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Pixel State Value Network for Combined Prediction and Planning in Interactive Environments
Automated vehicles operating in urban environments have to reliably interact with other traffic participants.
Planning on the fast lane: Learning to interact using attention mechanisms in path integral inverse reinforcement learning
Furthermore, the temporal attention mechanism learns persistent interaction with other vehicles over an extended planning horizon.
Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving
In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions.
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
Behavior and motion planning play an important role in automated driving.
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