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BEGIN:VEVENT
DTSTAMP:20241014T203108Z
LOCATION:FLW Salon G
DTSTART;TZID=America/Phoenix:20240911T083000
DTEND;TZID=America/Phoenix:20240911T093000
UID:HFESAM_ASPIRE - Presented by HFES_sess175@linklings.com
SUMMARY:ST6: Driver Attentional States and Situation Awareness—Part 2
DESCRIPTION:Industry/Practitioner Case Study, Lecture\n\nSupporting Driver
  Attention Towards Potential Hazards During Takeover: A Preliminary Result
 \n\nThis study investigates the impact of a supportive system on takeover 
 transitions in conditional autonomous driving. The supportive system is de
 signed to direct drivers' attention toward potential hazards in the enviro
 nment when a takeover request occurs, excluding the event triggering the t
 akeover ...\n\n\nX.Jessie Yang and Doo Won Han (University of Michigan); J
 undi Liu (Iowa State University); Alicia Romo and William Horrey (AAA Foun
 dation for Traffic Safety); Dawn Tilbury and Lionel Robert (University of 
 Michigan); Feng Zhou (University of Michigan, Dearborn); and Lisa Molnar (
 University of Michigan Transportation Institute)\n---------------------\nE
 stimating Mode Awareness of Drivers Interacting With an Automated Vehicle\
 n\nDriving automation introduces multiple modes to enhance benefits, but d
 rivers must monitor and stay aware of these modes, which can sometimes lea
 d to mode confusion. We modified Degani and Heymann’s (2002) state diagram
  method to assess the mode structure of a hypothetical driving automation 
 s...\n\n\nHansol Rheem, Joonbum Lee, and John D. Lee (University of Wiscon
 sin - Madison) and Joseph F. Szczerba, Roy Mathieu, and Akilesh Rajavenkat
 anarayanan (General Motors)\n---------------------\nPost-Trip Mind-Wanderi
 ng Assessment: A Scale and Protocol for Evaluating Train Driver Focus Acro
 ss Multiple Journey Phases\n\nIn professional driving, maintaining alertne
 ss throughout the journey can be challenging. Despite efforts to address d
 ecreased alertness, there is no agreed method to investigate mind-wanderin
 g in transportation. Trigger-based data collection methods are most common
 , but can influence participants' ...\n\n\nJoep von Berg and Ellemieke van
  Doorn (NS)\n\nTrack: Surface Transportation\n\nSession Chairs: Joel Coope
 r (MEA Forensic) and Jundi Liu (Iowa State University)
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