# Designing an Artificial Intelligence Driving Assistance System (AIDAS) for train drivers

Jean-Valentin Merlevede, Simon Enjalbert, Frédéric Vanderhaegen, Francesco Flammini

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Jean-Valentin Merlevede, Simon Enjalbert, Frédéric Vanderhaegen, Francesco Flammini. Designing an Artificial Intelligence Driving Assistance System (AIDAS) for train drivers. 16th IFAC Symposium on Analysis, Design and Evaluation of Human – Machine Systems (IFAC HMS 2025), Nov 2025, Pékin, China. hal-05338863

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# Designing an Artificial Intelligence Driving Assistance System (AIDAS) for train drivers

Jean-Valentin Merlevede* Simon Enjalbert*
Frédéric vanderhaegen** Francesco Flammini***

* LAMIH, UMR 8201 - CNRS, Univ. Polytechnique Hauts-de-France, Valenciennes, France (e-mail: simon.enjalbert@uphf.fr)

** INSA Hauts-de-France, Valenciennes, France

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

**Abstract:** This paper propose system in order to promote a new task sharing approach for railway driving. The system depends on three monitoring modules for evaluating the driver's condition, driving performance, and the driver's instantaneous and anticipated workload, and two output modules to compute, compare and select the best approach. Initially, data on the driver and the train driving parameters are collected and utilised to generate indicators. Finally, the algorithms to propose a distribution of train driving tasks is analysed in relation to the indicator that has been previously calculated. The objective of the task distribution is twofold: firstly, to optimise the driver's workload, and secondly, to modify the distribution when the driver is under or overloaded. The algorithm has the capacity to enhance the driver's performance and skills, or to adapt the driver's workload.

**Keywords:** Human-Machine Systems; Adaptive Automation; Human-AI interaction.

## 1. INTRODUCTION

A significant proportion of the literature has highlighted the challenges faced by operators and drivers in maintaining an active supervisory role when overseeing highly automated systems (Bainbridge (1983)). The extant literature has demonstrated that this transition has a detrimental effect on operators' workload, situational awareness and reaction time (Brandenburger and Naumann (2019); Nordhoff et al. (2023)). Drowsiness has also been observed in Brandenburger et al. (2021). To ensure transport safety, it is imperative to closely monitor driver engagement in driving activities and adapt the degree of automation accordingly. Numerous studies have indicated that the implementation of highly automated systems in train driving has exerted a detrimental effect on situational awareness and the capacity of operators to regain control in the event of a system failure (Brandenburger et al. (2018)). Research on Advanced Driver Assistance Systems (ADAS) (Brandenburger and Jipp (2017)) indicates that a lack of information or training results in a decrease of driving skills and sub-optimal performance due to the driver's unfamiliarity with the automated system. 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 the railway domain, the Grades of Automation (GoA) system is characterised by a hierarchy regarding to task automation, wherein operational tasks are prioritised over tactical tasks (Merlevede et al. (2025)). There are extant

tactical assistance systems on trains; however, these have not been factored into GoA levels. The objective is the establishment of a unified support system that can oversee all support mechanisms. The development of Grades of Operational and Tactical Automation (GOTA) was initiated to provide a framework for an adaptive approach to defining the levels of assistance required for each of the two groups of tactical or operational tasks. The distinctive characteristic of GOTA is its ability to provide assistance in situations not encompassed by GoA, particularly with regard to skills management and driver involvement in driving supervision. The unifying structure that seamlessly transitions distinct levels of autonomy while accommodating different assistance modalities and control systems is expected to mitigate the necessity for subsequent augmentations. This approach maintains a simple and organised structure that can evolve independently in each area, thus preventing additions that are difficult to integrate. A scheduler system is capable of adapting to a variety of situations, hardware and software, legislation, standards, restrictions, advances by manufacturers, the condition of equipment, and network complexity or personnel based on training, skills, and experience.

