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End-to-End Urban Autonomous Driving With Safety Constraints

End-to-end autonomous driving systems aim to address the perception, decision-making, and control challenges in a self-contained way, facilitating easier adaptation to new scenarios and improved scalability. However, existing end-to-end approaches often lack safety constraints and are limited to sim...

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Principais autores: Changmeng Hou, Wei Zhang
פורמט: Artigo
שפה:Inglês
יצא לאור: IEEE 2024-01-01
סדרה:IEEE Access
נושאים:
End-to-end autonomous driving
deep reinforcement learning
probabilistic graphical model
safety critic
interpretability
גישה מקוונת:https://ieeexplore.ieee.org/document/10679712/
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