Citation

Dong M, Lee YY, Cha JS, Huang G. J. Saf. Res. 2024; 89: 1-12.

Copyright

(Copyright © 2024, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2024.01.006

PMID

38858032

Abstract

INTRODUCTION: Almost a third of car accidents involve driving after alcohol consumption. Autonomous vehicles (AVs) may offer accident-prevention benefits, but at current automation levels, drivers must still perform manual driving tasks when automated systems fail. Therefore, understanding how alcohol affects driving in both manual and automated contexts offers insight into the role of future vehicle design in mediating crash risks for alcohol-impaired driving.

METHOD: This study conducted a systematic review on alcohol effects on manual and automated (takeover) driving performance. Fifty-three articles from eight databases were analyzed, with findings structured based on the information processing model, which can be extended to the AV takeover model.

RESULTS: The literature indicates that different Blood Alcohol Concentration (BAC) levels affect driving skills essential for traffic safety at various information processing stages, such as delayed reacting time, impaired cognitive abilities, and hindered execution of driving tasks. Additionally, the driver's driving experience, drinking habits, and external driving environment play important roles in influencing driving performance.

CONCLUSIONS: Future work is needed to examine the effects of alcohol on driving performance, particularly in AVs and takeover situations, and to develop driver monitoring systems.

PRACTICAL APPLICATIONS: Findings from this review can inform future experiments, AV technology design, and the development of driver state monitoring systems.

Keywords: Ethanol impaired driving

Language: en

Keywords

Humans; Automobiles; *Alcohol Drinking; Systematic Review; Blood Alcohol Content; Blood Alcohol Concentration; *Automation; *Automobile Driving; Accidents, Traffic/prevention & control; Alcohol-impaired driving; Automated Driving; Driving Under the Influence/statistics & numerical data/prevention & control; Information Process Model