Abstract: A lane boundary line recognition apparatus acquires an image. Based on edge points extracted from the image, a lane boundary line candidate is extracted, and a lane boundary line probability thereof is calculated. A lane boundary line candidate of which the lane boundary line probability exceeds a threshold is recognized. As the threshold, a first threshold, larger than a second threshold in normal mode, is set when a first condition is met and also a second condition is met. The first condition is that a single lane boundary line is recognized on a roadside side of a lane in which the own vehicle is traveling, and a composite lane boundary line is recognized on a side opposite the roadside side. The second condition is that a state in which a lane boundary line cannot be recognized on at least the opposite side continues for a predetermined amount of time.

Abstract: In a location estimation device, a change detection unit detects a point of change in the width of a roadside structure as a width change point. A width correction unit calculates a width deviation between a first relative location of the roadside structure on a map data segment and a second relative location of the roadside structure obtained based on shape information of the roadside structure in the width direction of a vehicle. The width correction unit corrects, as a function of the width deviation and a positional accuracy of the vehicle in the travelling direction, an estimated location of the vehicle in the width direction when it is determined that there is the point of change in the width of the roadside structure in the travelling direction of the vehicle.

Abstract: In a road recognition apparatus mounted in a vehicle, a shape change point detector is configured to detect a shape change point along each of lane lines of an own lane. A turn-off lane determiner is configured to, if the shape change point is detected, determine whether or not a shape changing lane line that is one of the lane lines including the shape change point constitutes a border of a turn-off lane branching off from the own lane. A road recognizer is configured to, when the shape change point has been detected, use only feature points of the left and right lane lines of the own lane located within a distance from the own vehicle to the shape change point to recognize the shape of the own lane, before a result of determination by the turn-off lane determiner is produced.

Abstract: In a travel road shape recognition apparatus, a first travel road shape, which approximates a shape of a road boundary, is calculated based on feature points indicating the travel road boundary in a captured image. A worsening position, which is a position on the calculated first travel road shape at which a degree of coincidence with the feature points worsens, is detected over an area from near to far from an own vehicle. A second travel road shape, which approximates a shape of the road boundary in an area farther from the own vehicle, beyond the worsening position, is calculated. The road boundary in the area nearer to the own vehicle, up to the worsening position, is recognized from the first travel road shape. The road boundary in the area farther from the own vehicle, beyond the worsening position, is recognized from the second travel road shape.

Abstract: A vehicle localization system is provided which localizes a system-equipped vehicle. The vehicle localization system determines a position of the system-equipped vehicle on a map using a map matching technique. The vehicle localization system also calculates a variation in arrangement of feature points (e.g., edge points) of a roadside object around the system-equipped vehicle in a captured image and corrects the calculated position of the system-equipped vehicle on the map using the variation in arrangement of the feature points. This ensures a required accuracy in localizing the system-equipped vehicle.

Abstract: In an object detection apparatus and an object detection method, as first and second images, captured images of an area ahead of a vehicle in a vehicle advancing direction are acquired from first and second imaging units provided in the vehicle. Based on the first and second images, whether or not an object is present in a blind spot ahead of the vehicle in the vehicle advancing direction is determined. When the object is determined to be present in the blind spot, the first and second images are held in time series. As first and second image differences, differences in a feature quantity between a previous image and a current image in the first and second images are acquired. Based on the first and second image differences, whether or not the object is approaching the area ahead of the vehicle is determined.

Abstract: A position identifying apparatus includes a position detecting unit; an information acquiring unit; a feature extracting unit; an index value calculating unit; a feature selecting unit; a correlating unit; a weight setting unit; and a correcting unit. The position detecting unit performs a map matching to detect a vehicle position on a map. The information acquiring unit acquires road information from the map. The feature extracting unit extracts features of ground object. The index value calculating unit calculates an index value representing a likelihood of a stationary object. The feature selecting unit selects feature candidates among the features having the index values larger than or equal to a predetermined likelihood of the stationary object. The correlating unit correlates the feature candidates with the road information. The weight setting unit sets weights of the feature candidates. The correcting unit corrects the position of the vehicle using the weights.

