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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.

The Acoustic Startling Pre-stimulus (ASPS, i.e. a loud sound preceding a physical perturbation) was previously found to accelerate action execution in simple flexion exercises. Therefore in this study we examined if ASPS can accelerate take-over reaction times in restrained teen and adult drivers who were asked to reach for the steering wheel while experiencing sled lateral perturbations simulating a vehicle swerve. Results showed that adult drivers lift their hands toward the steering wheel faster with the ASPS versus without (161 ± 23 ms vs 216 ± 27 ms, p<0.003). However this effect was not found in teens or in trials where the drivers were engaged in a secondary task. Adults also showed reduced lateral trunk displacement out of the seat belt with the ASPS. The ASPS could represent a novel warning that reduces take over time and out-of-position movements in critical autonomous driving scenarios.

Driving simulators provide a safe, highly reproducible environment in which to assess driver behavior. Nevertheless, data reduction to standardized metrics can be time-consuming and cumbersome. Further, the validity of the results is challenged by inconsistent definitions of metrics, precluding comparison across studies and integration of data. No established tool has yet been made available and kept current for the systematic reduction of literature-derived safety metrics. The long term goal of this work is to develop DriveLab, a set of widely applicable routines for reducing simulator data to expert-approved metrics. Since Matlab™ is so widely used in the research community, it was chosen as a suitable environment. This paper aims to serve as a case study of data reduction techniques and programming choices that were made for simulator analysis of a specific research project, the Simulated Driving Assessment.