# Cooperative Trajectory Planning in Uncertain Environments with Monte Carlo Tree Search and Risk Metrics

arXiv: https://arxiv.org/abs/2203.04452 · HTML: https://mlsanity.com/paper/2203.04452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [How Should a Robot Assess Risk? Towards an Axiomatic Theory of Risk in Robotics](https://mlsanity.com/paper/1710.11040/) (2017)
- [Online Planning Algorithms for POMDPs](https://mlsanity.com/paper/1401.3436/) (2014)
- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search](https://mlsanity.com/paper/1809.03200/) (2018)
- [Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search](https://mlsanity.com/paper/2002.00497/) (2020)

## Abstract (arXiv)

Automated vehicles require the ability to cooperate with humans for smooth integration into today's traffic. While the concept of cooperation is well known, developing a robust and efficient cooperative trajectory planning method is still a challenge. One aspect of this challenge is the uncertainty surrounding the state of the environment due to limited sensor accuracy. This uncertainty can be represented by a Partially Observable Markov Decision Process. Our work addresses this problem by extending an existing cooperative trajectory planning approach based on Monte Carlo Tree Search for continuous action spaces. It does so by explicitly modeling uncertainties in the form of a root belief state, from which start states for trees are sampled. After the trees have been constructed with Monte Carlo Tree Search, their results are aggregated into return distributions using kernel regression. We apply two risk metrics for the final selection, namely a Lower Confidence Bound and a Conditional Value at Risk. It can be demonstrated that the integration of risk metrics in the final selection policy consistently outperforms a baseline in uncertain environments, generating considerably safer trajectories.