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# Grades of Operational and Tactical Automation in rail domain for an Artificial Intelligence Driving Assistance System

Simon Enjalbert, Jean-Valentin Merlevede, Frédéric Vanderhaegen

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Simon Enjalbert, Jean-Valentin Merlevede, Frédéric Vanderhaegen. Grades of Operational and Tactical Automation in rail domain for an Artificial Intelligence Driving Assistance System. Cognition, Technology and Work, 2025, 10.1007/s10111-025-00847-0. hal-05394649

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Distributed under a Creative Commons Attribution 4.0 International License
Grades of Operational and Tactical Automation in
rail domain for an Artificial Intelligence Driving
Assistance System

Jean-Valentin Merlevede¹*, Simon Enjalbert¹*
and Frédéric Vanderhaegen¹,²

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

²INSA Hauts-de-France, Valenciennes, France.

*Corresponding author(s). E-mail(s): jeanvalentin.merlevede@uphf.fr; simon.enjalbert@uphf.fr;

Contributing authors: frederic.vanderhaegen@uphf.fr;

Abstract

This work proposes new additional levels of progressive driver assistance, extend-
ing the traditional Grades Of Automation (GoA) in order to allow both dedicated
Operational and Tactical assistances. The second contribution is an Artificial
Intelligence Driving Assistance System (AIDAS) that aims to restore the driver
to their central role in the train driving activity, with the new Grades of Opera-
tional and Tactical Automation (GOTA) defined previously, taking into account
human factors. The framework of Digital Co-Driver (DCD) is comprised of mul-
tiple monitoring and modules, each addressing a distinct issue arising from the
augmented level of automation. The Driver State, Driving Performance and Envi-
ronmental Monitoring provide indicators of global driver involvement to maintain
a high level of performance in manual driving and to deal with system failures.
The GOTA Selector Module then helps the driver to be optimally engaged while
driving by adapting the GOTA to the specific needs. Finally, the Driver Com-
panion Module learns about the driver's preferences and needs to adapt the
assistance and help the driver to improve their own driving skills.

**Keywords:** Automation, Artificial Intelligence, Railway, Human-Machine System
# 1 Introduction

The interface of railway drivers has undergone significant changes in order to adapt to the gradual incorporation of various systems into trains. Authors have proposed a redesign of the cabin organisation, integrating new systems and interactions during the CARBODIN project (Merlevede et al. (2022)). This approach has revealed that drivers have a keen interest in innovative interfaces that leverage new technologies, such as haptic, gesture, and voice interfaces, which are currently not present in trains. The present study aims to extend this work by investigating the integration of Artificial Intelligence into train Driving Assistance System (AIDAS). The existing literature offers a variety of definitions for AI; in this paper, authors adopt the definition that sees AI as all technologies that enable the transition from an automated system to an autonomous system capable of adapting to its environment. The objective of Academics4Rail is to propose driver assistance systems for railway driving.

The initial section delineates new degrees of automation in the railway sector, called Grades of Operational and Tactical Automation (GOTA), with the objective of proposing supplementary levels of progressive driver assistance. A taxonomy of driving activities is proposed, with the aim of extracting the tasks that need to be automated. The integration of the concept of a Digital Co-Driver (DCD) is examined in the context of rail driving assistance, with a focus on the potential opportunities it presents. The DCD is an AI-driven system designed to support drivers in the operation of highly automated vehicles, ensuring their continued involvement in the driving process. The DCD is structured into monitoring and modules, each covering one of the requirements identified during the conceptual phase. The Driver State, Driving Performance and Environmental Monitoring have been developed to supervise the driver's involvement, situation awareness, and performance, with a view to tuning the behaviour of the DCD according to the driver. The GOTA Selector Module has been developed to improve driver's involvement in train driving by providing optimal task repartition between the driver and the system, regarding the tasks nature to be achieved, the driver's state, the driving conditions and the environmental situation.

