On Driver-Vehicle-Environment Integration for Multi-Actuated Ground Vehicles Safety Advancement: An Overview of the Interdisciplinary Training Network in Multi-Actuated Ground Vehicles

Abstract

Transportation systems are invariably burdened with dynamically changing environmental conditions and ill-defined human factor. To raise ground vehicle safety on a new supreme level and to boost autonomous vehicles development driver-vehicle-environment cooperation is inevitable. In this paper, an overview of several existing driver-vehicle-environment integration methods with purpose of vehicle safety enhancement are stressed. Five unique and fundamentally different solutions are proposed, which have common similarity: the solutions are accomplished with machine learning algorithms. The methods aim at modelling drivers' or vehicles' behaviour with reasonable prediction accuracy under various complex scenarios. All five solutions are developed in individual projects in a framework of a continuous interdisciplinary European network ITEAM. The aim of the paper is to underline significant benefit of man-machine-environment integration in vehicle safety systems by exploiting fairly received tremendous attention machine learning methods.