论文标题
关于自主驾驶中概率对象检测的综述和比较研究
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
论文作者
论文摘要
对于安全的自动驾驶，捕获对象检测的不确定性是必不可少的。近年来，深度学习已成为对象检测的事实上的方法，并且已经提出了许多概率对象检测器。但是，对深度对象检测的不确定性估计没有摘要，现有方法不仅是使用不同的网络体系结构和不确定性估计方法构建的，而且在具有广泛评估指标的不同数据集上进行了评估。结果，方法之间的比较仍然具有挑战性，选择最适合特定应用程序的模型也是如此。本文旨在通过对现有的自动驾驶应用程序的现有概率对象检测方法进行审查和比较研究来减轻此问题。首先，我们概述了深度学习中的通用不确定性估计，然后系统地调查了现有的方法和评估指标，以进行概率对象检测。接下来，我们根据图像检测器和三个公共自主驾驶数据集提出了一项严格的比较研究，以进行概率对象检测。最后，我们对剩余的挑战和未来作品进行了讨论。代码已在https://github.com/asharakeh/pod_compare.git上提供。
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
