Abstract: An object recognition method uses a sensor configured to acquire a position of an object existing in a surrounding environment as point clouds including a plurality of detection points in a top view. The method includes: grouping point clouds according to a proximity; determining, when performing polygon approximation on the grouped point clouds, whether at least part of the detection points constituting the grouped point clouds are located in a blind zone of an approximate polygon acquired by the polygon approximation with respect to the sensor; recognizing the grouped point clouds as point clouds corresponding to plural objects when it is determined that the detection points are located in the blind zone with respect to the sensor; and recognizing the grouped point clouds as point clouds corresponding to a single object of the approximate polygon when it is determined that the detection points are not located in the blind zone.

Abstract: An occlusion is identified in a vehicle transportation network. A visibility grid is identified on a second side of the occlusion for a vehicle that is on a first side of the occlusion. The visibility grid is identified with respect to a region of interest that is at least a predefined distance above ground. The visibility grid is used to identify first portions of roads sensed by a sensor positioned on the vehicle and second portions of the roads that are not sensed by the sensor. A driving behavior of the vehicle is altered based on the visibility grid.

Abstract: A first method includes identifying an occlusion in the vehicle transportation network; identifying, for a first world object that is on a first side of the occlusion, a visibility grid on a second side of the occlusion; and altering a driving behavior of the first vehicle based on the visibility grid. The visibility grid is used in determining whether other world objects exist on the second side of the occlusion. A second includes identifying a first trajectory of a first world object in the vehicle transportation network; identifying a visibility grid of the first world object; identifying, using the visibility grid, a second world object that is invisible to the first world object; and, in response to determining that the first world object is predicted to collide with the second world object, alerting at least one of the first world object or the second world object.

Abstract: An object recognition method including: acquiring a group of points of a plurality of positions of objects in surroundings of an own vehicle ; generating a captured image of surroundings of the own vehicle; grouping points in the group of points into a group of object candidate points; extracting, from among object candidate points, included in the group of object candidate points, a position at which change in distance from the own vehicle between adjacent object candidate points increases from a value equal to or less than a threshold value to greater than the threshold value as a boundary position candidate; extracting a partial region in which a person is detected in the captured image; and when, in the captured image, a position of the boundary position candidate coincides with a boundary position of the partial region in the predetermined direction, recognizing that a pedestrian exists in the partial region.

Abstract: An object recognition method uses a sensor configured to acquire a position of an object existing in a surrounding environment as point clouds including a plurality of detection points in a top view. The method includes: grouping point clouds according to a proximity; determining, when performing polygon approximation on the grouped point clouds, whether at least part of the detection points constituting the grouped point clouds are located in a blind zone of an approximate polygon acquired by the polygon approximation with respect to the sensor; recognizing the grouped point clouds as point clouds corresponding to plural objects when it is determined that the detection points are located in the blind zone with respect to the sensor; and recognizing the grouped point clouds as point clouds corresponding to a single object of the approximate polygon when it is determined that the detection points are not located in the blind zone.

Abstract: A first method includes identifying an occlusion in the vehicle transportation network; identifying, for a first world object that is on a first side of the occlusion, a visibility grid on a second side of the occlusion; and altering a driving behavior of the first vehicle based on the visibility grid. The visibility grid is used in determining whether other world objects exist on the second side of the occlusion. A second includes identifying a first trajectory of a first world object in the vehicle transportation network; identifying a visibility grid of the first world object; identifying, using the visibility grid, a second world object that is invisible to the first world object; and, in response to determining that the first world object is predicted to collide with the second world object, alerting at least one of the first world object or the second world object.

Abstract: Autonomous vehicle operational management with visual saliency perception control may include operating a perception unit and an autonomous vehicle operational management controller. Operating the perception unit may include generating external object information based on image data received from image capture units of the vehicle and saliency information received from the autonomous vehicle operational management controller.

Abstract: Autonomous vehicle operational management with visual saliency perception control may include operating a perception unit and an autonomous vehicle operational management controller. Operating the perception unit may include generating external object information based on image data received from image capture units of the vehicle and saliency information received from the autonomous vehicle operational management controller.

Abstract: An object tracking device tracks objects in a time series using an object detection sensor for detecting objects around a host vehicle. When a plurality of objects are detected, the object tracking device sorts the objects into groups in accordance with a degree of influence of the positions of the objects on the movement of the host vehicle. The object tracking device tracks each of the groups as a single object in a time series.

