Speed estimation of a car at impact with a W-beam guardrail using numerical simulations and machine learning

This paper aimed at developing a new method of estimating the impact speed of a passenger car at the moment of a crash into a W-beam road safety barrier. The determination of such a speed based on the accident outcomes is demanding, because often there is no access to full accident data. However, accurate determination of the impact speed is one of the key elements in the reconstruction of road accidents. A machine learning algorithm was used to create the speed estimation model. The model was based on regression trees algorithms, with base regressors forming a final voting ensemble. The model was trained, validated, and tested using a database containing results from full-scale crash tests and numerical simulations. The developed machine learning model had a mean absolute error of 6.76 km/h with a standard deviation of 1.01 km/h on the cross-validation set, and a coefficient of determination, R2, of 0.85. This model was used to estimate the impact speed of the vehicle in three real road accidents with the W-beam barrier, and then the determined speeds were used in additional simulations to verify the results. A good quantitative and qualitative agreement between the simulation and accident outcomes was achieved, and this confirmed that the proposed method and the developed ML models combined with numerical simulations and full-scale crash tests can be effective tools for estimating the speed of the vehicle at impact with a roadside barrier.