Classifying Group Emotions for Socially-Aware Autonomous Vehicle Navigation
share
We present a real-time, data-driven algorithm to enhance the social-invisibility of autonomous robot navigation within crowds. Our approach is based on prior psychological research, which reveals that people notice and--importantly--react negatively to groups of social actors when they have negative group emotions or entitativity, moving in a tight group with similar appearances and trajectories. In order to evaluate that behavior, we performed a user study to develop navigational algorithms that minimize emotional reactions. This study establishes a mapping between emotional reactions and multi-robot trajectories and appearances and further generalizes the finding across various environmental conditions. We demonstrate the applicability of our approach for trajectory computation for active navigation and dynamic intervention in simulated autonomous robot-human interaction scenarios. Our approach empirically shows that various levels of emotional autonomous robots can be used to both avoid and influence pedestrians while not eliciting strong emotional reactions, giving multi-robot systems socially-invisibility.
READ FULL TEXT