![Sample our Computer Science journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5928/TOC-Subject-Sample-2021-Computer-Science.jpg"})
ABSTRACT
A technological race toward always ever-increasing automation is engaged, a race in which automation users are hardly considered. This explains why more automation is not necessarily for the best. A variety of human–machine interaction (HMI) theories co-exist. First, those HMI theories and predictions for lane departure warnings systems (LDWS, assisted driving automation) are described. Second, a synthesis of the key questions currently addressed by empirical data on LDWS is offered. Lastly, a new model of human–machine cooperation modelling is proposed. The model has been inspired by previous theories and empirical data collected with LDWS-assisted driving. Interestingly, automation such as LDWS seems to avoid the ‘ironies of automation’ with no negative effects on human performance. However, a major issue to be addressed is poor automation acceptance. While the focus was set on LDWS to offer a complete overview for this type of device, the model may be extended to other warning assistance devices.
Acknowledgments
This study was performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the programme ‘Investissements d'Avenir’ (ANR-11-IDEX-0007) run by the French National Research Agency (ANR).
Disclosure statement
No potential conflict of interest was reported by the author.
Additional information
Notes on contributors
Jordan Navarro
Jordan Navarro is a lecturer in psychology and cognitive sciences at the University of Lyon, France. He received the PhD degree in cognitive ergonomics from the University of Nantes in 2008. He also spent one year at Monash University Research Accident Center as a research fellow. His research interests focus on the human factors of transportation systems, particularly with advanced vehicle technologies and automation.