A Digital Twin Framework for Metamorphic Testing of Autonomous Driving Systems Using Generative Model

2025-10-08

AI Summary

Autonomous driving safety testing faces two fundamental challenges: insufficient coverage of realistic scenarios and the oracle problem. To address these, this paper proposes a digital twin–driven generative metamorphic testing framework. It integrates high-fidelity digital twin simulation environments with generative models (e.g., Stable Diffusion) to synthesize diverse, realistic driving scenes—varying weather conditions, road topologies, and other critical factors. We introduce a semantics-preserving scene mutation mechanism and traffic-rule–based metamorphic relations to enable automated, interpretable safety validation. Experimental evaluation on the Udacity simulator demonstrates substantial improvements in testing effectiveness: true positive rate of 0.719, F1-score of 0.689, and precision of 0.662—outperforming baseline methods significantly.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Ensuring the safety of self-driving cars remains a major challenge due to the complexity and unpredictability of real-world driving environments. Traditional testing methods face significant limitations, such as the oracle problem, which makes it difficult to determine whether a system's behavior is correct, and the inability to cover the full range of scenarios an autonomous vehicle may encounter. In this paper, we introduce a digital twin-driven metamorphic testing framework that addresses these challenges by creating a virtual replica of the self-driving system and its operating environment. By combining digital twin technology with AI-based image generative models such as Stable Diffusion, our approach enables the systematic generation of realistic and diverse driving scenes. This includes variations in weather, road topology, and environmental features, all while maintaining the core semantics of the original scenario. The digital twin provides a synchronized simulation environment where changes can be tested in a controlled and repeatable manner. Within this environment, we define three metamorphic relations inspired by real-world traffic rules and vehicle behavior. We validate our framework in the Udacity self-driving simulator and demonstrate that it significantly enhances test coverage and effectiveness. Our method achieves the highest true positive rate (0.719), F1 score (0.689), and precision (0.662) compared to baseline approaches. This paper highlights the value of integrating digital twins with AI-powered scenario generation to create a scalable, automated, and high-fidelity testing solution for autonomous vehicle safety.

Problem

• Addresses autonomous driving safety challenges with digital twins
• Solves oracle problem through metamorphic testing of vehicle behavior
• Generates diverse driving scenarios using AI-based image models

Innovation

• Digital twin framework creates virtual replica for testing
• Generative models produce diverse realistic driving scenarios
• Metamorphic relations validate system against traffic rules