Development of a drowsy driving detection method based on self-attention autoencoder using RR interval data

Abstract

Drowsy driving is a problem that needs to be solved because it can lead to serious traffic accidents. Heart rate variability (HRV), which is a fluctuation of RR interval (RRI) in electrocardiogram, is expected to be practical input data for drowsy driving detection since it can be measured easily using wearable devices. In this study, a new driver drowsiness detection method using raw RRI time series as input instead of extracting HRV features was proposed. The proposed method is an anomaly detection method based on autoencoder and self-attention. As a result of an experiment using a driving simulator, the proposed method recorded the true positive rate of 0.80 and the false positive rate of 0.12, which were superior to those of methods using HRV features as inputs. This result suggests that raw RRI time series may be more suitable as inputs than HRV features.