Socially Invisible Navigation for Intelligent Vehicles

We present a real-time, data-driven algorithm to enhance the social-invisibility of autonomous vehicles 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 high 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 entitativity. This study establishes 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 entitativity modeling for trajectory computation for active surveillance and dynamic intervention in simulated robot-human interaction scenarios. Our approach empirically shows that various levels of entitative robots can be used to both avoid and influence pedestrians while not eliciting strong emotional reactions, giving multi-robot systems socially-invisibility.

@article{Bera2018TheSI,
  title={The Socially Invisible Robot Navigation in the Social World Using Robot Entitativity},
  author={Aniket Bera and Tanmay Randhavane and Emily Kubin and Austin Wang and Kurt Gray and Dinesh Manocha},
  journal={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2018},
  pages={4468-4475}
}