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5033694 
Technical Report 
Assessing the Feasibility of Vehicle-Based Sensors to Detect Alcohol Impairment. Appendices for NHTSA Technical Report 
Lee, JD; Fiorentino, D; Reyes, ML; Brown, TL; Ahmad, O 
2010 
GRA and I 
e 0 
Despite persistent efforts at the local, state, and federal levels, alcohol-impaired driving crashes still account for 31% of all traffic fatalities. The proportion of fatally injured drivers with blood alcohol concentrations (BAC) greater than or equal to 0.08% has remained at 31-32% for the past ten years. Vehicle-based countermeasures have the potential to address this problem and save thousands of lives each year. Many of these vehicle-based countermeasures depend on developing an algorithm that uses driver performance to assess impairment. The National Advanced Driving Simulator (NADS) was used to collect data needed to develop an algorithm for detecting alcohol impairment. Data collection involved 108 drivers from three age groups (21-34, 38-51, and 55-68 years of age) driving on three types of roadways (urban, freeway, and rural) at three levels of alcohol concentration (0.00%, 0.05%, and 0.10% BAC). The scenarios used for this data collection were selected so that they were both representative of alcohol-impaired driving and sensitive to alcohol impairment. The data from these scenarios supported the development of three algorithms. One algorithm used logistic regression and standard speed and lane-keeping measures; a second used decision trees and a broad range of driving metrics that are grounded in cues NHTSA has suggested police officers use to identify alcohol-impaired drivers; a third used a support vector machines. The results demonstrate the feasibility of a vehicle-based system to detect alcohol impairment based on driver behavior. The algorithms differentiate between drivers with BAC levels at and above and below 0.08%BAC with an accuracy of 73 to 86%, comparable to the standardized field sobriety test. This accuracy can be achieved with approximately eight minutes of driving performance data. Differences between drivers and between roadway situations have a large influence on algorithm performance, which suggests the algorithms should be tailored to drivers and to road situations.