Enhancing Autonomous Driving Safety Analysis with Generative AI: A Comparative Study on Automated Hazard and Risk Assessment

Abstract: The advent of autonomous driving technology has accentuated the need for comprehensive hazard analysis and risk assessment (HARA) to ensure the safety and reliability of vehicular systems. Traditional HARA processes, while meticulous, are inherently time-consuming and subject to human error, necessitating a transformative approach to fortify safety engineering. This paper presents an integrative application of generative AI as a means to enhance HARA in autonomous driving safety analysis. Generative AI, renowned for its predictive modeling and data generation capabilities, is leveraged to automate the labor-intensive elements of HARA, thus expediting the process and augmenting the thoroughness of the safety analyses. Through empirical research, the study contrasts conventional HARA practices conducted by safety experts with those supplemented by generative AI tools. The benchmark comparisons focus on critical metrics such as analysis time, error rates, and scope of risk identification. By employing generative AI, the research demonstrates a significant upturn in efficiency, evidenced by reduced timeframes and expanded analytical coverage. The AI-augmented processes also deliver enhanced brainstorming support, stimulating creative problem-solving and identifying previously unrecognized risk factors.