# Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/1807.09530 · HTML: https://mlsanity.com/paper/1807.09530/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [Optimizing delegation between human and AI collaborative agents](https://mlsanity.com/paper/2309.14718/) (2023)
- [Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.12717/) (2023)
- [Optimizing Delegation in Collaborative Human-AI Hybrid Teams](https://mlsanity.com/paper/2402.05605/) (2024)
- [HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions](https://mlsanity.com/paper/2510.12733/) (2025)
- [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://mlsanity.com/paper/2102.03053/) (2021)
- [Optimizing Risk-averse Human-AI Hybrid Teams](https://mlsanity.com/paper/2403.08386/) (2024)
- [Simultaneous Computation with Multiple Prioritizations in Multi-Agent Motion Planning](https://mlsanity.com/paper/2501.10781/) (2025)

## Abstract (arXiv)

Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments.