Research
I work on computer vision and multimodal learning for human-centered end-to-end autonomy.
My goal is to connect perception with decision-making in a way that is robust and interpretable.
I am especially interested in vision-language supervision and generalizable evaluation benchmarks.
(* indicates equal contribution, † indicates equal advising)
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Predicting the Next Move: A Systematic Review of Driver Intention Modeling for Autonomous Vehicles
Kaiser Hamid, Nade Liang, PhD
Under review
A systematic review of driver intention modeling for autonomous vehicles, summarizing datasets, features, modeling paradigms, prediction horizons, and evaluation practices.
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FSDAM: Few-Shot Driving Attention Modeling via Vision-Language Coupling
Kaiser Hamid, Can Cui, Khandakar Ashrafi Akbar, PhD, Ziran Wang, PhD, Nade Liang, PhD
Preprint
paper /
video /
code /
We propose FSDAM, a few-shot driver attention modeling framework that couples vision-language supervision with driving perception to improve data efficiency and generalization.
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A New Evaluation Metric for Takeover Maneuver Quality: Comparing Human Drivers with Autonomous Driving Agents
Kaiser Hamid, Nade Liang, PhD
HFES 2025 (Accepted)
poster /
We introduce a new evaluation metric for takeover maneuver quality, enabling systematic comparison between human drivers and autonomous driving agents.
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Assessing the Potential of Google Location History (GLH) Data for Travel Behavior Research in the Context of Developing Country
Kaiser Hamid, Md Sayem Noor, Annesha Enam, PhD
Proceedings of IEEE ITSC 2024
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An empirical assessment of Google Location History data for travel behavior research, focusing on feasibility, limitations, and practical considerations in developing-country contexts.
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Professional Service
Conference Review: HFES'25, TRB'26
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