Finally, the Driver Companion Module helps the driver to remain aware and able to take over the train control in case of emergencies, by maintaining their skill level and awareness. In order to keep the driver able to drive, the Driver Companion selects the combination of methodological approach and interface best suited for the driver, depending on their learning abilities. The module has been developed to enhance the driving skills of the driver or to update the artificial agent's driving abilities in cases where the driver's performance exceeds that of the system. The advice function has been designed to improve drivers' situational awareness by providing them with relevant information regarding the tasks to be achieved and the driving conditions. In the conclusion section, the development stages that were planned to ensure the successful implementation of the DCD are outlined.
## 2 From GoA to GOTA

In the domain of Human-Machine Systems, the subject of many studies has been the shared control of tasks between the human operator and an automated agent. The objective of these studies was twofold: firstly, to determine the level of assistance, and secondly, to identify the consequences of this level of assistance for the system and the operator. A significant area of research in the design of automated systems is the impact of assistance functions on driver involvement and workload. The Grades of Automation (GoA) framework defines the distribution of driving tasks between the driver and the system, based on the capabilities of the automated driving agent (Habib et al. (2021)).

### 2.1 Grades of Automation extension

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 and are listed below:

* GoA-0:Manual control only, without automatic protection device.

* GoA-1:Manual operation with automatic protection. The automated systems concern compliance with speed limits and danger signals (ATP), in-cab signal repetition (ERTMS) and VACMA.

* GoA-2:Semi-autonomous train: Semi-autonomous train: the driver retains final authority over all systems, while assistance tools can act only under the driver's supervision (ATO).

* GoA-3:Driverless train: no driver on board, the train attendant is responsible for opening and closing the doors, as well as detecting and managing emergencies.

* GoA-4:Autonomous train. The train is fully automated, under the supervision of the Central Control Station.

As illustrated in the Figure 1, the GoA-2 and GoA-3 assume responsibility for a proportion of the operational and tactical management (Merlevede et al. (2024)). This transition signifies a transfer of authority from human operators to automated systems. This transition has implications for user safety and legal accountability in the event of an accident. Furthermore, the initial implementations of GoA-2 trains give rise to questions regarding the transformation of the driver's role. The transition has been characterised by a shift from the traditional role of the driver as the primary controller of the locomotive to that of a supervisor overseeing a highly automated system.

A considerable corpus of research has been published on the challenges confronting operators in their efforts to sustain their role as active supervisor in the face of this transition. The extant literature provides a range of scales with which to measure the level of automation of systems (Vagia et al. (2016)). The transition from GoA-2 to GoA-3 involves replacing the on-board driver with train attendants. This proposal has not yet been accepted by some users, manufacturers and legislative authorities. Consequently, the proposal of additional GoA is necessary to facilitate an increase in
<table><caption>Fig. 1 Extension of GoA-2</caption><thead><tr><th rowspan="2">Grades of Automation</th><th>Levels</th><th>Operational</th><th>Operational</th><th>Tactical</th><th>Tactical</th><th>Strategic</th></tr><tr><th>Driving tasks</th><th>Speed regulation</th><th>Departure / stopping at station</th><th>Monitoring of driving environment</th><th>Doors closure / opening</th><th>Detection and management of emergencies</th></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>

automation grade while maintaining the presence of a driver on board. These grades, situated between GoA-2 and GoA-3, serve to facilitate the transition of authority and the augmentation of the automated system's competence. These grades also facilitate the development of human-centred systems, which can mitigate the adverse effects of high automation grades on driver attention. The proposed approach facilitates a smooth transition from GoA-2 to GoA-3. It is important to note that strategic tasks remain the responsibility of the driver and are not within the scope of this study. In order to be acceptable to drivers, the system must be designed in such a way that the driver retains final authority. Additionally, it is imperative that the driver possesses a comprehensive understanding of the system.

in the Figure 1, for the GoA-2.1 system, the functionality of the system is such that it is able to act autonomously in relation to actions concerning its own motion. However, the driver remains active and retains authority over the supervision of the train's movement in its environment. The system enhances the driver's situational awareness, supporting him in anticipating driving tasks. The system can then acquire the skills it needs for GoA-2.2. At GoA-2.2, a substantial transformation in the controller's role occurs, transitioning from active involvement to a supervisory capacity. Consequently, the driver assumes the role of supervisor for both driving and environment monitoring tasks that the system is unable to perform independently. It is important to note that the ultimate authority for decision-making remains with the driver. The implementation of this new grade is particularly relevant in situations where the driver lacks sufficient reaction time to complete the driving task while ensuring passenger safety. This can be illustrated by the implementation of measures such as reducing the distance between trains, in scenarios where network capacity is constrained due to increasing demand. The principal benefit of this novel approach is that it facilitates the development of a system that maintains the driver's involvement in the driving task. The system enables the driver to re-assume control at any time, thus keeping his skills up to date and better able to react when the system needs help in a new situation.

