Can Large Language Models Assist with SOTIF Scenario Generation?
Abstract
Combinations of operating conditions can trigger a system to behave in a hazardous manner, even in the absence of malfunction. ISO 21448 - Road vehicles - Safety of the intended functionality (referred to as "SOTIF") describes strategies for managing this type of risk in automotive systems. One strategy includes the identification of operating scenarios that might lead to the occurrence of a hazard. Crafting scenarios is a technically challenging and labor-intensive task that requires sustained creative engagement, and the consequence of inadequate SOTIF analyses can be severe. This paper introduces Heraclitus, an engineering method and prototype software tool for performing SOTIF scenario generation with the support of a large language model. Large language models are a novel type of generative artificial intelligence targeted at natural language processing and generation that exhibit remarkable performance in a range of natural language applications that have historically been difficult for conventional artificial intelligence systems. As such, there is an opportunity to use these models, in collaboration with humans, to support SOTIF scenario creation. The goal of Heraclitus is to allow analysts to rapidly produce a comprehensive set of SOTIF scenarios that can be used as the basis for on-going SOTIF risk management. A preliminary control trial of Heraclitus was conducted, in which six system safety experts were asked to create SOTIF scenarios with and without the support of a large language model. Results indicate that these models show promise in supporting SOTIF analysis and are capable of generating useful SOTIF scenarios.