Detection of driver stress in real-world driving environment using physiological signals

Detection of driver stress is an important component in many ADASs (Advanced Driver-Assistance Systems), and a challenging problem when it is applied to real-world driving environment. In this paper, we present a convolutional neural network (CNN) designed to detect driver’s stress levels with four physiological signals, i.e., heart rate, heart rate variability, breathing rate, and galvanic skin response. The proposed model is shift invariant and is capable of handling the imbalanced data set issue. The performances of the proposed models are evaluated using real-world driving data in three different types of driver stress detection tasks, i.e., the single-driver, the cohorts of drivers, and all-driver stress detection task respectively. The experimental results demonstrate that the proposed model is capable of reliably detecting the driver’s stress levels. More importantly we demonstrate that a model can be trained on the data collected from drivers with similar cognitive capabilities and then generalized to new drivers with similar cognitive capabilities for stress detection.