Despite persistent efforts at the local, state, and federal levels, alcohol-impaired crashes still contribute to approximately 30% of all traffic fatalities. Although enforcement and educational approaches have helped to reduce alcohol-impaired fatalities, other approaches will be required to further reduce alcohol-related fatalities. This paper describes an approach that detects alcohol impairment in real time using vehicle-based sensors to detect alcohol-related changes in drivers’ behavior.

Data were collected on the National Advanced Driving Simulator from 108 volunteer drivers. Three age groups (21-34, 38-51, and 55-68 years of age) drove through representative situations on three types of roadways (urban, freeway, and rural) at three levels of blood alcohol content (0.00%, 0.05%, and 0.10% BAC).

Driver control input, vehicle state, driving context and driver state data, individually and in combination, reveal signatures of alcohol impairment. Algorithms built on these signatures detect drivers with BAC levels that are over the legal limit with an accuracy of approximately 80%, similar to the Standardized Field Sobriety Test (SFST) used by law enforcement. Each of the three algorithms combined information across time to predict impairment. The time required to detect impairment ranged from eight minutes, for complex algorithms (i.e., support vector machines and decision trees applied to relatively demanding driving situations), to twenty-five minutes for simple algorithms (i.e., logistic regression). Timely impairment detection depends critically on the driving context:​ variables specific to the particular driving situation result in much more timely impairment detection than generic variables.