Abstract
This paper discusses the main architectural issues of a challenging application of real-time image processing: the vision-based automatic guidance of road vehicles. Two algorithms for lane detection and obstacle localization, currently implemented on the ARGO autonomous vehicle developed at the University of Parma, are used as examples to compare two different computing engines — a massively parallel special-purpose SIMD architecture and a general-purpose system — while future trends in this field are proposed, based on the experience of the ARGO project.
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