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.

本文研究了自主车辆在安全关键任务中使用神经网络时，面临的不可避免的误差问题，并给出了一些用于评估这种不确定性的方法和指标，比较了各种不确定性量化方法在特定 AV 任务和类型的优劣。