=Paper=
{{Paper
|id=Vol-2905/paper3
|storemode=property
|title=Towards a Comprehensive Understanding of Stakeholder Requirements for Automated Road Transport Logistics
|pdfUrl=https://ceur-ws.org/Vol-2905/paper3.pdf
|volume=Vol-2905
|authors=Peter Fröhlich,Michael Gafert,Lisa Diamond,Martin Reinthaler,Matthias Neubauer,Florian Hammer,Sami Koskinen
|dblpUrl=https://dblp.org/rec/conf/chi/FrohlichGDRNHK21
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==Towards a Comprehensive Understanding of Stakeholder Requirements for Automated Road Transport Logistics==
https://ceur-ws.org/Vol-2905/paper3.pdf
Towards a Comprehensive Understanding of Stakeholder Requirements for
Automated Road Transport Logistics
PETER FRÖHLICH, AIT Austrian Institute of Technology, peter.froehlich@ait.ac.at
MICHAEL GAFERT, AIT Austrian Institute of Technology, michael.gafert@ait.ac.at
LISA DIAMOND, AIT Austrian Institute of Technology, lisa.diamond@ait.ac.at
MARTIN REINTHALER, AIT Austrian Institute of Technology, martin.reinthaler@ait.ac.at
MATTHIAS NEUBAUER, University of Applied Sciences Upper Austria, matthias.neubauer@fh-steyr.at
FLORIAN HAMMER, Linz Center of Mechatronics, florian.hammer@lcm.at
SAMI KOSKINEN, VTT Technical Research Centre of Finland Ltd, sami.koskinen@vtt.fi
The logistics domain promises to be a first ground for the roll-out and business integration of automated vehicles, but so far
the needs and expectations by the manifold stakeholders have only been analyzed to a limited degree. This paper describes
a framework for capturing the requirements for automated road transport logistics, which integrates a comprehensive
stakeholder taxonomy, operational factors and key scenarios, as well as a tailored acceptance factors model. We conclude
with considerations for mixed-methods data capture and an outlook towards next steps of research.
CCS CONCEPTS • Social and professional topics → Automation.
Additional Keywords and Phrases: automated vehicles, requirements gathering, automated road transport
logistics
1 INTRODUCTION
Automation is introduced in just about any domain, and some of the most prominent examples are currently
demonstrated in transport and workplace environments [4]. The area of logistics has been affected by this trend
since a long time, and already now many specialized areas operate with systems for automated loading or
warehouse management [8]. A wide introduction of automated vehicles could be achieved sooner in freight
transport and logistics than in passenger transport, because environments are more controllable and thus
suitable for the operation of connected and automated vehicles within different parts of supply chains (e.g.
factories, warehouse, airports, ports and other logistics hubs). Fewer vulnerable road users, low driving speeds
and a well-defined layout of logistics areas are typical and useful characteristics of such areas. Furthermore,
autonomous commercial vehicles aim at increasing freight transport capacity through 24/7 driverless operation.
Acceptance by various stakeholders has been widely recognized as an overarching requirement for a
successful and responsible introduction of automated road transport logistics (ATL), asides managing risks of
reduction of operational flexibility, high initial costs, security and vulnerability [1]. First approaches towards a
systematic investigation of requirements have been undertaken, and test fields and innovation laboratories have
been set up that specialize on automated road transport logistics use cases, their further development and
certification [6]. Apart from these preliminary research efforts, a comprehensive and systematic level of
understanding the requirements for automated driving within this field of interwoven stakeholder groups and
Workshop proceedings Automation Experience at the Workplace
In conjunction with CHI'21, May 7th, 2021, Yokohama, Japan
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Website: http://everyday-automation.tech-experience.at
value chains has only partly been achieved. The European research and development project AWARD1 has
set out to close this gap. The project gathers 29 leading institutions who develop and deploy safe and efficient
connected and automated heavy-duty vehicles in real-life logistics operations. The requirements for such novel
systems are explored within a range of real-world applications and with different types of purpose-built vehicles
and fleet management system prototypes. The knowledge gained from real-world operations shall help to
validate solutions for example in terms of functional safety, availability, efficiency, scalability, or cost-benefits
for hub operators or fleets.
This paper outlines the underlying considerations and methodological approach for a stakeholder requirements
analysis within AWARD that systematically integrates three key dimensions: stakeholder types, operational
scenarios and acceptance factors. Section 2 introduces open issues and remaining challenges for a holistic,
systematic and comprehensive analysis of stakeholder requirements for ATL. Section 3 presents a taxonomy
of ATL stakeholders and section 4 introduces the considerations and cross-relationships with use cases and
the defined operational design domain. Section 5 then describes a framework for investigating expectations
and future acceptance. The paper concludes with an outline on the ongoing data capturing activities and issues
encountered in times of COVID-19.
