Title: Merging psychology and technology: Non-contact monitoring of stress, cognitive load and fatigue in automated driving systems
Description: Automated driving systems (ADS) represent a promising instance of leveraging artificial intelligence to enhance user experience and vehicle safety. To facilitate seamless interaction between the user and system, driver monitoring systems (DMS) are being developed which aim to monitor the physiological state of the driver and intervene when the user’s psychological state reaches critical levels. However, the existing technology relies on invasive sensors, and thus can be used only for validation purposes. The purpose of the present research is to develop and establish a protocol for the measurement of driver psychophysiology and classification of target states – stress, cognitive load and fatigue – using non-invasive, sensor-based methods. A secondary aim of the present research is to examine drivers’ psychophysiological responses to stress, cognitive load and fatigue while using automated driving systems. Physiological parameters (heart rate, respiration rate, electrodermal activity, eye-tracking) will be measured using state-of-the-art near-infrared imaging techniques. The extracted data will be classified using machine learning algorithms to obtain a measure of driver states. The results will inform the development of DMS in on-road settings as ADS continue to emerge at the consumer level. This research will be conducted in collaboration with Xperi, representing an exciting integration of behavioural science with technology.