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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.

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 offer a safe alternative to on-road driving for the evaluation of performance. In addition, simulated drives allow for controlled manipulations of traffic situations producing a more consistent and objective assessment experience and outcome measure of crash risk. Yet, few simulator protocols have been validated for their ability to assess driving performance under conditions that result in actual collisions. This paper presents results from a new Simulated Driving Assessment (SDA), a 35- to-40-minute simulated assessment delivered on a Real-Time® simulator. The SDA was developed to represent typical scenarios in which teens crash, based on analyses from the National Motor Vehicle Crash Causation Survey (NMVCCS). A new metric, failure to brake, was calculated for the 7 potential rear-end scenarios included in the SDA and examined according two constructs: experience and skill.

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.