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 …
We propose a framework that leverages Large Language Models (LLMs) for adversarial scenario analysis in Autonomous Vehicles (AVs), generating interpretable explanations for …
Autonomous Vehicles (AVs) have transformed transportation by reducing human error and enhancing traffic efficiency, driven by deep neural network (DNN) models that power image …
Scenario simulation is central to testing autonomous driving systems. Scenic, a domain-specific language (DSL) for CARLA, enables precise and reproducible scenarios, but …