Abstract: In a travel division line recognition apparatus, an extracting unit extracts a travel division line candidate from an image of a surrounding environment including a road, captured by an on-board camera. A calculating unit calculates a degree of reliability that the extracted travel division line candidate will be the travel division line. A recognizing unit selects the travel division line candidate based on the calculated degree of reliability, and recognizes the travel division line using the selected travel division line candidate. In the calculating unit, a solid object processing unit recognizes solid objects including vehicles, sets a suppression area including a frontal-face suppression area covering a frontal face of the other vehicle and a side-face suppression area covering a side face of the other vehicle, based on the recognized solid objects, and reduces the degree of reliability of the travel division line candidate present within the suppression area.

Abstract: In a travel division line recognition apparatus mounted in a vehicle, a dividing unit divides an area from which edge points, configuring a division line on a road, are extracted in a captured image of the road in the periphery of the vehicle into a nearby area within a predetermined distance from the vehicle and a distant area beyond the predetermined distance from the vehicle. An extraction area from which the edge points are extracted in a portion of the distant area is set. The edge points within the set extraction area are extracted. Distant road parameters are estimated based on the extracted edge points. An extraction area setting unit predicts a position of the division line in the distant area using a curvature of the road acquired in advance, and sets the extraction area so as to include the predicted position of the division line.

Abstract: An apparatus for determining the presence or absence of a turnoff in a roadway. In the apparatus, a turnoff determiner is configured to, for each of left and right white lane lines selected by a white-lane-line selector, smooth white lane lines previously selected by the white-lane-line selector over time to calculate a smoothed white lane line as a reference line, calculate a white-lane-line deviation that is a maximum one of deviations between the reference line and white-lane-line edge points along the white lane line currently selected by the white-lane-line selector. The turnoff determiner is configured to calculate a degree of belief that a turnoff exists based on the calculated left and right white-lane-line deviations, and based on the calculated left and right white-lane-line deviations, determine the presence or absence of a turnoff in the roadway.

Abstract: 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.

Abstract: An apparatus for recognizing a lane line. In the apparatus, when a three-dimensional object lies in the same lane as a subject vehicle and a distance between the three-dimensional object and the subject vehicle is small in an image acquired by an image capture unit, a masking area setter sets a masking area that is partially truncated at or near a lower end of the three-dimensional object in the image. A degree-of-belief calculator is configured to, for each of the edge points extracted by the edge-point extractor, calculate a degree of belief that the edge point is on the lane line. The degree of belief when the edge point is in the masking area is set less than the degree of belief when the edge point is outside the masking area. A lane-line recognizer is configured to recognize the lane line based on the degrees of belief calculated for the edge points.

Abstract: An apparatus for determining the presence or absence of a turnoff in a roadway. In the apparatus, a white-line candidate extractor applies image processing to an image of surroundings of a subject vehicle acquired by a vehicle-mounted camera to extract white-line candidates in the roadway. A white-line likelihood calculator calculates, for each of the white-line candidates extracted by the white-line candidate extractor, a likelihood of the white-line candidate. A white-line likelihood calculator calculates, for each of the white-line candidates extracted by the white-line candidate extractor, a likelihood of the white-line candidate. A turnoff determiner calculates a likelihood for one of a plurality of features of the white line selected by the white-line selector, and determines the presence or absence of a turnoff in the roadway based on the likelihood calculated by the turnoff determiner.

Abstract: An apparatus for recognizing lane lines including a broken line. An image capture unit is configured to acquire an image of the surroundings including a roadway ahead of a subject vehicle. An edge-point extractor is configured to extract edge points in the image. An first-edge-point detector is configured to detect first edge points facing at least one missing section of a broken line in the edge points extracted by the edge-point extractor. A lane-line recognizes is configured to recognize a lane line using the edge points extracted by the edge-point extractor other than all or some of the first edge points.

