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DiDrive: A Risk-Aware Hierarchical Diffusion Framework for Safe Offline Reinforcement Learning in Autonomous Driving
Author1, Author2, Author3, Author4, Author5
offline reinforcement learningautonomous drivingrisk-awarediffusion models
2609.01609
Builder Relevance
2h ago80%
Abstract
DiDrive proposes a distribution-guided offline diffusion framework to enhance safety in autonomous driving by addressing challenges in offline reinforcement learning.
Reality Card
Core Claim
DiDrive achieves an 85% success rate and a 4295.68 average reward in complex traffic scenarios, outperforming existing baselines.
Method / Result
85% success rate in high-density traffic scenarios with 60 vehicles.
Limitations
The complexity of the framework may pose challenges for reproducibility in different environments.
Paper to code
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