ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913791351980032 |
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| author | Yu, Bo Yuan, Chaoran Wan, Zishen Tang, Jie Kurdahi, Fadi Liu, Shaoshan |
| author_facet | Yu, Bo Yuan, Chaoran Wan, Zishen Tang, Jie Kurdahi, Fadi Liu, Shaoshan |
| contents | Autonomous driving systems continue to face safety-critical failures, often triggered by rare and unpredictable corner cases that evade conventional testing. We present the Autonomous Driving Digital Twin (ADDT) framework, a high-fidelity simulation platform designed to proactively identify hidden faults, evaluate real-time performance, and validate safety before deployment. ADDT combines realistic digital models of driving environments, vehicle dynamics, sensor behavior, and fault conditions to enable scalable, scenario-rich stress-testing under diverse and adverse conditions. It supports adaptive exploration of edge cases using reinforcement-driven techniques, uncovering failure modes that physical road testing often misses. By shifting from reactive debugging to proactive simulation-driven validation, ADDT enables a more rigorous and transparent approach to autonomous vehicle safety engineering. To accelerate adoption and facilitate industry-wide safety improvements, the entire ADDT framework has been released as open-source software, providing developers with an accessible and extensible tool for comprehensive safety testing at scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_09461 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems Yu, Bo Yuan, Chaoran Wan, Zishen Tang, Jie Kurdahi, Fadi Liu, Shaoshan Robotics Hardware Architecture Autonomous driving systems continue to face safety-critical failures, often triggered by rare and unpredictable corner cases that evade conventional testing. We present the Autonomous Driving Digital Twin (ADDT) framework, a high-fidelity simulation platform designed to proactively identify hidden faults, evaluate real-time performance, and validate safety before deployment. ADDT combines realistic digital models of driving environments, vehicle dynamics, sensor behavior, and fault conditions to enable scalable, scenario-rich stress-testing under diverse and adverse conditions. It supports adaptive exploration of edge cases using reinforcement-driven techniques, uncovering failure modes that physical road testing often misses. By shifting from reactive debugging to proactive simulation-driven validation, ADDT enables a more rigorous and transparent approach to autonomous vehicle safety engineering. To accelerate adoption and facilitate industry-wide safety improvements, the entire ADDT framework has been released as open-source software, providing developers with an accessible and extensible tool for comprehensive safety testing at scale. |
| title | ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems |
| topic | Robotics Hardware Architecture |
| url | https://arxiv.org/abs/2504.09461 |