Decomposition and Quantification of SOTIF Requirements for Perception Systems of Autonomous Vehicles

TL;DR

This paper presents a quantitative approach to decompose and derive perception safety requirements for autonomous vehicles, aiding safety verification by translating high-level safety goals into measurable perception criteria.

Contribution

It introduces a risk decomposition methodology, including a collision severity model and Bayesian analysis, to derive actionable perception requirements from system-level safety goals.

Findings

Effective decomposition of safety requirements into perception metrics

Use of Bayesian models for uncertainty quantification

Application of Shapley value for component-level safety analysis

Abstract

Ensuring the safety of autonomous vehicles (AVs) is paramount before they can be introduced to the market. More specifically, securing the Safety of the Intended Functionality (SOTIF) poses a notable challenge; while ISO 21448 outlines numerous activities to refine the performance of AVs, it offers minimal quantitative guidance. This paper endeavors to decompose the acceptance criterion into quantitative perception requirements, aiming to furnish developers with requirements that are not only understandable but also actionable. This paper introduces a risk decomposition methodology to derive SOTIF requirements for perception. More explicitly, for subsystemlevel safety requirements, we define a collision severity model to establish requirements for state uncertainty and present a Bayesian model to discern requirements for existence uncertainty. For component-level safety requirements, we proposed a decomposition method based on the Shapley value. Our findings indicate that these methods can effectively decompose the system-level safety requirements into quantitative perception requirements, potentially facilitating the safety verification of various AV components.