Abstract: A brightness value calculation apparatus includes a color image obtaining section which obtains a color image obtained by imaging a view outside a vehicle and a brightness value calculation section which calculates a brightness value A of a pixel of at least part of the color image based on an expression (1): A=?AR+?AG+?AB??(1) where AR is brightness of R (red) of the pixel for which the brightness value A is to be calculated, AG is brightness of G (green) of the pixel for which the brightness value A is to be calculated, AB is brightness of B (blue) of the pixel for which a brightness value A is to be calculated, and ?, ?, ? are constants satisfying a relationship ?>?>?.

Abstract: In vehicle-lane boundary line detection, low-luminance values are acquired from areas corresponding to below a tire and directly below a vehicle center based on a road surface image. A snow rut degree is calculated based on a luminance ration between the areas. A probability is calculated from a map based on the calculated snow rut degree. A parameter indicating the degree of snow rut likeness is calculated by a low-pass filtering process. A snow rut determination is made by the calculation result being compared with a predetermined threshold. A final determination of whether or not a snow rut is present is made, with reference to an outside temperature. When determined that a snow rut is present, a determination is made not to perform the detection. When determined that a snow rut is not present, a determination is made to perform the detection.

Abstract: A cruising zone division line recognition apparatus has an image acquisition device that acquires an image including a road surface ahead of a vehicle, and an image recognition device. The image recognition device adds blurring to an area including the road surface in the acquired image and recognizes a cruising zone division line from the image to which blurring has been added. When blurring is added, a cruising zone division line that is an intermittent double line included in a captured image can be made unclear. Therefore, the recognized cruising zone division line can be prevented from becoming a discontinuous, disjointed line.

Abstract: An apparatus for recognizing a lane is provided. The apparatus performs a near-field white line recognition process and calculates road parameters (lane position, lane inclination, lane curvature and lane width) near the vehicle. The road parameters are calculated using the extended Kalman filter. In the calculation, the calculated lane curvature is used as a lane curvature to be included in predicted values. The apparatus outputs the calculated road parameters to a warning/vehicle-control apparatus.

Abstract: In a boundary line recognition apparatus, a boundary line candidate extracting part extracts boundary line candidates from image data obtained by an on-vehicle camera based on known image processing such as pattern matching and Hough transform. One or more kinds of boundary line feature calculating parts calculate one or more likelihoods of each boundary line candidate. The likelihood indicates a degree of probability to be the boundary line. A boundary line feature combining means multiplies the likelihoods of each boundary line candidate and outputs a combined likelihood. A boundary line candidate selecting part selects the boundary line candidate having a maximum likelihood as the boundary line. The boundary line feature calculating part further calculates the likelihood of the boundary line candidate using a dispersion of brightness and an internal edge amount, and changes the likelihood based on an additional likelihood obtained by a driving lane surface feature extracting part.

Abstract: In a boundary line recognition apparatus, a boundary line candidate extracting part extracts boundary line candidates from image data obtained by an on-vehicle camera based on known image processing such as pattern matching and Hough transform. One or more kinds of boundary line feature calculating parts calculate one or more likelihoods of each boundary line candidate. The likelihood indicates a degree of probability to be the boundary line. A boundary line feature combining means multiplies the likelihoods of each boundary line candidate and outputs a combined likelihood. A boundary line candidate selecting part selects the boundary line candidate having a maximum likelihood as the boundary line. The boundary line feature calculating part further calculates the likelihood of the boundary line candidate using a dispersion of brightness and an internal edge amount, and changes the likelihood based on an additional likelihood obtained by a driving lane surface feature extracting part.

Abstract: An in-vehicle white line recognition apparatus is disclosed. Form an image captured by a camera, the apparatus extracts multiple white line candidates including a first white line candidate and a second white line candidate. The apparatus calculates a certainty factor of each white line candidate, the certainty factor indicating a degree of certainty that the each white line candidate represents the white line. When the first and second white line candidates are within a predetermined range and when a ratio of contrast of the first white line candidate to that of the second white line candidate is less than a predetermined value, the apparatus decreases the certainty factor of the first white line candidate.