Following the proposal of two new grades, GoA-2.1 and GoA-2.2, for railway driving, the question arises as to which tasks are to be automated. In order to address this question, a comparison will be made between a taxonomy of driving tasks and the GoA. The operational and tactical tasks of the task taxonomy proposed as part of the CARBODIN project (Merlevede et al. (2022)) are presented in Table 1.
<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><th>CRITICAL</th><td><strong>Speed regulation</strong></td><td><strong>Doors closure / opening</strong><br><strong>Monitoring driving environment</strong></td></tr><tr><th>AUXILIARY</th><td>Accessories<br>Power management</td><td><strong>Departure / stopping at station</strong></td></tr><tr><th>SIDE</th><td>Lighting<br>Comfort systems</td><td></td></tr></tbody></table>

A correlation between task taxonomy from CARBODIN and levels of decision-making can be observed. Indeed, for GoA-1, the operational task of speed regulation is the only one automated; from GoA-2 to GoA-4, the automated functions extend to the tactical decision-making level. Tasks requiring conscious cognitive process will be considered as tactical ones. The strategic tasks are not covered before GoA-4, and thus fall outside the scope of the present study because the driver in out of the loop in this context. The goal of the paper is to maintain human in the loop by researching the best compromise of task allocation between driver and assistance tools. This correlation enables the determination of the grade at which tasks within the various categories can be transferred to the system by the driver. These tasks have previously been automated separately in other studies, particularly in the context of energy saving. Drivers have indicated a preference for the incorporation of supplementary tasks, including accessories and power supply. Indeed, during interviews, drivers express a desire for automation that is comparable to that of their vehicle (Merlevede et al. (2022)). The support tasks, namely Lighting and Comfort Systems, are considered integral components of the operational tasks associated with driving and, as such, fall within the investigation's scope.

A comprehensive review of the literature about 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 framework of GoA implies the automation of operational tasks, whereas drivers express a preference for tactical assistance. It is therefore appropriate to propose a new system to enable automation designed to each individual driver.

## 2.2 Grades of Operational and Tactical Automation

The changes in the driver's role that have arisen from the higher GoA implementation have resulted in the driver moving from an active role in controlling the vehicle to a supervisory role with regard to the automated system that operates the train. This transition has been the subject of extensive research in the academic literature, with particular attention being paid to its implications for the human operator (Bainbridge (1983)). It has been claimed that this transition has a negative impact on the human operator's workload (Brandenburger and Naumann (2019)) and situational awareness. This has been shown to result in a reduction in involvement
in the driving process and a considerable increase in reaction time (Brandenburger et al. (2021)). In the most unfavourable circumstances, this may result in a state of drowsiness (Naujoks et al. (2018)). In the interests of maintaining safety and enabling the driver to re-establish control of the system in the event of an unforeseen event, it is imperative to monitor the driver's involvement in the driving task and adapt the distribution of tasks to minimise the risk of the driver becoming disengaged. Despite the importance of automation in railway driving, previous studies have indicated a detrimental effect of highly automated systems on situation awareness (Rad et al. (2021)) and the operator's capacity to restore situation awareness in the event of loss of automated functions (Wickens et al. (2020)). Consequently, to enhance the level of automation, it is imperative to consider the driver's situational awareness.

<figure><img src="image_3.png" /><figcaption>Fig. 2 Grades of Operational and Tactical Automation (GOTA)</figcaption></figure>

The GoA is a fixed system established when the rolling stock is designed, while new Grades of Operational and Tactical Automation (GOTA) in Figure 2 are levels that constantly evolve during driving, depending on the driver's workload and awareness of the situation, taking into account their cognitive state and their ability to handle the present situation. It is proposed that this methodology will facilitate the consideration of each driver's unique characteristics, thereby enabling the adaptation of their workload and the assignment of tasks that align with their strengths or areas requiring development. The use of this GOTA approach will facilitate the
identification of relevant assistance measures. The GoA system is characterised by
a predetermined allocation of tasks, with a focus on the automation of operational
tasks and, subsequently, tactical tasks. In contrast, GOTA facilitates the definition
of a higher or lower level of assistance for each of the two groups of tactical or opera-
tional tasks, independently of each other. The GOTA is distinguished by its capacity
to accommodate assistance scenarios that are not managed with GoA, primarily with
regard to the management of skills and the involvement of the driver in the super-
vision of driving. The provision of a drawer application that facilitates independent
navigation between varying levels of assistance and operational modes will serve to
minimise the necessity for subsequent augmentations. 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 system to adapt to different situations, in relation 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.

