Abstract: The vehicle of the present disclosure includes at least one processor and at least one memory storing a plurality of instructions executed on the at least one processor. The plurality of instructions causes the at least one processor to select a candidate route to a destination and acquire information on a traffic environment of the selected candidate route. The plurality of instructions further causes the at least one processor to simulate, based on the information on the traffic environment, reliability in a case where the vehicle travels on the selected candidate route by the autonomous driving using a trained model for autonomous driving, and output a result of the simulation.

Abstract: A travel storage system includes: a target vehicle behavior determination unit that is configured to: acquire a sensor value indicative of a behavior of each of surrounding vehicles around the host vehicle; and sequentially determine a relative behavior of a target vehicle with respect to the host vehicle based on the sensor value; a rule acquisition unit that acquires an accident liability rule in a current position of the host vehicle; a potential accident liability information determination unit that sequentially determines, using liability determination information, potential accident liability information indicative of whether the host vehicle is liable for a potentially assumed accident between the target vehicle and the host vehicle; and a storage unit that stores the potential accident liability information and the liability determination information in association with each other or in associable with each other.

Abstract: A road parameter calculator is provided which is equipped with an edge-point extracting unit, a road parameter calculating unit, a gradient detecting unit, and a modeling unit. The image acquiring unit. The edge-point extracting unit extracts edge points from an image of a frontal view of a vehicle. The parameter calculating unit calculates a road parameter using the edge points through a Kalman filter. The gradient detecting unit detects a change in gradient of the road in front of the vehicle. The modeling unit is responsive to a change in gradient to make a model as extending more straight than when the change in gradient is not detected. This minimizes adverse effects of the change in gradient of the road on the calculation of the road parameter.

Abstract: An apparatus for identifying a line marking on a road surface. In the apparatus, an extractor extracts a paint candidate that is a candidate for road surface paint used to identify a line marking in an image captured by a camera mounted on a vehicle to capture an image of an area including a road surface ahead of the vehicle. A determiner determines whether or not the paint candidate has at least one predefined flare feature. A line marking identifier identifies a line marking using the paint candidate which meets an identification condition used to identify a line marking. The line marking identifier sets the identification condition to be more stringent for a flare paint candidate that is the paint candidate determined by the determiner to have the at least one predefined flare feature than for the paint candidate determined by the determiner to have no predefined flare feature.

Abstract: A road parameter calculator is provided which is equipped with an edge-point extracting unit, a road parameter calculating unit, a gradient detecting unit, and a modeling unit. The image acquiring unit. The edge-point extracting unit extracts edge points from an image of a frontal view of a vehicle. The parameter calculating unit calculates a road parameter using the edge points through a Kalman filter. The gradient detecting unit detects a change in gradient of the road in front of the vehicle. The modeling unit is responsive to a change in gradient to make a model as extending more straight than when the change in gradient is not detected. This minimizes adverse effects of the change in gradient of the road on the calculation of the road parameter.

Abstract: A lane detecting apparatus is provided which obtains at least one type of parameters associated with lane lines on the road and selects one of the lane lines as a reference line using one of the parameters which is most useful in determining the configuration of a lane on a road. The lane detecting apparatus also calculates a lane boundary line using the reference lane line.

Abstract: An apparatus for identifying a line marking on a road surface. In the apparatus, an extractor extracts a paint candidate that is a candidate for road surface paint used to identify a line marking in an image captured by a camera mounted on a vehicle to capture an image of an area including a road surface ahead of the vehicle. A determiner determines whether or not the paint candidate has at least one predefined flare feature. A line marking identifier identifies a line marking using the paint candidate which meets an identification condition used to identify a line marking. The line marking identifier sets the identification condition to be more stringent for a flare paint candidate that is the paint candidate determined by the determiner to have the at least one predefined flare feature than for the paint candidate determined by the determiner to have no predefined flare feature.

Abstract: A boundary line recognition apparatus recognizes a boundary line that demarcates a driving lane in which an own vehicle is driving and includes a candidate detecting unit, a dowel bar determining unit, and a boundary line recognizing unit. The candidate detecting unit detects line types of boundary line candidates that are candidates for the boundary line and a quantity of boundary line candidates of the same line type, based on detection data of the boundary line candidates acquired from a detecting unit that detects the boundary line candidates. The dowel bar determining unit determines whether or not the boundary line candidates are formed by dowel bars embedded in a road, based on the line type and quantity detected by the candidate detecting unit. The boundary line recognizing unit eliminates the boundary line candidates that are determined to be formed by the dowel bars and recognizes the boundary line.

Abstract: A road parameter calculator is provided which is equipped with an edge-point extracting unit, a road parameter calculating unit, a gradient detecting unit, and a modeling unit. The image acquiring unit. The edge-point extracting unit extracts edge points from an image of a frontal view of a vehicle. The parameter calculating unit calculates a road parameter using the edge points through a Kalman filter. The gradient detecting unit detects a change in gradient of the road in front of the vehicle. The modeling unit is responsive to a change in gradient to make a model as extending more straight than when the change in gradient is not detected. This minimizes adverse effects of the change in gradient of the road on the calculation of the road parameter.

