  Estimating statistical uncertainties allows autonomous agents to communicate
their confidence during task execution and is important for applications in
safety-critical domains such as autonomous driving. In this work, we present
the uncertainty-aware imitation learning (UAIL) algorithm for improving
end-to-end control systems via data aggregation. UAIL applies Monte Carlo
Dropout to estimate uncertainty in the control output of end-to-end systems,
using states where it is uncertain to selectively acquire new training data. In
contrast to prior data aggregation algorithms that force human experts to visit
sub-optimal states at random, UAIL can anticipate its own mistakes and switch
control to the expert in order to prevent visiting a series of sub-optimal
states. Our experimental results from simulated driving tasks demonstrate that
our proposed uncertainty estimation method can be leveraged to reliably predict
infractions. Our analysis shows that UAIL outperforms existing data aggregation
algorithms on a series of benchmark tasks.
