no code implementations • 25 Jul 2018 • Karl Kurzer, Chenyang Zhou, J. Marius Zöllner
This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments.
no code implementations • 10 Sep 2018 • Peter Wolf, Karl Kurzer, Tobias Wingert, Florian Kuhnt, J. Marius Zöllner
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.
1 code implementation • 10 Sep 2018 • Karl Kurzer, Florian Engelhorn, J. Marius Zöllner
Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency.
no code implementations • 2 Feb 2020 • Karl Kurzer, Marcus Fechner, J. Marius Zöllner
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.
1 code implementation • 30 Mar 2020 • Karl Kurzer, Christoph Hörtnagl, J. Marius Zöllner
Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari.
no code implementations • 12 Feb 2021 • Karl Kurzer, Philip Schörner, Alexander Albers, Hauke Thomsen, Karam Daaboul, J. Marius Zöllner
Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability.
1 code implementation • 14 Feb 2022 • Karl Kurzer, Matthias Bitzer, J. Marius Zöllner
Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants.
no code implementations • 9 Mar 2022 • Philipp Stegmaier, Karl Kurzer, J. Marius Zöllner
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.