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| Document Number: | N2010-003 |
| Document Type: | Project report |
| Author(s): | John D. Lee Dary Fiorentino Michelle Reyes Timothy L. Brown Omar Ahmad Robert Dufour James Fell Nic Ward |
| Publication / Venue Name: | None listed |
| Publication Date: | 2010-05-14 |
| Abstract: | Despite persistent efforts at the local, state, and federal levels, alcohol-related crashes still contribute to approximately 40% of all traffic fatalities. Although regulatory and educational approaches have helped reduce alcohol-related fatalities, other approaches merit investigation. One such approach detects alcohol impairment in real time using the increasingly sophisticated sensor and computational platform that is available on many production vehicles. It may be possible to detect impairment based on driver state (e.g., eye movements), driver input (e.g., steering and accelerator modulation), and vehicle state (e.g., speed or lane position). Once detected, this information can support interventions that discourage drivers from driving while impaired and prevent alcohol-related crashes. This study examined which vehicle-based sensors can support algorithms to detect impairment in a robust and timely manner. |
| Body: |  |
| Copyright: | NADS |
| Keywords: | IMPACT |
