures an image. The ECU is configured to obtain a depth distance for each pixel area of the image based on the image; extract at least one potential object based on the depth distance; calculate an amount of change over time in the depth distance of the at least one potential object; and detect an object around the vehicle from among the at least one potential object using the amount of change over time. When a first potential object and a second potential object that are adjacent in the image are extracted, and a second amount of change over time is twice a first amount of change over time, the second potential object is determined to be the same object as the first potential object.

Object detection apparatus and object detection method

An object estimation apparatus estimates a position and a speed of an object in images based on the images taken by an image taking means from different positions. The object estimation apparatus includes a shift information taking means that takes temporal shift information of a position of a corresponding pixel from frames arranged in time series in a reference image, which is a reference between the images, a parallax information taking means that takes parallax information of each corresponding pixel from the images with reference to the reference image, an estimated value taking means that estimates estimated values of the position and the speed of the object in three-dimensional space by using a filter based on the shift information taken by the shift information taking means and the parallax information taken by the parallax information taking means, a determination means that determines whether or not each of the shift information taken by the shift information taking means and the parallax information taken by the parallax information taking means is an abnormal value, and a correction means that corrects the estimated value taken by the estimated value taking means based on a determination result of the determination means. The correction means corrects the estimated value taken by the estimated value taking means by using different methods between a case where the determination means determines that the shift information taken by the shift information taking means is an abnormal value and a case where the determination means determines that the parallax information taken by the parallax information taking means is an abnormal value.

Object estimation apparatus and object estimation method

In a lane line recognition apparatus, an abnormality determiner is configured to determine whether or not a width of a travel lane defined by left-side and right-side lane lines is abnormal, and a lane-line recognizer is configured to, when the width of the travel lane is not abnormal, recognize both of the left-side and right-side lane lines in a both-side line recognition mode, and when the width of the travel lane is abnormal, recognize one of the lane lines in a one-side line recognition mode. In the one-side line recognition mode, the lane-line recognizer is configured to, for each of the left-side and right-side lane lines, calculate two or more parameters of the lane line, and then integrate recognition results for the respective two or more parameters, and based on the integrated recognition results of the left-side and right-side lane lines, select one of the lane lines to be recognized.

Lane line recognition apparatus

There is provided an apparatus for generating a disparity map. According to the apparatus, an image acquisition section acquires a right-camera image and a left-camera image which are picked up from mutually different viewpoints by cameras. A disparity map generation section generates a disparity map that includes disparity expressed in terms of a difference in pixel positions between each of pixels and a corresponding one of pixels in the right-camera image and the left-camera image, respectively, as acquired. A weight determination section determines a weight that serves as a degree of contribution to disparity correction, for each of weight-calculation-target pixels composed of surrounding pixels. The weight-calculation-target pixels include a correction-target pixel or surrounding pixels that are present around the correction-target pixel in the disparity map. A disparity map correction section corrects the disparity of the correction-target pixel, using weighted average based on the determined weight.

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Okazaki, Japan

Hirotake Ishigami