Abstract: An inter-vehicle distance estimation method includes: estimating a screened area screened by an obstacle from a sensor and two unscreened areas sandwiching the screened area; and, based on speeds of two tracked vehicles traveling in a same traffic lane respectively within the two unscreened areas, estimating the inter-vehicle distances from the two tracked vehicles to a tracked vehicle traveling in the same traffic lane within the screened area.

Abstract: A vehicle control method includes: detecting a blocking object that is at a position at which the blocking object obstructs travel of the tracked vehicle in a traffic lane in which the tracked vehicle is traveling and that hides at least part of the traffic lane from the sensor, and an oncoming vehicle traveling in a direction toward the tracked vehicle in an oncoming lane opposite from the traffic lane; and slowing down the vehicle when the oncoming vehicle slows down.

Abstract: In a case where multiple objects detected by an object detector enter a hidden area calculated by a hidden area calculator, a positional relationship estimator estimates a positional relationship between the multiple objects in the hidden area based on: information about each of the multiple objects; and lanes and routes into which each of the multiple objects may change their courses, and which are calculated by a route calculator. When the objects come out of the hidden area, an identification number assigner determines identification information about each object based on the positional relationship estimated by the positional relationship estimator.

Abstract: An object detection method includes: acquiring three-dimensional data on an area around a host vehicle by use of a distance measurement sensor; based on map data on an area around a current position of the host vehicle, setting a planned travel area where the host vehicle is going to travel in the future; estimating crossing object existence areas where there currently exist objects which are likely to cross the host vehicle in the future in the set planned travel area; and detecting the object by use of the three dimensional data on insides of the estimated crossing object existence areas.

Abstract: An inter-vehicle distance estimation method includes: estimating a screened area screened by an obstacle from a sensor and two unscreened areas sandwiching the screened area; and, based on speeds of two tracked vehicles traveling in a same traffic lane respectively within the two unscreened areas, estimating the inter-vehicle distances from the two tracked vehicles to a tracked vehicle traveling in the same traffic lane within the screened area.

Abstract: In a case where multiple objects detected by an object detector enter a hidden area calculated by a hidden area calculator, a positional relationship estimator estimates a positional relationship between the multiple objects in the hidden area based on: information about each of the multiple objects; and lanes and routes into which each of the multiple objects may change their courses, and which are calculated by a route calculator. When the objects come out of the hidden area, an identification number assigner determines identification information about each object based on the positional relationship estimated by the positional relationship estimator.

Abstract: An object tracking device tracks objects in a time series using an object detection sensor for detecting objects around a host vehicle. When a plurality of objects are detected, the object tracking device sorts the objects into groups in accordance with a degree of influence of the positions of the objects on the movement of the host vehicle. The object tracking device tracks each of the groups as a single object in a time series.

Abstract: An object detection method includes: acquiring three-dimensional data on an area around a host vehicle by use of a distance measurement sensor; based on map data on an area around a current position of the host vehicle, setting a planned travel area where the host vehicle is going to travel in the future; estimating crossing object existence areas where there currently exist objects which are likely to cross the host vehicle in the future in the set planned travel area; and detecting the object by use of the three dimensional data on insides of the estimated crossing object existence areas.

Abstract: A vehicle control method includes: detecting a blocking object that is at a position at which the blocking object obstructs travel of the tracked vehicle in a traffic lane in which the tracked vehicle is traveling and that hides at least part of the traffic lane from the sensor, and an oncoming vehicle traveling in a direction toward the tracked vehicle in an oncoming lane opposite from the traffic lane; and slowing down the vehicle when the oncoming vehicle slows down.

Abstract: An information input apparatus includes an observation unit that observes an environment including a user and one or more apparatuses to be controlled and includes a sensor; a learning unit that separates a foreground including the user and the one or more apparatuses to be controlled and a background including the environment except for the foreground from observation data obtained by the observation unit and learns three-dimensional models of the foreground and the background; a state estimation unit that estimates positions and postures of already modeled foregrounds in the environment; a user recognition unit that identifies fingers of the user from the foreground and recognizes a shape, position, and posture of the fingers; and an apparatus control unit that outputs a control command to the one or more apparatuses to be controlled on the basis of the recognized shape, position, and posture of the fingers.

Abstract: There is provided an information processing apparatus including a manipulation model learning unit configured to learn a manipulation model regarding manipulation of a first object by a second object, by use of an actual image that is an actually observed image including the first object and the second object, the manipulation model associating a position and a change in state of the second object, when a state of the second object changes at a position in an object reference coordinate system with the first object regarded as a reference, with a change in state of the first object caused by the change in state of the second object.