# New definition of Grades of Operational and Tactical Automation in rail domain

Jean-Valentin Merlevede, Simon Enjalbert, Frédéric Vanderhaegen, Francesco Flammini, Ronghui Liu, Ariane Wettig, Borna Abramovic, Helen Chalkia

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Jean-Valentin Merlevede, Simon Enjalbert, Frédéric Vanderhaegen, Francesco Flammini, Ronghui Liu, et al.. New definition of Grades of Operational and Tactical Automation in rail domain. 16th International Conference TRANSBALTICA 2025: Transportation Science and Technology, VILNIUS TECH Faculty of Transport Engineering, Sep 2025, Vilnius, Lithuania. hal-05267769

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Distributed under a Creative Commons Attribution 4.0 International License
New definition of Grades of Operational and
Tactical Automation in rail domain

Jean-Valentin Merlevede¹, Simon Enjalbert¹ Frédéric Vanderhaegen¹,²,
Francesco Flammini³, Ronghui Liu⁴, Ariane Wettig⁵, Borna Abramović⁶, and
Helen Chalkia⁷

¹ LAMIH, UMR 8201 - CNRS, Univ. Polytechnique Hauts-de-France, Valenciennes,
France

simon.enjalbert@uphf.fr,

² INSA Hauts-de-France, Valenciennes, France

³ Dalle Molle Institute for Artificial Intelligence, SUPSI, Lugano, Switzerland

⁴ Institute for Transport Studies, University of Leeds, United Kingdom

⁵ Institute of Land and Sea Transport, TU Berlin, Germany

⁶ Faculty of Transport and Traffic Sciences, University of Zagreb, Croatia

⁷ Centre for Research & Technology Hellas, Thessaloniki, Greece

**Abstract.** The present paper puts forward a proposal for an extension to the conventional Grades of Automation (GoA) with a purpose of facilitating both dedicated operational and tactical assistance. The contribution aims to reintegrate the driver into the train driving activity with the new Grades of Operational and Tactical Automation (GOTA). The proposal of GOTA is intended to provide train drivers with the option of varying the level of automation over time. Furthermore, they will facilitate the breakdown of the assistance level into tactical and operational tasks. This breakdown will be according to the specific characteristics of the driver and a variety of criteria, taking into account human involvement and driving skills. The GOTA Selector which helps the driver to be optimally engaged while driving by adapting the GOTA to the specific needs is then planned.

**Keywords:** Automation, Railway, Human-Machine Systems, ADAS

# 1 Introduction

Given the advances in autonomous technology, readers may feel that our position is outdated and that the future of rail travel lies with autonomous trains. We would therefore like to draw readers' attention to the presence of human pilots on commercial aircraft, which have been capable of flying without them for several years now. Together, we will explore the reasons that have led us to propose a new approach to train automation, which aims to enable a new method of task distribution to facilitate collaboration between the train and its driver. The extant literature has demonstrated that this transition has a detrimental effect on operators' workload, situational awareness and reaction time [1]. Studies have
shown that using highly automated systems in train driving has had a negative impact on situational awareness and the ability of operators to regain control in the event of a system failure [2]. A lack of information or training, as indicated by research on Advanced Driver Assistance Systems (ADAS), results in a decrease of driving skills and sub-optimal performance [3]. This system will make it possible to maximise the contribution of our two agents on board, which are the human driver and the autonomous driving agent. The next stage of the study will be to examine Grades of Operational and Tactical Automation (GOTA) and ascertain how they differ from the more familiar Grades of Automation (GoA). This will demonstrate the advantages of this innovative approach to the distribution of tasks between human and artificial driving agents.

## 2 Balancing Human and Machine: A Shared Control Approach

Following the integration of Human-Machine Systems, the focus has shifted to the distribution of tasks between humans and automated systems. Two distinct areas have been the subject of detailed analysis in these studies. The first of these focused on the level of assistance provided, while the second concentrated on the consequences of this level on both parties involved.