Abstract: A road parameter estimation apparatus estimates road parameters and includes an image acquiring unit, an edge point extracting unit, an area setting unit, an estimating unit, and a vehicle speed acquiring unit. The image acquiring unit acquires an image that shows an area ahead of the vehicle. The edge point extracting unit extracts edge points in the image. The area setting unit sets an area in the image. The area is a part of the image and having a far-side borderline as a boundary thereof The far-side borderline is a virtual line that is ahead of the vehicle by a distance. The estimating unit estimates road parameters using a Kalman filter based on the edge points. The vehicle speed acquiring unit acquires a vehicle speed. The area setting unit increases the distance from the vehicle to the far-side borderline of the area in the image as the vehicle speed increases.

Abstract: In a pitch angle estimation apparatus, an image acquirer sequentially acquires images in a forward or rearward direction of a vehicle. Each image is referred to as a frame. A movement amount calculator extracts a feature point from each of the images acquired by the image acquirer and calculates an amount of movement of the feature point in a vertical direction of the image. A pitch angle calculator calculates a pitch angle of the vehicle based on the amount of movement of the feature point. An output unit outputs the pitch angle calculated by the pitch angle calculator. A low frequency component calculator calculates a low frequency component of the pitch angle calculated by the pitch angle calculator. An output determiner determines whether to output the pitch angle based on the magnitude of the low frequency component calculated by the low frequency component calculator.

Abstract: In an image processing apparatus, an image acquiring unit acquires a first image and a second image that form stereoscopic images. A first sub-image extracting unit extracts first sub-images from the first image. A second sub-image extracting unit extracts second sub-images from the second image. A matching unit matches each pair of the first and second sub-images to determine a degree of similarity therebetween. A similar sub-image setting unit sets the second sub-image having a highest degree of similarity to the first sub-image to be a similar sub-image to the first sub-image. A brightness comparing unit compares in brightness each pair of the first and second sub-images. The matching unit is configured to, if a result of comparison made by the brightness comparing unit between a pair of the first and second sub-images is out of a predetermined brightness range, exclude such a pair of the first and second sub-images.

Abstract: A lane detecting apparatus is provided which obtains at least one type of parameters associated with lane lines on the road and selects one of the lane lines as a reference line using one of the parameters which is most useful in determining the configuration of a lane on a road. The lane detecting apparatus also calculates a lane boundary line using the reference lane line.

Abstract: The demarcation line recognition device determines the line type and line color of the extracted candidate line, and the presence or absence of an influence of backlight, and determines whether the extracted candidate line constitutes a part of a multiple line. The demarcation line recognition device selects a candidate line corresponding to a demarcation line from extracted candidate lines, using the line type and line color determined, and the determination result of the multiple line determination, and according to the rules of the driving region. When a candidate line determined to be under the influence of backlight constitutes a part of a multiple line, the demarcation line recognition device compares the color information of the candidate lines constituting that multiple line with each other and re-determines the line color of the candidate line determined to be under the influence of backlight.

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 pitch angle estimation apparatus, an image acquirer sequentially acquires images in a forward or rearward direction of a vehicle. Each image is referred to as a frame. A movement amount calculator extracts a feature point from each of the images acquired by the image acquirer and calculates an amount of movement of the feature point in a vertical direction of the image. A pitch angle calculator calculates a pitch angle of the vehicle based on the amount of movement of the feature point. An output unit outputs the pitch angle calculated by the pitch angle calculator. A low frequency component calculator calculates a low frequency component of the pitch angle calculated by the pitch angle calculator. An output determiner determines whether to output the pitch angle based on the magnitude of the low frequency component calculated by the low frequency component calculator.

Abstract: A lane partition line recognition apparatus for recognizing left-side and right-side lane partition lines of a traveling lane of a roadway in which a vehicle carrying the apparatus is traveling based on a forward image captured by a vehicle-mounted camera. The apparatus includes an offset detector configured to detect an offset amount indicative of a positional relationship between each of the left-side and right-side recognized lane partition lines and the vehicle and a predictor configured to, if only one of the left and right lane partition lines of the traveling lane of the subject vehicle is recognized, stochastically predict a position of the unrecognized one of the left and right lane partition lines based on the offset amount detected by the offset detector.

Abstract: In an image processing apparatus, an image acquiring unit acquires a first image and a second image that form stereoscopic images. A first sub-image extracting unit extracts first sub-images from the first image. A second sub-image extracting unit extracts second sub-images from the second image. A matching unit matches each pair of the first and second sub-images to determine a degree of similarity therebetween. A similar sub-image setting unit sets the second sub-image having a highest degree of similarity to the first sub-image to be a similar sub-image to the first sub-image. A brightness comparing unit compares in brightness each pair of the first and second sub-images. The matching unit is configured to, if a result of comparison made by the brightness comparing unit between a pair of the first and second sub-images is out of a predetermined brightness range, exclude such a pair of the first and second sub-images.

Abstract: A travel lane marking recognition apparatus includes a recognizing unit that is mounted to a vehicle. The recognizing unit uses a recognition model to recognize a lane marking that demarcates a lane on a road, based on an image of an area ahead of the vehicle captured by an on-board camera. The recognition model is configured by a plurality of recognition models that includes at least two recognition models among a first-order model that takes into consideration a straight line, a second-order model that takes into consideration a steady curve, and a third-order model that takes into consideration a clothoid curve. The recognizing unit is configured to recognize the lane marking by integrating recognition results acquired using the plurality of recognition models based on a state of the vehicle.