Abstract
This paper describes LEXLUTHER (likelihood-based experiments evaluating the efficacy of radar), an algorithm for detecting roads and obstacles in radar data taken from an imaging platform mounted on a stationary automobile. Such an algorithm would be useful in systems that provide all-weather driving assistance. Road boundaries are detected first. The prior shape of the road boundaries is modeled as a deformable template that describes the road edges in terms of its curvature, orientation and offset. This template is matched to the underlying gradient field of the road data using a new and novel matching criteria. The Metropolis algorithm is used to deform the template so that it best matches the underlying gradient field. Obstacles are detected next The radar returns from image pixels that are identified by LEXLUTHER as being part of the road are processed again and their power levels are compared to a threshold. Pixels belonging to the road that return a significant (greater than threshold) amount of incident radar power are identified as potential obstacles. The performance of LEXLUTHER on a large all-weather data set is documented. The road edges and obstacles detected are consistently closer to groung truth over the entire data set.
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