Understanding users' characteristics in the selection of vehicle seating configurations and positions in fully automated vehicles.

Francisco J López-ValdésKatarina BohmanJesus Jimenez-OctavioDavid LoganWassim RaphaelLeonardo Quintana JiménezRocio Suarez Del FueyoSjaan Koppel

Published in: Traffic injury prevention (2020)

Previous work had shown differences in participants' preferences for seating configurations and positions depending on age, sex and country. While increasing the sample size, the current study analyses other factors that were associated with choosing one vehicle configuration and seating position over others. As these factors are directly related to the likelihood of sustaining injuries in the event of a crash, the current study provides important insights regarding the potential risk factors for FAV occupants.

Keyphrases

machine learning
deep learning
high throughput
risk assessment
climate change
decision making