测试图像扰动对自动驾驶感知系统鲁棒性的影响，提升安全性和泛化能力。
Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems
- 系统评估38类图像扰动，发现均能暴露ADAS的脆弱点。
- 基于扰动的数据增强与持续学习使系统在新环境性能显著提升。
- 适用于自动驾驶研发中模型鲁棒性验证与部署优化。
基于深度神经网络（DNN）的高级驾驶辅助系统（ADAS）广泛应用于自动驾驶车辆的关键感知任务，如目标检测、语义分割和车道识别。然而，这些系统对输入变化（如噪声、光照变化）高度敏感，可能影响其有效性并导致安全关键性故障。本研究对图像扰动技术进行了全面的实证评估，旨在验证并提升ADAS感知系统的鲁棒性与泛化能力。我们首先系统回顾文献，识别出38类扰动；随后在两个不同的ADAS系统上，分别从组件和系统层面评估其揭示故障的有效性；最后探索了基于扰动的数据增强和持续学习策略，以提升ADAS对新型运行设计域的适应能力。结果表明，所有扰动类别均成功暴露了ADAS的鲁棒性问题，且数据增强与持续学习显著提升了系统在未见环境中的表现。
原文摘要 · Abstract (English)
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.
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