Jingwen Hu

Research Associate Professor

University of Michigan

United States

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Dr. Jingwen Hu is a Research Associate Professor at the University of Michigan, USA. His research primarily focuses on computational human modeling, injury risk predictions, and the development of innovative restraint technologies for improving safety equity in vehicular environments. Recent articles highlight his work on diverse human models aimed at simulating occupant responses during vehicle crashes and addressing equity issues in traffic injury prevention.

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2020

Collaborators (3)

Kathleen Klinich

University of Michigan

UNITED STATES

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Jason Forman

University of Virginia

UNITED STATES

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Nichole Orton

University of Michigan

UNITED STATES

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Jingwen Hu

Research Interests

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Articles (20)

Predicting head impact conditions in vehicle-to-pedestrian impacts through computational human modeling

Development and validation of diverse active human models for simulating stochastic occupant responses during pre-crash vehicle maneuvers

Exploring advanced restraint technologies for improving safety equity for rear-seat occupants using diverse human body models

Development of A Generic Nearside Impact Test Fixture for Evaluating In-Vehicle Crashworthiness of Wheelchairs

Development and validation of a set of wheelchair finite element models in frontal, side, and rear impact conditions

Efficient injury risk predictions for a diverse population using parametric human modeling and inducing points in Gaussian processes

Digital human models in automotive engineering applications: a bibliometric analysis of research progress and prospects

Editorial: Addressing equity issues in traffic injury prevention

Optimizing frontal impact occupant protection systems for passengers seated in wheelchairs

Child occupant safety in unconventional seating for vehicles with automated driving systems

Understanding the new trends in pedestrian injury distribution and mechanism through data linkage and modeling

Adaptive restraint design for a diverse population through machine learning

Approximating subject-specific brain injury models via scaling based on head–brain morphological relationships

Improving protection system for wheelchair-seated occupants in vehicle side impacts

Evaluation of a diverse population of morphed human body models for prediction of vehicle occupant crash kinematics

Influence of morphological variation on brain impact responses among youth and young adults

Effect of Class I–III obesity on driver seat belt fit

Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models

Restraint systems considering occupant diversity and pre-crash posture

Should airbags be deactivated for wheelchair-seated drivers?

Collaborators (3)

Kathleen Klinich

Jason Forman

Nichole Orton

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