测试自动驾驶辅助系统的图像扰动基准测试
Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems
AI总结：
本研究系统评估了38类图像扰动对ADAS感知系统鲁棒性的影响，证明扰动测试能有效暴露故障，且数据增强与持续学习可显著提升系统在新环境中的性能。
摘要：
基于深度神经网络（DNNs）的高级驾驶辅助系统（ADAS）广泛应用于自动驾驶汽车的关键感知任务，如物体检测、语义分割和车道识别。然而，这些系统对输入变化（如噪声和光照变化）高度敏感，这可能削弱其有效性，并可能导致安全关键的故障。本研究对图像扰动（常用于评估DNN鲁棒性的技术）进行了全面的实证评估，以验证并提升ADAS感知系统的鲁棒性和泛化能力。我们首先对文献进行了系统性回顾，识别出38类扰动。接着，我们在两个不同的ADAS中，从组件级和系统级两个层面评估了这些扰动在揭示故障方面的有效性。最后，我们探索了基于扰动的数据增强和持续学习策略，以提升ADAS对新操作设计域的适应能力。我们的结果表明，所有类别的图像扰动都能成功暴露ADAS中的鲁棒性问题，并且使用数据集增强和持续学习能显著提升ADAS在新颖、未见环境中的性能。
英文摘要：
Advanced Driver Assistance Systems (ADAS) based on deep neural networks (DNNs) are widely used in autonomous vehicles for critical perception tasks such as object detection, semantic segmentation, and lane recognition. However, these systems are highly sensitive to input variations, such as noise and changes in lighting, which can compromise their effectiveness and potentially lead to safety-critical failures. This study offers a comprehensive empirical evaluation of image perturbations, techniques commonly used to assess the robustness of DNNs, to validate and improve the robustness and generalization of ADAS perception systems. We first conducted a systematic review of the literature, identifying 38 categories of perturbations. Next, we evaluated their effectiveness in revealing failures in two different ADAS, both at the component and at the system level. Finally, we explored the use of perturbation-based data augmentation and continuous learning strategies to improve ADAS adaptation to new operational design domains. Our results demonstrate that all categories of image perturbations successfully expose robustness issues in ADAS and that the use of dataset augmentation and continuous learning significantly improves ADAS performance in novel, unseen environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
