Abstract: A vehicle controller includes a processor configured to: detect a lane being traveled by the vehicle, set a planned trajectory to be traveled by the vehicle along the detected lane, detect a lane line demarcating the lane being traveled by the vehicle and an obstacle in an area around the vehicle, identify an effective section of the planned trajectory, the effective section being a section in which the planned trajectory and the detected lane line match, and control motion of the vehicle to avoid a collision between the obstacle and the vehicle in the case that the obstacle is located in the effective section and on the planned trajectory.

Abstract: A roadside object detection apparatus to detect a roadside object by analyzing images, detecting height information of an object in the images in a neighboring region having a distance less than or equal to a threshold from a vehicle, detecting a change of a height of a road surface due to the roadside object from the height information in the neighboring region, determining positions having the change of the height detected as characteristic points of the roadside object, extrapolating the characteristic points into a far-off region having a distance greater than or equal to the threshold from the vehicle, based on a road model, setting a search range for the roadside object in the far-off region based on a virtual line that is obtained by the characteristic points being extrapolated by the characteristic point extrapolation unit, and detecting the roadside object in the far-off region based on the search range.

Abstract: A boundary detection apparatus includes an acquisition unit, an extraction unit and a detection unit. The acquisition unit acquires a disparity image based on information obtained by capturing an image of a peripheral environment of a vehicle. The extraction unit extracts predetermined pixel regions from first and second pixel regions of the disparity image based on a disparity gradient direction of the first pixel region and a disparity gradient direction of the second pixel region. The detection unit detects a boundary of a step surface existing alongside a road by joining together at least some of the predetermined pixel regions extracted by the extraction unit. The first and second pixel regions sandwich the predetermined pixel region. An angle formed by the disparity gradient direction of the first pixel region and the disparity gradient direction of the second pixel region is within a predetermined angular range of a right angle.

Abstract: A marking line detection system includes an imaging device and an ECU. An information extraction unit of the ECU is configured to extract depth distance information from the imaging device to an imaging object based on image information in an imaging area captured by the imaging device. A road surface area identification unit of the ECU is configured to identify a distant road surface area based on the depth distance information, the distant road surface area being a road surface area that excludes an immediately-preceding road surface area of the vehicle in the image information and is more distant from the vehicle than the immediately-preceding road surface area. A marking line detection unit of the ECU is configured to detect a marking line in the distant road surface area based on image information corresponding to a position of the distant road surface area.

Abstract: 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.

Abstract: A lane boundary marking line detection device includes an imaging device, a lane boundary detection unit, a lane boundary marking line search unit. The lane boundary detection unit detects first and second lane boundaries based on image information in an imaging area. The lane boundary marking line search unit searches for a lane boundary marking line on a road surface on a second lane boundary's side based on a position of the first lane boundary, sets search lines on the road surface on the second lane boundary's side based on shape information on the first lane boundary, and acquires search line brightness information based on the image information. When not determining that there is the search line that is most probable as the lane boundary marking line, the lane boundary marking line search unit does not select any of the search lines as the lane boundary marking line candidate line.

Abstract: A black eye position existence probability density distribution learning unit that records a black eye position which is detected in the daytime to a black eye position existence probability density distribution, a red-eye candidate detection unit that detects red-eye candidates from the image of the driver at night, and a red-eye determination unit that determines the red eye from the red-eye candidates. The red-eye determination unit determines the red eye on the basis of the relationship between a change in the direction of the face and the behavior of the red-eye candidate and determines, as the red eye, the red-eye candidate disposed at the position of high black eye position existence probability density with reference to the black eye position existence probability density distribution.

Abstract: A lane boundary estimation device includes: a level difference detection unit detecting a first part of a solid lane boundary; a base image setting unit setting a first image area in a most distant area in the first part as a template image; a search area setting unit setting a search area from the most distant area; and a comparison determination unit detecting a boundary candidate point for a second part of the solid lane boundary by performing template comparison in the search area. When a detection evaluation value of the first part is lower than a predetermined value and the search area includes a low-evaluation search area, the base image setting unit re-sets a second image area, nearer to a vehicle than the low-evaluation search area, as the template image. The search area setting unit skips the low-evaluation search area and re-sets a new search area.

Abstract: An ECU 30 includes a face position and face feature point detection unit 32 that detects the feature points of the face of the driver, a face pose estimation unit 33 that fits the feature points of the face detected by the face position and face feature point detection unit 32 to a 3D face model to estimate the direction of the face of the driver, an eyelid range setting unit 34 that sets an upper eyelid presence range and a lower eyelid presence range including the positions of the upper and lower eyelids on the basis of the pose of the face estimated by the face pose estimation unit 33, and an eyelid detection unit 35 that detects the positions of the upper and lower eyelids in the upper eyelid presence range and the lower eyelid presence range set by the eyelid range setting unit 34.

Abstract: An ECU connected to an image sensor includes a face position and face feature point detection unit that detects the feature points of the face of the driver, a red-eye detection unit that detects the red eye with template matching using a red-eye template, an eye opening degree calculation unit that calculates the degree of eye opening, a relative eye opening degree calculation unit that calculates the relative degree of eye opening which is 0% in an eye-closed state and is 100% in an eye-open state, and a red-eye template update unit that generates a red-eye template on the basis of the relative degree of eye opening and updates a red-eye template used for the next template matching with the generated red-eye template.

Abstract: A lane boundary marking line detection device includes an imaging device, a lane boundary detection unit, a lane boundary marking line search unit. The lane boundary detection unit detects first and second lane boundaries based on image information in an imaging area. The lane boundary marking line search unit searches for a lane boundary marking line on a road surface on a second lane boundary's side based on a position of the first lane boundary, sets search lines on the road surface on the second lane boundary's side based on shape information on the first lane boundary, and acquires search line brightness information based on the image information. When not determining that there is the search line that is most probable as the lane boundary marking line, the lane boundary marking line search unit does not select any of the search lines as the lane boundary marking line candidate line.

