Abstract
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 gradient boosting machine (lightGBM) were used as the representative models of NNs and GBDTs in the investigation, respectively. Furthermore, this work exploited Shapley additive explanations (SHAP) to interpret the contributions of the input features, upon which we further developed two ensemble modes. Experimental results demonstrated that the proposed model outperformed the baseline models, highlighting its effectiveness. In addition, the interpretation results can also provide practitioners with references regarding the eye-tracking features that are more relevant to SA recognition.
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References
Endsley, M.R.: Measurement of situation awareness in dynamic systems. Hum. Factors 37(1), 65–84 (1995)
de Winter, J.C., Eisma, Y.B., Cabrall, C., Hancock, P.A., Stanton, N.A.: Situation awareness based on eye movements in relation to the task environment. Cogn. Technol. Work 21(1), 99–111 (2019). https://doi.org/10.1007/s10111-018-0527-6
Li, R., Wang, L., Sourina, O.: Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness. Methods 202, 136–143 (2022)
Zhou, F., Yang, X.J., de Winter, J.C.F.: Using eye-tracking data to predict situation awareness in real time during takeover transitions in conditionally automated driving. IEEE Trans. Intell. Transp. Syst. 23(3), 2284–2295 (2022)
Li, F., Chen, C.-H., Lee, C.-H., Feng, S.: Artificial intelligence-enabled non-intrusive vigilance assessment approach to reducing traffic controller’s human errors. Knowl.-Based Syst. 239, 108047 (2022)
Kadra, A., Lindauer, M., Hutter, F., Grabocka, J.: Well-tuned simple nets excel on tabular datasets. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 23928–23941. Curran Associates Inc. (2021)
Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting deep learning models for tabular data. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol. 34, pp. 18932–18943. Curran Associates Inc. (2021)
McElfresh, D., Khandagale, S., Valverde, J., Ramakrishnan, G., Goldblum, M., White, C., et al.: When do neural nets outperform boosted trees on tabular data? arXiv preprint arXiv:2305.02997 (2023)
Arik, S.O., Pfister, T.: TabNet: attentive interpretable tabular learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 8, pp. 6679–6687 (2021)
Shi, Q., Katuwal, R., Suganthan, P.N., Tanveer, M.: Random vector functional link neural network based ensemble deep learning. Pattern Recogn. 117, 107978 (2021)
Malik, A.K., Ganaie, M.A., Tanveer, M., Suganthan, P.N., Alzheimer’s Disease Neuroimaging Initiative Initiative: Alzheimer’s disease diagnosis via intuitionistic fuzzy random vector functional link network. IEEE Trans. Comput. Soc. Syst. 1–12 (2022)
Hu, M., Chion, J.H., Suganthan, P.N., Katuwal, R.K.: Ensemble deep random vector functional link neural network for regression. IEEE Trans. Syst. Man Cybern. Syst. 53(5), 2604–2615 (2023)
Malik, A., Gao, R., Ganaie, M., Tanveer, M., Suganthan, P.N.: Random vector functional link network: recent developments, applications, and future directions. Appl. Soft Comput. 143, 110377 (2023)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates Inc. (2017)
Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4765–4774. Curran Associates Inc. (2017)
Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: the Bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 525–532 (1999)
Lu, Z., Happee, R., de Winter, J.C.: Take over! A video-clip study measuring attention, situation awareness, and decision-making in the face of an impending hazard. Transport. Res. F: Traffic Psychol. Behav. 72, 211–225 (2020)
Galton, F.: Regression towards mediocrity in hereditary stature. J. Anthropol. Inst. Great Br. Irel. 15, 246–263 (1886)
Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3), 61–74 (1999)
Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Pao, Y.-H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5), 76–79 (1992)
Suganthan, P.N., Katuwal, R.: On the origins of randomization-based feedforward neural networks. Appl. Soft Comput. 105, 107239 (2021)
Acknowledgements
This work was partially supported by the STI2030-Major Projects 2021ZD0200201, the National Natural Science Foundation of China (Grant No. 62201519), Key Research Project of Zhejiang Lab (No. 2022KI0AC02), Exploratory Research Project of Zhejiang Lab (No. 2022ND0AN01), and Youth Foundation Project of Zhejiang Lab (No. K2023KI0AA01).
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Li, R., Hu, M., Cui, J., Wang, L., Sourina, O. (2024). Ensemble of Randomized Neural Network and Boosted Trees for Eye-Tracking-Based Driver Situation Awareness Recognition and Interpretation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14449. Springer, Singapore. https://doi.org/10.1007/978-981-99-8067-3_37
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