MS defense: Impaired Driving Detection Using Multiple Textile & Inertial Sensors

MS Thesis Defense

distratto: Real-time Impaired Driving Detection Using Multiple Textile and Inertial Sensors

Tsu An Chen

1:00-3:00pm Tuesday, 23 December 2014, ITE 341

Statistical data shows that driving-related accidents and human casualties caused by vehicles are on the rise in the US and globally. Most of these accidents are cause by impaired or distracted driving. Existing systems that detect impaired driving use cameras that perform eye and head tracking and do not capture full-body movements that are indicative of dangerous driving. To address this problem, in this thesis we present a system, distratto, that uses capacitive textile sensors embedded into car seats, headrests, and arm rests to capture whole body motion, and inertial and GPS sensors for determining vehicle speed and turns. Using a combination of these sensors and a tiered signal processing algorithm, we infer attributes that are indicative of impaired driving. We have developed a fully functional prototype of distratto that we evaluate in a real vehicle setting. We show that our system can detect impaired driving instances and driver movements with high accuracy.

Committee: Drs. Nilanjan Banerjee (chair), Ryan Robucci, Chintan Patel

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