A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving

TL;DR

This paper reviews and compares probabilistic object detection methods for autonomous driving, highlighting their architectures, uncertainty estimation techniques, and evaluation metrics to guide future research and application choices.

Contribution

It provides the first comprehensive survey and systematic comparison of probabilistic object detection methods tailored for autonomous driving.

Findings

Probabilistic detectors vary significantly in architecture and uncertainty estimation methods.

Evaluation metrics differ widely, complicating direct comparison of methods.

The study identifies key challenges and future directions in probabilistic object detection.

Abstract

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