  End-to-end trained neural networks (NNs) are a compelling approach to
autonomous vehicle control because of their ability to learn complex tasks
without manual engineering of rule-based decisions. However, challenging road
conditions, ambiguous navigation situations, and safety considerations require
reliable uncertainty estimation for the eventual adoption of full-scale
autonomous vehicles. Bayesian deep learning approaches provide a way to
estimate uncertainty by approximating the posterior distribution of weights
given a set of training data. Dropout training in deep NNs approximates
Bayesian inference in a deep Gaussian process and can thus be used to estimate
model uncertainty. In this paper, we propose a Bayesian NN for end-to-end
control that estimates uncertainty by exploiting feature map correlation during
training. This approach achieves improved model fits, as well as tighter
uncertainty estimates, than traditional element-wise dropout. We evaluate our
algorithms on a challenging dataset collected over many different road types,
times of day, and weather conditions, and demonstrate how uncertainties can be
used in conjunction with a human controller in a parallel autonomous setting.
