March 6 – 11 , 2022, Dagstuhl Seminar 22102

Computational Models of Human-Automated Vehicle Interaction


Martin Baumann (Universität Ulm, DE)
Shamsi Tamara Iqbal (Microsoft – Redmond, US)
Christian P. Janssen (Utrecht University, NL)
Antti Oulasvirta (Aalto University, FI)

For support, please contact

Dagstuhl Service Team


List of Participants
Shared Documents
Dagstuhl Seminar Schedule [pdf]


The capabilities of automated vehicles are rapidly increasing, and are changing human interaction considerably. Despite this technological progress, the path to fully self-driving vehicles without any human intervention is long, and for the foreseeable future human interaction is still needed with automated vehicles. The principles of human-automation interaction also guide the future outlook of the European Commission.

Human-automated vehicle interaction can take at least two forms. One form is a partnership, in which the human and the automated vehicle both contribute in parallel to the control of the vehicle. Another form is in transitions of control, where the automated system at times takes over full control of the vehicle, but transitions control back to the human when desired by the human, or when required due to system limitations. For both the partnership and the transition paradigm it is beneficial when the car and the human have a good model of each other’s capabilities and limitations. Accurate models can make clear how tasks are distributed between the human and the machine. This helps avoid misunderstandings, or mode confusion, and thereby reduces the likelihood of accidents and incidents.

A key tool in this regard is the use of computational (cognitive) models: computational instantiations that simulate the human thought process and/or their interaction with an automated vehicle. Computational models build on a long tradition in cognitive science, human factors and human-computer interaction, neuroscience, and AI and engineering. By now, there are a wide set of varieties that can be applied to different domains, ranging from constrained theoretical problems to capturing real-world interaction. Computational models have many benefits, ranging from enforcing a working ethic of "understanding by building" to testing "what if" scenarios. For human-automated vehicle interaction in particular, it allows testing of future adaptive systems that are not yet on the road.

Automated driving is a domain where computational models can be applied. Three approaches have only started to scratch the surface. First, the large majority of models focus on engineering aspects (e.g., computer vision, sensing the environment, flow of traffic) that do not consider the human extensively. Second, models that focus on the human mostly capture manual, non-automated driving. Third, models about human interaction in automated vehicles are either conceptual or qualitative, and do not benefit from the full set of advantages that computational models offer.

In summary, there is a disconnect between the power and capabilities that computational models offer for the domain of automated driving, and today’s state-of-the-art research. This is due to a set of broad challenges that the field is facing and that need to be tackled over the next 3 to 10 years. These challenges include the following:

  • Challenge 1: What phenomena and driving scenarios need to be captured?
  • Challenge 2: What technical capabilities do computational models possess?
  • Challenge 3: How can models benefit from advances in AI while avoiding pitfalls?
  • Challenge 4: What insights are needed for empirical research?
  • Challenge 5: How can models inform design and governmental policy?

The aim of this Dagstuhl Seminar is to further identify and specify the methods and challenges that the field has, to inform a roadmap for research to solve them. We look forward to work with top researchers and practitioners from academia, industry, and government on this exciting field.

Motivation text license
  Creative Commons BY 4.0
  Martin Baumann, Shamsi Tamara Iqbal, Christian P. Janssen, and Antti Oulasvirta

Related Dagstuhl Seminar


  • Artificial Intelligence
  • Human-Computer Interaction
  • Machine Learning


  • Human-automation interaction
  • Computational models
  • Automated vehicles


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


Download overview leaflet (PDF).

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.


Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.