Unfreezing autonomous vehicles with game theory, proxemics, and trust

Fanta Camara*, Charles Fox

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Recent years have witnessed the rapid deployment of robotic systems in public places such as roads, pavements, workplaces and care homes. Robot navigation in environments with static objects is largely solved, but navigating around humans in dynamic environments remains an active research question for autonomous vehicles (AVs). To navigate in human social spaces, self-driving cars and other robots must also show social intelligence. This involves predicting and planning around pedestrians, understanding their personal space, and establishing trust with them. Most current AVs, for legal and safety reasons, consider pedestrians to be obstacles, so these AVs always stop for or replan to drive around them. But this highly safe nature may lead pedestrians to take advantage over them and slow their progress, even to a complete halt. We provide a review of our recent research on predicting and controlling human–AV interactions, which combines game theory, proxemics and trust, and unifies these fields via quantitative, probabilistic models and robot controllers, to solve this “freezing robot” problem.

Original languageEnglish
Article number969194
Number of pages18
JournalFrontiers in Computer Science
Publication statusPublished - 13 Oct 2022

Bibliographical note

Funding Information:
This work has received funding from EU H2020 interACT: Designing cooperative interaction of automated vehicles with other road users in mixed traffic environments under grant agreement no. 723395 and from InnovateUK grant 5949683 C19-ADVs.

Publisher Copyright:
Copyright © 2022 Camara and Fox.


  • autonomous vehicles
  • freezing robot
  • game theory
  • interactions
  • pedestrians
  • proxemics
  • robotics
  • trust

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