  Advances in autonomy offer the potential for dramatic positive outcomes in a
number of domains, yet enabling their safe deployment remains an open problem.
This work's motivating question is: In safety-critical settings, can we avoid
the need to have one human supervise one machine at all times? The work
formalizes this scalable supervision problem by considering remotely located
human supervisors and investigating how autonomous agents can cooperate to
achieve safety. This article focuses on the safety-critical context of
autonomous vehicles (AVs) merging into traffic consisting of a mixture of AVs
and human drivers. The analysis establishes high reliability upper bounds on
human supervision requirements. It further shows that AV cooperation can
improve supervision reliability by orders of magnitude and counterintuitively
requires fewer supervisors (per AV) as more AVs are adopted. These analytical
results leverage queuing-theoretic analysis, order statistics, and a
conservative, reachability-based approach. A key takeaway is the potential
value of cooperation in enabling the deployment of autonomy at scale. While
this work focuses on AVs, the scalable supervision framework may be of
independent interest to a broader array of autonomous control challenges.
