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This paper presents a dynamic road model for unstructured road area detection with applications to intelligent vehicles. Road area is the region of interest in many intelligent vehicle systems. We present a road detection algorithm, Road Model, that utilizes a planefitting technique based on image features of road area followed by a region growing process. The road model algorithm is fast and robust to a variety of unstructured road conditions.

This paper presents a simulation and comparison of two different infra-red imaging systems in terms of their use in automotive collision avoidence applications. The first half of this study concerns the simulations of an “cooled” focal plane array infra-red imaging system, and an “uncooled” focal plane array infra-red imaging system. This is done using the United States Army's Tank-Automotive Command Thermal Image Model - (TTIM). Visual images of automobiles - as seen through a forward looking infra-red sensor - are generated, by using TTIM, under a variety of viewing range, and rain conditions. The second half of the study focuses on a comparison between the two simulated sensors. This comparison is undertaken from the standpoint of the ability of a human observer to detect potential (collision) targets, when seeing through the two different sensors.