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 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 detecting obstacles in off-road applications. A stereo camera and specific image-processing techniques enable a vehicle's vision system to identify drivable terrain in front of the vehicle. The method uses non-drivable residuals (NDR), where the NDR is zero for all terrain that can be easily traversed by the vehicle and greater than zero for terrain that may not be traversable by the vehicle. The method utilizes a depth map having a point cloud that represents the depth to objects within the field of view of the stereo cameras. The depth map is tiled such that the point cloud data is represented by an average (smoothed) value. The method scans pixels in the smoothed depth map to find sequences of “good” points that are connected by line segments having an acceptable slope. Points that lie outside of the acceptable slope range will have an NDR that is greater than zero. The vehicle control system can use the NDRs to accurately make decisions as to the trajectory of the vehicle.

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.