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Automation and Human Performance in Aviation
Published in Pamela S. Tsang, Michael A. Vidulich, Principles and Practice of Aviation Psychology, 2002
Raja Parasuraman, Evan A. Byrne
The expected-value analysis proposed by Sheridan and Parasuraman (2000) considered only the accuracy of decision choices. In many systems, the speed of such responses may be important; that is, the timeliness of response by human or automation may be a critical factor in the decision to automate. Inagaki (1999) carried out a related expected-value analysis comparing human versus automation control in time-critical conditions. Time criticality was defined as the window of time following an abnormal event (e.g., an engine failure just prior to VI speed during takeoff) within which corrective action must be applied. Inagaki’s model showed that control of a task should be allocated to automation when the human cannot respond effectively within the time window, but to human operators otherwise.
Human Factors and Healthcare
Published in Mark W. Wiggins, Introduction to Human Factors for Organisational Psychologists, 2022
While highly coordinated activities have proven successful in the context of emergency medicine where behaviour is centred around a singular event, less time-critical situations may require the coordination of healthcare resources over an extended period, involving a number of medical specialities. For example, a cancer diagnosis will normally require staging, during which radiologists will identify the course of the malignancy throughout the body. This information will be used by surgeons and oncologists to determine whether surgery, chemotherapy, and/or radiation therapy constitutes the most appropriate treatment.
Driver response and recovery following automation initiated disengagement in real-world hands-free driving
Published in Traffic Injury Prevention, 2023
Pnina Gershon, Bruce Mehler, Bryan Reimer
Concerns over driver inattention, coupled with known limitations of partial automation, has led research to focus on time-critical, system-initiated disengagements where drivers are required to take over either the lateral (steering) or both lateral and longitudinal (steering and speed) vehicle control (Louw et al. 2017; Gaspar and Carney 2019). Essentially, drivers may be unprepared to regain control when automation reaches its operational limits (Lin et al. 2018; Parasuraman and Riley 1997; Reagan et al. 2020). Furthermore, in these situations, it is also unknown how the use of systems like SC, that allows hands-free driving, will impact a driver’s ability to regain control in a timely manner. Driver attention monitoring systems are one mechanism designed to mitigate lapses in driver engagement by providing feedback to the driver or adapting the automation functionality in real-time (Donmez et al. 2009; Lee et al. 2013; Reimer 2020). Current driver monitoring systems use steering wheel torque-based sensors and/or driver facing cameras that track gaze and/or head position to infer driver state and intervene when a threshold for apparent inattention is exceeded. SC, for example, has a camera-based driver monitoring system that employs multimodal cues (visual, auditory, and haptics) to support driver attention on a moment-to-moment basis. While partial automation systems are increasingly available, the literature to date is limited by the lack of objective data on the extent to which drivers use partial automation, the context and frequencies in which the automation initiates disengagements, and how drivers respond to and recover from such events.