Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

Advances in autonomy offer the potential for dra- matic 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 onehumansuperviseonemachineatalltimes?Theworkformalizes this scalable supervision problem by considering remotely located human supervisors and investigating how autonomous agents can cooperatetoachievesafety.Thisarticlefocusesonthesafety-critical context of autonomous vehicles (AVs) merging into traffic consist- ing 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 relia- bility 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.