Finally, research on driving with Advanced Driving Assistance Systems (ADAS, see
(Enjalbert et al. (2021))) indicates that when drivers are not adequately informed
or trained, the use of ADAS not only results in a decrease in their driving skills, as
evidenced in Nordhoff’s study (Nordhoff et al. (2023)), but also leads to suboptimal
performance due to driver unfamiliarity with the operation of the automated system,
as highlighted in the study by Endsley (Endsley (2023a)). Consequently, it is impera-
tive for the automated system integrated into the train to closely monitor the driver’s
performance and provide targeted training to enhance collaboration in the event of
a skill deficit. In consideration of the potential risks to the driver and/or the train,
the integration of Artificial Intelligence in rail transport necessitates a comprehensive
examination of human factors and the impact of the system on the driver. This is
essential to optimise involvement and safety, ensuring the well-being of passengers
and the integrity of rolling stock.
## 3 Design of Digital Co-Driver

A substantial body of research has been dedicated to the study of railway driver assistance systems, with a predominant focus on improving network management (Scheepmaker et al. (2017); Zhu et al. (2016)) and promoting energy efficiency (Fu et al. (2023); Boukal and Enjalbert (2019)). However, the majority of these studies do not take the human factor into account, or merely verify that the driver is able to apply the system's instructions with sufficient speed and precision for the system to be effective. Concurrently, there are already articles highlighting the negative impact of these systems on driver condition (see Rad et al. (2021); Brandenburger et al. (2021); Brandenburger and Naumann (2019)). A great deal of work has been studied on train automation and the use of artificial intelligence to optimise various criteria and automate tasks, but few systems that emphasise the establishment of healthy collaboration between humans and machines. Among the articles that caught our attention were studies in which AI-based tools calculate strategies to optimise energy consumption, with experimental tests in which the driver simply copies the AI's recommendations onto the train's throttle (Yao et al. (2022); Xiao et al. (2021); Zhu et al. (2018)). Those systems provide very good results concerning energy consumption or disturbance management, but they are not about Human in the loop nor situation awareness concepts that authors are investigating. Finally, the closest contribution concerns a cooperative tool without the tactical/operational task taxonomy called CEDRICS (La Delfa et al. (2021)).

<figure><img src="image_4.png" /><figcaption>Fig. 3 Digital Co-Driver operating framework</figcaption></figure>
Consequently, there is an imperative to propose an assistance system that places the human back at the centre of decision-making. In order to counterbalance the potential undesirable effects of increasing levels of automation in the rail sector, the Digital Co-Driver (DCD) illustrated in Figure 3 is designed to focus on keeping the driver active in the driving task. This approach is substantiated by recent research in the domain of semi-autonomous vehicles (La Delfa et al. (2021)), as well as in industry, where operators engage with progressively intelligent robots. In addition to enhancing the safety and performance of the Human-AI Team, the Digital Co-Driver also tackles the concerns surrounding operator well-being that are central to the Industry 5.0 model (Lu et al. (2022)). A significant challenge in ensuring the safety of AI-based systems for both operators and users is the so-called 'Black Box problem'. This issue with AI-based systems, which means that the system has become so complex that even the designer cannot predict its behaviour (Endsley (2023a)), makes the interaction between humans and AI unpredictable.

In order to establish an effective collaboration between the DCD and the driver, human involvement is reintroduced to ensure the DCD adapts to various human behaviours, including cooperation, competition and non-collaboration. The objective is to enhance driver involvement in driving activities, regardless of the driver's attitude towards the system. The functionality of the Digital Co-Driver is delineated in Table 2, comprises three monitoring and two output modules, designed to address the bottlenecks slowing down the integration of highly automated systems, or artificial intelligence-based systems on trains. The DCD is based on the same theory as a model proposed by Vanderhaegen (Vanderhaegen (2021a)) to allocate tasks between a human operator and an assistance system, utilising the triplet (Competence, Availability, Possibility-to-Act (CAP)). This model inspired the approach of DCD which use performance and driver state to select the most suitable agent for a specific task.

