Evidence-Aware Adaptive Recognition for Intelligent Traffic Sign Systems with Calibrated Uncertainty under Distribution Shift
Year: 2026
Type: preprint
Abstract: Traffic-sign recognition under image shiftraises two related questions: which label is supportedand whether the crop still contains the visual evidenceon which that decision is based. Adaptive signconditionedrecognition (ASCR) keeps these quantitiesseparate. A temperature-calibrated classifier withregularized adaptive prediction sets (RAPS) returnssemantic probabilities and label sets; a label-free monitormeasures preserved color, area, shape, and componentstructure. Track-disjoint traffic-sign data andcontrolled corruptions reveal a high-confidence/lowevidenceregime: the lowest-evidence group retains 0.913confidence at 0.162 error. The evidence–error slopealso changes sign across the evaluated environments,indicating that a single global evidence coefficient canbe misleading under these shifts. ASCR therefore actsat the decision stage: semantic ambiguity expands thelabel set, while evidence departure triggers intervention.Within the evaluated settings, this separation providesa transparent decision interface for calibrated classifiersand visual-evidence monitors.
Source: Research Square
Author: Abdulrahman S. Alturki
Institution: Qassim University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Qassim University
Awards: G1639986404, G6806168746
OpenAlex ID: W7225121712