Abstract
The issue of heavy vehicles rollover appears to be central in various sectors. This is due to the consequences entailed in terms of driver and passenger safety, other than considering aspects as environmental damaging and pollution. Therefore, several studies proposed estimative and predictive techniques to avoid this critical condition, with especially good results obtained by the using of Artificial Intelligence (AI) systems based on neural networks. Unfortunately, to conduct these kind of analyses a great quantity of data is required, with the same often difficult to be retrieved in sufficient numbers without incurring in unsustainable costs. To answer the problem, in this paper is presented a methodology based on synthetic data, generated in a specifically designed Matlab environment. This has been done by defining the characteristics of an heavy vehicle, a three axles truck, and making it complete maneuvers on surfaces and circuits purposely created to highlight rollover issues. After this phase, represented by the generation and processing of the data, follows the analysis of the same. This is the second major phase of the methodology, and contains the definition of a neural networks based algorithm. Referring to the nets, these are designed to obtain both the estimate and the prediction of four common rollover indexes, the roll angle and the Load Transfer Ratios (LTR, one for each axle). Very promising results were achieved in particular for the estimative part, offering new possibilities for the analysis of rollover issues both for the generation and the analysis of the data.
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References
Azad, N., Khajepour, A., McPhee, J.: A survey of stability enhancement strategies for articulated steer vehicles. Int. J. Heavy Veh. Syst. 16(1–2), 26–48 (2009). https://doi.org/10.1504/IJHVS.2009.023853
Baldi, M.M., Perboli, G., Tadei, R.: Driver maneuvers inference through machine learning. In: Pardalos, P.M., Conca, P., Giuffrida, G., Nicosia, G. (eds.) MOD 2016. LNCS, vol. 10122, pp. 182–192. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-51469-7_15
Braghin, F., Cheli, F., Corradi, R., Tomasini, G., Sabbioni, E.: Active anti-rollover system for heavy-duty road vehicles. Veh. Syst. Dyn. 46(SUPPL.1), 653–668 (2008). https://doi.org/10.1080/00423110802033064
Chen, X., Chen, W., Hou, L., Hu, H., Bu, X., Zhu, Q.: A novel data-driven rollover risk assessment for articulated steering vehicles using RNN. J. Mech. Sci. Technol. 34(5), 2161–2170 (2020). https://doi.org/10.1007/s12206-020-0437-4
Evans, J.L., Batzer, S.A., Andrews, S.B.: Evaluation of heavy truck rollover accidents (2005)
Evans, J.L., Batzer, S.A., Andrews, S.B., Hooker, R.M.: Evaluation of heavy truck rollover crashworthiness. 2006, 91–96 (2006)
Ghazali, M., Durali, M., Salarieh, H.: Path-following in model predictive rollover prevention using front steering and braking. Veh. Syst. Dyn. 55(1), 121–148 (2017). https://doi.org/10.1080/00423114.2016.1246741
Iida, M., Nakashima, H., Tomiyama, H., Oh, T., Nakamura, T.: Small-radius turning performance of an articulated vehicle by direct yaw moment control. Comput. Electron. Agric. 76(2), 277–283 (2011). https://doi.org/10.1016/j.compag.2011.02.006
Imine, H., Benallegue, A., Madani, T., Srairi, S.: Rollover risk prediction of heavy vehicle using high-order sliding-mode observer: Experimental results. IEEE Trans. Veh. Technol. 63(6), 2533–2543 (2014). https://doi.org/10.1109/TVT.2013.2292998
Li, H., Zhao, Y., Wang, H., Lin, F.: Design of an improved predictive LTR for rollover warning systems. J. Braz. Soc. Mech. Sci. Eng. 39(10), 3779–3791 (2017). https://doi.org/10.1007/s40430-017-0796-7
Li, L., Lu, Y., Wang, R., Chen, J.: A three-dimensional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC. IEEE Trans. Industr. Electron. 64(4), 3389–3401 (2017). https://doi.org/10.1109/TIE.2016.2583400
Li, X., Wang, G., Yao, Z., Qu, J.: Dynamic model and validation of an articulated steering wheel loader on slopes and over obstacles. Veh. Syst. Dyn. 51(9), 1305–1323 (2013). https://doi.org/10.1080/00423114.2013.800893
Rahman, A., Srikumar, V., Smith, A.D.: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl. Energy 212, 372–385 (2018). https://doi.org/10.1016/j.apenergy.2017.12.051
Sellami, Y., Imine, H., Boubezoul, A., Cadiou, J.C.: Rollover risk prediction of heavy vehicles by reliability index and empirical modelling. Veh. Syst. Dyn. 56(3), 385–405 (2018). https://doi.org/10.1080/00423114.2017.1381980
Service de l’Observation et des Statistiques: Bilan social annuel du transport routier de marchandises. Technical report, SOeS (2015)
US Department of Transportation: Traffic safety facts 2016: a compilation of motor vehicle crash data from the fatality analysis reporting system and the general estimates system. Technical report, NHTSA (2017)
Zhang, X., Yang, Y., Guo, K., Lv, J., Peng, T.: Contour line of load transfer ratio for vehicle rollover prediction. Veh. Syst. Dyn. 55(11), 1748–1763 (2017). https://doi.org/10.1080/00423114.2017.1321773
Zhu, T., Yin, X., Li, B., Ma, W.: A reliability approach to development of rollover prediction for heavy vehicles based on SVM empirical model with multiple observed variables. IEEE Access 8, 89367–89380 (2020). https://doi.org/10.1109/ACCESS.2020.2994026
Zhu, T., Yin, X., Na, X., Li, B.: Research on a novel vehicle rollover risk warning algorithm based on support vector machine model. IEEE Access 8, 108324–108334 (2020). https://doi.org/10.1109/ACCESS.2020.3001306
Zhu, T., Zong, C., Wu, B., Sun, Z.: Rollover warning system of heavy duty vehicle based on improved TTR algorithm. Jixie Gongcheng Xuebao/J. Mech. Eng. 47(10), 88–94 (2011). https://doi.org/10.3901/JME.2011.10.088
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Velardocchia, F., Perboli, G., Vigliani, A. (2023). Analysis of Heavy Vehicles Rollover with Artificial Intelligence Techniques. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_22
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