Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability.
Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari.
Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others.
Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency.
This ensures a consistent model of the environment across scenarios as well as a behavior adaptation function, enabling on-line changes of desired behaviors without re-training.
This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments.