This dataset was collected from a fixed-base driving simulator experiment designed to examine driver responses to takeover requests in Level 3 conditionally automated driving. Each of the 57 participants (33 male, 24 female; mean age = 38.51 ± 17.23 years) completed nine takeover scenarios. These scenarios were generated by combining three levels of traffic density (0, 10, 20 vehicle/km) with three levels of cognitive workload induced by non-driving-related tasks (0-back, 1-back, 2-back). The order of nine scenarios was balanced using a Latin Square design, thereby reducing the potential order and learning effects and enhancing the dataset’s reliability for experimental replication and the development of generalizable models.
The dataset includes multimodal data capturing (1) driver characteristics (takeover style, risk-taking attitude, etc.), (2) scenario information (traffic density and non-driving-related task), (3) vehicle operational data (velocity, acceleration, steering wheel angle, etc.), (4) subjective scenario experience (situational awareness, spare capacity, etc.), and (5) physiological signals (eye movements and heart rate). Detailed descriptions of the variables and formats are provided in the data_dictionary.csv.
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