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TZID:America/Phoenix
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TZOFFSETFROM:-0700
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DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203101Z
LOCATION:FLW Salon G
DTSTART;TZID=America/Phoenix:20240911T083000
DTEND;TZID=America/Phoenix:20240911T085000
UID:HFESAM_ASPIRE - Presented by HFES_sess175_LECT546@linklings.com
SUMMARY:Estimating Mode Awareness of Drivers Interacting With an Automated
  Vehicle
DESCRIPTION:Industry/Practitioner Case Study, Lecture\n\nHansol Rheem, Joo
 nbum Lee, and John D. Lee (University of Wisconsin - Madison) and Joseph F
 . Szczerba, Roy Mathieu, and Akilesh Rajavenkatanarayanan (General Motors)
 \n\nDriving automation introduces multiple modes to enhance benefits, but 
 drivers must monitor and stay aware of these modes, which can sometimes le
 ad to mode confusion. We modified Degani and Heymann’s (2002) state diagra
 m method to assess the mode structure of a hypothetical driving automation
  system and the likelihood of discrepancies between drivers’ mode awarenes
 s and actual modes. The modified diagram maps all possible combinations of
  actual modes and drivers’ awareness, highlighting where they diverge, and
  estimates the probabilities of the divergence. We tested the method by cr
 eating a state diagram using driving simulation data from participants who
  weren't fully informed about all possible automation modes. The diagram i
 dentified areas where human-machine interface (HMI) design can help driver
 s maintain accurate mode awareness. The modified state diagram method prov
 ides actionable insights for designing HMIs to reduce mode confusion and c
 an be used to develop and test computational models of automation mode str
 uctures.\n\nTrack: Surface Transportation\n\nSession Chairs: Joel Cooper (
 MEA Forensic) and Jundi Liu (Iowa State University)
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