The Prediction of Autonomous Vehicle Occupants’ Pre-Crash Motion during Emergency Braking Scenarios

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Abstract

This research investigates a computational method, which can assist the development of occupants’ passive safety in future autonomous vehicles, more particularly in the definition of head kinematics in rotated seat arrangement during emergency braking. To capture these head motions, the methodology utilised an Active Human Model, whose head kinematics were validated in a previous work in 3-point and lapbelt restraint configuration scenarios. A sled model was then built where the seat backrest angle (SBA) and the seat orientation, modelled by rotating the acceleration angle (AA), could be adjusted to represent various “living room” seating conditions. A Design of Experiments study was then performed by varying AA from 0° to 360° in steps of 22.5° and SBA from 20° to 60° in steps of 8°. The responses were subsequently converted into a Reduced Order Model (ROM), which was then successfully validated through a comparison with the kinematic responses predicted with simulations. In terms of simulation time it was found that the ROM was able to calculate the head kinematics in 3 seconds instead of the 1.5 hours taken using Simcenter Madymo, without compromising predicted responses accuracy. This research has provided a unique method to define head kinematics corridors for seated occupants in autonomous vehicle interiors, including maximum head excursion, head kinematics as a function of time, and define for the first time a) the safe “no-contact” head envelope within the cabin interior, and b) capture the seated scenarios where head proximity to airbag systems could be of concern, following emergency braking.
Original languageEnglish
Pages (from-to)3304-3312
Number of pages9
JournalProceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Volume237
Issue number14
Early online date12 Feb 2023
DOIs
Publication statusPublished - Dec 2023

Bibliographical note

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Keywords

  • Occupant protection
  • autonomous vehicles
  • active human body models
  • machine learning
  • reduced order modelling

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