Quantitative validation of artificial precognition adaptive cognized control: real-world performance evaluation across automotive and railway operational deployments

Abstract

This paper presents the quantitative validation of Artificial Precognition Adaptive Cognised Control (APACC), a dual-layer neuro-symbolic architecture for safety-critical autonomous transport. APACC integrates Type-2 fuzzy symbolic reasoning for high-level decision-making with linearised predictive optimisation for trajectory control, synchronised through Diophantine Frequency Synthesis over a 2.5 s precognition horizon. We establish a theoretical framework that shows how these components interact to ensure bounded uncertainty propagation and temporal coherence across layers. Validation combines high-fidelity simulation with real-world field deployment. Automotive trials using Honda Civic vehicle dynamics achieved 51% reduction in peak deceleration (0.45 g to 0.22 g) and 67% decrease in jerk during pedestrian-crossing scenarios (p \lt 0.01) compared with reactive baselines. Operational railway deployment under UK SBRI Project Edge on 32 km of live Network Rail infrastructure achieved 96% base-station-handover prediction accuracy, 18.6% latency reduction, and complete route coverage through predictive multi-modal connectivity management across four mobile-network operators with satellite backup—Long-short-term-memory (LSTM) signal-strength prediction achieved 5–8% RSRP error, confirming simulation-to-reality transfer. The system operated continuously for 168 h without failure, demonstrating robustness for mission-critical control. Across more than 10,000 simulation scenarios, APACC outperformed proportional-integral-derivative, model-predictive-control, and deep-reinforcement-learning baselines while maintaining interpretability consistent with ISO 26262 certification. All validation datasets, simulation environments, and operational telemetry are released via Zenodo for independent verification and reproducibility.