Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology. To address this issue, this study proposes the Safedrive Dreamer navigation framework, which aims to reduce...

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Bibliographic Details
Main Authors: Haitao Li, Tao Peng, Bangan Wang, Ronghui Zhang, Bolin Gao, Ningguo Qiao, Zhiwei Guan, Jiayin Li, Tianyu shi
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Alexandria Engineering Journal
Subjects:
Reinforcement learning
World models
Reality gap
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S1110016824011943
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