Interaction research has been initially focusing on partially and conditionally automated vehicles. Augmented Reality (AR) may provide a promising way to enhance drivers' experience when using autonomous driving (AD) systems. This study sought to gain insights on drivers' subjective assessment of a simulated driving automation system with AR-based support. A driving simulator study was conducted and participants' rating of the AD system in terms of information imparting, nervousness and trust was collected. Cumulative Link Models (CLMs) were developed to investigate the impacts of AR cues, traffic density and intersection complexity on drivers' attitudes towards the presented AD system. Random effects were incorporated in the CLMs to account for the heterogeneity among participants. Results indicated that AR graphic cues could significantly improve drivers' experience by providing advice for their decision-making and mitigating their anxiety and stress. However, the magnitude of AR's effect was impacted by traffic conditions (i.e. diminished at more complex intersections). The study also revealed a strong correlation between self-rated trust and takeover frequency, suggesting takeover and other driving behavior need to be further examined in future studies.

Recent AR research efforts have explored the use of virtual environments to test augmented reality (AR) user interfaces. However, it is yet to be seen what effects the visual fidelity of such virtual environments may have on AR interface assessment, and specifically to what degree assessment results observed in a virtual world would apply to the real world. Automotive AR head-up (HUD) interfaces provide a meaningful application area to examine this problem, especially given that immersive, 3D-graphics-based driving simulators are established tools to examine in-vehicle interfaces safely before testing in real vehicles. In this work, we present an argument that adequately assessing AR interfaces requires a suite of different measures, and that such measures should be considered when debating the appropriateness of virtual environments for AR interface assessment. We present a case study that examines how an AR interface presented via HUD effects driver performance and behavior in different virtual and real environments. Twelve participants completed the study measuring driver task performance, eye gaze behavior and situational awareness during AR guided navigation in low-and high-fidelity virtual simulation, and an on-road environment. Our results suggest that the visual fidelity of the environmental in which an AR interface is assessed, could impact some measures of effectiveness. Discussion is guided by a proposed initial assessment classification for AR user interfaces that may serve to guide future discussions on AR interface evaluation, as well as the suitability of virtual environments for AR assessment.

Automotive manufactures are rapidly developing more advanced in-vehicle systems that seek to provide a driver with more active safety and information in real-time, in particular human machine interfaces (HMIs) using mixed or augmented reality (AR) graphical elements. However, it is difficult to properly test novel AR interfaces in the same way as traditional HMIs via on-road testing. Instead, simulation could likely offer a safer and more financially viable alternative for testing AR HMIs, inconsistent simulation quality may confound HMI research depending on the visual fidelity of each simulation environment. We investigated how visual fidelity in a virtual environment impacts the quality of resulting driver behavior, visual attention, and situational awareness when using the system. We designed two large-scale immersive virtual environments; a “low” graphic fidelity driving simulation representing most current research simulation testbeds and a “high” graphic fidelity environment created in Unreal Engine that represents state of the art graphical presentation. We conducted a user study with 24 participants who navigated a route in a virtual urban environment via direction of AR graphical cues while also monitoring the road scene for pedestrian hazards, and recorded their driving performance, gaze patterns, and subjective feedback via situational awareness survey (SART). Our results show drivers change both their driving and visual behavior depending upon the visual fidelity presented in the virtual scene. We further demonstrate the value of using multi-tiered analysis techniques to more finely examine driver performance and visual attention.

The automotive industry is rapidly developing new in-vehicle technologies that can provide drivers with information to aid awareness and promote quicker response times. Particularly, vehicles with augmented reality (AR) graphics delivered via head-up displays (HUDs) are nearing mainstream commercial feasibility and will be widely implemented over the next decade. Though AR graphics have been shown to provide tangible benefits to drivers in scenarios like forward collision warnings and navigation, they also create many new perceptual and sensory issues for drivers. For some time now, designers have focused on increasing the realism and quality of virtual graphics delivered via HUDs, and recently have begun testing more advanced 3D HUD systems that deliver volumetric spatial information to drivers. However, the realization of volumetric graphics adds further complexity to the design and delivery of AR cues, and moreover, parameters in this new design space must be clearly and operationally defined and explored. In this work, we present two user studies that examine how driver performance and visual attention are affected when using fixed and animated AR HUD interface design approaches in driving scenarios that require top-down and bottom-up cognitive processing. Results demonstrate that animated design approaches can produce some driving gains (e.g., in goal-directed navigation tasks) but often come at the cost of response time and distance. Our discussion yields AR HUD design recommendations and challenges some of the existing assumptions of world-fixed conformal graphic approaches to design.

This study presents the preliminary attempt to investigate the usage of driver physiology signals, including electrocardiography (ECG) and respiration wave signals, to predict driving maneuvers. While most studies on driving maneuver prediction uses direct measurements from vehicle or road scene, we believe the mental state changes from the driver when making plans for maneuver can be reflected from the physiological signals. We extract both time and frequency domain features from the physiological signals, and use them as the features to predict the drivers' future maneuver. We formulate the prediction of driver maneuver as a multi-class classification problem by using the features extracted from signal before the driving maneuvers. The multi classes correspond to various types of driving maneuvers including Start, Stop, Lane Switch and Turn. We use the support vector machine (SVM) as the classifier, and compare the performance of using both physiological and car signals (CAN bus) with the baseline classifier that is trained with only car signal. An improved performance is observed when using the physiological features with 0.04 in F-score on average. This improvement is more obvious as the prediction is made earlier.