Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control

Abstract-- 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 safetycritical 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. 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. I. INTRODUCTION Deep learning, and in particular Deep Neural Networks (DNNs), have seen a surge in popularity over the past decade, and their use has become widespread in many fields. This increase in popularity, attributed to (i) more powerful GPU implementations and (ii) the availability of large amounts of data, has led to significant performance gains.