The Grades of Operational and Tactical Automation (GOTA) Selector Module furnishes the driver with all the requisite and pertinent information, empowering them to perform in accordance with energy efficiency and safety criteria. The Driver Companion Module determines the most appropriate teaching method for the driver.
<table><caption>**Table 2** Digital Co-Driver Inputs/Outputs</caption><thead><tr><th></th><th>Inputs</th><th>Outputs</th></tr></thead><tbody><tr><td>Monitoring/Module</td><td></td><td></td></tr><tr><td>Driving Performance Monitoring</td><td>Commercial skills<br>Drivers skills<br>Safety skills</td><td>Driving Performance Indicator: <strong>DPI</strong><br>Driver Learning Needs: <strong>DLN</strong></td></tr><tr><td>Driver State Monitoring</td><td>Cognitive State<br>Activity</td><td>Driver State Indicator: <strong>DSI</strong></td></tr><tr><td>Environmental Monitoring</td><td>Network State<br>Route complexity<br>Cabin complexity</td><td>Environmental Indicator: <strong>EI</strong></td></tr><tr><td>Operational Involvement Monitoring</td><td>DSI<br>DPI - motion performance<br>DPI - safety performance<br>EI</td><td>Operational Involvement Indicator: <strong>OII</strong></td></tr><tr><td>Tactical Involvement Monitoring</td><td>DPI - commercial performance<br>DSI<br>EI</td><td>Tactical Involvement Indicator: <strong>TII</strong></td></tr><tr><td>GOTA Selector module</td><td>OII + TII<br>Driver adaptation/choice</td><td>Operational Assistance + Tactical Assistance: <strong>GOTA</strong></td></tr><tr><td>Driver Companion Module</td><td>GOTA + DLN<br>DSI<br>DPI</td><td>Learning function<br>Advice function<br>Driver information tuning function</td></tr></tbody></table>
The framework utilises three distinct input modules that generate indicators that are used to compute a driver assistance strategy. The initial module, designated as Driver State Monitoring, focuses on the driver and aims to estimate their cognitive state. The resultant metric is known as the Driver State Indicator (DSI). The second module, entitled Driving Performance Monitoring, examines alterations in the driver's proficiency. The results of this module provide the Driving Performance Indicator (DPI) and Driver Learning Needs (DLN). The third input module is designed to address all external factors that have the potential to disrupt driving. This module provides the Environmental Indicator (EI).

The integration of these indicators, provided by the three previously depicted input modules, is managed by two separate output modules. The initial module, designated GOTA Selector, is responsible for monitoring operational involvement using the Operational Involvement Indicator (OII) and tactical involvement using the Tactical Involvement Indicator (TII). The GOTA Selector module provides the level of assistance according to operational and tactical tasks. The second output module, designated the Driver Companion, is designed to perform a number of functions, including the maintenance and acquisition of driving skills, the provision of relevant driving information, and the adaptation of other modules to accommodate interpersonal variations and driver preferences.

