Ensuring safety in autonomous vehicles (AVs) requires addressing hazards beyond functional failures, especially those arising from Performance Limitations (PLs) and Triggering Conditions (TCs) under varying Operational Design Domains (ODDs). This paper proposes AV-SLAF, a scenario-layered safety analysis framework integrating System-Theoretic Process Analysis (STPA) with Cause Tree Analysis (CTA) and internal algorithm modeling. By incorporating layered ODD scenarios into the control structure and modeling internal logic of AV modules, AV-SLAF systematically identifies PLs and TCs critical for Safety of the Intended Functionality (SOTIF). Unlike traditional methods focusing solely on structural-level interactions, the proposed framework bridges external scenario modeling with internal algorithms, enabling a more complete view of hazard propagation. A case study on autonomous port vehicles demonstrates the framework’s applicability, yielding a structured set of 84 PL-TC pairs and a partial cause tree for the Planning and Control module. The resulting causal structure reveals dependencies among algorithmic components and their safety-relevant conditions. The proposed framework enhances the traceability and completeness of safety analysis for complex AV applications.