Sensor Fusion Models in Autonomous Systems: A Review

This survey presents a comprehensive examination of sensor fusion research spanning four decades, tracing the methodological evolution, application domains, and alignment with classical hierarchical models. Building on this long-term trajectory, the foundational approaches such as probabilistic inference, early neural networks, rule-based methods, and feature-level fusion established the principles of uncertainty handling and multi-sensor integration in the 1990s. The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering, Bayesian–Dempster–Shafer hybrids, distributed consensus algorithms, and machine learning ensembles for more robust and domain-specific implementations. From 2011 to 2020, the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain. A key contribution of this work is the assessment of contemporary methods against the JDL model, revealing gaps at higher levels- especially in situation and impact assessment. Contemporary methods offer only limited implementation of higher-level fusion. The survey also reviews the benchmark multi-sensor datasets, noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage. By synthesizing developments across decades and paradigms, this survey provides both a historical narrative and a forward-looking perspective. It highlights unresolved challenges in transparency, scalability, robustness, and trustworthiness, while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions. This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.