Out-of-Distribution Detection for Safety Assurance of AI and Autonomous Systems

TL;DR

This paper reviews out-of-distribution detection techniques crucial for ensuring the safety of AI-powered autonomous systems, emphasizing their integration into safety assurance processes across various domains.

Contribution

It provides a comprehensive analysis of OOD detection methods, their role in safety assurance, and discusses integration challenges and future research directions.

Findings

Identifies key OOD detection techniques for safety assurance.

Highlights challenges in integrating OOD detection into system lifecycles.

Outlines future research needs for safe autonomous system deployment.

Abstract

The operational capabilities and application domains of AI-enabled autonomous systems have expanded significantly in recent years due to advances in robotics and machine learning (ML). Demonstrating the safety of autonomous systems rigorously is critical for their responsible adoption but it is challenging as it requires robust methodologies that can handle novel and uncertain situations throughout the system lifecycle, including detecting out-of-distribution (OoD) data. Thus, OOD detection is receiving increased attention from the research, development and safety engineering communities. This comprehensive review analyses OOD detection techniques within the context of safety assurance for autonomous systems, in particular in safety-critical domains. We begin by defining the relevant concepts, investigating what causes OOD and exploring the factors which make the safety assurance of autonomous systems and OOD detection challenging. Our review identifies a range of techniques which can be used throughout the ML development lifecycle and we suggest areas within the lifecycle in which they may be used to support safety assurance arguments. We discuss a number of caveats that system and safety engineers must be aware of when integrating OOD detection into system lifecycles. We conclude by outlining the challenges and future work necessary for the safe development and operation of autonomous systems across a range of domains and applications.