TY - JOUR
T1 - Understanding Human Avoidance Behavior
T2 - Interaction-Aware Decision Making Based on Game Theory
AU - Turnwald, Annemarie
AU - Althoff, Daniel
AU - Wollherr, Dirk
AU - Buss, Martin
N1 - Publisher Copyright:
© 2016, The Author(s).
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
KW - Game theory
KW - Human motion analysis
KW - Interaction-aware decision making
KW - Interactivity during navigation
UR - http://www.scopus.com/inward/record.url?scp=84963782217&partnerID=8YFLogxK
U2 - 10.1007/s12369-016-0342-2
DO - 10.1007/s12369-016-0342-2
M3 - Article
AN - SCOPUS:84963782217
SN - 1875-4791
VL - 8
SP - 331
EP - 351
JO - International Journal of Social Robotics
JF - International Journal of Social Robotics
IS - 2
ER -