Search Results for author:
Found 8 papers, 3 papers with code
Cooperative Trajectory Planning in Uncertain Environments with Monte Carlo Tree Search and Risk Metrics
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.
Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree Search
Cooperative trajectory planning methods for automated vehicles can solve traffic scenarios that require a high degree of cooperation between traffic participants.
Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning
Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability.
Parallelization of Monte Carlo Tree Search in Continuous Domains
Monte Carlo Tree Search (MCTS) has proven to be capable of solving challenging tasks in domains such as Go, chess and Atari.
Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search
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.
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States
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.
Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search
Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency.
Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search
This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments.
