Abstract
Autonomous Vehicles (AVs) rely on artificial intelligence (AI) to accurately detect objects and interpret their surroundings. However, even when trained using millions of miles of real-world data, AVs are often unable to detect rare failure modes (RFMs). The problem of RFMs is commonly referred to as the "long-tail challenge", due to the distribution of data including many instances that are very rarely seen. In this paper, we present a novel approach that utilizes advanced generative and explainable AI techniques to aid in understanding RFMs. Our methods can be used to enhance the robustness and reliability of AVs when combined with both downstream model training and testing. We extract segmentation masks for objects of interest (e.g., cars) and invert them to create environmental masks. These masks, combined with carefully crafted text prompts, are fed into a custom diffusion model. We leverage the Stable Diffusion inpainting model guided by adversarial noise optimization to generate images containing diverse environments designed to evade object detection models and expose vulnerabilities in AI systems. Finally, we produce natural language descriptions of the generated RFMs that can guide developers and policymakers to improve the safety and reliability of AV systems.
Abstract (translated)
自主车辆（AVs）依赖于人工智能（AI）来准确检测物体并解释周围环境。然而，即使经过数百万英里的实际道路数据训练后，自动驾驶汽车仍然经常无法识别罕见故障模式（RFMs）。由于数据分布中包含许多极少出现的实例，这个问题被称为“长尾挑战”。在本文中，我们提出了一种新颖的方法，该方法利用先进的生成式和可解释的人工智能技术来帮助理解RFMs。我们的方法可以与下游模型训练和测试相结合，增强AVs的鲁棒性和可靠性。 我们提取感兴趣物体（例如汽车）的分割掩码，并将其反转以创建环境掩码。然后，我们将这些掩码结合精心设计的文本提示输入自定义扩散模型中。我们利用由对抗性噪声优化引导的Stable Diffusion修复模型生成包含多样环境的图像，这些环境旨在避开目标检测模型并揭示AI系统的漏洞。 最后，我们为生成的RFMs提供自然语言描述，以指导开发人员和政策制定者提高AV系统的安全性和可靠性。
URL
https://arxiv.org/abs/2504.17179
