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The popularity of new Human-Machine-Interfaces (HMIs) comes with growing concerns for driver distraction. In part, this concern stems from a rising challenge to design systems that can make functions accessible to drivers while maintaining drivers’ ability to cope with the complex driving task. Therefore, engineers need assessment methods which can evaluate how well a user interface achieves the dual-goal of making secondary tasks accessible, while allowing safe driving. Most prior methods have emphasized measuring off-road glances during HMI use. An alternative to this is to consider both on-road and off-road glances, as done in Kircher and Ahlstrom’s AttenD algorithm [1]. In this study, we compared two types of prevalent visual-manual user interfaces based on AttenD. The two HMIs of interest were a touchscreen-based interface (already in production) and a remote-rotary-controller-based interface (a high-fidelity prototype).

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

There is a strong evidence that the overrepresentation of teen drivers in motor vehicle crashes is mainly due to their poor hazard perception skills, i.e., they are unskilled at appropriately detecting and responding to roadway hazards. This study evaluates two cuing systems designed to help teens better understand their driving environment. Both systems use directional color-coding to represent different levels of proximity between one’s vehicle and outside agents. The first system provides an overview of the location of adjacent objects in a head-up display in front of the driver and relies on drivers’ focal vision (focal cuing system). The second system presents similar information, but in the drivers’ peripheral vision, by using ambient lights (peripheral cuing system). Both systems were retrofitted into a test vehicle (2014 Toyota Camry). A within-subject experiment was conducted at the University of Michigan Mcity test-track facility.