Non-Linear Method of Vehicle Pre-Crash Velocity Estimation Based on Random Forest Regression and Energy Equivalent Speed for Compact Vehicle Class

Until now, there have been no published attempts to utilize ensemble learning approaches to pre-crash velocity estimation. In this research article, we focus on the method of vehicle crash velocity prediction based on the random forest regression approach. In particular, the study aims to develop and validate a random forest-based non-linear model for estimating pre-crash velocity using EES-related parameters for compact vehicles in a crash scenario against an immovable, stationary barrier. The estimation technique is trained and evaluated using the compact vehicle class from the NHTSA database, which consists of 399 records of frontal impacts against a rigid barrier. The relative error obtained for the presented calculation method is 7.57%, with absolute error being equal to 1.12 m/s. We subsequently compare our results with some other techniques which were tested on this dataset. Despite the simplicity of random forest regression, we obtain surprisingly good results, as the method outperforms linear regressor and artificial neural network predictors, which have relative errors of 8.17% and 9.63%, respectively. The independence of Event Data Recorders along with the ease of obtaining the necessary data makes the proposed approach a highly desirable tool in forensic analysis, especially in cases involving older vehicles.