PMID- 31995390
OWN - NLM
STAT- MEDLINE
DCOM- 20210910
LR  - 20240328
IS  - 1547-8181 (Electronic)
IS  - 0018-7208 (Print)
IS  - 0018-7208 (Linking)
VI  - 62
IP  - 2
DP  - 2020 Mar
TI  - Redesigning Today's Driving Automation Toward Adaptive Backup Control With 
      Context-Based and Invisible Interfaces.
PG  - 211-228
LID - 10.1177/0018720819894757 [doi]
AB  - OBJECTIVE: We investigated a driver monitoring system (DMS) designed to 
      adaptively back up distracted drivers with automated driving. BACKGROUND: Humans 
      are likely inadequate for supervising today's on-road driving automation. 
      Conversely, backup concepts can use eye-tracker DMS to retain the human as the 
      primary driver and use computerized control only if needed. A distraction DMS 
      where perceived false alarms are minimized and the status of the backup is 
      unannounced might reduce problems of distrust and overreliance, respectively. 
      Experimental research is needed to assess the viability of such designs. METHODS: 
      In a driving simulator, 91 participants either supervised driving automation 
      (auto-hand-on-wheel vs. auto-hands-off-wheel), drove with different forms of 
      DMS-induced backup control (eyes-only-backup vs. eyes-plus-context-backup; 
      visible-backup vs. invisible-backup), or drove without any automation. All 
      participants performed a visual N-back task throughout. RESULTS: Supervised 
      driving automation increased visual distraction and hazard non-responses compared 
      to backup and conventional driving. Auto-hand-on-wheel improved response 
      generation compared to auto-hands-off-wheel. Across entire driving trials, the 
      backup improved lateral performance compared to conventional driving. Without 
      negatively impacting safety, the eyes-plus-context-backup DMS reduced unnecessary 
      automated control compared to the eyes-only-backup DMS conditions. 
      Eyes-only-backup produced low satisfaction ratings, whereas 
      eyes-plus-context-backup satisfaction was on par with automated driving. There 
      were no appreciable negative consequences attributable to the invisible-backup 
      driving automation. CONCLUSIONS: We have demonstrated preliminary feasibility of 
      DMS designs that incorporate driving context information for distraction 
      assessment and suppress their status indication. APPLICATION: An appropriately 
      designed DMS can enable benefits for automated driving as a backup.
FAU - Cabrall, Christopher D D
AU  - Cabrall CDD
AUID- ORCID: 0000-0003-1357-0160
AD  - 2860 Delft University of Technology, The Netherlands.
FAU - Stapel, Jork C J
AU  - Stapel JCJ
AUID- ORCID: 0000-0002-8445-1014
AD  - 2860 Delft University of Technology, The Netherlands.
FAU - Happee, Riender
AU  - Happee R
AD  - 2860 Delft University of Technology, The Netherlands.
FAU - de Winter, Joost C F
AU  - de Winter JCF
AUID- ORCID: 0000-0002-1281-8200
AD  - 2860 Delft University of Technology, The Netherlands.
LA  - eng
PT  - Journal Article
DEP - 20200129
PL  - United States
TA  - Hum Factors
JT  - Human factors
JID - 0374660
SB  - IM
MH  - Attention
MH  - *Automation
MH  - *Automobiles
MH  - Computer Simulation
MH  - *Distracted Driving/psychology
MH  - Eye-Tracking Technology
MH  - Feasibility Studies
MH  - Female
MH  - Humans
MH  - Male
MH  - *Man-Machine Systems
MH  - Young Adult
PMC - PMC7054641
OTO - NOTNLM
OT  - adaptive automation
OT  - driver monitoring system (DMS)
OT  - eye-tracking
OT  - situated cognitive design
OT  - transitions of control (ToC)
OT  - vigilance decrement
EDAT- 2020/01/30 06:00
MHDA- 2021/09/11 06:00
PMCR- 2020/03/04
CRDT- 2020/01/30 06:00
PHST- 2020/01/30 06:00 [pubmed]
PHST- 2021/09/11 06:00 [medline]
PHST- 2020/01/30 06:00 [entrez]
PHST- 2020/03/04 00:00 [pmc-release]
AID - 10.1177_0018720819894757 [pii]
AID - 10.1177/0018720819894757 [doi]
PST - ppublish
SO  - Hum Factors. 2020 Mar;62(2):211-228. doi: 10.1177/0018720819894757. Epub 2020 Jan 
      29.