  A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any
abnormal behaviors under undesirable and unpredicted circumstances. As AVs
increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical
tasks, different methods for uncertainty quantification have recently been
proposed to measure the inevitable source of errors in data and models.
However, uncertainty quantification in DNNs is still a challenging task. These
methods require a higher computational load, a higher memory footprint, and
introduce extra latency, which can be prohibitive in safety-critical
applications. In this paper, we provide a brief and comparative survey of
methods for uncertainty quantification in DNNs along with existing metrics to
evaluate uncertainty predictions. We are particularly interested in
understanding the advantages and downsides of each method for specific AV tasks
and types of uncertainty sources.