In the process of designing automated systems, it is imperative to consider the repercussions that assistance functions have on the automated agent, particularly with regard to its integration and the operator's workload. The latter decreases as a consequence of the fundamental transformation in the nature of assistance functions. A variety of scales have been developed for the purpose of measuring the level of automation of systems [4]. It has been observed that certain users, manufacturers and administrative regulations have temporarily rejected proposed increases in the level of assistance, which would have involved a change in the driver's position on board by cabin crew. These automations, which resulted in an elevated automation level, precipitated a transition in the driver's role from an active to a supervisory position within the operation of the train managed by an automated system.

Several academic research have been conducted to emphasise the repercussions of these automation measures on human operators [5]. A significant proportion of the literature has underscored the challenges encountered by operators and drivers in maintaining an active supervisory role when overseeing such highly automated systems. The extant literature has demonstrated that the transition from manual system to highly automated system has a deleterious effect on operators, in terms of both their workload [1] and their situational awareness. This has been shown to result in a decrease in driver involvement and a substantial increase in reaction time [3]. As indicated in [6], drowsiness has also been observed. In order to circumvent these issues and ensure transport safety, it is imperative to closely monitor the driver's engagement in driving activities and adapt the degree of automation in these diverse tasks accordingly, thereby sustaining the driver's attention and involvement.
Numerous studies have previously indicated that the implementation of highly automated systems in train driving has exerted a detrimental effect on situational awareness [7] the capacity of operators to regain control in the event of a system failure [8]. In order to enhance the level of automation, it is imperative to consider the driver's level of situational awareness. Research on Advanced Driver Assistance Systems (ADAS) indicates that a lack of information or training among drivers results in a decrease of driving skills, as evidenced by the Nordhoff study [9], and to sub-optimal performance due to the driver's unfamiliarity with the operation of the automated system, as elucidated by the Endsley study [10]. In order to provide appropriate training, it is necessary to be constantly aware of the driver's behaviour. The objective is to ensure the continuity of collaboration in the event of a decline in skills.

In light of the considerable risks involved for both the driver and the train, the integration of artificial intelligence in the railway sector necessitates a deep understanding of human factors, as well as the interplay between automation and the driver. This approach is imperative for effective driver integration and safety, ensuring seamless operation for both passengers and railway equipment.

# 3 From GoA to GOTA

The grade of automation is comparable to the SAE Levels of Driving Automation in the context of road transport [11]. Like their road transport counterpart, GoAs are used to allocate tasks related to driving tasks between assistance systems and the driver. The GoA have defined an international standard (IEC 62290 – 1) for the development and compatibility of train driving assistance and automated train control systems. They integrate Automatic Train Protection (ATP) and Automatic Train Operation (ATO) systems.

