© 2022 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
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.
automatic train operation, autonomous vehicles, machine learning, self-driving cars
[1] IRRB Webinar Autonomous technologies in rail – Anticipating expectations, 9.6.2021. Online, https://uic.org/events/IMG/pdf/ato_webinar.pdf. Accessed on: 2.3.2022.
[2] Liu, P., Yang, R. & Xu, Z., How safe is safe enough for self-driving vehicles? Risk Analysis, 39(3), pp. 315325, 2018.
[3] Erskine, M., et al., Digital train control functional safety for AI based systems. Presented at the International Railway Safety Council Conference, Perth, Australia, 2019.
[4] Richard, P., Boussif, A. & Paglia, Ch., Rule-based and managed safety: a challenge for railway autonomous driving systems. Proc. of the 31st European Safety and Reliability Conference (ESREL), pp. 2363–2369, Angers, France, 2021.
[5] EN50126 (1–2), Railway applications – The specification and demonstration of reliability, availability, maintainability and safety (RAMS) – European standard, 2017.
[6] IEC 61508 (1–7), Functional safety of electrical/electronic/programmable electronic safety-related systems, European standard, 2010.
[7] EN50128, Railway applications – communication, signalling and processing systems – software for railway control and protection systems. European standard, 2011.
[8] EN 50129, Railway applications – safety related electronic systems for signalling. European standard, 2018.
[9] ISO 26262 (1–10), Road vehicles – Functional safety. International standard, 2018.
[10] Regulation (EU) No. 402/2013 of 30 April 2013 on the common safety method for risk evaluation and assessment and repealing Regulation (EC) No. 352/2009.
[11] ISO/PAS 21448, Road Vehicles – Safety of the intended functionality (SOTIF). International standard, 2019.
[12] UL 4600, Standard for safety – evaluation of autonomous products. American National Standard, 2020.
[13] ISO/TR 4804, Road vehicles – Safety and cybersecurity for automated driving systems – design, verification and validation. Technical report, 2020.
[14] Filip, A., et al., D2.3 System requirements specification. H2020 HELMET project, 2020. Online, https://www.researchgate.net/publication/342673852_HELMET_ SYSTEM_REQUIREMENTS_SPECIFICATION. Accessed on: 16 May 2022.
[15] Aviation safety. Online, https://en.wikipedia.org/wiki/Aviation_safety. Accessed on: 16 May 2022.
[16] Jovicic, D., Guideline for the application of harmonised design targets (CSM-DT) for technical systems as defined in (EU) Regulation 2015/1136 within the risk assessment process of Regulation 402/2013. European Union Agency for Railways, 2017. Online, https://www.era.europa.eu/sites/default/files/activities/docs/era_gui_harmonised_ design_targets_en.pdf. Accessed on 16 May 2022.
[17] Filip, A. et al., Clarification of Discrepancies in the Classification of 1oo2 and 2oo2 Architectures Used for Safety Integrity in Land Transport. Proc. of the 31st European Safety and Reliability Conference (ESREL), pp. 2172–2179, Angers, France, 2021.
[18] Filip, A., Certification of EGNOS Safety-of-Life service for ERTMS according to IEC 61508 and EN 50129. Proc. of the COMPRAIL 2020 conference – Computers in Railways XVII, 1-3.7.2020, vol 199, on-line, pp. 115–125, 2020.