Generative AI Driven Sensor Fusion for Secure Digital Twin Ecosystems in Cyber Physical and Autonomous Delivery Systems

Abstract

The convergence of cyber physical systems, digital twin architectures, and autonomous delivery platforms has created an unprecedented demand for secure, reliable, and intelligent sensor fusion frameworks capable of operating under uncertainty, heterogeneity, and real-world adversarial conditions. Modern delivery robots, vehicle to everything communication environments, and automated mobility platforms depend upon continuous synchronization between physical entities and their virtual counterparts, yet this synchronization is increasingly challenged by noisy sensors, non-line of sight perception failures, dynamic operational design domains, and emerging cyber threats. This article develops a comprehensive theoretical and methodological investigation of generative artificial intelligence driven sensor fusion as the central mechanism for enabling secure digital twin ecosystems. Grounded in multidisciplinary theories of multisensory integration, probabilistic perception, and cyber physical security, the paper integrates classical neuroscience inspired sensor fusion theory with contemporary cyber infrastructure and standardization frameworks.

Drawing upon foundational work on statistically optimal multisensory integration (Ernst and Banks, 2002), neurocomputational models of perception (Angelaki et al., 2009; Ernst and Bulthoff, 2004), and modern cyber physical sensor fusion architectures (Yeong et al., 2021), the paper positions generative artificial intelligence as the missing epistemic layer that allows digital twins to reason under uncertainty, detect anomalies, and maintain synchronization across distributed systems. A central contribution of this work is its integration of a recently standardized, generative artificial intelligence-based framework for secure digital twin ecosystems that explicitly aligns with ISO and 3GPP requirements while embedding probabilistic logic, fault detection, and cyber resilience into the sensor fusion pipeline (Hussain et al., 2026). Rather than treating digital twins as static mirrors of physical assets, this framework reconceptualizes them as living cognitive systems capable of predictive inference, self-verification, and security aware decision making.