用扩散模型生成新驾驶场景，提升自动驾驶测试覆盖率。
Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models
- 融合扩散模型与物理仿真，生成符合语义的新驾驶场景
- 自动化验证器误检率低至3%，保障生成图像真实可靠
- 成功提前发现自动驾驶系统潜在缺陷，适合测试团队使用
基于仿真的测试被广泛用于评估自动驾驶系统（ADS）的可靠性，但其有效性受限于模拟器中可用的操作设计域（ODD）条件。为解决此问题，本文探索将生成式人工智能技术与物理基仿真器结合，以增强ADS系统级测试能力。研究评估了三种基于扩散模型的生成策略：指令编辑、图像修复及带精修的图像修复，重点考察其生成代表新ODD的驾驶场景图像的能力。采用新型基于语义分割的自动化无效输入检测器，确保生成图像在语义上一致且逼真。随后开展系统级测试，评估ADS对新合成ODD的泛化能力。结果表明，扩散模型显著提升了系统测试的ODD覆盖范围；自动化语义验证器的误报率最低达3%，有效保持生成图像的正确性与质量；本方法成功在真实测试前识别出新的自动驾驶系统故障。
原文摘要 · Abstract (English)
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 perform system-level testing to evaluate the ADS's generalization ability to newly synthesized ODDs. Our findings show that diffusion models help increase the ODD coverage for system-level testing of ADS. Our automated semantic validator achieved a percentage of false positives as low as 3%, retaining the correctness and quality of the generated images for testing. Our approach successfully identified new ADS system failures before real-world testing.
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