Redundancy-Aware Multi-Sensor Fusion for Resilient Perception in Intelligent Vehicles

Abstract

Reliable perception in intelligent vehicles rests on redundant sensing and fault-tolerant data fusion rather than any single modality. This study explains how redundancy is realized in practice across cameras, LiDAR, radar, and GNSS/IMU, and organizes fusion techniques from raw-level integration to feature- and decision-level reasoning. Emphasis is placed on reliability awareness—detecting, isolating, and mitigating sensor faults—so that the fused estimate degrades gracefully instead of catastrophically. Quantitative evidence from recent evaluations anchors the discussion: learning-based health monitoring on real vehicle bus signals achieves near-ceiling fault-detection accuracy with usable fault identification/isolation; fault-aware vision fusion consistently recovers mean intersection-over-union by roughly 0.08–0.22 under defocus and field-of-view truncation; and localization pipelines that adapt exclusion thresholds with a deep model reduce extreme position-error tails when GNSS measurements become unreliable. Persisting obstacles include uncertainty miscalibration under distribution shift, limited explainability of deep fusion, compute–latency constraints on embedded platforms, and the absence of fault-rich, standardized benchmarks for certification. The paper closes by outlining practical directions—self-auditing fusion that reports sensor contributions and uncertainty, context-aware reliability modeling, resource-conscious implementations, and datasets that “fail on purpose”—with the goal of enabling certifiable, fail-operational perception that maintains situational awareness in the adverse and ambiguous conditions where safety matters most.