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, multimodal and self-adaptive framework that operationalizes Human-Centric Requirements Engineering (HCRE) by treating contextual signals as non-functional requirements (NFRs). The proposed framework integrates driver emotions, behaviors, traffic conditions, and vehicle dynamics within an interpretable neural architecture to deliver proactive behavior recommendations aligned with drivers’ needs. Unlike prior approaches which rely on static rules or thresholds, our framework continuously elicits, monitors, and fulfills latent human-centric goals, such as cognitive comfort, trust, and perceived safety, 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 integrated behavior recommendations. It satisfies real-time constraints with an average inference latency of 106.84 ms and maintains interpretability through explicit mappings from multi-modal input to adaptive output. The results demonstrate the feasibility of embedding the HCRE principles, particularly dynamic NFR fulfillment, into the core of AV control architectures, enabling emotionally responsive and stakeholder-aligned autonomous systems.