LAUSR

In the field of advanced driver assistance systems (ADAS), effective learning of driver fatigue characteristics representation is a major challenge due to uncertainties of both real roads and drivers. To tackle this problem, this paper proposes a novel model of learning interpretable representations for fatigue features, so as to improve the performance of the monitoring system on real roads through the learned features. First, the approximate entropy of the Steering Wheel Angle (SWA)sequence is used to crop adaptive lengths of the Recurrent Neural Network (RNN) input data. Then, it will learn statistical indexes to discover the random steering characteristics. The Long Short-Term Memory (LSTM) unit can memorize drivers’ long-term operating characteristics and instantaneous change patterns, and mine their fatigue characteristics. Finally, the information gain method is used to discover the strong correlation between potential characteristics and fatigue levels, thereby obtaining the best feature representation for driver fatigue. The proposed method makes use of the advantages of recurrent neural network learning to explicitly capture various potential characteristics of fatigue driving and interactions between non-linear characteristics in driving. Experimental results on real driving data validate the effectiveness of our proposed method.