A Two-Stage Machine Learning Method for Highly-Accurate Drunk Driving Detection
Hasanin Harkous, Hassan Ali Artail · 2019
Abnormal driving refers mostly to drunk driving, fatigue driving, and aggressive driving behaviors. This work addresses drunk driving detection, which actually can be extended to apply to detection of the other abnormal driving behaviors. The approach learns about the related on-board vehicle sensors for detecting drunk driving behaviors based on learning using certain drunk driving cues. In a previous work, we developed a Hidden Markov Model (HMM) method and applied it to each time series of the selected sensors measurements. The prediction accuracy was highest for the longitudinal acceleration, with a maximum of 79%. Here, we extend our early work that was based on HMMs and employ Recurrent Neural Networks that specialize in time series, where our testing results indicate accuracy percentages that go into the upper nineties.