Denso Corporation

Yuu Maeda

Naoki Kawasaki

Shunsuke Suzuki

Osamu Shimomura

Syunya Kumano

Tetsuya Takafuji

Yusuke Ueda

Junichiro Funabashi

Kazuhisa Ishimaru

A parking space recognition apparatus includes an onboard camera, a wireless receiver, and a parking space determination portion. The onboard camera is mounted to a first vehicle and captures a peripheral image around the first vehicle. The wireless receiver is mounted to the first vehicle and receives a parking-space related information that is transmitted from an outside of the first vehicle. The parking space determination portion is mounted to the first vehicle and determines a parking space to park the first vehicle on a basis of the peripheral image and the parking-space related information.

Parking space recognition apparatus and parking space recognition system

In an image processing apparatus, an image data acquisition unit acquires image data including R-pixels which are red pixels, holding red luminance values, B-pixels which are blue pixels, holding blue luminance values, G-pixels which are green pixels, holding green luminance values, and C-pixels which are clear pixels, holding luminance values in a wavelength range including wavelength ranges corresponding to red, blue and green. In the apparatus, a determination unit determines whether or not an inequality given by Valc&#x2212;Valx&#x3c;&#x3b1; is satisfied, where Valx is a luminance value of a pixel of interest that is one of the R-, B-, and G-pixels in the image data, Valc is a luminance value of the C-pixel or pixels closest to the pixel of interest, and &#x3b1; is a constant, and if the inequality is satisfied, a correction unit corrects the luminance value Valx to be less than the luminance value Valc.

Image processing apparatus and lane partition line recognition system including the same

An in-vehicle camera obtains image frames of a scene surrounding an own vehicle on a roadway. An extracting section in a lane boundary line recognition device extracts white line candidates from the image frames. The white line candidates indicate a degree of probability of white lines on an own vehicle lane on the roadway and a white line of a branch road which branches from the roadway. A branch judgment section calculates a likelihood of the white line as the white line of the branch road, and judges whether or not the white line candidate is the white line of the branch road based on the calculated likelihood. The branch judgment section decreases the calculated likelihood when a recognizable distance of the lane boundary line candidate monotonically decreases in a predetermined number of the image frames.

Lane boundary line recognition device and computer-readable storage medium storing program of recognizing lane boundary lines on roadway

A traffic lane marking recognition apparatus includes a candidate detecting unit, a gap detecting unit, and a recognition reducing unit. The candidate detecting unit detects a lane dividing line candidate which is a candidate for a lane dividing line that defines a traffic lane on a road, based on an image of the road captured by an on-board camera that is mounted in a vehicle. The gap detecting unit detects a gap included in the lane dividing line candidate detected by the candidate detecting unit. When the gap is detected by the gap detecting unit, the recognition reducing unit reduces a probability of recognition of the lane dividing line candidate as a lane dividing line to a first probability that is lower than the probability when the gap detecting unit does not detect the gap, in a region from the gap closest to the vehicle towards a direction away from the vehicle.

Traffic lane marking recognition apparatus and traffic lane marking recognition program

A lane boundary line recognition device detects lane boundary line candidates of a roadway from images captured by an in-vehicle camera, judges that the lane boundary line candidate is a lane boundary line of a branch road, and calculates a curvature of the lane boundary line candidate, and recognizes the lane boundary line based on the calculated curvature. The device removes the lane boundary line candidate, which has been judged as the lane boundary line of the branch road, is removed from a group of the lane boundary line candidates, and calculates the curvature of the lane boundary line candidate based on an estimated rate of change of the curvature. The device uses a past curvature calculated predetermined-number of images before when the lane boundary line candidate is the lane boundary line of the branch road, and resets the estimated rate of change of the curvature to zero.

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Ichinomiya, Japan

Yuu Maeda