Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there is no summary on uncertainty estimation in deep object detection, and existing methods are not only built with different network architectures and uncertainty estimation methods, but also evaluated on different datasets with a wide range of evaluation metrics. As a result, a comparison among methods remains challenging, as does the selection of a model that best suits a particular application. This paper aims to alleviate this problem by providing a review and comparative study on existing probabilistic object detection methods for autonomous driving applications. First, we provide an overview of generic uncertainty estimation in deep learning, and then systematically survey existing methods and evaluation metrics for probabilistic object detection. Next, we present a strict comparative study for probabilistic object detection based on an image detector and three public autonomous driving datasets. Finally, we present a discussion of the remaining challenges and future works. Code has been made available at https://github.com/asharakeh/pod_compare.git
翻译：获取物体探测的不确定性是安全自主驾驶所必不可少的。近年来,深层学习已成为物体探测的“实际法”方法,并提出了许多概率物体探测器;然而,没有关于深物体探测中不确定性估计的概述,现有方法不仅采用不同的网络架构和不确定性估计方法,而且采用各种评价指标对不同的数据集进行评价。因此,方法之间的比较仍然具有挑战性,选择最适合特定应用的模型也是如此。本文件的目的是通过对自主驾驶应用的现有概率物体探测方法进行审查和比较研究来缓解这一问题。首先,我们提供了深度学习中一般不确定性估计的概况,然后系统调查概率物体探测的现有方法和评估指标。接下来,我们提出一份严格的比较研究,根据图像探测器和公共自主驾驶数据集对概率物体探测进行严格比较。最后,我们介绍了其余的挑战和未来的工作。代码已在 https://github.com/asharake/pod_compare_comp上公布。
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