CHS: SMALL: Methods to Assess Automotive Augmented Reality Head-up Display Effects on Driver Performance

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Augmented Reality (AR) windshield displays have the potential to provide fundamentally new driving experiences by displaying computer graphics directly on the roadway, and with the increasing commercialization of AR technologies are likely to become more common in cars. If designed properly, AR has the potential to enhance driving and driver safety. However, we currently do not know how to evaluate the safety of emerging AR driving interfaces, as existing methods used for evaluating other in-vehicle information displays (such as navigation and entertainment systems) are not rich enough to capture effects of AR displays on driver performance. This project will develop new methods for assessing drivers' visual distraction when using AR displays. These methods will be specifically tuned for developing AR user interfaces for driving that reduce injuries and minimize loss of life and property. If successful, this project will help auto manufacturers more quickly create and release new and safe AR driving applications, and may have a positive impact on other areas where AR is used, such as in construction and manufacturing. The project will also support educational impacts, through the training of researchers better able to consider both human and technical factors in system design and evaluation, and through efforts to recruit students who are members of populations traditionally underrepresented in engineering.The project will address open questions that arise from expected increases in the field of view provided by AR heads-up displays (HUDs). This will support a wider variety of possible interfaces and information locations than current displays; however, we currently do not know how to effectively quantify the effects of AR user interfaces on driving. This project will evaluate glance allocation and visual attention capabilities of drivers with AR HUDs, and apply this knowledge to inform new methods of AR HUD assessment. Specifically, the team will establish thresholds for AR HUD glance durations along with a set of ecologically valid visual AR HUD tasks that can be broadly adopted by both researchers and designers of automotive AR HUDs. The team will further create a set of methods to assess the effects of AR HUD visual demand on drivers' ability to detect events occurring in both the drivers' central and peripheral fields of view. Lastly, the research will employ user studies to test the refined methods on an actual roadway and recommend best practices for applying the methods.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.