Using vehicle-based sensors of driver behavior to detect alcohol impairment.
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2011-06-13
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Abstract: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.
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