A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications

TL;DR

This paper surveys various uncertainty estimation methods in deep learning for autonomous vehicles, highlighting their advantages, limitations, and suitability for safety-critical applications.

Contribution

It provides a comparative analysis of uncertainty quantification approaches in DNNs tailored for autonomous vehicle tasks, emphasizing their computational costs and effectiveness.

Findings

Different methods vary in computational load and accuracy

Uncertainty sources include data noise and model errors

Trade-offs exist between complexity and real-time applicability

Abstract

A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical tasks, different methods for uncertainty quantification have recently been proposed to measure the inevitable source of errors in data and models. However, uncertainty quantification in DNNs is still a challenging task. These methods require a higher computational load, a higher memory footprint, and introduce extra latency, which can be prohibitive in safety-critical applications. In this paper, we provide a brief and comparative survey of methods for uncertainty quantification in DNNs along with existing metrics to evaluate uncertainty predictions. We are particularly interested in understanding the advantages and downsides of each method for specific AV tasks and types of uncertainty sources.