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The VAIPOSA project successfully demonstrated that AI can be part of a safety-critical navigation system when governed by a verifiable supervisory architecture.
VAIPOSA combined a classical GNSS-based engine with an AI-based visual odometry engine, then supervises them through a “safety cage” architecture that detected faults, evaluates confidence, and dynamically adjusted the contribution of each engine.
The safety cage used both qualitative cut-off rules and quantitative weighting to manage anomalies such as poor luminosity, frozen images, camera failures, and GNSS degradation.
Results showed that the fused approach improves navigation robustness and accuracy, especially in faulty conditions.
The slides of the closing event @ESA can be accessed here.
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