## 3.1 Grades of Automation

As shown in the Figure 1, system in GoA-2 and GoA-3 take responsibility for some of the management of operational and tactical tasks ([12]). This transition denotes a transfer of authority from human operators to automated systems. This change raises questions about the role of the driver. The transition has seen the driver's traditional role as the primary controller of the locomotive change to that of a supervisor overseeing a highly automated system. To make this transition smoother, we have proposed extending the GoAs by adding two additional transition levels, expending GoA2. The aim of these levels is to enable the introduction of rolling stock that is both suited to human driving requirements and capable of autonomous driving at different levels of supervision. From a system perspective, it allows the technology to mature while relying on a human operator to mitigate any potential problems. From a human perspective, this solution makes drivers' jobs easier by offering them a system designed for collaboration that is capable of adapting to the driver. Since the driver retains control of the system, the driver retains authority over the system.
<table><caption>Fig. 1. Extension of GoA-2</caption><thead><tr><th>Grades of Automation</th><th>Levels</th><th>Operational</th><th>Operational</th><th>Tactical</th><th>Tactical</th><th>Strategic</th></tr><tr><td></td><td>Driving tasks</td><td>Speed regulation</td><td>Departure / stopping at station</td><td>Monitoring of driving environment</td><td>Doors closure / opening</td><td>Detection and management of emergencies</td></tr></thead><tbody><tr><td>GoA-0</td><td></td><td>Driver</td><td>Driver</td><td>Driver</td><td>Driver</td><td>Driver</td></tr><tr><td>GoA-1</td><td>ATP</td><td>Driver\System</td><td>Driver</td><td>Driver</td><td>Driver</td><td>Driver</td></tr><tr><td>GoA-2</td><td>ATP+ATO</td><td>System\Driver</td><td>Driver\System</td><td>Driver</td><td>Driver</td><td>Driver</td></tr><tr><td>GoA-2.1</td><td></td><td>System\Driver</td><td>Driver\System</td><td>Driver\System</td><td>Driver\System</td><td>Driver</td></tr><tr><td>GoA-2.2</td><td></td><td>System\Driver</td><td>System\Driver</td><td>System\Driver</td><td>System\Driver</td><td>Driver</td></tr><tr><td>GoA-3</td><td>Driverless</td><td>System</td><td>System</td><td>System</td><td>Train attendant</td><td>Train attendant</td></tr><tr><td>GoA-4</td><td></td><td>System</td><td>System</td><td>System</td><td>System</td><td>System\Operational Control Center</td></tr></tbody></table>

In Figure 1, at the grade 2.1, the driver is still responsible for monitoring the environment in which the train is operating, but the train is capable of moving under the driver's supervision and provides the driver with information about the situational awareness helping to improve anticipation. Regarding grade 2.2, the driver's role transforms significantly, becoming supervisory. Consequently, the driver assumes the role of supervisor for driving and environmental monitoring tasks that the system cannot perform independently. The driver retains ultimate authority for decision-making. The proposed framework is designed to ensure the continued engagement of the driver in the driving task. The proposal to extend GoA-2 to three sub-grades requires the classification of different train driving tasks. This classification must coordinate the tasks and the grade at which they will be automated. The operational and tactical tasks of the task taxonomy proposed as part of the CARBODIN project ([13]) are presented in Table 1.

There is a relationship between the GoA and the distribution of driving tasks

<table><caption>**Table 1.** Driving tasks distribution regarding authors taxonomy and decision making levels</caption><thead><tr><th></th><th>Operational<br>GoA-1</th><th>Tactical<br>GoA-2 to GoA-4</th></tr></thead><tbody><tr><td>CRITICAL</td><td><strong>Speed regulation</strong></td><td><strong>Doors closure / opening</strong><br><strong>Monitoring driving environment</strong></td></tr><tr><td>AUXILIARY</td><td>Accessories<br>Power management</td><td><strong>Departure / stopping at station</strong></td></tr><tr><td>SIDE</td><td>Lighting<br>Comfort systems</td><td></td></tr></tbody></table>

proposed by CARBODIN. The operational task of speed regulation is automated in GoA-1, while tactical tasks are automated from GoA-2 to GoA-4. In our research, we will consider tasks requiring a deliberate cognitive process to be tactical or strategic tasks. Strategic tasks and GoA-4 will not be studied in this
work because their automation only concerns fully autonomous trains. This classification allows driving tasks to be allocated between the different GoA. Speed regulation tasks must be automated, but their automation is not the subject of our research. Critical tasks such as speed control and environmental monitoring must be automated. These tasks have already been automated separately in other studies, particularly in the context of energy savings. Drivers have indicated that they prefer the integration of additional tasks, including accessories and power supply. Assistance tasks, namely lighting and comfort systems, are considered integral components of the operational tasks associated with driving and, as such, fall within the scope of the study. A comprehensive review of the literature on train driving automation indicates that the delineation of tactical and operational tasks reveals a limitation of GoA. Indeed, the automation of tactical tasks within the GoA framework implies the automation of operational tasks, whereas drivers express a preference for tactical assistance. A new system allowing for automation tailored to each driver should therefore be proposed.

