The objective of this paper is to propose a methodology to estimate nationwide traffic safety impacts of automated vehicle technologies using multi-agent traffic simulations. The influence of three levels of driver trust in the automation system (appropriate, over trust , distrust) is considered in the simulation and takes different transition modes of control between the driver and the system into account. The nationwide estimation of crashes is obtained by projecting results of the simulations using traffic data for three different and representative municipalities. Results indicated that Automated Driving Systems and Advanced Driver Assistance Systems significantly reduced the number of casualties and fatalities compared to manual driving. Simulation results in consideration of the influence of driver trust also found that this reduction may be negatively affected by over-and under-trust parameters. However, even with the introduction of these parameters, the reduction rate was still significant compared to manual driving. The proposed methodology using multi-agent traffic simulations may thus address concerns surrounding the deployment of automated driving systems which is a feature not found in conventional simulations, provide useful insight for interested parties to develop research and policy making strategies that accelerate traffic safety improvements, and to support social acceptance efforts.
Human factors research that addresses driver-automation interaction is required to mitigate crashes related to the deployment of automated vehicles. While Automated Driving Systems still require driver supervision, there will remain room for human error. This paper presents results from the first in a series of experiments that aim to estimate driver situation awareness using spontaneous gaze behavior and to ensure the driver is ready for takeover. Spontaneous gaze behavior was studied and compared among 13 participants between partial driving automaton and driver assistance automations condition to extract indicators of the out of the loop phenomenon.
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