Vehicle trajectories classification using Support Vectors Machines for failure trajectory prediction

Abstract

The vehicles real trajectories analysis on dangerous zones is an important task to improve the road safety. The objective of this study is to provide tools for driving behaviour identification with the associated risk as regards of handling loss. This study aims to take into account the infrastructure, driver and the vehicle interactions, which are useful to evaluate this risk accurately.We propose in this paper a vehicles trajectories analysis in bend within a suitable Support Vector Machine (SVM) algorithm framework. At first, we will be interested on vehicle trajectory definition and experimental data acquisition. Then, we will make an experimental trajectories classification in order to determine several classes of trajectories. Afterwards, we will make a vehicle trajectories stability analysis in order to identify safe and unsafe fields of the observed trajectories. Lastly, one will use machine learning methods to predict failure trajectories.