A vehicle on the same lane, but in front of the DSS constitutes an obstacle, albeit of a special kind if it moves at a limited velocity differential with respect to the DSS. On highways and rural roads, preceding vehicles have to be detected and tracked in order to decide whether they should be followed or overtaken.
Two approaches have been devised which both take advantage of the special type of `obstacle' expected in this case. The first approach assumes that the rear side of the preceding vehicle exhibits a marked symmetry around its central vertical axis in the image and that the silhouette contrasts well against the background along the horizontal direction. A Hough Transform is used with tentatively paired edge elements in order to search for edge element pairings with a lateral distance compatible with the rear view of a vehicle and a center position corresponding approximately to the middle of the lane. Depending on the sophistication of the approach, a simple rectangle might be fitted to image gradients, initialized by the size and position estimates obtained by the Hough search for symmetric pairs of vehicle side edge segments. A further step in sophistication consists in fitting the projection of a simple 3-D vehicle model in the form of a parallelepiped to the image edges, to initialize a Kalman Filter and to track such a model from frame to frame.
A different detection approach exploits the observation that the rear side of a road vehicle causes a shadow on the road which very frequently can be clearly detected as a fairly sharp gray value transition to very dark values (corresponding to the visible road area underneath the preceding vehicle). As soon as an approximately horizontal edge segment of appropriate length and location can be detected in the image area corresponding to the road in front of the DSS, it is hypothesized to constitute the rear shadow edge of a preceding vehicle. One may then search for vertical edge segments corresponding to the left and right sides of the preceding vehicle and use these three cues in order to initialize either a 2-D rectangle or a 3-D box model for tracking.
Both of the approaches mentioned can be implemented to work in real-time on a 1996 vintage VLSI CPU. It depends on what information about the preceding vehicle will be required for the driving manouver to be supported by the DSS whether more complex model-based tracking and recognition procedures are initialized. In any case, if the hypothesis that a preceding vehicle has been detected in the lane of the DSS has been firmly established, the distance to this preceding vehicle can be easily inferred from its rear end projected onto the road plane, exploiting the camera calibration of the DSS. Given a reliable estimate of this distance to the preceding vehicle, longitudinal control of the DSS vehicle can be based on this distance estimate (Follow_Preceding_Vehicle). Alternatively, a warning to the driver can be generated if this distance drops below a safety threshold.