2 CHALLENGES FOR THE REQUIREMENTS ANALYSIS OF AUTOMATED ROAD TRANSPORT
LOGISTICS
Trends such as digitalization, automation and Industry 4.0 also transform the work roles in the logistics sector.
Cimini et al [2] introduce the paradigm of the “Logistics Operator 4.0”, who is highly skilled and is supported by
various advanced technologies, such as smart supervisory control of increasingly automated functions as well
as by task assistance and augmentation. However, at the current stage most innovations are focusing on
automating single vehicles, which leaves uncertainty about where, how often, and how humans should be
enabled to configure, monitor, or intervene with automated vehicles. When integrating automated vehicle fleets,
it is likely that also here the automation paradox can be observed: the less humans are involved in automated
processes, the more crucial is their involvement in the planning, refinement and intervention [1]. Another aspect
that is specific to the logistics sector, is the highly specialized and multifaceted appearance and behavior of
vehicles and machinery used for a large variety of mobility and goods handling tasks, ranging from long-haul
transport to small distances between hubs and intralogistics operations. Here, also other automated tasks such
as loading and unloading and warehouse robotics are extending and interfacing with transportation tasks.
Given these novel developments and transitions of new work roles and use cases, we are missing a sufficiently
systematic analysis on the requirements from different operators and stakeholders of automated road transport
logistics. Most fundamentally, no reference frame is available that could help to categorize involved ATL
stakeholders of the dynamically developing ecosystem, value chains and work role models, in order to enable
a common ground for communication. Furthermore, so far, the structured empirical analysis of expectations of
future users towards automated road transport logistics, has most often been restricted to singular use cases
such as the future workplace in a truck. As a notable exception, Neubauer et al. [9] investigated different
stakeholder groups on the institutional level, but they did not break this further down towards the need for direct
operation of the system.
1
H2020 Project AWARD: www.award-h2020.eu/
2
3 THE STAKEHOLDER PERSPECTIVE
Figure 1 shows the stakeholder taxonomy that has been derived from expert-based consultations among ATL
experts from the 29 partners of the AWARD project. The taxonomy is divided into three main categories. Direct
process participants are those persons who get in touch with or are affected by automated vehicles. This
includes staff remotely managing the vehicles, persons close to the vehicle working in a logistics hub or
production site, as well as other road users on public roads. For human–computer interaction (HCI), this group
is the most relevant one, as it is related to direct contact of human operators and technology. However, for a
more holistic discussion of requirements for logistics processes, also indirect process participants are relevant,
which go beyond those who directly are in remote or on-site contact with an automated vehicle. Then, beyond
a concrete logistics process, those groups of persons and institutions are listed who should an overarching
economic or social interest in (the future use of) ATL.
Figure 1: Stakeholder Taxonomy for automated road transport logistics
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4 THE OPERATIONS PERSPECTIVE
In order to demonstrate and evaluate the technical improvements for all-weather operation of automated
vehicles, the AWARD concept includes specific real-world use cases. The use cases address vehicle tasks in
different settings, from operational area to public roadways as well as with different automated vehicles and
users. The AWARD project aims at demonstrating the automated vehicles working in all weather conditions and
addressing challenges related to the deployment of these vehicles in real logistics operations through several
strategic use cases that meet market needs, from the factory to logistics hubs.
Figure 2 describes the general scope of the use cases and related tests planned within the AWARD project.
For each use case, ODD (Operational Design Domain) elements and associated parties (users and
stakeholders) are defined. Each use case is linked to several operational scenarios, where the use case will be
tested in a specific situation. The purpose of a use case description is to have an initial, semi-formal description
of the use of the system and the test details. Subsequently, use case support to derive formal and more detailed
operational scenarios. Use cases are applicable for any abstraction level within the system hierarchy to capture
the needs of the stakeholders, e.g. the AD vehicle in its environment in certain situation to fulfill a specific
purpose or intended behavior. Operational scenarios are refined, more detailed, formalized and structured
descriptions of the system of interest in its context (environment, situation) to validate a use case. Operational
scenarios can be used to trigger for requirements elicitation, detailed system development, test case variation
and test development.
