Predicting Driver Takeover Time in Conditionally Automated Driving
dc.contributor.author | Ayoub, Jackie | |
dc.contributor.author | Du, Na | |
dc.contributor.author | Yang, X. Jessie | |
dc.contributor.author | Zhou, Feng | |
dc.date.accessioned | 2022-03-11T00:56:22Z | |
dc.date.available | 2022-03-11T00:56:22Z | |
dc.date.issued | 2022-03-10 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/171905 | en |
dc.description.abstract | It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests, and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset of 129 previous studies. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of in-vehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle. | en_US |
dc.language.iso | en_US | en_US |
dc.rights | CC0 1.0 Universal | * |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.subject | Takeover time prediction, takeover control, explainable machine learning models | en_US |
dc.title | Predicting Driver Takeover Time in Conditionally Automated Driving | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | |
dc.subject.hlbtoplevel | Engineering | |
dc.contributor.affiliationumcampus | Dearborn | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/171905/1/Predicting_Takeover_Time_ITS__Final_Files (2).pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/4206 | |
dc.description.filedescription | Description of Predicting_Takeover_Time_ITS__Final_Files (2).pdf : Main article | |
dc.description.depositor | SELF | en_US |
dc.working.doi | 10.7302/4206 | en_US |
dc.owningcollname | Industrial and Manufacturing Systems Engineering (IMSE, UM-Dearborn) |
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