Search Results for author: Karl Kurzer

Found 6 papers, 0 papers with code

Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning

no code implementations12 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.

Decision Making

Parallelization of Monte Carlo Tree Search in Continuous Domains

no code implementations30 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.

Trajectory Planning

Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search

no code implementations2 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.

Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

no code implementations10 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.

Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States

no code implementations10 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.

Autonomous Driving

Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search

no code implementations25 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.

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