Computational Models of Human-Automated Vehicle Interaction

Motivation

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.