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DTSTART:19700101T000000
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BEGIN:VEVENT
DTSTAMP:20241014T203103Z
LOCATION:FLW Salon G
DTSTART;TZID=America/Phoenix:20240913T100500
DTEND;TZID=America/Phoenix:20240913T102500
UID:HFESAM_ASPIRE - Presented by HFES_sess282_LECT548@linklings.com
SUMMARY:Analyzing Human-Automation Interdependence Based on the Teammate, 
 Situational Properties, and Interaction Sequence
DESCRIPTION:Industry/Practitioner Case Study, Lecture\n\nHansol Rheem, Joo
 nbum Lee, and John D. Lee (University of Wisconsin - Madison) and Joshua E
 . Domeyer (Toyota Collaborative Safety Research Center)\n\nAutomation’s im
 perfection requires teaming with drivers for certain tasks, creating inter
 dependent relationships where both parties influence each other. Therefore
 , it is important to support driver-automation interdependence to align th
 eir behaviors with team goals. We expand the Interdependence Analysis (IA)
  method to evaluate interdependent relationships within driver-automation 
 teams, considering their situational contexts (e.g., cost of task failure)
  and the timescales of team tasks. By analyzing driver-automation teaming 
 in a lane-changing scenario, our proposed IA offered suggestions to improv
 e the collaboration. Specifically, adding collaboration features can enhan
 ce the trajectory planning aspect of lane-changing, which carries a high c
 ost of failure. Timescale decomposition further suggested that trajectory 
 planning relies on short-term road user movement and lane predictions—task
 s solely assigned to automation but where the driver can offer support. Th
 is study demonstrates the benefits of considering situational contexts and
  task timescales in analyzing interdependence within driver-automation tea
 ms and designing supportive strategies for them.\n\nTrack: Surface Transpo
 rtation\n\nSession Chairs: Gayoung Ban (Virginia Polytechnic Institute and
  State University) and Jing Zang (Purdue University)
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