A Safety Cage for Reliable GNSS and AI-Based PNT: An Experience Report

Reliable Positioning, Navigation, and Timing (PNT) is a fundamental requirement for the safe operation of autonomous vehicles. While traditional GNSS-based PNT is typically very accurate in open sky conditions, its performance can degrade significantly in environments where satellite visibility is limited or signals are obstructed. To ensure continuity, it is beneficial to complement GNSS with other methods, such as AI-enhanced Visual SLAM, which remain robust under challenging environmental conditions. However, AI-based components can produce untrustworthy results and necessitate a safety cage to monitor and filter their output. This experience report presents the VAIPOSA architecture, which introduces a safety cage to perform runtime verification and fusion of hybrid GNSS and AI-based PNT sources. The safety cage monitors a variety of parameters, including GNSS signal characteristics, AI-based precision metrics, image quality, and vehicle state information, to determine the trustworthiness of each source. We describe the integration of these monitors within a supervisor that fuses data into a reliable pose estimate and discuss its validation in the CARLA simulator using automated test case generation and fault injection scenarios.