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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.

Autonomous and/or automated vehicles offer a host of future opportunities but leave many questions unanswered regarding their impact on crash avoidance or the ability of drivers to effectively scan and re-engage from self-driving mode when necessary to avoid crash scenarios. Considering a 16-year-old is several times more likely to die in an automobile crash than other licensed drivers, it was crucial to test both teenage drivers and adults to determine head-on collision avoidance abilities when subjected to a failing autopilot in a simulated autonomous vehicle. In this study, eight teenagers ages 16-19 and four experienced adults underwent four simulated drives (one manual practice drive and three simulated autonomous drives) using a hi-fidelity, Real Time Technologies SimDriver Simulator to represent being in a self-driving vehicle.

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.

Inadequate situation awareness and response are increasingly recognized as prevalent critical errors that lead to young driver crashes. To identify and assess key indicators of young driver performance (including situation awareness), we previously developed and validated a Simulated Driving Assessment (SDA) in which drivers are safely and reproducibly exposed to a set of common and potentially serious crash scenarios. Many of the standardized safety measures can be calculated in near real-time from simulator variables. Assessment of situation awareness, however, largely relies on time-consuming data reduction and video coding. Therefore, the objective of this research was to develop a near real-time automated method for analyzing general direction and location of driver's gaze in order to assess situation awareness.

Driving simulators offer a safe alternative to on-road driving for the evaluation of driving performance. Standardized procedures for providing individualized feedback on driving performance are not readily available. The aim of this paper is to describe a methodology for developing standardized procedures that provide individualized feedback (“LiveMetrics”) from a simulated driving assessment used to measure driving performance. A preliminary evaluation is presented to test the performance of the LiveMetrics methodology. Three key performance indicators are used to evaluate the performance and utility of the method in the context of the preliminary evaluation. The results from the preliminary evaluation suggest abilities to customize reporting features for feedback and integrate these into existing driver training and education programs.

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.