Research 01
Proactive Crash Risk Prediction and Prevention
Can risk be identified before a crash? I combine connected-vehicle trajectories, traffic states, and driving behavior to support timely, context-sensitive prevention.
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Postdoctoral Researcher
Chenzhu Wang, Ph.D.
University of Central Florida
Smart & Safe Transportation Lab
Transportation Safety. Autonomous Driving. Multimodal Learning.
I study how human behavior, roadway conditions, and vehicle technologies shape crash risk and injury outcomes. My work combines crash records, connected-vehicle trajectories, traffic video, and simulation, linking data-driven methods with behavioral and physical mechanisms. Current research includes safety-critical recovery in automated driving, connected-vehicle resilience, and multimodal understanding of near-miss and crash events.
Research
Four directions for safer mobility
Research 02
Crash Mechanisms, Human Factors, and Roadway Design
How do people and roads shape safety? I examine injury mechanisms, driver perception, vulnerable road users, and context-sensitive roadway design.
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Research 03
Autonomous Driving, ADAS, and CAV Safety
How can driving technologies respond safely to hazards? I study safety recovery, cyber-physical resilience, and the roadway conditions affecting ADAS performance.
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Research 04
Multimodal Traffic Scene Understanding
What happened, why, and when should a warning occur? I connect video, trajectories, and structured signals to interpret and evaluate safety-critical traffic events.
Explore researchFeatured project
Roadway Infrastructure and ADAS Safety
Investigates roadway geometric design and infrastructure factors affecting ADAS technologies for mitigating rural roadway departure crashes.
Project & PI role- Role
- Principal Investigator
- Award
- FHWA · 2026
- Project funding
- $246,950
Selected publications
Recent contributions
2026Published
From prediction to explanation: A machine learning and causal mediation framework for roadway crash risk with connected vehicle data
Transportation Research Part C: Emerging Technologies, 183, 105479
Pairs connected-vehicle crash-risk prediction with causal mediation to investigate the pathways linking roadway conditions, driving behavior, and risk.
2025Published
Joint analysis on pedestrian injury severity across vehicle movements at intersections: Addressing temporal instability and spatial correlations
Analytic Methods in Accident Research, 48, 100406
Jointly models pedestrian injury severity across left-turn, straight, and right-turn vehicle movements while accounting for temporal instability and spatial correlations.
2025Published
Assessing the safety effectiveness of advanced driver assistance systems
Journal of Safety Research, 95, 345-361
Evaluates ADAS safety effectiveness using real-world two-vehicle crash records, examining how safety effects vary across traffic conditions.
2026Published
Vision-language and generative models in traffic video safety analysis: a computational framework and research agenda
Accident Analysis & Prevention, 236, 108673
Organizes vision-language traffic-safety research around event understanding, grounded evidence, and verification, with an agenda for generative and counterfactual analysis.
Contact
Let's connect.
I welcome collaborations on proactive traffic safety, human factors and roadway design, automated vehicle safety, and multimodal traffic scene understanding.
chenzhu.wang@ucf.edu
Smart & Safe Transportation Lab
University of Central Florida
Orlando, Florida, USA