PhD Student · University of South Florida · MOTIF-Lab

Yuhang Wang 王雨航

Vision–language models that decide when to alert the driver.

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CoRL 2026 IEEE ITSC 2026 IEEE ITSC 2025 TRB 2026 SMI 2024 · Best Paper

My PhD builds vision–language models, large-scale real-world datasets and benchmarks for safer driving automation.

◈Multimodal LearningVideo, CAN, radar & language — fused for driving understanding
◎Human-Centered AIAlerting, trust and shared control that drivers accept
◉Driver Behavior ModelingTakeovers, driving style and body motion at scale
➤Autonomous DrivingSafety of production ADAS & automated vehicles

Featured Projects

TriDrive
Under review 2026

TriDrive

Joint driver, vehicle and road modeling for human-centered driving — a world model that forecasts driver kinematics, vehicle dynamics and road demands, running in real time on comma four and validated with 14 drivers on the road.
VLAlert
CoRL 2026

VLAlert

Observe Before You Alert: a vision–language framework for adaptive driver alerting (SILENT / OBSERVE / ALERT), with VLAlert-Bench — 192,892 labeled ticks from six datasets.
ADAS-TO
IEEE ITSC 2026

ADAS-TO

15,000+ real-world ADAS takeover events from 327 drivers across 22 brands — multimodal and vision-language-annotated.
OpenLKA
IEEE ITSC 2025

OpenLKA

The first large-scale open dataset of Lane Keeping Assist from 62 production vehicle models, pairing decoded CAN-bus logs with synchronized video.
BATON
Preprint 2026

BATON

A multimodal benchmark for bidirectional human–automation control transitions: 136.6 h of naturalistic driving with three prediction tasks.
DriveDNA
Preprint 2026

DriveDNA

Driving-style identification — 4,121 drives from 465 drivers, 975 h — isolating driver-specific behavior from vehicle, route and environment confounds.
DriveMotion
Dataset 2026

DriveMotion

Driver body-motion forecasting: 400 h of in-cabin skeleton motion (133 keypoints/frame, privacy-reduced) with an 8 s → 4 s prediction task.

About Me

Yuhang Wang

I am Yuhang Wang (Henry, 王雨航), a PhD student in Civil Engineering Transportation at the University of South Florida, working as a research assistant at the MOTIF-Lab advised by Prof. Hao Zhou. I previously spent a year with Prof. Peng Song at the Singapore University of Technology and Design.

My research applies deep learning and the capabilities of self-driving vehicles to transportation problems — building the large-scale, real-world datasets the field is missing, and pursuing human-centered driving automation that earns drivers' trust. If any of this interests you, I would love to chat and collaborate: yuhangw@usf.edu.

Vision–Language ModelsDatasets & Benchmarks Autonomous Driving SafetyMultimodal Learning Human-Centered AIDriver Behavior Modeling
  • Aug 2024 – Present
    University of South Florida — Transportation PhD student, advised by Prof. Hao Zhou
  • Oct 2023 – Jul 2024
    Singapore University of Technology and Design — Visiting student, advised by Prof. Peng Song
  • Sep 2020 – Jun 2024
    Nanjing University of Information Science and Technology — BEng in CS, advised by Prof. Xiaolong Xu

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