Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory

Annemarie Turnwald, Daniel Althoff, Dirk Wollherr, Martin Buss

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Being aware of mutual influences between individuals is a major requirement a robot to efficiently operate in human populated environments. This is especially true for the navigation among humans with its mutual avoidance maneuvers. While humans easily manage this task, robotic systems are still facing problems. Most of the recent approaches concentrate on predicting the motions of humans individually and deciding afterwards. Thereby, interactivity is mostly neglected. In this work, we go one step back and focus on understanding the underlying principle of human decision making in the presence of multiple humans. Non-cooperative game theory is applied to formulate the problem of predicting the decisions of multiple humans that interact which each other during navigation. Therefore, we use the theory of Nash equilibria in static and dynamic games where different cost functions from literature rate the payoffs of the individual humans. The approach anticipates collisions and additionally reasons about several avoidance maneuvers of all humans. For the evaluation of the game theoretic approach we recorded trajectories of humans passing each other. The evaluation shows that game theory is able to reproduce the decision process of humans more accurately than a decision model that predicts humans individually.

Original languageEnglish
Pages (from-to)331-351
Number of pages21
JournalInternational Journal of Social Robotics
Issue number2
StatePublished - 1 Apr 2016


  • Game theory
  • Human motion analysis
  • Interaction-aware decision making
  • Interactivity during navigation


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