Gaussian Process Regression as a precrash velocity determination method–subcompact vehicle class

The following paper presents an innovative approach to determining vehicle precrash velocity when hitting an immovable obstacle facing forward. Precrash velocity is necessary in order to perform a crash reconstruction. It is needed for the time-space analysis of the events, as well as to assess crash mitigation and to evaluate drivers’ technique and tactics. For this task, the authors are using Gaussian Process Regression (GPR). Such an approach offers a number of advantages over the currently used methods that prove to be outdated when considering modern vehicles. The mathematical model was trained on a database shared by the National Highway Traffic Safety Administration. This database covers a large number of crash tests of different kind, however authors focus on frontal collisions of the subcompact car class. Due to low accuracy of linear methods used up till now, Authors developed an innovative approach to determine the EES parameter utilizing Gaussian process regression. The newly developed method is an effective and accurate way to determine the vehicle’s velocity and shows promising results, as is demonstrated in this paper.