Abstract

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.