4 papers
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis
David Fernandez, Pedram MohajerAnsari, Amir Salarpour +1
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decisi…
FedVLM: Scalable Personalized Vision-Language Models through Federated Learning
Arkajyoti Mitra, Afia Anjum, Paul Agbaje +2
Vision-language models (VLMs) demonstrate impressive zero-shot and few-shot learning capabilities, making them essential for several downstream tasks. However, fine-tuning these mo…
FuzzSense: Towards A Modular Fuzzing Framework for Autonomous Driving Software
Andrew Roberts, Lorenz Teply, Mert D. Pese +3
Fuzz testing to find semantic control vulnerabilities is an essential activity to evaluate the robustness of autonomous driving (AD) software. Whilst there is a preponderance of di…
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI
Muhammet Anil Yagiz, Pedram MohajerAnsari, Mert D. Pese +1
In the evolving landscape of autonomous vehicles, ensuring robust in-vehicle network (IVN) security is paramount. This paper introduces an advanced intrusion detection system (IDS)…