A review of the literature on task sharing reveals that, once tasks have been identified and can be distributed between the machine and the controller, there are two paradigms for choosing this distribution. The first paradigm is characterised by the distribution of tasks being left to the operator, who must therefore anticipate and adapt the automation to the difficulty of the task ahead (Calhoun (2022)). In contrast, the second paradigm involves dele-
gating the distribution process to a dedicated component of the system (Bernabei and Costantino (2024)). Finally, hybrid systems have been developed which combine these two approaches. It is important to emphasise several points in the literature that should be given due consideration in the design of our Artificial Intelligence Driver Assistance Systems (AIDAS) (Ruff et al. (2018); Sauer et al. (2012); Kidwell et al. (2012)).

* Adaptable systems are more acceptable by operators;

* Operators have a preference for adaptable systems;

* Operators have been found to show a preference for intermediate levels of automation. However, it has also been determined that each operator possesses their own personal distribution that allows them to maximise their performance;

* Operators have been observed to maintain a consistent distribution of tasks, often at the expense of their individual workload.

In this paper, we present the concept of DcD to validate the theoretical functioning of its operation. The concepts on which our framework is based have been established. The architecture, structure of functions and their operations are presented. Some of the functions are still under development, and certain features and approaches are subject to change during the final implementation and experimentation.

## 2. MONITORING HUMAN-MACHINE TRAIN DRIVING

In order to offer drivers an assistance strategy tailored to their individual needs, indicators have been generated to assess driving and driver performance. This process enables the evaluation of the relevance of the assistance strategies. These indicators should provide information on the driver's condition, performance, and current and future workload.

### 2.1 Driver State Monitoring

In order to evaluate the condition of a human operator, some of the literature suggests studying the activity of the autonomic nervous system. The autonomic nervous system is influenced by an operator's activity, state of alertness, and stress levels, and can be used to estimate an operator's cognitive state based on physically measurable data collected using non-invasive sensors. It is composed of two subsystems, the parasympathetic and sympathetic automatic body responses, which are markers of increased or decreased activity. It is important to note that the relationship between activity level and the activity of the parasympathetic and sympathetic nervous systems is complex and non-linear.

Heart Rate Monitoring (HRM) and Heat Rate Variability (HRV) data are indicators of cardiac activity. Cardiac activity is influenced by both the parasympathetic and sympathetic automatic body responses Lu et al. (2022). HRM is the heart rate and HRV is the variation in the time between heartbeats. The study of heart rate variability complements the study of heart rate and is a widely studied indicator in the literature. Time is critical during

<figure><img src="image_2.png" /><figcaption>Fig. 1. DSI computation with first aggregating algorithm</figcaption></figure>

studying HRV Shaffer et al. (2020) Electro Dermal Activity (EDA) data is the measurement of the variation in the electrical properties of the skin in response to stimulation of the sweat glands. The activity of the sweat glands is influenced by the activity of the sympathetic nervous system resulting from environmental stimuli, it can be used to detect drowsiness Nawawi et al. (2023). However, it is important to differentiate the location of the measurement, as some sweat glands are sensitive to heat while others are sensitive to sympathetic activity. The most relevant areas for diagnosing the condition of operators are the feet and hands, as their activity is little or not at all related to heat. It comprises two variables: the tonic component and the phasic component. The tonic component represents a change in the driver's general condition, while the phasic component indicates the presence of peaks in driver stress/arousal in Yang and Huang (2024).

PERcentage of eyelid CLOSure (PERCLOS) is a measurement coordinated with parasympathetic activity. It represents the percentage of time during which the eyelids are closed at least 80% over the pupil. It can be used to detect whether a driver is awake or asleep Abe (2023). It is a widely recognized indicator of fatigue due to its simplicity of measurement and high accuracy Dang et al. (2021); Junaedi and Akbar (2018).

**First Aggregating Algorithm** Extreme values of the HRM, HRV, EDA, and PERCLOS sensors change the value of the Driver State Indicator (DSI). We can see in fig 1, an example of DSI computation based on this proposition, where DSI values range from 0 to 10, with values below 2 considered as indicative of underload and values up to 8 considered as indicative of overload.

