Predicting Takeover Performance in Conditionally Automated Driving

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dc.contributor.author Du, Na
dc.contributor.author Zhou, Feng
dc.contributor.author Pulver, Elizabeth
dc.contributor.author Tilbury, Dawn
dc.contributor.author Robert, Lionel + "Jr"
dc.contributor.author Pradhan, Anuj
dc.contributor.author Yang, X. Jessie
dc.date.accessioned 2020-02-14T01:04:36Z
dc.date.available 2020-02-14T01:04:36Z
dc.date.issued 2020-02-13
dc.identifier.citation Du, N., Zhou, F., Pulver, E., Tilbury, D., Robert, L.P., Pradhan, A., and Yang, J.X. (2020). Predicting Takeover Performance in Conditionally Automated Driving, In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, April 25-30, 2020, Honolulu, Hawaii, USA. en_US
dc.identifier.uri http://hdl.handle.net/2027.42/153789
dc.description.abstract In conditionally automated driving, drivers decoupled from operational control of the vehicle have difficulty taking over control when requested. To address this challenge, we conducted a human-in-the-loop experiment wherein the drivers needed to take over control from an automated vehicle. We collected drivers’ physiological data and data from the driving environment, and based on which developed random forest models for predicting drivers’ takeover performance in real time. Drivers’ subjective ratings of their takeover performance were treated as the ground truth. The best random forest model had an accuracy of 70.2% and an F1-score of 70.1%. We also discussed the implications on the design of an adaptive in-vehicle alert system. en_US
dc.description.sponsorship University of Michigan Mcity en_US
dc.description.sponsorship National Science Foundation en_US
dc.language.iso en_US en_US
dc.publisher CHI ’20 Extended Abstracts en_US
dc.subject automated driving en_US
dc.subject human-in-the-loop experiment en_US
dc.subject physiological data en_US
dc.subject vehicle alert system en_US
dc.subject autonomous vehicle en_US
dc.subject automated vehicle en_US
dc.subject advanced driving systems en_US
dc.subject self-driving car en_US
dc.subject driving environment en_US
dc.subject human-automation interaction en_US
dc.subject adaptive in-vehicle alert system en_US
dc.subject Transition of control en_US
dc.subject take over request en_US
dc.subject driving en_US
dc.subject takeover performance en_US
dc.title Predicting Takeover Performance in Conditionally Automated Driving en_US
dc.type Conference Paper en_US
dc.subject.hlbsecondlevel Information and Library Science
dc.subject.hlbtoplevel Social Sciences
dc.description.peerreviewed Peer Reviewed en_US
dc.contributor.affiliationum Information, School of en_US
dc.contributor.affiliationum College of Engineering en_US
dc.contributor.affiliationum Robotics Institute en_US
dc.contributor.affiliationother State Farm Mutual Automobile Insurance Company en_US
dc.contributor.affiliationother University of Massachusetts Amhest en_US
dc.contributor.affiliationumcampus Ann Arbor en_US
dc.description.bitstreamurl https://deepblue.lib.umich.edu/bitstream/2027.42/153789/1/Du et al. 2020.pdf
dc.identifier.doi 10.1145/3334480.3382963
dc.identifier.source Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems en_US
dc.description.filedescription Description of Du et al. 2020.pdf : Main File
dc.owningcollname Information, School of (SI)
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