Predicting Takeover Performance in Conditionally Automated Driving
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 | https://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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