Trustworthy and human-centric adaptation remains a central challenge for autonomous vehicles (AVs), which operate in dynamic and uncertain environments. This paper proposes a real-time, multi-modal, and self-adaptive framework that operationalizes Human-Centric Requirements Engineering (HCRE)by treating contextual signals as non-functional requirements(NFRs), where NFRs denote stakeholder-oriented soft goals such as trust, cognitive comfort, and perceived safety that must be continuously fulfilled alongside functional driving tasks.The framework integrates driver emotions, behaviors, traffic conditions, and vehicle dynamics within an interpretable neural architecture to deliver proactive behavior recommendations aligned with drivers’ needs, and unlike prior approaches thatrely on static rules or thresholds, it continuously elicits, monitors,and fulfills latent human-centric goals through transparent and context-aware adaptation. Trained and evaluated on the AIDE dataset, the system achieves high accuracy across perception modules (83–93%) and 89.32% exact match accuracy for inte- grated behavior recommendations, satisfies real-time constraints with an average inference latency of 106.84 ms, and main-tains interpretability through explicit mappings from multi-modal input to adaptive output. The results demonstrate the feasibility of embedding HCRE principles, particularly dynamic NFR fulfillment, into the core of AV control architectures, thereby enabling emotionally responsive and stakeholder-aligned autonomous systems.