**Algorithm: DSI Calculation**

**Input:**

HR,HR_min,HR_max,ΔHRV,ΔHRV_ths,ΔEDA,ΔEDA_ths, perclos80

**Output:** DSI

**Initialization:**

DSI := 5

**Heart Rate (HR):**

If HR < HR_min then DSI := DSI - 1
Else if $HR > HR_{max}$ then DSI := DSI + 1

Else DSI := DSI

**Delta HRV [5 min]:**

If $\Delta HRV < -\Delta HRV_{ths}$ then DSI := DSI - 1

Else if $\Delta HRV > \Delta HRV_{ths}$ then DSI := DSI + 1

Else DSI := DSI

**Delta EDA [5 min]:**

If $\Delta EDA < -\Delta EDA_{ths}$ then DSI := DSI - 1

Else if $\Delta EDA > \Delta EDA_{ths}$ then DSI := DSI + 1

Else DSI := DSI

**Perclos:**

If perclos80 > 14% then DSI := DSI - 2

Else if perclos80 > 8% then DSI := DSI - 1

Else DSI := DSI

**DSI Capping:**

If DSI > 10 then DSI := 10

Else if DSI < 0 then DSI := 0

**Return:**

return DSI

**Second Aggregating Algorithm** The values obtained from the sensors are utilised to calculate factors that are then employed to generate the DSI. The DSI ranges from -10 to 10. The HRM and HRV cardiac factors are calculated based on variations in the HRM HRV sensors values. The EDA factor is calculated based on the variation in the phasic component and the presence of stimulation peaks detected by the tonic component. The PERCLOS factor is calculated on the basis of the PERCLOS threshold values, as well as recent signal variations. The indicators are then added to calculate the DSI. The reliability of the DSI is determined by evaluating the correlation of the indicators. The reliability value is employed to modify the behaviour of the task distribution algorithm. The tolerance value is derived from the reliability assessment of the DSI. This mechanism enables the adjustment of the performance indicator's behaviour, thereby ensuring that drivers experiencing ill health, stress, or fatigue are not subject to unfavourable modifications in their performance records.

As previously stated, these indicators are used to measure biological activity levels. The influence of external factors, including living conditions, emotions, and stress, has been demonstrated to play a significant role in the development of these conditions. Indeed, even a high DSI score does not guarantee that the driver is focused and capable of driving. In order to surmount this issue, a performance indicator has been developed that can be utilised in the short term to evaluate instantaneous driving ability and in the long term to assess the impact of assistance systems on driver skills.

## 2.2 Driving Performance Monitoring

GOTA's new levels of assistance have been categorised into two distinct domains: tactical and strategic automation. The same applies to our performance indicator, which is used to determine the operator's performance in each domain, as required by the GOTA selection algorithm. The performance indicator also incorporates a safety indicator, the purpose of which is to assess the driver's safety

performance, which is the most significant aspect of rail driving.

**Commercial performance** The first group is commercial performance, which includes two main factors: energy efficiency and adherence to schedules.

* The primary consideration is that of energy efficiency, defined as the operating state in which energy consumption is at its lowest for the anticipated service. In order to provide a robust framework for analysis, it is proposed that a comparison be drawn between the performance of the driver and a theoretical result. This theoretical result may have been defined prior to the commencement of the service, or alternatively, it may have been calculated by an algorithm during the driving process. In view of the extant literature on the subject, it is pertinent to mention the use of new algorithms based on artificial intelligence. These algorithms are capable of calculating an optimised command that takes into account user preferences.

* Secondly, adherence to scheduled timelines is imperative in a performance criterion about railways. This criterion is of particular significance in the context of rail transport for two primary reasons. The initial issue pertains to customer satisfaction, which is further compounded by constraints in connection. The subsequent issue is the impact that a delay can have on the entire network. The evaluation of timetable compliance can be approached in two distinct yet interconnected ways. Firstly, absolute adherence to the designated route timetable can be assessed. Secondly, as the journey progresses, the assessment can be adapted to evaluate the driver's performance in relation to prevailing environmental conditions. Such delays may be attributed to unforeseen circumstances, and it would be inequitable to penalise a driver who is not culpable for the disruption. In such cases, it is necessary to propose an evaluation of the recovery strategy implemented by the driver.