## 3.2 Grades of Operational and Tactical Automation

<table><caption>**Fig. 2.** Grades of Operational and Tactical Automation (GOTA)</caption><thead><tr><th rowspan="2">Operational and<br>Tactical Automation</th><th rowspan="2">Grades of<br>Automation</th><th colspan="1">Levels</th><th colspan="1">Operational</th><th colspan="1">Tactical</th></tr><tr><th>Driving tasks</th><th>Speed regulation<br>Departure / stopping at station</th><th>Monitoring of driving environment<br>Doors closure / opening</th></tr></thead><tbody><tr><td>0.0</td><td rowspan="5">GoA-0</td><td></td><td>Driver</td><td>Driver</td></tr><tr><td>0.1</td><td></td><td>Driver</td><td>Driver \ System</td></tr><tr><td>0.2</td><td></td><td>Driver</td><td>System \ Driver</td></tr><tr><td>0.3</td><td></td><td>Driver</td><td>System</td></tr><tr><td>1.0</td><td rowspan="5">GoA-1</td><td>ATP</td><td>Driver \ System</td><td>Driver</td></tr><tr><td>1.1</td><td></td><td>Driver \ System</td><td>Driver \ System</td></tr><tr><td>1.2</td><td></td><td>Driver \ System</td><td>System \ Driver</td></tr><tr><td>1.3</td><td></td><td>Driver \ System</td><td>System</td></tr><tr><td>2.0</td><td rowspan="5">GoA-2</td><td>ATP+ATO</td><td>System \ Driver</td><td>Driver</td></tr><tr><td>2.1</td><td rowspan="2">GoA-2.1</td><td></td><td>System \ Driver</td><td>Driver \ System</td></tr><tr><td>2.2</td><td></td><td>System \ Driver</td><td>System \ Driver</td></tr><tr><td>2.3</td><td></td><td></td><td>System \ Driver</td><td>System</td></tr><tr><td>3.0</td><td rowspan="3">GoA-3</td><td></td><td>System</td><td>Driver</td></tr><tr><td>3.1</td><td></td><td>System</td><td>Driver \ System</td></tr><tr><td>3.2</td><td rowspan="2">GoA-4</td><td>Driverless</td><td>System</td><td>System \ Train attendant</td></tr><tr><td>3.3</td><td></td><td></td><td>System</td><td>System</td></tr></tbody></table>

The GoA is a fixed system whose configuration is determined during the design of the rolling stock, while the new Grades of Operational and Tactical Automation (GOTA) illustrated in Figure 2 are levels that adapt to the driver and their condition. The utilisation of the GoA as the sole representative of a train's automation level entails the exclusion of numerous solutions. The GoA's proposed grouping is not subject to review here. However, the GOTA unifies and standardises all the assistance proposed in the train. This will facilitate the incorporation and management of Driver Advisory System (DAS) tools, which
assist drivers in tactical planning, into the assessment of the requisite level of
assistance. This methodology will make it easier to take into account the specific
characteristics of each driver, thereby adapting their workload and assigning
them tasks that correspond to their strengths or areas for improvement. The use
of this GOTA approach will facilitate the identification of relevant assistance
measures.

The GoA system has been designed with a specific hierarchy of automation, prioritising operational tasks before tactical tasks. However, there are already tactical assistance systems, the DAS system, in place on trains, though these are not factored into the GOA levels. It was not considered either that a driver would want to delegate the management of stops in stations to the system while retaining traction control. However our objective is to facilitate a consolidated support system, thereby ensuring that a unified framework is capable of overseeing all support mechanisms. That is why GOTA has been developed to provide a framework for a more nuanced approach to defining the levels of assistance required for each of the two groups of tactical or operational tasks, independently of each other. The unique attribute of GOTA is its capacity to furnish assistance scenarios that are not encompassed by GoA, particularly with regard to skills management and driver involvement in driving supervision. The provision of a unifying structure that enables distinct levels of autonomy to be seamlessly transitioned between, while accommodating diverse assistance modalities and control systems, is expected to mitigate the necessity for subsequent augmentations. There are different paradigms for task distribution, and their impact on driver performance and well-being will need to be studied. This approach facilitates the maintenance of a simple and organised structure that can evolve independently in each area, thus avoiding additions that are difficult to integrate. This will enable a single scheduler system to adapt to different situations, hardware and software, to adapt to national regulations, legislation, standards, restrictions, or in relation to advances made by manufacturers, the condition of equipment, the complexity of networks, or in relation to personnel based on their training, skills, and experience.

