论文标题
对自动驾驶使用的深度学习组件中不确定性估计方法的比较
A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications
论文作者
论文摘要
确保自动驾驶汽车安全性（AV）的关键因素是避免在不良和不可预测的情况下进行任何异常行为。随着AV越来越多地依赖深度神经网络（DNN）来执行关键安全任务，最近已经提出了不同的不确定性量化方法来衡量数据和模型中不可避免的错误源。但是，DNN中的不确定性量化仍然是一项艰巨的任务。这些方法需要更高的计算负载，更高的内存足迹并引入额外的延迟，这在安全至关重要的应用中可能会过高。在本文中，我们对DNN中不确定性定量的方法以及现有指标进行了简短的比较调查，以评估不确定性预测。我们特别有兴趣了解特定的AV任务和不确定性源类型的每种方法的优势和缺点。
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.
