The Changing Role of Staff in Automated Railway Operation and why Human Cognition is Here to Stay

Automated mainline railway operation is challenging the traditional role of the operational staff ensuring safe and punctual service. Nevertheless, there are sound operational, economic, regulatory and societal reasons for valuing and maintaining central contributions of human staff to railway operation in future automated service. Instead of a linear transferal of tasks from the human to the automation technology a human-machine collaboration setting becomes apparent that enables both, automation-driven benefits in terms of capacity or energy consumption and benefits in terms of efficient human intervention in case of operational uncertainty, where human decision-making and communication skills are key to safety

From GoA2 to Remote Operation. Workplaces in highly automated rail

During the last years, the projects Next Generation Railway System, Next Generation Train, and Digitalization and Automation of the Railway System, enabled a series of studies regarding the train driver’s workplace at the German Aerospace Center. This research focused the changes in train drivers’ tasks and workplace environments as well as changes in demands on train drivers with increasing digitalization and automation of the Railway System. Increasing grades of automation impact the ways in which the staff can contribute to overall system performance and safety (Brandenburger, Naumann, & Jipp, 2019). First, we present experimental results on the effects of intermediate grades of automation (GoA2, International Association of Public Transport, 2012) on train driver performance, fatigue, workload, situation awareness and attention allocation. At GoA2, Automatic Train Protection (ATP) is side lined by technological automatic train operation (ATO) responsible for automatic speed adjustment, while the train driver in the cabin remains in charge of monitoring instrument panel and track, thus ensuring the safety of the trip. This implies continuous monitoring of an automated system but at the same time having to react quickly and correctly in a critical situation. In line with broader theory, our results reveal unfavourable effects on workload, fatigue and performance (e.g. Brandenburger & Jipp, 2017; Brandenburger, Thomas-Friedrich, Naumann, & Grippenkoven, 2018). Second, these results concerning GoA2 are compared to research on high grades of automation and its effect on the involved human operator. There, we currently focus on GoA3, where the train driver is no longer available in the cabin, while speed adjustment, track integrity checks and all safety- relevant tasks are executed by ATP and ATO technology. Nevertheless, the driverless operations need to be monitored from a control room. We propose a role change from the train driver to the role of a train operator, a member of staff that is automatically requested by the driverless trains to remotely diagnose or manually drive any train in a particular area, once the ATO functionality reaches its limits and disruptions occur (Brandenburger & Naumann, 2018b). The task environment of a train operator comprises tasks that are not necessarily related to a particular train (e.g. communication, planning, or monitoring the traffic in the area) as well as tasks that come up after manual intervention is requested by a particular train in cases of irregularity (e.g. on- sight driving, speed adjustment, communication with passengers, or diagnosis; Brandenburger & Naumann, 2018a). At the German Aerospace Center, we developed a prototypical workplace environment for the future train operator. This prototype is in continuous further development and improvement. Here, the current state of the design will be presented, and first results on the effects on the train operator will be contrasted with our results for GoA2. In summary, we assume that limited capacity gains as well as the human factors challenges of GoA2 may turn the favourability towards GoA3 operation with an operator role that enables a more effective human contribution to the overall railway system performance, safety and reliability (Brandenburger & Naumann, 2019)

Comparing different types of the track side view in high speed train driving

The introduction of high speed trains featuring an increasing number of automated components raises imperative questions concerning the future tasks and the general role of the train driver. Previous work showed that train protection systems provoke train drivers to relocate their visual attention from the track side towards the displays within the cabin. The introduction of high speed routes allowing automatic train operation (ATO) has major implications that question the importance of the track side view for the train driver: (1) all relevant driving parameters are displayed within the cabin in high speed railway operations. (2) Supervisory tasks based on in-cab display information shift into the train driver´s focus. This study investigated the influence of three differently sized track side views (real size, monitor size, none) on a) the allocation of visual attention towards displays and the track, measured by Eye-Tracking parameters and b) the situation awareness of the train driver supervising a high speed train featuring ATO measured with the SPAM method. Empirical data are presented for both research questions. The implications are discussed in order to identify how the delivery of relevant information in the context of the changing train driver’s task can be facilitated

