2018/06/01 by Karl Kurzer, Chenyang Zhou, J. Marius Zöllner · 52 citations
Computer Science · Engineering · Mathematics · #Action (physics) #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Distributed computing #Machine learning #Macro #Mathematical optimization #Mathematics #Monte Carlo method #Monte Carlo tree search #Plan (archaeology) #Reinforcement Learning in Robotics #Robotic Path Planning Algorithms #Tree (set theory) #cs.AI
paper · pdf · doi:10.1109/ivs.2018.8500712
openalex publication_date 2018/06/01 · arxiv created 2018/07/25 · arxiv updated 2020/02/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments.