Synergies Between Road and Rail Transport in the Development of Safe Self-driving Vehicles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F22%3A39919633" target="_blank" >RIV/00216275:25530/22:39919633 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.witpress.com/elibrary/tdi-volumes/6/3/2928" target="_blank" >https://www.witpress.com/elibrary/tdi-volumes/6/3/2928</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2495/TDI-V6-N3-313-325" target="_blank" >10.2495/TDI-V6-N3-313-325</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Synergies Between Road and Rail Transport in the Development of Safe Self-driving Vehicles

Popis výsledku v původním jazyce

In recent years, artificial intelligence (AI) has found numerous applications in medicine, energy, industry and various transport sectors, including rail and road. The use of AI for autonomous train operation is listed as one of the research challenges in the new Master Plan of the European Railway Joint Undertaking (October 2021). Nowadays, AI and machine learning (ML) algorithms are also widely used in connected self-driving cars (SDCs) for detection, classification and localization of objects on roads. Naturally, the rail industry also wants to benefit from recent advances in SDCs. While the current level of safety on the railways is acceptable to society, mass deployment of SDCs is expected to significantly reduce the number of accidents caused by human driver behaviour. Safety is thus currently a major challenge in the development of driverless cars. In contrast, various driverless automatic train operation (ATO) systems supported by automatic train protection with guaranteed high safety integrity level (SIL 4) have been introduced in the last decades, but mainly on segregated networks such as the metro. Therefore, the aim of SDC technology transfer is to go beyond segregated lines and develop fully autonomous driverless trains for open rail networks. In this paper, a comparative analysis was used to show how the required safety is assured in automated driving of trains and cars. The results of the analysis describe the differences, intersections and synergies in these two different application areas, in particular in terms of the basic pillars of safety, the safety standards and regulations used, interoperability requirements, safety demonstration, certification and independent assessment. Finally, the paper summarises how the rail experience in safety could be used to improve SDC safety, or conversely, how the ATO could benefit from transferring the latest AI and ML technologies developed specifically for SDCs.

Název v anglickém jazyce

Synergies Between Road and Rail Transport in the Development of Safe Self-driving Vehicles

Popis výsledku anglicky

In recent years, artificial intelligence (AI) has found numerous applications in medicine, energy, industry and various transport sectors, including rail and road. The use of AI for autonomous train operation is listed as one of the research challenges in the new Master Plan of the European Railway Joint Undertaking (October 2021). Nowadays, AI and machine learning (ML) algorithms are also widely used in connected self-driving cars (SDCs) for detection, classification and localization of objects on roads. Naturally, the rail industry also wants to benefit from recent advances in SDCs. While the current level of safety on the railways is acceptable to society, mass deployment of SDCs is expected to significantly reduce the number of accidents caused by human driver behaviour. Safety is thus currently a major challenge in the development of driverless cars. In contrast, various driverless automatic train operation (ATO) systems supported by automatic train protection with guaranteed high safety integrity level (SIL 4) have been introduced in the last decades, but mainly on segregated networks such as the metro. Therefore, the aim of SDC technology transfer is to go beyond segregated lines and develop fully autonomous driverless trains for open rail networks. In this paper, a comparative analysis was used to show how the required safety is assured in automated driving of trains and cars. The results of the analysis describe the differences, intersections and synergies in these two different application areas, in particular in terms of the basic pillars of safety, the safety standards and regulations used, interoperability requirements, safety demonstration, certification and independent assessment. Finally, the paper summarises how the rail experience in safety could be used to improve SDC safety, or conversely, how the ATO could benefit from transferring the latest AI and ML technologies developed specifically for SDCs.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/EF17_049%2F0008394" target="_blank" >EF17_049/0008394: Spolupráce Univerzity Pardubice a aplikační sféry v aplikačně orientovaném výzkumu lokačních, detekčních a simulačních systémů pro dopravní a přepravní procesy (PosiTrans)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Transport Development and Integration

ISSN

2058-8305

e-ISSN

2058-8313

Svazek periodika

6

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

313-325

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85142308433