# Cooperation for Scalable Supervision of Autonomy in Mixed Traffic

arXiv: https://arxiv.org/abs/2112.07569 · HTML: https://mlsanity.com/paper/2112.07569/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 87 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Safe Model-based Reinforcement Learning with Stability Guarantees](https://mlsanity.com/paper/1705.08551/) (2017)
- [Quantifying Generalization in Reinforcement Learning](https://mlsanity.com/paper/1812.02341/) (2018)
- [A Lyapunov-based Approach to Safe Reinforcement Learning](https://mlsanity.com/paper/1805.07708/) (2018)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances](https://mlsanity.com/paper/1709.07523/) (2017)
- [Trial without Error: Towards Safe Reinforcement Learning via Human Intervention](https://mlsanity.com/paper/1707.05173/) (2017)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving](https://mlsanity.com/paper/1904.07189/) (2019)
- [On Infusing Reachability-Based Safety Assurance within Planning Frameworks for Human-Robot Vehicle Interactions](https://mlsanity.com/paper/2012.03390/) (2020)
- [Scaled Autonomy: Enabling Human Operators to Control Robot Fleets](https://mlsanity.com/paper/1910.02910/) (2019)
- [Probabilistic Guarantees for Safe Deep Reinforcement Learning](https://mlsanity.com/paper/2005.07073/) (2020)
- [A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety](https://mlsanity.com/paper/1904.02697/) (2019)
- [A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior](https://mlsanity.com/paper/2006.08832/) (2020)
- [Scalable Operator Allocation for Multi-Robot Assistance: A Restless Bandit Approach](https://mlsanity.com/paper/2111.06437/) (2021)
- [Robust Supervisors for Intersection Collision Avoidance in the Presence of Uncontrolled Vehicles](https://mlsanity.com/paper/1603.03916/) (2016)

## Built on by (most cited first, 4 of 4)

- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems](https://mlsanity.com/paper/2507.09836/) (2025)
- [Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark](https://mlsanity.com/paper/2501.16728/) (2025)

## Abstract (arXiv)

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.