## 3.1 Driver State, Driving Performance and Environmental Monitoring

The Driver State, Driving Performance and Environmental Monitoring Modules are responsible for the assessment of driver involvement in the operation of the train, with the objective of ensuring their continued engagement and preventing their disengagement from the operational and tactical processes. In addition to this, the module assesses the driver's driving skills, including their driving performance and reaction time. The purpose of these assessments is to personalise the operation of the Digital Co-Driver. The Driver State and Performance Monitoring Module must therefore be able to evaluate three aspects: the ability to perform the driving task, the performance, and the situational awareness. A multitude of factors, which are challenging to quantify, influence the driver's ability to drive competently, including concentration, workload, motivation, and fatigue. The measurements intended to assess the driver's condition are divided into three categories. The first category concerns the evaluation of the driver's cognitive availability. Monitoring an operator's cognitive activity has been the subject of numerous studies. Although there is already a satisfactory body of research in the field of railway driving, new and interesting findings can be found in the broader context of driving road vehicles or flying aircraft. Eye-tracking (Dang et al. (2021)), thermal imaging (Zhou et al. (2023)), heart rate variability, electromyography (EMG), electroencephalogram (EEG), and Inertial Measurement Unit (IMU) (Lambay et al. (2024)) are among the most relevant. Another possible indicator is the interaction between the driver and the train, as in automotive vehicles, where the interaction between the driver and the steering wheel can be monitored. In order to calculate the DSI as part of a proof of concept, authors selected four indicators: heart rate, heart rate variability, electrodermal activity, and PERCLOS. The variation in
each of these indicators is evaluated to determine whether it indicates a change in the
driver's condition. A voting mechanism is then used to assess the driver's condition.
The voting mechanism takes extreme values into account to issue alerts to other com-
ponents of the DCD. Other indicators will be studied within the framework, as the
specific characteristics of rail driving must be taken into account. For example, the
existence of tunnels imposes additional constraints on oculometers. The second cat-
egory is concerned with the evaluation of driving skills, which can be assessed using
indicators such as driving energy efficiency, adherence to schedules, material wear
and tear, and passenger comfort. This is achieved by studying acceleration, braking,
and jerk variations. Finally, safety is evaluated by taking into account the speed of
approach to signals and danger zones. The third category relates to indicators to
monitor the driver's situational awareness and evaluating the workload of the route,
authors are considering the possibility of the system asking the driver questions to
check the driver's perception, comprehension and projection based on the information
extracted by the GOTA Selector Module, either using a tactile screen or voice inter-
action. The utilisation of a connected watch is a very interesting option, as it would
also provide information on the driver's general condition.

The Driver State and Performance Monitoring investigates the impact of varying degrees of assistance on the driver’s engagement in supervising or performing the driving task. Depending on the driver’s degree of involvement, the DCD can decide to return tasks to the driver to mitigate feelings of boredom or drowsiness. However, the driver must obtain system approval to automate these tasks once more. In instances where the system considers the driver to be disengaged, or if the system detects a significant decline in the driver’s competence, it forces the driver back into lower GOTA and the system acts only as a supervisor of the driving. In order to mitigate the risk of driver fatigue during extended periods of inactivity, it is proposed to share traction control with the driver or to use this time to train a novice driver. Indeed, a number of studies have demonstrated encouraging results in terms of the ability to regain control of an automated system when the operator is engaged in a secondary task (Wandtner et al. (2018); Lu et al. (2021)).

The Environment Monitoring module has been developed to estimate the workload that will be demanded of drivers while they are driving. The recording of information from the driver will be used to ensure that the driver remains in the loop. The impact of this workload is divided into two categories: operational and tactical. The workload generated by the environment is comprised of the following sources: firstly, information specific to the train, including its composition, condition, level of wear, and faults. The second source pertains to the configuration of the network, the track layout, its condition, load density, and disruptions. Finally, the final source of variation is attributed to external events, including meteorological conditions.

3.2 GOTA Selector Module

The GOTA Selector Module is tasked with the responsibility of monitoring opera-
tional involvement. This is achieved through the use of the DSI, the DPI in terms of
movement and safety, and the EI. This provides the Operational Involvement Indi-
cator (OII). The GOTA selector also monitors tactical involvement, a multifaceted
concept that encompasses the DSI, the commercial performance of the DPI, and the EI. The processing of these three indicators will produce the Tactical Involvement Indicator (TII). In accordance with the determined OII and TII, the GOTA selector will have information on the how the task affects the driver (DSI), how the driver performs under the load (DPI), and the next workload (EI), then it will be able to determine the best assistance strategy OA+TA (Operational and Tactical Automation) according to the circumstances, OA+TA entities constitute a combination of the GOTA. The refreshment rate of such the GOTA Selector will be determined during the experimental process and can vary for operational and tactical levels separately.

## 3.3 Driver Companion Module

The first function of the Driver Companion is to ensure that the driver is still able to drive the train, in order to overcome any automation failures and help newly trained driver to learn experienced driver knowledge. The second is to provide an advice function for the driver even when using high automation assistance, by helping him to collect meaningful information regarding to the driving environment. The last function of the Driver Companion aims to adapt the system behaviour to the driver's willingness and ability to cooperate.

