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

TL;DR

This paper presents an advanced cooperative trajectory planning method for automated vehicles that explicitly models environmental uncertainties using Monte Carlo Tree Search and risk metrics, resulting in safer trajectories.

Contribution

It extends existing MCTS-based trajectory planning by incorporating uncertainty modeling and risk-aware decision metrics for improved safety in uncertain environments.

Findings

Risk-aware planning outperforms baseline in safety.

Explicit uncertainty modeling improves trajectory robustness.

Use of kernel regression for result aggregation enhances decision quality.

Abstract

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.