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Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a challenging and less examined research problem.
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network
In this work, a deep reinforcement learning based decision-making approach for high-level driving behavior is proposed for urban environments in the presence of pedestrians.
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