Evaluating Effects of Cognitive Load, Takeover Request Lead Time, and Traffic Density on Drivers’ Takeover Performance in Conditionally Automated Driving
dc.contributor.author | Du, Na | |
dc.contributor.author | Kim, Jinyong | |
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 | 2020-07-20T19:13:59Z | |
dc.date.available | 2020-07-20T19:13:59Z | |
dc.date.issued | 2020-07-20 | |
dc.identifier.citation | Du, N., Kim, J., Zhou, F., Pulver, E., Tilbury, D., Robert, L. P., Pradhan, A., & Yang, X. J. (2020). Evaluating Effects of Cognitive Load, Takeover Request Lead Time, and Traffic Density on Drivers’ Takeover Performance in Conditionally Automated Driving, Proceedings of 12th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, September 21-22, 2020, Washington, DC, USA. | en_US |
dc.identifier.uri | https://doi.org/10.1145/3409120.3410666 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/156045 | |
dc.description | The views expressed are those of the authors and do not reflect the official policy or position of State Farm®. | en_US |
dc.description.abstract | In conditionally automated driving, drivers engaged in non-driving related tasks (NDRTs) have difficulty taking over control of the vehicle when requested. This study aimed to examine the relationships between takeover performance and drivers’ cognitive load, takeover request (TOR) lead time, and traffic density. We conducted a driving simulation experiment with 80 participants, where they experienced 8 takeover events. For each takeover event, drivers’ subjective ratings of takeover readiness, objective measures of takeover timing and quality, and NDRT performance were collected. Results showed that drivers had lower takeover readiness and worse performance when they were in high cognitive load, short TOR lead time, and heavy oncoming traffic density conditions. Interestingly, if drivers had low cognitive load, they paid more attention to driving environments and responded more quickly to takeover requests in high oncoming traffic conditions. The results have implications for the design of in-vehicle alert systems to help improve takeover performance. | 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 | AutomotiveUI ’20) | en_US |
dc.subject | conditionally automated driving | en_US |
dc.subject | takeover transition | en_US |
dc.subject | cognitive load | en_US |
dc.subject | traffic density | en_US |
dc.subject | takeover lead time | en_US |
dc.subject | automated driving | en_US |
dc.subject | automated vehicles | en_US |
dc.subject | vehicles | en_US |
dc.subject | cars | en_US |
dc.subject | self driving cars | en_US |
dc.subject | human factors | en_US |
dc.subject | takeover request | en_US |
dc.subject | in-vehicle alert systems | en_US |
dc.subject | advance driving systems | en_US |
dc.subject | driving systems | en_US |
dc.subject | traffic | en_US |
dc.subject | drivers’ cognitive load | en_US |
dc.subject | vehicle drivers | en_US |
dc.subject | Automotive User Interfaces | en_US |
dc.subject | Interactive Vehicular Applications | en_US |
dc.subject | AutomotiveUI ’20 | en_US |
dc.subject | driving automation | en_US |
dc.title | Evaluating Effects of Cognitive Load, Takeover Request Lead Time, and Traffic Density on Drivers’ 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.affiliationum | University of Michigan, Dearborn | 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/2027.42/156045/1/Du et al. 2020.pdf | |
dc.identifier.doi | 10.1145/3409120.3410666 | |
dc.identifier.source | 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications | en_US |
dc.identifier.orcid | 0000-0002-1410-2601 | en_US |
dc.identifier.name-orcid | Robert, Lionel P.; 0000-0002-1410-2601 | en_US |
dc.owningcollname | Information, School of (SI) |
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