Abstract: An apparatus for recognizing lane partition lines on opposite sides of a traveling lane in a processing area of a forward image captured by a camera mounted in a vehicle. In the apparatus, a lane change determiner is configured to determine whether or not there is a lane change made by the vehicle. A processing area changer is configured to, while it is determined by the lane change determiner that there is a lane change, change the processing area from a predefined processing area to a processing area that can accommodate the lane change.

Abstract: A road shape recognition apparatus is mounted in a vehicle. The road shape recognition apparatus acquires a road image, determines a road shape using the road image, recognizes the road shape based on the road image within a predetermined recognition range, and estimates the road shape outside of the recognition range based on the road shape within the recognition range. The road shape recognition apparatus estimates the road shape outside of the recognition range and within a first distance as a curved line of which a curvature change rate is constant, and the road shape farther than the first distance as a curved line of which a curvature is constant.

Abstract: 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?Valx<? 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 ? 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.

Abstract: An in-vehicle system as a road curvature detection device calculates a curvature of a road in front of a vehicle based on an acquired front scene image. The in-vehicle system receives gradient information from the data map. The gradient information corresponds to a current road section on the road on which the vehicle drives. The in-vehicle system detects a gradient accuracy of the received gradient information. When the current road section has a gradient, i.e. the road is an uphill or downhill road, the in-vehicle system selects an appropriate special detection methods based on the gradient accuracy of the received gradient information, and calculates a road curvature by using the selected special detection method. Each of the special detection methods calculates a road curvature while effectively suppressing influence of a road gradient indicated by the received gradient information.

Abstract: A travel lane marking probability calculating unit calculates a travel lane marking probability of each of travel lane marking candidates based on a calculation condition. A travel lane marking recognizing unit recognizes, as a travel lane marking, a travel lane marking candidate having a travel lane marking probability that is a threshold value or higher, among the travel lane marking candidates. A lane change detecting unit detects that an own vehicle is in the midst of a lane change. When the own vehicle is in the midst of a lane change, a condition changing unit changes the calculation condition to allow the travel lane marking probability to be more easily increased compared to when the own vehicle is not in the midst of a lane change, or changes the threshold value to be lower than that when the own vehicle is not in the midst of a lane change.

Abstract: An on-board camera captures an image of a travel lane ahead of an own vehicle. At least one state acquisition apparatus acquires an acquired value indicating a state of the own vehicle. A travel lane marking recognition system recognizes a travel lane marking that demarcates the travel lane from an image captured by the on-board camera. The travel lane marking recognition system sets a reliability level of the recognized travel lane marking based on the acquired value of the at least one state acquisition apparatus. When the reliability level is higher than a first predetermined threshold, the travel lane marking recognition system performs driving control of the own vehicle based on the recognized travel lane marking. When the reliability level is lower than the first predetermined threshold, the travel lane marking recognition system performs a deviation warning of deviation from the travel lane based on the recognized travel lane marking.

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: 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.

Abstract: In an apparatus for recognizing a location of an own vehicle, a detector is configured to detect one or more own-vehicle location candidates on roads based on map data. A partition line determiner is configured to, based on a captured image of a road around the own vehicle, determine a line type of each of partition lines extending along edges of the road. A determiner is configured to, for each of the one or more own-vehicle location candidates, under assumption that the own vehicle is at the own-vehicle location candidate, if the own vehicle is passing an intersection, determine, based on the line type of each of partition lines, a degree of confidence indicative of a likelihood that the own-vehicle location candidate at which it is assumed that the own vehicle is present is a location of the own vehicle.

Abstract: A driving support apparatus installed in a host vehicle calculates an overlap ratio relating to a target object positioned ahead of the host vehicle within the same traffic lane, as the ratio of the width of the target object to its lateral distance from a lane marker line of the traffic lane, and also detects the motion condition of the object (i.e., stationary, moving towards, or in the same direction as the host vehicle, or moving laterally with respect to the forward direction of the host vehicle). An amount of compensation for retarding or advancing the commencement of a collision avoidance operation by the driving support apparatus is determined based on the overlap ratio and/or the motion condition of the target object.

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 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.

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.