  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.
