Guiding Driver Responses During Manual Takeovers from Automated Vehicles (VTTI-00-026)

Project Description: This research was conducted during the 2019-2020 academic year at the Virginia Tech Transportation Institute (VTTI). This study sought to investigate the effects of multi-modal takeover requests (TORs) on driver’s situation awareness (SA) and takeover performance. Participants rode in a simulated SAE L3 vehicle for a series of 30 drives in which they experienced takeover requests involving auditory, visual, and haptic cues. Researchers measured participant SA after each trial. The driving simulator collected takeover performance data, which was used to calculate metrics such as time to collision and initial response time. Upon completion of the drivers, participants gave their overall impressions of each TOR cue in the form of preference data. Data Scope: This dataset consists of data from 21 participants who each experienced a series of 30 drives. We collected situation awareness data (SAGAT method) which involves binary (yes or no) responses from participants which was coded and added to the Microsoft Excel file. We also collected data from the driving simulator which gave us takeover performance metrics (such as time to collision and initial response time) as well as eye-gaze data from the built in eye-tracking system in the virtual reality headset. We also used Qualtrics to collect demographic and HMI preference data from participants. Data Specification: Due to the size of the dataset, a detailed description of each variable from the dataset can be found under the “Key” tab in the dataset file.