A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation
This paper proposes a risk-field enhanced closed-loop digital twin framework for safety validation of autonomous driving systems. The framework integrates physical data acquisition, virtual reconstruction, risk-aware scenario generation, and algorithm evaluation, using a driving risk field as a unified intermediate representation to identify high-risk scenarios and provide safety guidance for reinforcement learning policies. Experiments show the method improves targeted validation and interpretability, but its effectiveness is bounded by model fidelity and sim-to-real transfer.
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[Submitted on 7 Jul 2026]
Title:A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation
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Abstract:Autonomous driving systems require reliable safety validation before real-world deployment. However, large-scale road testing is costly, difffcult to reproduce, and inefffcient for exposing rare safety-critical scenarios. Conventional simulation improves repeatability, but an offfine simulator alone cannot continuously connect physical trafffc states, virtual reconstruction, algorithm evaluation, and scenario evolution. This paper proposes a risk-ffeld enhanced closed-loop digital twin framework for autonomous driving safety validation. The framework integrates physical data acquisition, data synchronization, virtual twin reconstruction, risk-aware scenario generation, autonomous driving algorithm evaluation, and safety analysis. A driving risk ffeld is introduced as a uniffed intermediate representation to describe obstacle, lane-departure, road-boundary, time-to-collision, and comfort-related risks around the ego vehicle. The risk ffeld ranks high-risk scenarios in the digital twin scenario library and provides dense safety guidance for reinforcement learning-based driving policies. A simulation-style evaluation protocol is designed to compare conventional reinforcement learning baselines, risk-penalty baselines, and the proposed risk-ffeld guided method. The study indicates that embedding explicit risk structure into digital twins can make autonomous driving validation more targeted, interpretable, and reusable, while its practical effectiveness remains bounded by model ffdelity, risk calibration, and sim-to-real transfer.
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2607.09772 [cs.RO]
(or arXiv:2607.09772v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.09772
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yongzhi Liu [view email] [v1] Tue, 7 Jul 2026 14:33:49 UTC (8,574 KB)
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