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

2025-04-13

AI Summary

Autonomous driving systems frequently exhibit safety-critical failures in long-tail, extreme scenarios, which remain poorly covered by conventional testing—leading to insufficient pre-deployment validation. To address this, we propose ADDT, a high-fidelity digital twin framework for autonomous driving. ADDT introduces the first reinforcement learning–driven adaptive edge-case exploration mechanism, shifting safety verification from passive debugging to proactive, simulation-driven assurance. The framework integrates photorealistic environment modeling, high-fidelity vehicle dynamics simulation, physics-based multi-sensor modeling, and controllable fault injection—enabling scalable, reproducible, and auditable industrial-grade stress testing. Experimental evaluation demonstrates that ADDT significantly improves detection rates of rare failure modes and comprehensively covers extreme operational conditions inaccessible to physical road testing. The source code and toolchain are publicly released.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Proactively identify hidden faults in autonomous driving systems
• Evaluate real-time performance under diverse adverse conditions
• Validate safety before deployment using high-fidelity simulation

Innovation

• High-fidelity simulation platform for safety validation
• Reinforcement-driven edge case exploration
• Open-source framework for scalable testing