SmartCert: A Multi-modal framework for automated guided vehicle screening

Global used vehicle market is undergoing rapid transformation with proliferating demand for scalable, efficient and trustworthy inspection systems that is capable of meeting stringent requirements of online marketplaces and regulatory standards. This paper introduces SmartCert, a multi-modal inspection framework engineered for robust, scalable and pervasive vehicle screening. The novelty of SmartCert lies in synergistic integration of tailored multi-modal transformer architecture with fine-grained temporal diagnostics and optimized edge deployment. An embedded cross-attention mechanism fosters seamless fusion of visual data with on-board diagnostic signals to simultaneously detect exterior damages and internal performance anomalies. To ensure reliable evaluation, SmartCert incorporates reinforcement learning agent with human-in-the-loop reward scheme for adaptive certification thresholding that reduces false positive rates by 6.8% and false negative rates by 4.2% compared to optimally tuned static thresholds. Rigorously evaluated on large-scale dataset of 10240 vehicles with edge deployment validated exclusively on 240 vehicles (2.3%) collected from diverse mobile inspection locations, SmartCert achieves F1-score of 95% for damage classification and 92% anomaly detection rate. These results demonstrate statistically significant improvements over same-dataset baseline implementations by average of 7.4% in classification accuracy and 9.6% in anomaly detection ( p < 0 . 001 ). Furthermore in ablation study, SmartCert improves processing efficiency by 40%, reduces certification-to-sale by 30% and decreases post-sale complaints by 25% compared to traditional manual methods. By integrating explainable AI with optimized edge deployment achieve 18 FPS inference on resource-constrained hardware, SmartCert articulates end-to-end solution for next generation of trustworthy and efficient vehicle certification ecosystem.