Abstract: A boundary detection apparatus includes an acquisition unit, an extraction unit and a detection unit. The acquisition unit acquires a disparity image based on information obtained by capturing an image of a peripheral environment of a vehicle. The extraction unit extracts predetermined pixel regions from first and second pixel regions of the disparity image based on a disparity gradient direction of the first pixel region and a disparity gradient direction of the second pixel region. The detection unit detects a boundary of a step surface existing alongside a road by joining together at least some of the predetermined pixel regions extracted by the extraction unit. The first and second pixel regions sandwich the predetermined pixel region. An angle formed by the disparity gradient direction of the first pixel region and the disparity gradient direction of the second pixel region is within a predetermined angular range of a right angle.

Abstract: A marking line detection system includes an imaging device and an ECU. An information extraction unit of the ECU is configured to extract depth distance information from the imaging device to an imaging object based on image information in an imaging area captured by the imaging device. A road surface area identification unit of the ECU is configured to identify a distant road surface area based on the depth distance information, the distant road surface area being a road surface area that excludes an immediately-preceding road surface area of the vehicle in the image information and is more distant from the vehicle than the immediately-preceding road surface area. A marking line detection unit of the ECU is configured to detect a marking line in the distant road surface area based on image information corresponding to a position of the distant road surface area.

Abstract: A roadside object detection apparatus to detect a roadside object using a plurality of captured images includes a height information detection unit configured to analyze the captured images, and to detect height information of an object in the captured images in a neighboring region having a distance less than or equal to a threshold from a vehicle having the roadside object detection apparatus installed; a height change detection unit configured to detect a change of a height of a road surface due to the roadside object from the height information in the neighboring region; a characteristic point extrapolation unit configured to determine positions having the change of the height detected as characteristic points of the roadside object, and to extrapolate the characteristic points into a far-off region having a distance greater than or equal to the threshold from the vehicle, based on a road model; and a roadside object detection unit configured to set a search range for the roadside object in the far-off re

Abstract: A lane separation mark detection apparatus 100 includes imaging units 11 and 12 to capture a lane separation mark separating a lane on which a vehicle is traveling; a lane separation mark detection unit 24 to detect the lane separation mark from at least one piece of image data among pieces of image data, each piece of the image data being generated by the corresponding one of the imaging units; a road form estimation unit 25 to estimate a road form from the lane separation mark detected by the lane separation mark detection unit; and an image data determination unit 26 to determine, according to the lane separation mark depending on the road form estimated by the road form estimation unit, the piece of the image data to be used by the lane separation mark detection unit for detecting the lane separation mark.

Abstract: An ECU which is connected to an image sensor and an illuminance sensor includes an eyelid detection unit that detects the positions of the upper and lower eyelids from a face image, an eyelid determination unit that determines the positions of the upper and lower eyelids detected by the eyelid detection unit, and an eye opening degree calculation unit that calculates the degree of eye opening. The eyelid determination unit searches for a red-eye candidate in the range in which the skin is assumed to be present from the positions of the upper and lower eyelids detected by the eyelid detection unit. When the red-eye candidate is searched in the range, the eyelid determination unit determines that the eyelid detection unit falsely detects the positions of the upper and lower eyelids.

Abstract: An ECU connected to an image sensor includes a face position and face feature point detection unit that detects the feature points of the face of the driver, a red-eye detection unit that detects the red eye with template matching using a red-eye template, an eye opening degree calculation unit that calculates the degree of eye opening, a relative eye opening degree calculation unit that calculates the relative degree of eye opening which is 0% in an eye-closed state and is 100% in an eye-open state, and a red-eye template update unit that generates a red-eye template on the basis of the relative degree of eye opening and updates a red-eye template used for the next template matching with the generated red-eye template.

Abstract: An ECU 30 includes a face position and face feature point detection unit 32 that detects the feature points of the face of the driver, a face pose estimation unit 33 that fits the feature points of the face detected by the face position and face feature point detection unit 32 to a 3D face model to estimate the direction of the face of the driver, an eyelid range setting unit 34 that sets an upper eyelid presence range and a lower eyelid presence range including the positions of the upper and lower eyelids on the basis of the pose of the face estimated by the face pose estimation unit 33, and an eyelid detection unit 35 that detects the positions of the upper and lower eyelids in the upper eyelid presence range and the lower eyelid presence range set by the eyelid range setting unit 34.

Abstract: A black eye position existence probability density distribution learning unit that records a black eye position which is detected in the daytime to a black eye position existence probability density distribution, a red-eye candidate detection unit that detects red-eye candidates from the image of the driver at night, and a red-eye determination unit that determines the red eye from the red-eye candidates. The red-eye determination unit determines the red eye on the basis of the relationship between a change in the direction of the face and the behavior of the red-eye candidate and determines, as the red eye, the red-eye candidate disposed at the position of high black eye position existence probability density with reference to the black eye position existence probability density distribution.

Abstract: An ECU which is connected to an image sensor and an illuminance sensor includes an eyelid detection unit that detects the positions of the upper and lower eyelids from a face image, an eyelid determination unit that determines the positions of the upper and lower eyelids detected by the eyelid detection unit, and an eye opening degree calculation unit that calculates the degree of eye opening. The eyelid determination unit searches for a red-eye candidate in the range in which the skin is assumed to be present from the positions of the upper and lower eyelids detected by the eyelid detection unit. When the red-eye candidate is searched in the range, the eyelid determination unit determines that the eyelid detection unit falsely detects the positions of the upper and lower eyelids.