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

中文翻译：

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自动驾驶中概率目标检测的回顾与比较研究

捕获对象检测中的不确定性对于安全的自动驾驶必不可少。近年来，深度学习已成为事实上的对象检测方法，并且已经提出了许多概率对象检测器。然而，关于深度目标检测中的不确定性估计尚无总结，现有方法不仅具有不同的网络体系结构和不确定性估计方法，而且可以在具有广泛评估指标的不同数据集上进行评估。结果，方法的比较仍然具有挑战性，最适合特定应用的模型选择也是如此。本文旨在通过对现有的用于自动驾驶应用的概率目标检测方法进行回顾和比较研究，来缓解这一问题。第一，我们概述了深度学习中的一般不确定性估计，然后系统地调查了概率对象检测的现有方法和评估指标。接下来，我们提出基于图像检测器和三个公共自动驾驶数据集的概率目标检测的严格对比研究。最后，我们讨论了剩余的挑战和未来的工作。代码已在https://github.com/asharakeh/pod_compare.git中提供 我们将讨论剩余的挑战和未来的工作。代码已在https://github.com/asharakeh/pod_compare.git中提供 我们将讨论剩余的挑战和未来的工作。代码已在https://github.com/asharakeh/pod_compare.git中提供