A two-stage context-aware meta-ensemble framework for collision risk prediction in Internet of Vehicles

Abstract

A key challenge in Internet of Vehicles (IoV) environments is accurately predicting collision risks, given the dynamic nature of traffic systems and the variety of available data sources. Existing approaches often rely on structured IoV data, while visual contextual information is not fully incorporated into the prediction process. This limitation motivates a unified framework that integrates environmental perception with sensor-derived information to enable more accurate collision-risk prediction. In this study, we propose a two-stage context-aware meta-ensemble framework that combines image-based perception and IoV-based predictive analysis for collision-risk prediction. The first stage uses multiple pre-trained convolutional neural networks to obtain rich visual representations from vehicle images through feature-level fusion. The second stage employs a hybrid stacked ensemble architecture that combines the probabilistic outputs of heterogeneous machine learning models with IoV and image-derived features, which a meta-learner then processes for final collision-risk prediction. The proposed feature-fusion model performs best in the perception stage, with an accuracy of 0.97. In the IoV-based prediction stage, the proposed stacked ensemble architecture outperforms all baseline models, achieving an accuracy of 0.9847. Robustness analysis shows that the perception module tolerates blur and rotation but remains sensitive to Gaussian noise. Cross-stage integration incorporates image-derived features into the IoV feature space, providing complementary visual context for collision-risk prediction and demonstrating its potential for intelligent transportation and cyber-physical IoV applications.