@inproceedings{raptors2026-c094,
  title     = {{The Ghost Toll: How ML Caught the Errors Silent Monitoring Missed}},
  author    = {Mullangi, Venkateswarlu},
  booktitle = {Proceedings of the Raptors Conference},
  volume    = {1},
  year      = {2026},
  publisher = {Hackathon Raptors},
  address   = {London, United Kingdom},
  doi       = {10.68495/rc.2026.C094},
  url       = {https://doi.org/10.68495/rc.2026.C094},
  abstract  = {Satellite-based free-flow tolling systems silently accumulate GPS positioning errors that rule-based monitoring never flags, until a pattern becomes a financial liability. This paper walks through a real production incident in which systematic GNSS error went undetected across thousands of transactions, the machine learning error-classification model built from production logs to catch it in real time, and the deployment tradeoffs made to run that model at the roadside edge. The failure is characterised precisely: the errors were statistically masked rather than absent, so every individual transaction looked acceptable and only the distribution was wrong, which is exactly what threshold-based validation cannot see. Readers take away a framework for identifying silent failure modes in location-dependent ML pipelines, an account of why rule-based validation fails at the edge of GPS accuracy and which features actually matter for error classification in positioning systems, and a realistic picture of what edge deployment demands.}
}