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There are direct correlations between drunk driving and car-related injuries, disabilities, and death. Autonomous vehicles (AVs) may provide useful driver support systems to prevent or reduce road accidents. However, AVs are not yet fully automated and require human drivers to take over the vehicle at times. Therefore, understanding how alcohol affects driving performance in both manual and automated driving is important because manual drives may offer insights into the takeover process in AVs. A systematic review of 53 articles from eight databases was conducted. Findings were categorized based on the human information processing model, which can be extended to the AV takeover model. The results demonstrated that different blood alcohol concentration (BAC) levels affect driving performance in various stages of the information processing model and the takeover model. However, existing studies tested limited levels of BAC, and there are few studies on AV takeover performance. Future work may focus on AVs and takeover performance. This review can also provide implications for future driving experiments and AV technology design.
MIAOMIAO DONG
Miaomiao Dong is a graduate student majoring in M.S. Human Factor Ergonomics at San José State University. She received her B.S. in Industrial Design from San Francisco State University in 2020.
YUNI Y LEE
Yuni Y. Lee is a graduate student majoring in M.S. Human Factor Ergonomics at San José State University. She received her B.S. in Psychobiology from the University of California, Los Angeles in 2019.
JACKIE S CHA, PHD
Dr. Cha is an assistant professor in the Department of Industrial Engineering at Clemson University. She received her PhD in Industrial Engineering from Purdue University in 2020.
GAOJIAN HUANG, PHD
Dr. Huang is an assistant professor in the Department of Industrial and Systems Engineering at San José State University. He received his PhD in Industrial Engineering from Purdue University in 2021.
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