Abstract: An information processing device includes a travel request acquisition unit that acquires travel request information for requesting travel permission of a specific road by a first vehicle, a number-of-vehicles acquisition unit that acquires a number of traveling vehicles indicating a number of a plurality of second vehicles traveling on the specific road, a determination unit that determines traveling availability on a basis of the number of traveling vehicles and an appropriate number of vehicles indicating an appropriate number of vehicles traveling on the specific road, and a transmission unit that transmits information indicating a determination result of the traveling availability to the first vehicle.

Abstract: An acquisition unit (22) acquires a value corresponding to the number of aggregation targets that are present, the number being collected in a predetermined time unit for each region obtained by virtually dividing a real space, a calculation unit (24) calculates each of three different indexes from the acquired value, and a control unit (26) displays, on a display unit, a visualized image in which a pixel value obtained by associating the three indexes calculated with three elements of a color space, respectively, is set as a pixel value indicating the region.

Abstract: A recognition device acquires a time-series image acquired in an environment in which a vehicle travels, detects characters of a predetermined character string from the image, and evaluates relationship between detected characters of the character string and recognizes a shape of a target including the character string.

Abstract: In a dimension estimation device, an image acquired in a moving body is acquired, and a vanishing point and a predetermined decision line in the image are calculated. An object in the image is detected and a detection rectangle is detected for the object, the detection rectangle being a rectangle including a first surface that is a surface close to a photographing position, the first surface being in proximity to the decision line, and a second surface that is a surface far from the photographing position. A dimension of the object is estimated on the basis of a ratio between a height of the first surface and a height of the second surface obtainable from a relationship between the detection rectangle and the vanishing point.

Abstract: A recognition device that detects a boundary of a roadway by use of time-series images captured by a camera mounted on a vehicle, the recognition device including: an acquisition unit that acquires the time-series images; and a detection unit that classifies areas in the acquired time-series images into a roadway, which is an area having a small change amount, and a portion other than the roadway, which is an area having a large change amount, according to magnitude of the change amounts, and detects a boundary between the area having the small change amount and the area having the large change amount as a boundary of the roadway.

Abstract: A technique of estimating, based on a camera image captured from a mobile object, the position coordinates of an ambient object or the mobile object itself is offered. A plurality of camera images that are captured at different geographical locations and contain at least one identical stationary object are acquired; position coordinates of the imaging locations of the corresponding camera images are acquired; and the position coordinates of the stationary object are estimated based on the position coordinates of the imaging locations and gaze vectors oriented from a camera to the stationary object in each of the camera images.

Abstract: A lane pricing technique for charging a mobile body traveling on a road for use of a lane is provided. A lane pricing device according to one embodiment is configured to: acquire from a mobile body traveling on a road, at least one of position information of a mobile body measured based on a positioning signal, and/or a camera image that is acquired by capturing an image from the mobile body and includes a part of a lane in which the mobile body is moving; specify, based on at least one of the position information and/or the camera image, a lane in which the mobile body is moving; acquire lane identification information for identifying a specified lane along with time information indicative of time when the mobile body is moving; and determine and output a toll to be charged to the mobile body in units of lanes based on the lane identification information and the time information.

Abstract: An image acquisition unit 20 acquires a group of time-series images captured by a camera mounted at the observation vehicle. A speed difference estimation unit 24 estimates a speed difference between the target object and the observation vehicle by using a time-series change in a region representing the target object captured in the group of time-series images. The determination unit 28 determines whether the target object or the observation vehicle is in a dangerous state based on the speed difference.

Abstract: A sidewalk information generation device includes a thinning unit configured to perform thinning processing for extracting, from image data indicating an area of a sidewalk, a centerline in a width direction of the area of the sidewalk, a segmentation unit configured to perform segmentation processing for dividing the centerline extracted by the thinning unit in a longitudinal direction to generate a plurality of lines, and a vectorization unit configured to convert each of the plurality of lines generated by the segmentation unit to vector data indicating a coordinate sequence of a plurality of line segments.

Abstract: A sidewalk network information generation device in an embodiment includes a sidewalk centerline data storage device configured to store sidewalk centerline data indicating a plurality of sidewalk centerlines, a detection unit configured to detect an end point and a corner point of each of the plurality of sidewalk centerlines from the sidewalk centerline data stored in the sidewalk centerline data storage unit, and a link information generation unit configured to select, from among the end points and the corner points of the plurality of sidewalk centerlines detected by the detection unit, first coordinates indicating an end point or a corner point of a first sidewalk centerline and second coordinates indicating an end point or a corner point of a second sidewalk centerline within a certain distance from the first coordinates, and generate link information for linking the plurality of sidewalk centerlines on the basis of the selected first and second coordinates.

