Dataset of physiological signals in the use of advanced driver assistance systems (ADAS)

This article introduces a dataset that investigates the physiological responses of drivers when using advanced driver assistance systems (ADAS) in real-world traffic conditions. The study, conducted in the Federal District, Brazil, involved seven drivers in controlled driving sessions. The time of day and the days of the week were standardized to ensure comparable traffic conditions. The data collection was centered on ADAS Level 2 systems, specifically the Lane Keeping Assist System (LKAS) and the Forward Collision Warning System (FCWS). The dataset includes five physiological signals: respiration, heart rate, galvanic skin response (GSR), leg muscle activity, and brain activity. These signals were continuously acquired using a dedicated instrumentation system installed in the vehicle. Given the complexity of collecting data under real traffic conditions, the acquisition sessions generated a large volume of raw data. Considerable post-processing was conducted to identify and segment portions of the signals with sufficient integrity for subsequent analysis. The dataset is structured as time-stamped raw signal spreadsheets, each corresponding to a specific driver and direction of the pre-established route (outbound and return). Such organization enables researchers to navigate the dataset easily, explore specific segments of interest, and conduct comparative analyses across participants and varying traffic conditions. The dataset is relevant to researchers in biomedical signal processing, driver state monitoring, intelligent transportation systems, and human–machine interaction. It may be used by academic laboratories investigating physiological responses during driving tasks, as well as by engineers and developers working on advanced driver assistance systems (ADAS), including automotive manufacturers and ADAS technology suppliers. The dataset, which includes synchronized physiological and vehicle dynamics data collected under real traffic conditions may contribute to the study of human responses during semi-automated driving, supporting research and development of driver-centered mobility technologies.