17 citations · 17 across the 4 of their papers we have counts for
4 papers
How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study
Roberto Brusnicki, Mattia Piccinini, Johannes Betz
Vision-Language Models (VLMs) are increasingly proposed for autonomous driving tasks, yet their performance on sequential driving scenes remains poorly characterized, particularly…
Can VLMs Unlock Semantic Anomaly Detection? A Framework for Structured Reasoning
Roberto Brusnicki, David Pop, Yuan Gao +2
Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution semantic anomalies. While VLMs have emerged as promising tools for perception,…
NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving
Yuan Gao, Mattia Piccinini, Roberto Brusnicki +2
Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Model…
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang +12
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing hav…