Artificial Intelligence (AI) models are increasingly used in complex systems such as autonomous vehicles (AVs), where safety and explainability are critical. However, existing explainable AI (xAI) methods focus on model-level transparency while neglecting system-level safety explanations (Gap 1), and prior applications of large language models (LLMs) in AVs often view the AV as a whole, overlooking potential risks arising from interactions among its internal components (Gap 2). To address these gaps, we propose a framework that integrates LLMs with System Theoretic Process Analysis (STPA), a structured method to analyse hazards and assess safety, to improve AV safety assurance. Our framework leverages LLMs for scenario analysis while incorporating STPA to identify unsafe control actions (UCAs) and filter them with real-world video data. We evaluated our method against Lingo-2 (a vision-language-action model developed by Wayve) in a simulated environment, demonstrating superior STPA-based explanations. To evaluate the framework, we employed two ground truth references for accuracy verification and conducted robustness testing, which outperforming traditional LLM-based explainers, as also confirmed by expert evaluations.