Novel Approach in Vehicle Front-End Modeling for Numerical Analyses of Pedestrian Impact Scenarios

Vychytil, Jan; Špička, Jan; Hynčík, Luděk; Manas, Jaroslav; Pavlata, Petr; Striegler, R; Moser, Tomas; Valášek, Radek

doi:10.4271/2017-01-1451

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The main advantage of the MBS method is reduced computational time. [15] The Virthuman model is validated [15] and used for prediction of injury risk in various not only road traffic scenarios [9,10,16]. For the PTW impact scenarios, the head, neck and extremities usually undergo complex multi-directional loading due to the fact that the body moves almost freely by inertia in the space.…”

Section: Basic Modelmentioning

confidence: 99%

Virtual hybrid human body model for PTW safety assessment

Hynčík

Bońkowski

Vychytil

2017

ACM

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Road traffic accidents cause one of the highest numbers of severe injuries. The numbers of deaths and seriously injured citizens prove that traffic accidents and their consequences are still a serious problem to be solved. Virtual human body models play an important role to assess injuries during impact loading especially for scenarios, where complex dynamical loading is taken into account. The most suffering group is so called vulnerable road users (VRU) like powered two-wheelers (PTW) riders. The presented work contributes to increasing safety of PTW riders by implementing virtual human body model for injury risk analysis. The scalable hybrid virtual human body model Virthuman, which was formerly developed, validated and demonstrated in impact scenarios, is improved by updated neck and shoulder models in order to describe the realistic kinematics during complex long duration impact loading and presented in the oblique impact scenario compared to the THUMS results.

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Section: Basic Modelmentioning

confidence: 99%

Virtual hybrid human body model for PTW safety assessment

Hynčík

Bońkowski

Vychytil

2017

ACM

Self Cite

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Injury Assessment in Non-Standard Seating Configurations in Highly Automated Vehicles Using Digital Twin and Active Learning

Hynčík¹,

Talimian²,

Vychytil³

et al. 2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Human-driven vehicles are going to be replaced by highly automated vehicles as one of the future mobility trends. Even though highly automated vehicles’ active safety systems can protect against vehicle-to-vehicle accidents, the traffic mix between human-driven vehicles and highly automated vehicles is still a potential source of vehicle collisions. Additionally, occupants in highly automated vehicles will be passengers not necessarily dealing with driving anymore, so there will be a considerable number of non-standard seating configurations. Those configurations are not able to be assessed for safety by hardware testing due to their number, variability and complexity. The objective of the paper is the development of a fast virtual approach to identify the passengers’ injury risk in non-standard seating configurations under multi-directional impact scenarios and severity. We deploy the concept of surrogate modeling, where we introduce a digital twin for the expected automated vehicle interiors. Non-standard seating configurations are represented by a simplified model of four seats located in the vehicle. These seats are occupied by a previously developed scalable human body model representing passengers of variable anthropometry. Thanks to the vehicle interior simplification and the hybrid human body model, thousands of simulations describing the impacts identified can be run. Based on the numerical simulations describing impact scenarios, a fast and lean artificial intelligence (AI) model actively learns a digital twin to approximate injury risk predictions for a huge number of possible crash scenarios fast. An impact scenario concerns a seating configuration occupied by up to 4 passengers (a seat can be empty) of variable height, weight, age and gender and crash direction and severity (velocity). Behind AI, the machine learning method uses supervised classifiers that are trained to predict injury severity based on the given input. There are 8 trained classifiers per passenger, each one for a body segment, where multi (predicting 4 injury severity levels) and binary (predicting injury or non-injury levels) classifications are compared. The machine learning accuracy is compared by the Mattheus correlation coefficient, where the presented digital twin AI approach reasonably approximates the numerical solution.</div></div>

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Novel Approach in Vehicle Front-End Modeling for Numerical Analyses of Pedestrian Impact Scenarios

Cited by 2 publications

References 13 publications

Virtual hybrid human body model for PTW safety assessment

Virtual hybrid human body model for PTW safety assessment

Injury Assessment in Non-Standard Seating Configurations in Highly Automated Vehicles Using Digital Twin and Active Learning

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