# ADDT -- A Digital Twin Framework for Proactive Safety Validation in Autonomous Driving Systems

arXiv: https://arxiv.org/abs/2504.09461 · HTML: https://mlsanity.com/paper/2504.09461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [Causal Parametric Drift Simulation: A Digital Twin Framework for Classifier Robustness Evaluation](https://mlsanity.com/paper/2605.09663/) (2026)

## Abstract (arXiv)

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.