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Motor vehicles crashes are the leading cause of injury and death of US teens. Driving simulators offer a way to safely expose drivers to specific events in a controlled and repeatable manner. They empower researchers by enabling them to compare different groups and driving behaviors and assess the cognitive and attention skills that are essential to safe driving. Classically, assessment of eye glances and gaze duration relies largely on time-consuming data reduction and video coding. In addition, the synchronization of eye tracker and simulator data is essential to a valid analysis of the eye glances patterns in relation to the driving scenario. To better understand and quantify eye glances in relation to a driving scene, Eyesync was developed as a synchronization bridge between an eye tracker and a driving simulator. It allows the real time synchronization and logging of eye tracking and simulator data. The design of the software is presented in this paper.

As vehicles with SAE level 2 of autonomy become more widely deployed, they still rely on the human driver to monitor the driving task and take control during emergencies. It is therefore necessary to examine the Human Factors affecting a driver’s ability to recognize and execute a steering or pedal action in response to a dangerous situation when the autonomous system abruptly requests human intervention. This research used a driving simulator to introduce the concept of level 2 autonomy to a cohort of 60 drivers (male: 48%, female: 52%) of different age groups (teens 16 to 19: 32%, adults: 35 to 54: 37%, seniors 65+: 32%). Participants were surveyed for their perspectives on self-driving vehicles. They were then assessed on a driving simulator that mimicked SAE level 2 of autonomy. Participants’ interaction with the HMI was studied.