Toggle Main Menu Toggle Search

Open Access padlockePrints

Analysis of the Safety Level of Obstacle Detection in Autonomous Railway Vehicles

Lookup NU author(s): Dr Cristian UlianovORCiD

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

© 2022, Budapest Tech Polytechnical Institution. All rights reserved. Traffic safety of fully automated train operations is one of the most complex challenges in the field of railway traffic automation. One of the biggest problems with the introduction of driverless trains to the public railway infrastructure are the risks associated with the obstacles on the line, which represent one of the most common and most significant safety risks in railway traffic. The Obstacle Detection System (ODS), should meet the safety requirements, but also should not lead to a deterioration of the railway traffic. In addition to the purely technical issues of ODS development, the issue of determining the necessary requirements in terms of safety, reliability and efficiency must be considered. The paper analyses the current European regulations in the field of railway safety, safety requirements for certification of ODS, as well as risk control measures by the types of obstacles on the line. A survey of train drivers in the Republic of Serbia was conducted to understand the significance of particular obstacles and the manner of reaction of train drivers in case of their occurrence. The results of the survey and the available statistical indicators were used to assess the impact of certain categories of obstacles on railway safety. The criteria for defining the safety requirements necessary for the certification of ODS in autonomous vehicles have been proposed.


Publication metadata

Author(s): Rosic S, Stamenkovic D, Banic M, Simonovic M, Ristic-Durrant D, Ulianov C

Publication type: Article

Publication status: Published

Journal: Acta Polytechnica Hungarica

Year: 2022

Volume: 19

Issue: 3

Pages: 187-205

Acceptance date: 02/04/2018

ISSN (print): 1785-8860

Publisher: Obudai Egyetem,Obuda University

URL: https://doi.org/10.12700/APH.19.3.2022.3.15

DOI: 10.12700/APH.19.3.2022.3.15


Altmetrics

Altmetrics provided by Altmetric


Share