Cost-Aware Conformal Learning for Reliable Accident Detection Under Exchangeability Violations
Year: 2026
Type: article
Abstract: Conformal prediction can give an accident detector coverage guarantees on its confidence, but only when the calibration and test data are exchangeable. Accident image benchmarks break it: a hash audit of three benchmarks finds 9% to 59% of test images have an exact copy in training, confirmed by learned embeddings and structural similarity. The damage falls on confidence, not accuracy: after grouping, accuracy stays near 98%, yet a detector calibrated on leaky data appears to hold a 90% guarantee while covering only 67% of novel images, a gap of 22.1 points with the calibration sets matched. We build a duplicate grouped split and report on a locked test set, where empirical coverage is near 90%. A cost-aware allocation of the coverage budget, our main contribution, is model-agnostic: it raises accident coverage from 91% to 98% and lowers accidents declared normal for every base method in every run, at the price of normal coverage falling to 80%, false alerts rising from seven to 33, and 9% human review, so the 90% budget is a frequency-weighted average, not a per-class level. On a rare severe class, 0.4% of records, it cuts missed cases from 34 to three.
Source: Sensors
DOI: 10.3390/s26227125
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225789940