Development of Implementable Omni-Directional Chest, Spine and Head Injury Criteria for Human Body Models
Research Project , 2016 – 2018

Moving towards autonomous driving there is a need for tools that can support the development of the autonomous vehicle braking and avoidance systems by comparing the outcome of crashes (when a crash is unavoidable) at various crash directions and severity levels. Crash test dummies are only biofidelic in specific impact directions such as pure frontal or pure lateral impacts. Therefore there is a need for a tool that can predict injuries in all loading directions (frontal to side) at various severities. Human Body Models (HBM) have the potential to predict injuries to vehicle occupants in all crashes that occur in the real world. 

The current project is addressing important injuries to three body parts of vehicle occupants in crashes, both when considering serious injuries as well as injuries leading to long term disability. These are thorax injuries (rib fractures), thoraco-lumbar spine injuries and head injuries. A biofidelic human body model capable of predicting these injuries will be used by the industrial partners Volvo Cars and Autoliv to develop pre-crash triggered restraint system and also for the development of the autonomous braking and avoidance systems as such.

The partner responsible for the application is Autoliv. Partners are Volvo Cars, Chalmers University, Umeå University and Gothenburg University (Sahlgren Hospital).



Johan Davidsson (contact)

Person Injury Prevention

Karin Brolin

Person Injury Prevention

Johan Iraeus

Person Injury Prevention

Mats Svensson

Person Injury Prevention


Autoliv AB

Vårgårda, Sweden

Umeå University

Umeå, Sweden

University of Gothenburg

Gothenburg, Sweden

Volvo Cars

Göteborg, Sweden



Project ID: 2015-04864
Funding Chalmers participation during 2016–2018

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Sustainable development

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C3SE (Chalmers Centre for Computational Science and Engineering)


Innovation and entrepreneurship

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Life Science Engineering (2010-2018)

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