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

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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