Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models

Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in this work, we explore the integration of generative a...

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Vydáno v:Proceedings / International Conference on Software Engineering s. 398 - 410
Hlavní autoři: Baresi, Luciano, Xian Hu, Davide Yi, Stocco, Andrea, Tonella, Paolo
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 26.04.2025
Témata:
autonomous driving systems
Autonomous vehicles
Deep learning
deep learning testing
Detectors
Diffusion models
Generative AI
Reliability engineering
Semantic segmentation
Semantics
Software engineering
Testing
ISSN:1558-1225
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Shrnutí:Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in this work, we explore the integration of generative artificial intelligence techniques with physics-based simulators to enhance ADS system-level testing. Our study evaluates the effectiveness and computational overhead of three generative strategies based on diffusion models, namely instruction-editing, inpainting, and inpainting with refinement. Specifically, we assess these techniques' capabilities to produce augmented simulator-generated images of driving scenarios representing new ODDs. We employ a novel automated detector for invalid inputs based on semantic segmentation to ensure semantic preservation and realism of the neural generated images. We then performed system-level testing to evaluate the ability of the ADS to generalize to newly synthesized ODDs. Our findings show that diffusion models help to increase the coverage of ODD for system-level ADS testing. Our automated semantic validator achieved a percentage of false positives as low as 3%, retaining the correctness and quality of the images generated for testing. Our approach successfully identified new ADS system failures before real-world testing.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00206