Investigating the Effects of Alcohol Consumption on Manual and Automated Driving: A Systematic Review

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.

University: 
Mineta Consortium for Transportation Mobility
San José State University
Principal Investigator: 
Gaojian Huang
PI Contact Information: 

gaojian.huang@sjsu.edu

San Jose State University

Agency ID or Contract Number: 
69A3551747127
Dates: 
February 2024
Project Number: 
2302

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CSUTC
MCEEST
MCTM
NTFC
NTSC

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