AUTHENTICATION: Identifying Rare Failure Modes in Autonomous Vehicle Perception Systems using Adversarially Guided Diffusion Models
Journal:
arXiv
Published Date:
Apr 24, 2025
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.
