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• W2893071470 abstract "As technological advances lead to a fast progress of driving automation, human-machine interaction issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aimed to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry, and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate, heart rate variability, and skin conductance level. Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during, and 10 s after reported discomfort to check for overall parameter relevance, direction, and strength of effects; timings of increase/decrease; variability as well as filtering, standardization, and artefact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, heart rate decreased significantly during discomfort periods whereas heart rate variability diminished as expected. No effects could be observed for skin conductance level. Body motion showed the expected push-back movement during the close approach situation. Overall, besides skin conductance level, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies." @default.
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• W2893071470 date "2018-09-24" @default.
• W2893071470 modified "2023-10-17" @default.
• W2893071470 title "Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving" @default.
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• W2893071470 doi "https://doi.org/10.3389/fnhum.2018.00338" @default.
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