Optimisation of vehicle front-end geometry for adult and pediatric pedestrian protection
This study proposes a method of achieving an optimised vehicle front-end profile for improved protection for both adult and child pedestrian groups, which at the same time is able to avoid designs that may cause Run-over scenarios. A hybrid model of a seven-parameter vehicle front-end geometry and...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2014
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/39144/1/Optimisation_of_vehicle_front-end_geometry_for_adult_and_pediatric_pedestrian_protection.pdf http://irep.iium.edu.my/39144/ http://www.tandfonline.com/doi/abs/10.1080/13588265.2013.879506#.VGGb0cmleSk http://dx.doi.org/10.1080/13588265.2013.879506 |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English |
| Summary: | This study proposes a method of achieving an optimised vehicle front-end profile for improved protection for both adult
and child pedestrian groups, which at the same time is able to avoid designs that may cause Run-over scenarios. A hybrid
model of a seven-parameter vehicle front-end geometry and a pedestrian dummy is used. Latin Hypercube sampling is
utilised to generate a Plan of Experiments for the adult and child pedestrian cases. Head injury criteria results from the
simulations that are tabulated as the response functions. The radial basis function method is used to obtain mathematical
models for the response functions. Optimised front-end geometries are obtained using the Genetic Algorithm method. The
optimised vehicle front-end profile for the adult pedestrian is shown to be different from that of the optimised profile for
the child pedestrian, and optimised profiles are shown to be not mutually applicable for safety. Furthermore, Run-over
scenario is observed in child pedestrian optimised profiles, where its occurrence invalidates the optimisation. A simple
weighting method is used to optimise the geometry for both adult and child pedestrian groups. The Run-over occurrences
are mapped using Logistic Regression and is subsequently used as a constraint for optimisation. The final optimised model
is shown to achieve a safe vehicle front-end profile which equally caters for both adult and child pedestrians while
simultaneously avoiding Run-over scenarios. |
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