**Motion performance** The second group is motion performance, which includes the following factors: anticipation, speed control, stopping accuracy, and driving consistency.

* Anticipation is the driver's ability to adapt their behavior in advance based on predictable events, such as accelerating before approaching an uphill slope;

* Speed control is the driver's ability to maintain the optimum speed for a given journey or section of journey. It will be assessed based on variations in jerk;

* Stop accuracy is defined as the driver's ability to halt the train at the designated locations, not only in relation to the car positions indicated on the platform, but also in relation to the doors. This facilitates passenger transfers, including those with reduced mobility or with bulky or heavy luggage;

* Driving consistency that defines acceleration and deceleration. This criterion has two direct consequences: passenger comfort and energy efficiency. It will be evaluated based on jerk values.

**Safety performance** The third group is safety performance, which includes the following factors: attention control, speeding, deceleration, speed when crossing sections,
speed when passing disturbances such as roadworks, and
finally compliance with signals and instructions.

* *Attention control* involves measuring the time it takes a driver to adjust their speed in response to a given event;

* *Overspeeding* corresponds to the number of times and the duration of periods when the driver exceeds the speed recommended by the system;

* *Deceleration curve* reflects the method used by drivers, particularly in terms of speed, when signals indicating upcoming blockages are displayed;

* *Passing speeds* are also taken into account, whether in terms of signals or disturbances;

* Finally, and among the most important criteria, is the imperative respect for *stop signs*.

## 2.3 Environmental Monitoring

To maintain consistency in the differentiation between tactical and operational tasks in the DcD, we wanted to split the elements of environmental monitoring according to their impact on the driver. In order to assess the driver's workload, we generate an indicator that is calculated based on the train's driving environment. Among the elements that have been classified in the tactical category, this study has identified two main groups.

* *Road Geometry* The first group covers the geometry of the track, which can be broken down into three characteristics: gradients, curves, and any defects in the track;

* *Infrastructure* The second group covers various types of equipment, which are divided into four categories. The first category concerns stations, where the impact is assessed differently depending on whether the train has to stop or not. The second category concerns superstructures and substructures. The third category concerns track crossings. And finally, the fourth category concerns points.

In the operational category, this study identified three main groups. The rankings we provide are subject to change.

* *Weather condition* The first group covers factors related to weather conditions. The main weather conditions considered are wind, the presence of leaves, frost or ice, rain, and finally snow;

* *Interaction* The second group covers interactions with the environment. The interactions that have been selected are as follows: the presence of other trains, signaling, any work on the tracks or in the immediate vicinity, areas where animals are likely to be present, and finally areas where people are likely to be present;

* *Train* The third group covers elements specific to the train. Among the elements selected are faulty equipment, wear and tear, and finally the train's driving interface itself.

The utilisation of these three monitoring systems facilitates the collection of data that enables the observation of changes in the driver's condition, with these changes being based on the workload generated by the environment. Furthermore, cross-referencing the various indicators has been demonstrated to provide additional useful information. For

instance, it can be utilised to evaluate the impact of the environment on the driver and their performance.

# 3. GRADES OF OPERATIONAL AND TACTICAL ASSISTANCE

In the context of the research conducted on task distribution mechanisms, we present solutions for the allocation of tasks between the operator and the machine. The extant literature in the field of railway studies highlights the capacity of operators to modify their conduct in response to variations in load, thereby substantiating the initial system's design. In this configuration, the identification of abnormal situations is prioritised, with task allocation superseding Dead man's switch. A secondary system has been proposed with the objective of maintaining the driver in an optimal state.