### 3.3.1 Learning function

A number of studies have been conducted in order to investigate the possibility of using ADAS for driver training in the area of private vehicles. The learning capabilities of ADAS have already been validated for parking (Wada (2019); Tada et al. (2016)), and a previous paper has succeeded in improving drivers' skills in economic driving (Wada et al. (2011)). Authors want to check if that kind of interesting results could be obtained once applied to rail driving. In order to avoid any potential loss of skills caused by the use of a highly automated system, Driver's companion learning function is used to maintain driver's skill at an optimal level to keep human knowledge and human ability to monitor the system. Research has already demonstrated the ability of AIs to learn and reproduce the driving skills of human drivers in the railway sector, using deep learning approaches (Huang et al. (2019)). Following adequate training, these AIs can serve as a reference for the Driver Companion module. It is noteworthy that AIs can be trained to learn from novice drivers, helping them to drive efficiently (Vanderhaegen (2021b)).

In order to facilitate the adaptability of our framework, a study was carried out on the various available pedagogies that must be adapted for use in the training of drivers. It should improve the driver's willingness to use the learning function by assuring that they will find a learning solution which matches with their faculties and tastes. The following list comprises the all 6 pedagogies that should be implemented in the learning function. They will allow the adaptation of the learning curve and learning methods to the driver, but can also be used to adapt the learning material to environment.

1. **Magistral:** presentation of knowledge;

* No shared Control: Train gives speed curve / right speed;
* Train show theoretical knowledge / tutorials;

2. **Demonstrative:** the trainer does and explains how he does it;

* Shared Haptic Control: train can force manipulator position;

* HUD: train show driving efficiency and correct speed;

* Train show theoretical knowledge related to the task set up;

* Train show tutorial related to the task set up;

3. **Participative:** the trainer and the learner try to solve the problem together;

* Shared haptic control: train can use force feedback to move the manipulator

* HUD: train show driving efficiency

* Train might show theoretical knowledge related to the task set up

* Train might show tutorial related to the task set up

4. **Interrogative:** the learner tries to solve the problem;

* Shared haptic control: train can use force feedback to give hints the manipulator

* HUD: train show driving efficiency

* Train show theoretical knowledge related to the task set up on driver request

* Train show tutorial related to the task set up on driver request

5. **Exploratory:** the learner tries to solve the problem by himself under trainer supervision;

* Shared haptic control: train can use force feedback to give hints the manipulator

* HUD: train show driving efficiency

* Train don't show theoretical knowledge nor tutorials

6. **Practical experiments:** the learner tries to solve the problem without supervision.

* No driving assistance, driving quality is checked at the station;

Once the various interactive modules would have been implemented, tests will be needed to be carried out. Initially, this will involve checking that the systems really do provide additional comfort for the driver and that they result in a more stable workload for the driver. In a second phase, a study could be carried out to determine the factors affecting the use of assistance systems.

### 3.3.2 Advice function

In accordance with the research findings on the necessity to enhance drivers' situational awareness (Dadashi et al. (2013)), and the role of enhanced situation awareness in increasing rail drivers' efficiency and anticipation (see Panou et al. (2013); Verstappen (2017); Verstappen et al. (2022)), the Advice function filters network state information from the Environmental Monitoring such as delay, tracks state, tracks maintenance operation, the information from ground control, and train information to provide the driver with relevant data regarding driving tasks and planned timetable. The availability of this information is used to optimise the operation of the train. To further facilitate the driver's task, a display can be selected to show the recommended
speed and traction by the system to optimise driving behaviour. The Advice function facilitates communication with the driver via a visual interface (touch screen or Head Up Display) and a voice interface, in accordance with driver requests and the theory of limited resources, with a view to avoiding saturation of the optical channel, which is already heavily used in rail driving (Verstappen (2017)).

### 3.3.3 Driver information tuning function

The Tuning function of the Driver Companion is responsible for the adjustment of other functions in order to optimize the driving assistance according to the driver's condition and cooperation with the system. Authors will log how the drivers behave toward the assistance, to detect defiance or over-reliance and adapt assistance strategies to optimise the cooperation efficiency between driver and DCD. The Tuning function evaluates the efficiency of the driver's learning sessions and feeds it to the Learning function. The learning strategy evaluation is based on the results provided by the Driver State and Performance Monitoring. The system determines the most suitable task distribution for each individual, thereby personalising the collaborative process by adapting it to the capabilities of each driver. The determination of the optimum strategy for the driver is based on the most appropriate human-machine interface for the current driver (haptic or visual), the most effective teaching method with this driver, but also the driver's willingness to cooperate and receptiveness to advice by having a database about previous learning sessions and how they affected driving performance.

