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This paper describes an algorithm for detecting roads and obstacles in radar data taken from a millimeter-wave imaging platform mounted on a stationary automobile. Such an algorithm is useful in a system that provides 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 radar data using a new criterion. 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 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 a fixed threshold) amount of incident radar power are identified as potential obstacles. The performance of the algorithm on a large all-weather data set is documented. The road edges and obstacles detected are consistently close to ground truth over the entire data set. A new method for computing the gradient field of radar data is also reported, along with an exposition of the millimeter-wave radar imaging process from a signal-processing perspective.
Kaliyaperumal et al. (Mon,) studied this question.