## 3.1 Adaptable GOTA

The present algorithm employs the first method of calculating the DSI and is designed to modify task distribution only in the event of an abnormal driver status value. It is considered that the situation is normal if the DSI level is found to be between 2 and 8. Should the DSI level exceed 8, the situation is categorised as an overload, and the system will increase the GOTA. The GOTA is increased in the area where the driver exhibits the lowest performance, thus helping them to compensate for their difficulties. In circumstances where the DSI level is less than 2, the situation is categorised as underload, and the system will initiate a reduction in the GOTA. The GOTA is reduced in the area where the driver has the highest performance, thus enabling them to perform the tasks in which they are most competent.

## 3.2 Adaptive GOTA

This algorithm uses the second method of calculating the DSI and is designed to generate a more consistent workload for the driver.

* *Nominal DSI* If the DSI level is between 5 and -5, the situation is considered normal and no action is generated at the GOTA level.

* *High DSI* The algorithm will then focuses on an increase in the DSI, which translates into an increase in stress. If the level is between 5 and 10, the principle would be to increase the GOTA in the same proportion as the DSI. If the DSI level is between 10 and 15, the GOTA level is increased taking into account the driver's choice and performance.

* *Low DSI* The study then focuses on a decrease in the DSI, which can translate into a decrease in attention. If the level is between -5 and -10, the principle would be to decrease the GOTA in the same proportion as the DSI. If the DSI level is between -10 and -15, the GOTA level is decreased.

To decide which tasks will be automated, the system has several algorithms based on previously calculated indicators. The choice of operating mode would be left to the driver.
*   **GOTA increment** The first prioritizes the area in which the driver has the most difficulty. The second focuses on the area where the operator's workload is highest. The third prioritizes increasing assistance in the area where the driver has lost the most skill. And finally, the last one requires a history of the impact of changes in the EI and the repercussions on the DSI in order to increase the level that has the most impact on the driver.

*   **GOTA decrement** Regarding the method for reducing GOTA, the same principle would be used as for increasing it, taking into account the driver's choice and performance.

### 3.3 AI management perspective

The following three solutions are proposed for integrating artificial intelligence. The initial approach involves the incorporation of driver status diagnostics into the DSM module, utilising artificial intelligence techniques, such as dynamic basian network. Indeed, Bayesian networks are a probabilistic artificial intelligence technique that are robust, and several articles have already used these networks in the context of assessing the condition of an operator, in an industrial setting or in the field of transport. Dynamic Bayesian networks are particularly interesting in our approach because they will be able to adapt in the event of a measurement being unavailable or unreliable, for example, eye tracking in the context of changes in light levels caused by tunnels. The subsequent strategy entails the refinement of the coefficients of the DSI or GOTA Selector algorithms through generating usage data, tracking driver interaction with DcD and the employment of data driven artificial intelligence tools. The final approach involves using deep learning technique to access DcD operation and failure detection. Neural networks are excellent tools for detecting faults or inconsistencies. This implementation will be studied once the DcD has reached the experimental phase.

## 4. CONCLUSION

Once the DcD has been fully implemented, validation is planned. For this evaluation, different perspectives will be proposed. A proof of concept will be carried out by evaluating the separate modules, questionnaires will be given to train drivers to enable them to give their opinion on the system, its acceptability and the modifications they would like to see, and finally, an experimental validation will be carried out by comparing the results obtained by drivers with and without the system. The various distribution mechanisms will also be evaluated. The system that has been presented proposes the establishment of a division of railway driving tasks. This distribution is predicated on the principles of adaptable or adaptive sharing. The objective of the task distribution is twofold: firstly, to optimise the driver's workload, and secondly, to modify the distribution when the driver is under or overloaded. The algorithm has the capacity to enhance the driver's performance and skills, or to adapt the driver's workload. The dispatching of drivers is based on the values recorded by three monitoring systems. These systems are responsible for evaluating the driver's condition, driving performance, and the driver's instantaneous and anticipated workload. The system has

been designed to compensate for the negative effects of driver assistance systems.

## 5. 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.

## ACKNOWLEDGEMENTS

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 Innovante pour un Futur Solidaire, Inclusif et Durable).

The authors express gratitude for the support of these institutions.

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