DLR Workload Assessment Tool (DLR-WAT)-Official English Version

This article presents an official English translation of the "DLR - Workload Assessment Tool" (DLRWAT), an originally German language questionnaire for subjective self-assessment of workload originally published in 2018. The DLR- WAT assesses deviations from a subjective optimum of workload more explicitly than existing measurement tools such as the NASA-TLX. The rationale behind the development of this tool is found in the increasing coexistence of humans and automation technology in sociotechnical systems across application domains such as transportation. Automation technology assists and takes over tasks formerly executed by humans as actors, resulting in changing human roles ranging for example from more passive monitoring tasks to short term interventions in cases of malfunction. In general, automation can relieve humans and increase their comfort. Yet, the issue of unbalanced workload and especially more prevalent underload needs to be targeted given the changing task environments faced by staff in the transportation domain. However, instruments for the subjective assessment of workload have so far lacked clear differentiation between underload and overload anchored in relation to a subjectively optimal level of workload. The DLR-WAT was developed to fill that gap, while greatly relying on the general format of the widely established NASATLX. The tool comprises a total of eight subscales. On six of the eight subscales (information acquisition, knowledge retrieval, decision-making, motor and physical demand, temporal demand, effort), the respondent can indicate his or her state of workload in relation to the personal optimum, which is located in the middle of each subscale. The two other subscales of the DLR-WAT (frustration, performance) are designed one-dimensionally, since an optimal level of frustration is characterised by the absence of frustration and the highest possible performance equals the theoretical optimum. The consideration of the personal optimum of workload in the first six subscales is thought to enable more detailed workload analyses distinctively imaging underload and overload in the areas represented by the subscales. In designing future transportation systems, this tool enables identification of the targeted balance between overload and underload across subscales and allows informed subsequent allocation of tasks between humans and automation accordingly

Implementation of a Remote Control Workplace to Realize Remote Train Control over 5G-Network in Real-World Testing

Remote diagnosis, control and recovery of malfunctioning automated and unmanned trains is seen as a key enabler for automatic train operation along the Grades of Automation taxonomy. Therefore, a major aim of the 5G-Reallabor project, which is set out to bring technology under research into field-testing setups, is the technical demonstration of remote train control over physical distance via a 5G mobile network connection. This technical demonstration requires the development and implementation of a remote control workplace, equipped with all necessary functionalities to attain remote control of a connected train in real-world employing 5G mobile network technology. Significant available knowledge from the prior development and setup of a remote control simulator, called the Train Operator Workplace, served as a starting point for the current topic under investigation. The key step in the process of implementing the remote control workplace for real-world purposes was the derivation of functional user requirements in terms of information needs and control functionality. In the domain of information needs the presentation of in-train train protection system information (European Train Control System), video footage, vehicle status data and traction/braking data was identified, documented in several functional user requirements and implemented accordingly. In the domain of the required control functionality user control over traction, several braking systems, vehicle functions such as horn, light or door release, video cameras and direction of travel were identified and technically realized. Additional safety-related functional requirements mainly related to network quality, connection or package loss were also identified and implemented. The process of scenario-based user requirement derivation and subsequent implementation into a real-world demonstration case ready to satisfy safetyrequirements for physically operating a train on railway infrastructure is presented. Lessonslearned in the field of automatic train operation are discussed to derive insights and bestpractice for further testing in this promising new field of research

ATO-Cargo: Betriebsverfahren für die Rückfallebenen des hochautomatisierten Bahnbetriebes / ATO-Cargo: Operating procedures for the fallback levels of highly automated railway operation

DE: Im Projekt ATO-Cargo wird ein vollautomatisierter Betrieb von Güterzügen getestet, bei dem ein Remote Supervision and Control Center (RSC) insbesondere Aufgaben in der Rückfallebene übernehmen soll. Dies umfasst bspw. die Betriebsart Remote Train Operation. Das Deutsche Zentrum für Luft- und Raumfahrt entwirft in diesem Projekt unter anderem Betriebsverfahren für Use Cases, in denen das RSC involviert ist. Anschließend werden Human Factors Analysen zu den Arbeitsprozessen eines RSC-Operators durchgeführt sowie die möglichen Veränderungen in den Anforderungen an die menschliche Leistungsfähigkeit für die neue Rolle des RSC-Operators identifiziert. EN: In the ATO-Cargo project, a fully automated operation of freight trains is being tested, in which a Remote Supervision and Control Centre (RSC) is to take over tasks in the fallback level in particular. This includes, for example, the Remote Train Operation mode. In this project, the German Aerospace Center (DLR) is designing, among other things, operating procedures for use cases in which the RSC is involved. Subsequently, human factors analyses are carried out on the work processes of an RSC operator and the possible changes in the human performance requirements for the new role of the RSC operator are identified

A technical demonstration of remote train operations using 5G mobile communications

The 5G-Reallabor in Braunschweig-Wolfsburg project has demonstrated the technical feasibility of Remote Train Operation (RTO) using 5G mobile communications with special consideration for any human-factor user requirements for the RTO workplace. During the demonstration, a rail vehicle was remotely controlled in two operating scenarios involving remote-controlled shunting and remote-controlled driving for the purpose of clearing an open track on infrastructure in Schlettau (Saxony, Germany) from an RTO workplace situated at the German Aerospace Center (DLR) in Braunschweig

Remote Control of Automation: Workload, Fatigue, and Performance in Unattended Railway Operation

