IASCI Research Publishing
Journal of Advanced Discovery and Artificial Intelligence
Research Article Published October 9, 2026 Article jadai20260009

Causal Shortcut Control for Reliable Traffic Sign Recognition across Weather and Road Scenes

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Abstract

Traffic sign recognition systems may learn shortcut features from background scenery, camera exposure, road texture, weather conditions, or country-specific sign placement instead of sign shape and symbol content. This limits model reliability in unseen driving environments. This study develops a Causal Shortcut Control Network (CSCN) for robust traffic sign recognition under weather and scene shift. The model identifies shortcut variables by measuring prediction instability across weather, illumination, camera angle, and road-background environments. It then applies sign-region causal attention, background counterfactual replacement, and environment-invariant loss to encourage decisions from sign-relevant visual evidence. The dataset contained 148,300 traffic sign images from 11 cities, covering 43 sign categories, six weather conditions, daytime and nighttime scenes, and three camera types. External testing used 26,400 images from two unseen cities and unseen rainy-night conditions. Compared with EfficientNet-B3, CSCN improved external accuracy from 82.7% to 90.4% and reduced misclassification under heavy rain from 21.8% to 11.6%. Attention evaluation showed that the proportion of model focus located within the sign region increased from 68.2% to 84.9%. These results indicate that causal shortcut control can strengthen traffic sign recognition under real-world driving variation.

Keywords
Traffic sign recognitionshortcut controlcausal attentionautonomous drivingweather shiftvisual robustness
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Publication details
Journal
Journal of Advanced Discovery and Artificial Intelligence
Volume
1 (2026)
Issue
1 · Forthcoming issue
Article number
jadai20260009
License
CC BY 4.0