## 4 Perspectives

In the context of ongoing initiatives to enhance rail safety and efficiency, a
novel assistance system is set to be introduced. The GOTA framework will be
integrated into this railway driving assistance tool with the objective of incor-
porating human factors into its design. The human factors and the adaptation
of assistance systems, which were the subject of the first part of this article,
are taken into account to optimise the performance of the Human-Machine Sys-
tem by avoiding the inherent pitfalls of supervisory tasks. The system has been
designed to assess and mitigate the impact on the driver in real time, thereby
ensuring that they are able to regain control in the event of a critical failure.
Instead of a predetermined level of assistance at the system's initiation, the Dig-
ital Co-Driver (DCD) will offer assistance levels that are adapted to the driver's current status. This will include performance and state indicators that continuously assess the driver and their condition. The system must be designed to efficiently adjust the level of assistance to the driving task.

As part of the assessment and monitoring of the system's impact on the driver, it is essential to evaluate the following three indicators: The first indicator assesses the driver's cognitive state and their involvement in the driving process. We used several biomarkers to monitor changes in the driver's condition to estimate the driver's workload, vigilance, and situation awareness. This proposed biofeedback system aims to gather several non-invasive markers and combine them to provide reliable tracking of the driver without disturbing them. The second indicator aims to assess driver performance. Performance will be compared both to the theoretical optimum achievable results and to previous driver assessment results. The purpose of this evaluation is to ensure that the competence of the drivers does not deteriorate due to repeated use of the assistance systems. The third indicator assesses the environmental conditions in which the train is operating. It takes into account various criteria, such as the complexity of the track, stops and weather conditions, to estimate the workload that the environment will generate for the driver.

Accounting for these different indicators aims to enable the rail driving assistance tool to detect and mitigate the multiple negative consequences of automation in order to preserve the best of both machines and humans. It will then be possible to assess the relevance of the assistance strategies deployed in real time, but is also used in the longer term to generate a new assistance strategy adapted to the anticipated load. The system will be capable to determine how the driver is affected by the current task and to assess their need for assistance with future tasks. The recording of this data will enable the driver to be offered driving phases and knowledge reviews to maintain their driving ability at a level that allows them to detect any problems with the automated systems and to be able to react.

# 5 Acknowledgments

This research was supported by the European Union's Horizon Europe research and innovation framework programme (project Academics4Rail, grant agreement no. 101121842). This research was sponsored by RITMEA, the Regional Delegation for Research and Technology, the Hauts-de-France Region, the European Community, the Ministry of Higher Education, Research and Innovation, and the French National Center for Scientific Research (CNRS). The Scientific Research Network on Integrated Automation and Human-Machine Systems (GRAISyHM) and the Regional Council of Hauts-de-France supported the present research work, as part of the SACRe TIFSID project (Technologie Inno-vante pour un Futur Solidaire, Inclusif et Durable). The authors express gratitude for the support of these institutions.
# 6 Disclaimer

Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Europe's Rail Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them.