Human failure performance of staff working in increasingly automated work environments, particularly control rooms, has been reported to deteriorate along with related constructs such as workload and fatigue once degrees of automation increased. At the same time, mediation effects of domain‐specific contextual factors were stressed, questioning the generalizability of the proposed automation effects onto domain‐specific applications. This thesis examined whether a specific contextual factor—monotony—mediated the proposed detrimental effects of degrees of automation on workload, fatigue, and human failure performance in domain‐specific railway automation, described by the taxonomy of grades of railway automation. Empirical evidence on workload, fatigue, and human failure performance, obtained in multiple laboratory studies with professional train drivers participating in simulated work at three different grades of railway automation, was presented. In line with the hypotheses, results showed detrimental effects of intermediate grades of railway automation in combination with increasing monotony on workload, fatigue, and human failure performance. Further results showed beneficial effects of the domain‐specific decrease in monotony on workload, fatigue, and human failure performance at high grades of railway automation featuring remote control and recovery of unattended railway operation. The presented results stress the impact of domain‐ specific contextual factors, such as monotony in railway automation, on automation effects. Task characteristics were shown to play a major role in shaping workload, fatigue, and human failure performance. The influence of contextual factors on the generalizability of established effects of degrees of automation on workload, fatigue and human failure performance is discussed

DLR-WAT: Ein Instrument zur Untersuchung des optimalen Beanspruchungsniveaus in hochautomatisierten Mensch-Maschine-Systemen

In diesem Artikel wird das „DLR - Workload Assessment Tool“ (DLR–WAT) vorgestellt, ein Fragebogen zur Selbsteinschätzung der Beanspruchung, der gezielter als bestehende Messwerkzeuge wie z.B. der NASA-TLX Abweichungen von einem subjektiven Optimum der Beanspruchung berücksichtigt. Automatisierung und Digitalisierung sind derzeit die bestimmenden Trends im Personen- und Güterverkehr. Ob auf der Straße, der Schiene oder in der Luft, die Rolle des Menschen im Verkehr wandelt sich im Angesicht dieser Entwicklungen rapide. Technische Assistenzen unterstützen den Menschen und hochautomatisierte Systeme übernehmen in einzelnen Bereichen bereits Aufgaben des Menschen wie z.B. die Steuerung und Navigation. Die aktive Rolle des Menschen, zum Beispiel in der Rolle des Fahrers, wandelt sich sukzessive hin zu einer passiveren Rolle, die durch kontinuierliches Überwachen geprägt ist. Generell kann der Mensch durch Automation entlastet und sein Komfort gesteigert werden. Die Anforderung an den Menschen, seine Aufmerksamkeit konsequent aufrecht zu erhalten, auch wenn er nicht handeln muss, um in unsicheren oder kritischen Situationen kurzfristig in der Lage zu sein, die Steuerung zu übernehmen, stellt allerdings eine Schattenseite des Automationskomforts dar. Unter der Berücksichtigung der zunehmend passiven Rolle des Menschen in hochautomatisierten Verkehrssystemen, gewinnt das Thema der Unterbeanspruchung mit wachsender Automatisierung an Bedeutung. Erhebungsinstrumente zur subjektiven Einschätzung der eigenen Beanspruch differenzieren bislang jedoch nicht klar zwischen einem Unterforderungs- und einem Überforderungsbereich. Vor diesem Hintergrund wurde der DLR-WAT entwickelt. Der DLR–WAT umfasst insgesamt acht Subskalen. Auf sechs der acht Subskalen (Beanspruchung durch Informationsaufnahme, Beanspruchung durch Wissensabruf, Beanspruchung durch Entscheidungsfindung, motorische und körperliche Beanspruchung, zeitliche Beanspruchung und Anstrengung) kann der Befragte seinen Beanspruchungszustand angeben, ausgehend von seinem persönlichen Optimum, das in der Mitte jeder Subskala verortet ist. Die zwei weiteren Subskalen des DLR-WAT (Frustration, Aufgabenbewältigung) sind eindimensional gestaltet, da ein optimales Frustrationsniveau durch Abwesenheit der Frustration gekennzeichnet ist und es nicht möglich ist, eine Aufgabe weniger als gar nicht zu bewältigen. Die Berücksichtigung des persönlichen Optimums der Beanspruchung im DLR–WAT in den ersten sechs Subskalen ermöglicht im Zuge der Entwicklung hochautomatisierter Verkehrssysteme eine detailliertere Beanspruchungsanalyse als bestehende Fragebögen. Dies eröffnet in der Gestaltung zukünftiger interaktiver Verkehrssysteme die Chance, den schmalen Grat zwischen Überforderung und Unterforderung zielgerichtet zu identifizieren und Aufgaben zwischen Mensch und Automation entsprechend zu verteilen

Railway Simulators at the German Aerospace Center - Testing, Research and Development

The presentation includes an overview of the existing rail simulation technology at the Institute for Transportation Systems of the DLR. In Addition, current projects making use of this infrastructure as well as a future Outlook are provided