Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable …
Vision-language models are emerging for autonomous driving, yet their robustness to physical adversarial attacks remains unexplored. This paper presents a systematic framework for …
Autonomous vehicles rely on perception systems with deep neural networks for traffic sign recognition (TSR), automated lane centering (ALC), and object detection (OD). While these …
Large Language Models face significant deployment challenges in enterprise environments, including high computational costs, data privacy concerns, and network dependencies. This …
We propose a framework that leverages Large Language Models (LLMs) for adversarial scenario analysis in Autonomous Vehicles (AVs), generating interpretable explanations for …
We propose SASA (Sequence-Aware Shadow Attack), a black-box adversarial framework that uses physically realistic, differentiable shadow patterns to deceive traffic sign recognition …
We present a framework for black-box adversarial attacks on traffic signs using dynamic, temporally coherent shadows. Unlike prior work that focuses on single-image attacks or …
Autonomous Vehicles (AVs) have transformed transportation by reducing human error and enhancing traffic efficiency, driven by deep neural network (DNN) models that power image …