Probabilistic Safety Verification for an Autonomous Ground Vehicle: A Situation Coverage Grid Approach

TL;DR

This paper introduces a probabilistic safety verification method for autonomous ground vehicles using a situation coverage grid, enabling systematic safety assessment and compliance with safety standards.

Contribution

It presents a novel approach combining situation coverage grids with probabilistic modeling and model checking for autonomous vehicle safety verification.

Findings

Effectively identifies high-risk situations

Provides quantitative safety guarantees

Supports regulatory compliance

Abstract

As industrial autonomous ground vehicles are increasingly deployed in safety-critical environments, ensuring their safe operation under diverse conditions is paramount. This paper presents a novel approach for their safety verification based on systematic situation extraction, probabilistic modelling and verification. We build upon the concept of a situation coverage grid, which exhaustively enumerates environmental configurations relevant to the vehicle's operation. This grid is augmented with quantitative probabilistic data collected from situation-based system testing, capturing probabilistic transitions between situations. We then generate a probabilistic model that encodes the dynamics of both normal and unsafe system behaviour. Safety properties extracted from hazard analysis and formalised in temporal logic are verified through probabilistic model checking against this model. The results demonstrate that our approach effectively identifies high-risk situations, provides quantitative safety guarantees, and supports compliance with regulatory standards, thereby contributing to the robust deployment of autonomous systems.