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Ensuring traffic safety is crucial in the pursuit of sustainable transportation. Across diverse traffic systems, maintaining good situation awareness (SA) is important in promoting and upholding traffic safety. This work focuses on a regression problem of using eye-tracking features to perform situation awareness (SA) recognition in the context of conditionally automated driving. As a type of tabular dataset, recent advances have shown that both neural networks (NNs) and gradient-boosted decision trees (GBDTs) are potential solutions to achieve better performance. To avoid the complex analysis to select the suitable model for the task, this work proposed to combine the NNs and tree-based models to achieve better performance on the task of SA assessment generally. Considering the necessity of the real-time measure for practical applications, the ensemble deep random vector functional link (edRVFL) and light …