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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2019-11-01


    Size :

    1090207 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Robust Control for Over-Actuated Vehicles*

    Kissai, Moad / Monsuez, Bruno / Martinez, Didier et al. | IEEE | 2019



    Global force potential of over-actuated vehicles

    Jonasson, M. / Andreasson, J. / Jacobson, B. et al. | Tema Archive | 2010


    ELECTROMECHANICALLY ACTUATED CONTROL ROD FOR FLIGHT VEHICLES

    CHARAFEDDINE ABBAS M / EDWARDS DAVID E | European Patent Office | 2020

    Free access