Abstract: A technology is provided whereby a lane can be correctly estimated without relying on lane markers. A shape indicating a road region is extracted from image data obtained by capturing images in the progression direction of a vehicle 6 and, on the basis of information indicating the shape, the angle of inclination for one contour line, the center-of-gravity coordinates for a diagram indicating the shape of the road region, the angle between two consecutive contour lines across one vertex of the diagram indicating the shape, and the area of the diagram indicating the shape are calculated as feature values for the shape of the road region. In addition, the lane in which the vehicle 6 is traveling can be estimated by determining whether or not the calculated feature values are included in a threshold value range preset for each lane.

Abstract: A traffic congestion determination device includes an acquisition unit configured to acquire a total number of automobiles for each mesh obtained by virtually dividing a determination target region of traffic congestion and for each unit time, and a determination unit configured to determine whether occurrence of traffic congestion is sudden for each of the meshes based on the acquired total number of automobiles for each of the meshes and unit time.

Abstract: The traffic flow estimation apparatus acquires images and position information and speed information, the images being captured at different timings by a first moving object that is in motion in a target area and including a second moving object around the first moving object, the position information and speed information being of the first moving object at the timings; estimates a lane in which the second moving object is in motion; and estimates a traffic flow for each lane in the target area based on the speed information of the first moving object, the information indicative of a change over time of positions of the second moving object, and the lanes in which the first moving object and the second moving object are in motion.

Abstract: An estimation method includes acquiring sensor information acquired by a sensor mounted on a first vehicle belonging to a vehicle line, acquiring position information on a location of the first vehicle, and estimating a cause of the vehicle line by using the sensor information and the position information. The estimating of the cause includes estimating the cause by using information on a facility or a feature present near the first vehicle, information indicating a state of the first vehicle, information on an object present ahead of the first vehicle included in the sensor information, or information on a road that affects traveling. The information on the facility or the feature is searched based on the position information, the information on the object is estimated based on the sensor information, and the information on the road is searched based on the position information.

Abstract: A position estimation unit 25 estimates, for each movement in a predetermined section, a plurality of position candidates of a position estimation target on the basis of position information and sensor information. A position determination unit 26 evaluates, for each movement in the predetermined section, on the basis of the plurality of position candidates of the position estimation target obtained by the movement in the predetermined section, whether the position information used to estimate the position candidates has an error caused by any of a position of a GPS satellite, a position of a mobile body in a real space, or the mobile body or does not have any error caused thereby.

Abstract: A state estimation method executed by a state estimation apparatus including a processor and a memory storing program instructions that cause the processor to estimate a state related to congestion on a subject lane, includes acquiring a state related to congestion of a monitoring vehicle traveling on a non-subject lane, counting a number of vehicles traveling on the subject lane, the vehicles being overtaken by the monitoring vehicle or being oncoming vehicles passed by the monitoring vehicle, and estimating the state related to the congestion on the subject lane, using the state related to the congestion of the monitoring vehicle and the number of vehicles. In the estimating, when the number of the vehicles is equal to or greater than a threshold value according to the state related to the congestion of the monitoring vehicle, the state related to the congestion on the subject lane is estimated as traffic congestion.

Abstract: A vehicle condition estimation method performed by an information processing device including a processor and a memory connected to the processor to estimate a position or a condition of a vehicle, includes: acquiring an image including a vehicle to be estimated; and estimating the position or the condition of the vehicle to be estimated, using a line segment connecting at least two points selected from a region in which the vehicle to be estimated is captured in the image, with reference to an imaging device that has captured the image.

Abstract: The traffic flow estimation apparatus acquires images and position information and speed information, the images being captured at different timings by a first moving object that is in motion in a target area and including a second moving object around the first moving object, the position information and speed information being of the first moving object at the timings; estimates a lane in which the second moving object is in motion; and estimates a traffic flow for each lane in the target area based on the speed information of the first moving object, the information indicative of a change over time of positions of the second moving object, and the lanes in which the first moving object and the second moving object are in motion.

Abstract: A crosswalk detection device according to an embodiment includes a processor, and the processor configured to perform image scanning processing for scanning aerial image data in unit of window, multiple line segment creation processing for creating a plurality of line segments corresponding to a distance based on a dimension of a crosswalk in any direction within a region scanned in the aerial image data, first calculation processing for calculating an absolute value of a first derivative value indicating a difference in brightness between both ends of the line segment and calculating an average value of the absolute value of the first derivative value for each angle, second calculation processing for calculating an absolute value of a second derivative value indicating a difference between a difference in brightness between a first end and an intermediate point of the line segment and a difference in brightness between the intermediate point and a second end of the line segment and calculating an average val

Abstract: A driving assistance device according to an embodiment includes a hardware processor and a memory, in which the hardware processor is configured to execute: acquiring image data captured in a vehicle and sensor data relating to a driving operation; converting an image in each frame included in the image data into an image feature amount indicated by the image; combining the image feature amount and the acquired sensor data; performing, based on the combined data, a learning process for a driving assistance information learning model used when a target value relating to the driving operation by a driver of the vehicle is generated from the image feature amount included in the combined data; and generating, when new image data captured in the running vehicle is acquired, the target value relating to the driving operation based on the image feature amount converted from the acquired new image data and the driving assistance information learning model.