Abstract: In general, techniques are described for coordinating actions of a plurality of agents or subsystems using a machine learning system that implements a Capability Graph Network (CGN). In an example, a method includes generating a control policy model comprising a plurality of nodes and a plurality of edges interconnecting the plurality of nodes, wherein the plurality of nodes represents a plurality of agents or subsystems and the plurality of edges represent information exchange between the plurality of agents or subsystems; and encoding agent behavior control policy within the control policy model for executing to coordinate a plurality of the actions of the plurality of agents or subsystems.

Abstract: The present invention provides a system and method for detecting edges of an object in a pre-crash sensing environment. The method includes extracting vertical edges from peaks in an array of column sums of absolute values of normalized horizontal derivates computed in the intensity image. The pixels with corresponding range image depths proximate the estimated object's closest surface contribute the column sums. The search is conducted for all likely combinations of left/right edge pairs preferably within a pre-determined horizontal range about the object's detected center. The left/right edge pairs that delimit a width within the pre-determined range of possible widths for the object are selected. Alternatively, an additional search is performed on each pair of edges to support any angular or inward slant on the edges of the object.

Abstract: A system and method for fusing depth and radar data to estimate at least a position of a threat object relative to a host object is disclosed. At least one contour is fitted to a plurality of contour points corresponding to the plurality of depth values corresponding to a threat object. A depth closest point is identified on the at least one contour relative to the host object. A radar target is selected based on information associated with the depth closest point on the at least one contour. The at least one contour is fused with radar data associated with the selected radar target based on the depth closest point to produce a fused contour. Advantageously, the position of the threat object relative to the host object is estimated based on the fused contour. More generally, a method is provided for aligns two possibly disparate sets of 3D points.

Abstract: The present invention provides a collision avoidance apparatus and method employing stereo vision applications for adaptive vehicular control. The stereo vision applications are comprised of a road detection function and a vehicle detection and tracking function. The road detection function makes use of three-dimensional point data, computed from stereo image data, to locate the road surface ahead of a host vehicle. Information gathered by the road detection function is used to guide the vehicle detection and tracking function, which provides lead motion data to a vehicular control system of the collision avoidance apparatus. Similar to the road detection function, stereo image data is used by the vehicle detection and tracking function to determine the depth of image scene features, thereby providing a robust means for identifying potential lead vehicles in a headway direction of the host vehicle.

Abstract: A method and apparatus for producing a fused image is described. In one embodiment, a first image at a first wavelength and a second image at a second wavelength are generated. Next, range information is generated and subsequently used to warp the first image in a manner that correlates to the second image. In turn, the warped first image is fused with the second image to produce the fused image.

Abstract: A method and apparatus for classifying an object in an image is disclosed. A plurality of sub-classifiers is provided. An object is classified using input from each of the plurality of sub-classifiers.

Abstract: The present invention provides a system and method for detecting edges of an object in a pre-crash sensing environment. The method includes extracting vertical edges from peaks in an array of column sums of absolute values of normalized horizontal derivates computed in the intensity image. The pixels with corresponding range image depths proximate the estimated object's closest surface contribute the column sums. The search is conducted for all likely combinations of left/right edge pairs preferably within a pre-determined horizontal range about the object's detected center. The left/right edge pairs that delimit a width within the pre-determined range of possible widths for the object are selected. Alternatively, an additional search is performed on each pair of edges to support any angular or inward slant on the edges of the object.

Abstract: A vision system that forms a map of a scene proximate a platform, e.g., a vehicle, that determines the actual ground plane form the map, and that corrects the map for differences between the actual ground plane and an assumed ground plane. The vision system may remove the actual ground plane from the map to prevent false positives. The vision system can further identify and classify objects and, if appropriate, take evasive action.

Abstract: A method and apparatus for generating stereo imagery scenario data to be used to test stereo vision methods such as detection, tracking, classification, steering, collision detection and avoidance methods is provided.

Abstract: A method and apparatus for classifying an object in an image is disclosed. A depth image is provided. At least one area of the depth image unsatisfactory for object identification are identified. A plurality of two-dimensional projections of surface normals in the depth image is determined without considering the unsatisfactory at least one area. One or more objects are classified based on the plurality of two-dimensional projections of surface normals.

Abstract: A method and apparatus for classifying an object in an image is disclosed. Edges of an object are detected within a region of interest. Edge analysis is performed on a plurality of sub-regions within the region of interest to generate an edge score. The object is classified based on the edge score.

Abstract: A method and apparatus for detecting a target in an image is disclosed. A plurality of depth images is provided. A plurality of target templates is compared to at least one of the plurality of depth images. A scores image is generated based on the plurality of target templates and the at least one depth image.

Abstract: A method and apparatus for performing collision detection is described. An object is detected within a first operational range of an object tracker. A classification of the object is determined using the object tracker. The object tracker tracks the object. The object is detected within a second operational range of a collision detector. A safety measure is activated based on the classification using the collision detector.

Abstract: A method and apparatus for classifying an object in an image is disclosed. Edge energy information and/or intensity variation information is generated for an object in a region of interest of the image. The object occupying the region of interest is classified based on the edge energy information and/or intensity variation information.

Abstract: A vehicle vision system that uses a depth map, image intensity data, and system calibration parameter to determine a target's dimensions and relative position. Initial target boundary information is projected onto the depth map and onto the image intensity. A visibility analysis determines whether the rear of a target is within the system's field of view. If so, the mapped image boundary is analyzed to determine an upper boundary of the target. Then, vertical image edges of the mapped image boundary are found by searching for a strongest pair of vertical image edges that are located at about the same depth. Then, the bottom of the mapped image boundary is found (or assumed from calibration parameters). Then, the target's position is found by an averaging technique. The height and width of the target are then computed.