AI Sensor Fusion Technology for Self-Driving Intelligent Systems

Abstract

Sensor fusion plays a critical role in enabling reliable perception for autonomous driving systems by integrating heterogeneous data from multiple sensors such as cameras, LiDAR, radar, and inertial units. However, inconsistencies in spatial alignment, temporal synchronisation, and data representation present significant challenges to achieving a unified understanding of the driving environment. This paper proposes a conceptual framework that interprets sensor fusion as a reconstructive process, transforming diverse sensory inputs into a coherent environmental model. The study systematically analyses three levels of fusion: data-level, feature-level, and decision-level, and examines how artificial intelligence enhances each stage through learned alignment, cross-modal feature representation, and uncertainty-aware decision making. Furthermore, the paper connects fusion strategies to key autonomous driving tasks, including object detection, tracking, localisation, and planning, highlighting the relationship between task requirements and fusion architecture design. Finally, major challenges such as domain shift, long-tail scenarios, sensor failure, and interpretability are discussed. This work provides a structured perspective on AI-driven sensor fusion and its role in building robust and adaptive intelligent driving systems.