Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations

Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations

Despite the substantial advancements in reinforcement learning (RL) in recent years, ensuring trustworthiness remains a formidable challenge when applying this technology to safety-critical autonomous driving domains. One pivotal bottleneck is that well-trained driving policy models may be particula...

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Bibliographic Details
Main Authors: He, Xiangkun, Huang, Wenhui, Lv, Chen
Other Authors: School of Mechanical and Aerospace Engineering
Format: Journal Article
Language:English
Published: 2024
Subjects:
Engineering
Autonomous vehicle
Traffic safety
Online Access:https://hdl.handle.net/10356/179385

Internet

https://hdl.handle.net/10356/179385

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