5 papers
When2Talk: When Should a Proactive In-Car Agent Talk?
Kaiser Hamid, Peihang Li, Nade Liang
Proactive in-cabin agents can help passengers understand automated-vehicle (AV) behavior, but communicating every ride event may introduce unnecessary interruptions. We investigate…
CoRE: Weakly Supervised Coarse-to-Fine Risk Evidence Learning in Driving Videos
Kaiser Hamid, Can Cui, Nade Liang
Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level judgments. Learning \emph{wh…
ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving
Kaiser Hamid, Can Cui, Nade Liang
Recent progress in vision-language-action (VLA) models has enabled language-conditioned driving agents to execute natural-language navigation commands in closed-loop simulation, ye…
FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling
Kaiser Hamid, Can Cui, Khandakar Ashrafi Akbar +2
Understanding not only where drivers look but also why their attention shifts is essential for interpretable human-AI collaboration in autonomous driving. Driver attention is not p…
Interpretable Modeling of Driver Attention Shifts with a Vision-Language Model
Kaiser Hamid, Khandakar Ashrafi Akbar, Peihang Li +1
Driver gaze is commonly modeled as a spatial heatmap, but heatmaps alone are difficult for humans to interpret because they do not explain which road object or region is being moni…