Matching Items (2)
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- All Subjects: Automation
- Creators: Chiou, Erin
Description
Highly automated vehicles require drivers to remain aware enough to takeover
during critical events. Driver distraction is a key factor that prevents drivers from reacting
adequately, and thus there is need for an alert to help drivers regain situational awareness
and be able to act quickly and successfully should a critical event arise. This study
examines two aspects of alerts that could help facilitate driver takeover: mode (auditory
and tactile) and direction (towards and away). Auditory alerts appear to be somewhat
more effective than tactile alerts, though both modes produce significantly faster reaction
times than no alert. Alerts moving towards the driver also appear to be more effective
than alerts moving away from the driver. Future research should examine how
multimodal alerts differ from single mode, and see if higher fidelity alerts influence
takeover times.
during critical events. Driver distraction is a key factor that prevents drivers from reacting
adequately, and thus there is need for an alert to help drivers regain situational awareness
and be able to act quickly and successfully should a critical event arise. This study
examines two aspects of alerts that could help facilitate driver takeover: mode (auditory
and tactile) and direction (towards and away). Auditory alerts appear to be somewhat
more effective than tactile alerts, though both modes produce significantly faster reaction
times than no alert. Alerts moving towards the driver also appear to be more effective
than alerts moving away from the driver. Future research should examine how
multimodal alerts differ from single mode, and see if higher fidelity alerts influence
takeover times.
ContributorsBrogdon, Michael A (Author) / Gray, Robert (Thesis advisor) / Branaghan, Russell (Committee member) / Chiou, Erin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Rapid advancements in Artificial Intelligence (AI), Machine Learning, and Deep Learning technologies are widening the playing field for automated decision assistants in healthcare. The field of radiology offers a unique platform for this technology due to its repetitive work structure, ability to leverage large data sets, and high position for clinical and social impact. Several technologies in cancer screening, such as Computer Aided Detection (CAD), have broken the barrier of research into reality through successful outcomes with patient data (Morton, Whaley, Brandt, & Amrami, 2006; Patel et al, 2018). Technologies, such as the IBM Medical Sieve, are growing excitement with the potential for increased impact through the addition of medical record information ("Medical Sieve Radiology Grand Challenge", 2018). As the capabilities of automation increase and become a part of expert-decision-making jobs, however, the careful consideration of its integration into human systems is often overlooked. This paper aims to identify how healthcare professionals and system engineers implementing and interacting with automated decision-making aids in Radiology should take bureaucratic, legal, professional, and political accountability concerns into consideration. This Accountability Framework is modeled after Romzek and Dubnick’s (1987) public administration framework and expanded on through an analysis of literature on accountability definitions and examples in military, healthcare, and research sectors. A cohesive understanding of this framework and the human concerns it raises helps drive the questions that, if fully addressed, create the potential for a successful integration and adoption of AI in radiology and ultimately the care environment.
ContributorsGilmore, Emily Anne (Author) / Chiou, Erin (Thesis director) / Wu, Teresa (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05