Velocities measured by radar sensors suffer from ambiguities caused by aliasing effects associated with signal processing. These ambiguities are highly undesired as they affect otherwise very accurate speed measurements. For automotive applications utilizing radar sensors, this means that measured velocities are potentially unreliable which can lead to unsafe conditions for automated driving functionalities. This work presents the first approach to disambiguate radial velocities measured by radar sensors based on Deep Learning methods. By utilizing the presented method, radial velocity estimates can be obtained which are both highly accurate while being reliable as ambiguities are resolved.

This paper proposes a framework for cost-sensitive classification under a generalized cost function. By combining decision trees with sequential binary programming, we can handle unequal misclassification costs, constrained classification, and complex objective functions that other methods cannot. Our approach has two main contributions. First, it provides a new method for cost-sensitive classification that outperforms a traditional, accuracy-based method and some current cost-sensitive approaches. Second, and more important, our approach can handle a generalized cost function, instead of the simpler misclassification cost matrix to which other approaches are limited.