Quantification of Uncertainties in Deep Learning-based Environment
  Perception
Marco Braun Moritz Luszek Jan Siegemund Kevin Kollek Anton Kummert
In this work, we introduce a novel Deep Learning-based method to perceive the
environment of a vehicle based on radar scans while accounting for
uncertainties in its predictions. The environment of the host vehicle is
segmented into equally sized grid cells which are classified individually.
Complementary to the segmentation output, our Deep Learning-based algorithm is
capable of differentiating uncertainties in its predictions as being related to
an inadequate model (epistemic uncertainty) or noisy data (aleatoric
uncertainty). To this end, weights are described as probability distributions
accounting for uncertainties in the model parameters. Distributions are learned
in a supervised fashion using gradient descent. We prove that uncertainties in
the model output correlate with the precision of its predictions. Compared to
previous concepts, we show superior performance of our approach to reliably
perceive the environment of a vehicle.