Extracting tendency and stability from time series and random forest for classifying a car driver's cognitive load
Yutaka Yoshida, Hayato Ohwada, Fumio Mizoguchi · 2014
This paper presents a novel temporal feature extraction method and random forest (RF) for classifying a car driver's cognitive load. Temporal value, tendency, and stability are important features for classifying a car driver's state. We present the classification problem of the car driver's state. We need a function in the in-vehicle information service that judges the user's cognitive load. We define the driver's cognitive load based on the driving situation. The experiment confirmed that classification accuracy improved using the tendency and stability of a time series. Moreover, the results confirmed that the tendency and stability of steering angle, accelerator rate, and car speed contribute to the classification of cognitive load.