# Bibliography

[1] Brandenburger N, Naumann A (2019) On Track: A Series of Research about the Effects of Increasing Railway Automation on the Train Driver. IFAC-PapersOnLine 52(19):288-293, DOI 10.1016/j.ifacol.2019.12.115, URL https://linkinghub.elsevier.com/retrieve/pii/S2405896319319500

[2] Brandenburger N, Thomas-Friedrich B, Naumann A, Grippenkoven J (2018) Automation in Railway Operations: Effects on Signaller and Train Driver Workload. In: Proceedings of the 3rd German Workshop on Rail Human Factors, ITS Mobility, Brunswick, Germany, pp 51-60

[3] Brandenburger N, Jipp M (2017) Effects of expertise for automatic train operations. Cognition, Technology & Work 19(4):699-709, DOI 10.1007/s10111-017-0434-2, URL http://link.springer.com/10.1007/s10111-017-0434-2

[4] Vagia M, Transeth AA, Fjerdingen SA (2016) A literature review on the levels of automation during the years. What are the different taxonomies that have been proposed? Applied Ergonomics 53:190-202, DOI 10.1016/j.apergo.2015.09.013, URL https://linkinghub.elsevier.com/retrieve/pii/S0003687015300855

[5] Bainbridge L (1983) IRONIES OF AUTOMATION. In: Analysis, Design and Evaluation of Man-Machine Systems, Elsevier, pp 129-135, DOI 10.1016/B978-0-08-029348-6.50026-9, URL https://linkinghub.elsevier.com/retrieve/pii/B9780080293486500269

[6] Naujoks F, Höfling S, Purucker C, Zeeb K (2018) From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and take-over performance. Accident Analysis & Prevention 121:28-42, DOI 10.1016/j.aap.2018.08.018, URL https://linkinghub.elsevier.com/retrieve/pii/S0001457518303944

[7] Rad MA, Lefsrud LM, Hendry M, Blais D (2021) Literature review on cognitive impacts of cab warning systems and train control technologies. Rail Research Conference

[8] Wickens CD, Onnasch L, Sebok A, Manzey D (2020) Absence of DOA Effect but No Proper Test of the Lumberjack Effect: A Reply to Jamieson and Skraaning (2019). Human Factors: The Journal of the Human Factors and Ergonomics Society 62(4):530-534, DOI 10.1177/0018720820901957, URL http://journals.sagepub.com/doi/10.1177/0018720820901957

[9] Nordhoff S, Lee JD, Calvert SC, Berge S, Hagenzieker M, Happee R (2023) (Mis-)use of standard Autopilot and Full Self-Driving (FSD) Beta: Re-
sults from interviews with users of Tesla's FSD Beta. Frontiers in Psy-
chology 14:1101,520, DOI 10.3389/fpsyg.2023.1101520, URL https://www.
frontiersin.org/articles/10.3389/fpsyg.2023.1101520/full

[10] Endsley MR (2023) Ironies of artificial intelligence. Ergonomics 66(11):1656-1668, DOI 10.1080/00140139.2023.2243404, URL https://www.tandfonline.com/doi/full/10.1080/00140139.2023.2243404

[11] Habib L, Oukacha O, Enjalbert S (2021) Towards Tramway Safety by Managing Advanced Driver Assistance Systems depending on Grades of Automation. IFAC-PapersOnLine 54(2):227-232, DOI 10.1016/j.ifacol.2021.06.027, URL https://linkinghub.elsevier.com/retrieve/pii/S2405896321004638

[12] Merlevede JV, Enjalbert S, Vanderhaegen F, Flammini F (2024) Towards artificial intelligence based rail driving assistance tool. In: ITM Web of Conferences, EDP Sciences, vol 69, p 03005, DOI 10.1051/itmconf/20246903005, URL https://www.itm-conferences.org/articles/itmconf/pdf/2024/12/itmconf_maih2024_03005.pdf

[13] Merlevede JV, Enjalbert S, Henon F, Baños AP, Ricci S, Vanderhaegen F (2022) Expectations of train drivers for innovative driving cabin. IFAC-PapersOnLine 55(29):144-149, DOI 10.1016/j.ifacol.2022.10.246, URL https://linkinghub.elsevier.com/retrieve/pii/S240589632202273X