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...

Szczegółowa specyfikacja

Opis bibliograficzny

Główni autorzy:	Changmeng Hou, Wei Zhang
Format:	Artigo
Język:	Inglês
Wydane:	IEEE 2024-01-01
Seria:	IEEE Access
Hasła przedmiotowe:	End-to-end autonomous driving deep reinforcement learning probabilistic graphical model safety critic interpretability
Dostęp online:	https://ieeexplore.ieee.org/document/10679712/
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