The Digital Co-Driver (DCD) is not only an assistance tool, but a new vision of Human-Machine System team in shared driving activities, with the goal of allowing the driver to monitor the system safely. Indeed monitoring a highly automated system is a complex action in term of underload and non-involvement. So the DCD framework brings a solution to help driver to be involded again in the monitoring process, allowing the driver to keep or learn the skill needed to be able to get back in the loop and assist the system. Moreover, it will bring back some attractiveness to the train driving task, in order to solve the vocational crisis among train drivers by putting them back in control of the train driving activities.
## 4 Conclusions and Perspectives

The next step of the project is to implement Driver State, Driving Performance and Environmental Monitoring to evaluate the newly proposed task assistance. This will involve testing the process in simulation to validate the hypothesis regarding the positive impact of varying the task assignment based on the driver state, with the aim of ensuring the driver remains engaged. The comparison of different algorithms, some of which using AI techniques, will be conducted using various paradigms. At the following step, the implementation of the Driver Companion Module, which aims to adapt the DCD's actions to the personal preferences of the driver, while also considering their willingness to cooperate with an automated agent, must be realized. This stage will assess the feasibility of employing artificial intelligence for the training of drivers. An initial prototype is under development with MATLAB software in order to test and evaluate different algorithms for each module. As sensors integration is not a scientific priority in this project, early testing is realized with random values. In next steps, Generative AI for could be applied for sensor values generation in Driver State Monitoring (DSM) module, and data provided by European project partners could be used in Driver Performance Monitoring (DPM) module. DCD is planned to be integrated progressively in a simulator then on a simulation platform.

The third and final step will be to complete the implementation of the Driver Companion module. The aim of this module is to generate data to train artificial intelligence to improve the individual adaptation of the DCD and its robustness to inter-individual differences. Ways to integrate artificial intelligence tools into the DCD are already investigated. First, the hard logic used in the driver state monitoring could be replaced by Dynamic Bayesian Networks, which have the advantage to cope with the safety standard required for integration into rolling stock. Next, deep learning methods based on neural networks to detect system anomalies and adjust the various coefficients are planned. Possibility to use a hybrid human-artificial intelligence approach (Ning et al. (2022)) to enhance human-artificial intelligence collaboration by implementing transparency and explainability (Endsley (2023b)) is studied. Neuro-Symbolic artificial intelligence emerges as a promising solution for facilitating collaboration between artificial intelligence systems and humans. This field integrates logic programming and deep learning methodologies to enhance human comprehension of the artificial agent. This paper puts forward a pioneering approach, in which the artificial system takes into account not only the strengths and weaknesses of the human operator, but also its impact on the human operator and the willingness of the operator to cooperate with the DCD. The two major contributions of Digital Co-Driver are the ability to monitor the driver's level of availability and involvement in order to adapt the task distribution. Secondly, it is able to assess driving skills and propose a training programme based on the learning of driving practices from expert drivers. The DCD was enabled following the introduction of Grades of Operational and Tactical Automation (GOTA), which facilitated task sharing between the automated agent (the DCD) and the train driver. These novel grades empower the system to acquire knowledge from the human operator, thereby facilitating the accumulation of data, thus enabling a systematic transition from a train operated by a driver to one that is autonomous. The use of Artificial Intelligence in human teams has been identified as a potentially
viable solution for the development of novel driver-centred railway assistance systems. However, it will be crucial to avoid problems related not only to the involvement of the human operator, but also to the operator's ability to retain his/her driving skills and manage unforeseen events. In order to ensure that the Digital Co-Driver meets the standards set by Industry 5.0, a concerted effort will be made to enhance driver well-being.
# 5 Acknowledgements

This work was supported by the European Union's Horizon Europe research and innovation framework program, project Academics4Rail (HORIZON-ER-JU-2022-ExplR-04) No 101121842. This research is sponsored by RITMEA, the Hauts-de-France Region, the European Community, the Regional Delegation for Research and Technology, the Ministry of Higher Education, Research and Innovation, and the French National Center for Scientific Research (CNRS). The present research work has been supported by the Scientific Research Network on Integrated Automation and Human-Machine Systems (GRAISyHM), and by the Regional Council of "Hauts-de-France", project SACRe TIFSID (Technologie Innovante pour un Futur Solidaire, Inclusif, et Durable). The authors gratefully acknowledge 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.

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