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Found 6 papers, 0 papers with code
Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
In this work, we propose a method to employ a trained deep reinforcement learning policy for dedicated high-level behavior planning.
Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks
The present work proposes to leverage reinforcement learning and a graph-based scene representation for cooperative multi-agent planning.
An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management
In this work, we build upon a previously presented graph-based scene representation and graph neural network to approach the problem using reinforcement learning.
Cooperative Behavior Planning for Automated Driving using Graph Neural Networks
Learning-based behavior planning poses several challenges, demanding for a suited input and output representation as well as large amounts of ground-truth data.
Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks
To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed.
A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions
Already today, driver assistance systems help to make daily traffic more comfortable and safer.
