A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation
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 ...
| Authors | Yongzhi Liu |
|---|---|
| Primary category | cs.RO |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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