Operating-Condition Residual Normalization: A Bio-Inspired Operator for Sensor Integrity Monitoring in Automated...

Bio-inspired integrity monitors for automated vehicles are reported as single pooled detection figures, which conflates algorithmic performance with evaluation protocol. Transferring the reafference principle to inertial-channel integrity, we identify what actually governs the reported figure. A single-track forward model is identified from the data, and its residual is standardized by Operating-Condition Residual Normalization (OCRN), a bin-wise operator conditioned on an observable operating point; the design is resolved by an exhaustive constrained search and each component is isolated using ablation. Across 224,638 samples spanning five towns and four friction levels, the residual scale varies by a factor of 158, and OCRN recovers 21.8 F₁ points, more than the detection rule, the encoder, and the biological attenuation gate combined. A cross-comparison confirms this: changing the detection rule moves the result by 0.09 points, while changing the normalization moves this by 15 to 19. The monitor attains 97.34% precision, 76.35% recall, and 85.57% F₁ at a 0.76% false-alarm rate when counting every injection, and 97.30/93.99/95.62% above a 3σ detectability floor when covering 80.1% of them. The floor scales with forward-model error, which is correlated with but not determined by the road friction, and the gate, the most explicitly biological element, is not selected once the residual is conditionally normalized, with its best setting gaining at most one F₁ point at nearly twice the false-alarm rate.