Cooperation for Scalable Supervision of Autonomy in Mixed Traffic
This addresses the safety-critical challenge of deploying autonomy at scale in domains like transportation, offering a framework that could apply broadly, though it is incremental in focusing on a specific merging scenario.
The paper tackles the problem of scalable human supervision for autonomous vehicles in mixed traffic, showing that cooperation among AVs can improve supervision reliability by orders of magnitude and reduce the number of supervisors needed per AV as AV adoption increases.
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.