  A rise in popularity of Deep Neural Networks (DNNs), attributed to more
powerful GPUs and widely available datasets, has seen them being increasingly
used within safety-critical domains. One such domain, self-driving, has
benefited from significant performance improvements, with millions of miles
having been driven with no human intervention. Despite this, crashes and
erroneous behaviours still occur, in part due to the complexity of verifying
the correctness of DNNs and a lack of safety guarantees.
  In this paper, we demonstrate how quantitative measures of uncertainty can be
extracted in real-time, and their quality evaluated in end-to-end controllers
for self-driving cars. To this end we utilise a recent method for gathering
approximate uncertainty information from DNNs without changing the network's
architecture. We propose evaluation techniques for the uncertainty on two
separate architectures which use the uncertainty to predict crashes up to five
seconds in advance. We find that mutual information, a measure of uncertainty
in classification networks, is a promising indicator of forthcoming crashes.