Loading and transport with automated forklift Shuttle service from production site to logistics hub
Automated baggage tractor on airside Container transfer operations and boat loading
Figure 2: Key four use cases for automated road transport logistics discussed in AWARD
5 THE ACCEPTANCE PERSPECTIVE
Several technology acceptance models have been developed, adapted and extended in recent decades to
improve understanding of the processes underlying technology acceptance and to clarify the factors and
antecedents that clearly influence the acceptance of different types of technologies. Most prominent among
these models are the Technology Acceptance Model TAM, developed by Davis [3], that established “Perceived
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Usefulness” (U), “Perceived Ease of Use” (E) and “Behavioral Intention to Use” (BI) as core factors indicating
actual system use. This model was extended to TAM2 [12] and TAM3 [11], which include “Social/Subjective
Norms”, “Experience” and “Voluntariness” as further factors impacting BI, as well as a growing list of
antecedents to U and E. The Unified Theory of Acceptance and Use of Technology Model UTAUT [13] was an
attempt to improve the TAM by integrating it with a number of existing related models, therefore increasing the
explanatory powers and simplifying model choice for researchers.
These models serve as a base to assess user acceptance and understand the importance of a variety of factors
in shaping acceptance. They have been applied in a wide range of contexts including automotive technologies,
most prominently the C-TAM [10], which builds on the U-TAUT and extends it to include several trust-related
factors (towards the technology and oneself). Neubauer and Schauer [9] took a closer look at core acceptance
factors for automated road transport logistics through the development of scenarios and stakeholder interviews.
They identified “Perceived Usefulness”, “Job Relevancy” (as in clear definition of new job profiles & related
training), “Social Dimension” (as in acceptance by different stakeholder groups), and “Perceived Safety” as
factors central to the acceptance of automated road transport logistics. The authors further emphasized the
importance of clear communication in order to align expectations and technology performance, as well as
careful consideration of appropriateness of automation.
Based on these insights, we have developed an automated road transport logistics acceptance model
(ARTLAM) which includes the four dimensions emphasized by Neubauer & Schauer and the traditional ease of
use factor that we expect to be a sensitive and relevant acceptance component in this context. We have further
extended both safety and job relevancy into the broader concepts of trustworthiness and facilitating conditions,
therefore incorporating some of the spirit of the C-TAM, as well as learnings from behavioral models that point
to the high impact of situations constraints on adopting behaviors [7]. The developed model, on which serves
as the base for the data capturing activities and the derivation of requirements insights is depicted in Figure 3.
Figure 3: The Automated road transport logistics Acceptance Model (ARTLAM) developed for the Requirements Analysis
6 CAPTURING REQUIREMENTS FROM MULTIPLE PERSPECTIVES
In order to address the challenges for requirements analysis of automated road transport logistics (cf. section
2), an empirical requirements elicitation method has been designed that incorporates the stakeholder,
operations and expectations perspectives. To capture the data necessary for gaining insights related to the
ARTLAM factors, a mixed-methods approach is used that shall enable for quantitative modelling of expectations
factors across different stakeholder groups, as well as for deep insights into the workplace requirements of
5
future automated logistics. Contextual inquiry studies are conducted with the direct process participants within
the current work context at the selected use case test sites introduced in section 0, in combination with
contextual interviews structured along the ARTLAM factors. In order to complement this in-depth research with
a broader coverage across regions and a larger number of representatives of all stakeholder categories, an
electronic survey was developed. In order to take the perspectives of different stakeholders and their operational
expertise into account, respondents select the stakeholder category and the use case and are then asked
specific questions related to the ARTLAM factors. In order to discuss needs and requirements on an expert
consultation basis, workshops are being conducted among representatives of the stakeholder groups.
7 CONCLUSIONS
Acceptance of all involved stakeholders is considered a main success factor for the introduction of automated
transport in logistics. This paper introduces a new requirements elicitation framework that builds on a
comprehensive stakeholder taxonomy, integrates the operations perspective and enables qualitative and
quantitative capturing of expectations, both for directly affected human operators in their actual usage context
and on a broader regional and societal level. The proposed mixed-methods approach is expected to provide a
robust means for coping with constraints on-site investigation possibilities in the context of the COVID-19
outbreak, and it invites for the development of novel cross-media approaches for gathering contextual insights
at virtual site visits. The analysis of the currently gathered data will feed into the validation of the proposed
ARTLAM framework and will be elaborated for wider refinement.
ACKNOWLEDGMENTS
This work was performed within the European R&D project AWARD. The project has received funding from the
European Union’s Horizon 2020 research and innovation programme under grant agreement No 101006817.
The content of this paper reflects only the author’s view. Neither the European Commission nor CINEA is
responsible for any use that may be made of the information it contains.
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