1. A computer-implemented method for adaptive regulation of distance between a host vehicle and a lead vehicle, comprising: receiving, at an image processing unit, a stereo image of a roadway scene; locating, using a road detection and awareness module at said image processing unit, said roadway scene in a headway direction of said host vehicle, computing, using said road detection and awareness module, a plurality of vehicle detection gates, wherein each of said vehicle detection gates specifies a region of interest (ROI) of the roadway scene of the stereo image and a depth interval between said vehicle detection gates of the roadway scene of the stereo image, said each vehicle detection gate overlaps a portion of an adjacent detection gate; identifying, using said road detection and awareness module, at least two lane boundary markers in said stereo image of said roadway scene; retrieving, using said road detection and awareness module, ground plane data, said lane boundary markers, and said depth interval/range between said vehicle detection gates; computing, using said road detection and awareness module, depth for each said vehicle detection gate using the depth interval and computing said region of interest (ROI) for each said vehicle detection gate using the ground plane data and the lane marker positions, wherein said ROI is a projection in the image of the roadway scene of height and depth equal to the depth of the vehicle detection gate lying between two lane boundary makers and the ground; detecting, using a vehicular detection and tracking module at said image processing unit, potential lead vehicle detections among said vehicle detection gates on said roadway scene in said headway direction of said host vehicle, wherein said detecting comprising searching for edge features/pixels for the potential lead vehicle in said depth range within said ROI; receiving a plurality of left images and a plurality of right images of the stereo image for each said gate ROI; sub-sampling said plurality of the left images and the right images along a horizontal axis; and filtering said sub-sampled left images and said sub-sampled right images, applying a vertical sobel application to said sub-sampled left images to compute vertical left sobel edge images, wherein said vertical left sobel edge images are candidate image pixels for left edge pixels; selecting said left candidate image pixels as said left edge pixels based on a threshold; computing an intensity histogram for said selected left edge pixels to obtain a total number of said selected left sobel edge pixels and; thresholding the vertical left sobel image using the intensity histogram for said selected right edge pixels to produce a left edge pyramid, wherein said left edge pyramid is a binary image comprising said selected left sobel edge pixels.