Authors:
Kaushik Madala
and
Hyunsook Do
Affiliation:
Department of Computer Science and Engineering, University of North Texas, Denton, U.S.A.
Keyword(s):
Safety Of The Intended Functionality (SOTIF), ISO 21448, UL 4600, Machine Learning Safety, Autonomous Vehicle Safety, SAE Level 5 of Driving Automation.
Abstract:
Autonomous vehicles are susceptible to unknowns. In particular, vehicles with SAE level 5 of driving automation, which need to operate in complex operational design domain (ODD) conditions, have a very high chance to face unknowns. While the industrial standards ISO 21448 and UL 4600 hint at analyzing unknowns from the analysts and engineers’ perspective, the unknowns from different perspectives such as a autonomous vehicle or a machine learning model within an autonomous vehicle can differ from those perceived by engineers and analysts. In this paper, we discuss the different types of unknowns considering three different perspectives: analysts and engineers, autonomous vehicles, and machine learning (ML) models. We also clarify the often confused concepts of unknown knowns and unknowns unknowns for each perspective. Using a running example, we show how considering unknowns from different perspectives will aid in designing a safe autonomous vehicle.