  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
