一句话：提出用扩散模型增强仿真驾驶场景的 ODD，以提升自动驾驶系统级测试覆盖率与故障发现。
基于仿真的自动驾驶系统测试受限于模拟器已有的运行设计域（ODD）条件。本文将扩散模型与物理模拟器结合，探索指令编辑、图像修复以及带精修的图像修复三种生成策略，用于合成新的驾驶场景图像并扩展 ODD。为保持语义与真实感，作者提出基于语义分割的自动无效输入检测器。评估涵盖生成有效性、计算开销以及系统级 ADS 测试。结果表明，扩散模型能提升 ODD 覆盖率；语义验证器的假阳性率低至 3%，并能在真实道路测试前发现新的 ADS 系统故障。
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.
@inproceedings{paperbot8830,
title = {Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models},
author = {Luciano Baresi and Davide Yi Xian Hu and Andrea Stocco and Paolo Tonella},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00206}
}