Abstract: The vehicle-mounted alarm generating apparatus includes an extracting section configured to extract a road boundary marking representing a boundary of a lane from an image of a picture of a road surface ahead of a vehicle, a position setting section configured to set a first position and a second position more distant from the vehicle than the first position in accordance with extraction results by the extracting section, a first alarm generating section configured to generate a first alarm when a distance between the vehicle and the first position becomes shorter than a predetermined first distance, and a second alarm generating section configured to generate a second alarm, when a distance between the vehicle and the first position becomes shorter than a predetermined second distance. The configuration of the second alarm is different from that of the first alarm.

Abstract: An in-vehicle, white line recognition apparatus is disclosed. Form an image captured by a camera, the apparatus extracts multiple white line candidates including a first white line candidate and a second white line candidate. The apparatus calculates a certainty factor of each white line candidate, the certainty factor indicating a degree of certainty that the each white line candidate represents the white line. When the first and second white line candidates are, within a predetermined range and when a ratio of contrast of the first white line candidate to that of the second white line candidate is less than a predetermined value, the apparatus decreases the certainty factor of the first white line candidate.

Abstract: In a vehicle bumper structure, an optical fiber sensor (32) is provided in a vehicle width direction on the front surface of a front-bumper reinforcement (30). A front-bumper absorber (28) is provided in front of the optical fiber sensor (32), the front-bumper absorber (28) formed of a soft absorber (54) and a hard absorber (56). The hard absorber is disposed at the bumper center region where the thickness of the bumper is increased. Accordingly, the thickness of the soft absorber (54) becomes substantially uniform in the vehicle width direction, so that a substantially uniform load is inputted to the optical fiber sensor (32) regardless of the collision position of the bumper with an object. As a result, variations in the output of the load-sensing unit are reduced.

Abstract: In a vehicle bumper structure, an optical fiber sensor (32) is provided on the upper portion of the front surface of a front wall portion (30A) of a front-bumper reinforcement (30). A front-bumper absorber (28) is provided before the optical fiber sensor (32) and a load transfer plate (34) is disposed between the front-bumper absorber (28) and the optical fiber sensor (32). The vertical width (B3) of the front-bumper absorber (28) and the load transfer plate (34) are greater than the vertical width (B1) of the optical fiber sensor (32), and a lower end portion (34A) of the load transfer plate (34) extends below the lower end of the optical fiber sensor (32). Accordingly, when the optical fiber sensor (32) is offset to the upper portion of the front-bumper reinforcement (30), the front-bumper absorber (28) will not topple down and the impact load is input to the optical fiber sensor (32) even if the input direction of the impact load is slightly inclined.

Abstract: A vehicular pedestrian collision detection sensor detects a collision with a pedestrian which allows high-accuracy recognition of a collision with a pedestrian. A large number of load detection cells are arranged in a lateral direction (width direction) of the front side of a vehicle. The total collision load is calculated with respect to a cell group composed of some of the load detection cells which are adjacent to each other, wherein the cell group has a width generally equal to a pedestrian width. Based on the total collision load, an amount correlated to the mass of a pedestrian is calculated. When the amount correlated to the pedestrian mass is within a preset range, a collision with a pedestrian is recognized or determined.

Abstract: In a boundary line recognition apparatus, a boundary line candidate extracting part extracts boundary line candidates from image data obtained by an on-vehicle camera based on known image processing such as pattern matching and Hough transform. One or more kinds of boundary line feature calculating parts calculate one or more likelihoods of each boundary line candidate. The likelihood indicates a degree of probability to be the boundary line. A boundary line feature combining means multiplies the likelihoods of each boundary line candidate and outputs a combined likelihood. A boundary line candidate selecting part selects the boundary line candidate having a maximum likelihood as the boundary line. The boundary line feature calculating part further calculates the likelihood of the boundary line candidate using a dispersion of brightness and an internal edge amount, and changes the likelihood based on an additional likelihood obtained by a driving lane surface feature extracting part.