Modern vehicles are using AI and increasingly sophisticated sensor suites to improve Advanced Driving Assistance Systems (ADAS) and support automated driving capabilities. Heads-Up-Displays (HUDs) provide an opportunity to visually inform drivers about vehicle perception and interpretation of the driving environment. One approach to HUD design may be to reveal to drivers the vehicle’s full contextual understanding, though it is not clear if the benefits of additional information outweigh the drawbacks of added complexity, or if this balance holds across drivers. We designed and tested an Augmented Reality (AR) HUD in an online study (N = 298), focusing on the influence of HUD visualizations on drivers’ situation awareness and perceptions. Participants viewed two driving scenes with one of three HUD conditions. Results were nuanced: situation awareness declined with increasing driving context complexity, and contrary to expectation, also declined with the presence of a HUD compared to no HUD. Significant differences were found by varying HUD complexity, which led us to explore different characterizations of complexity, including counts of scene items, item categories, and illuminated pixels. Our analysis finds that driving style interacts with driving context and HUD complexity, warranting further study.

Electric vehicles’ (EVs) nearly silent operation has proved to be dangerous for bicyclists and pedestrians, who often use an internal combustion engine’s sound as one of many signals to locate nearby vehicles and predict their behavior. Inspired by regulations currently being implemented that will require EVs and hybrid vehicles (HVs) to play synthetic sound, we used a Wizard-of-Oz AV setup to explore how adding synthetic engine sound to a hybrid autonomous vehicle (AV) will influence how pedestrians interact with the AV in a naturalistic field study. Pedestrians reported increased interaction quality and clarity of intent of the vehicle to yield compared to a baseline condition without any added sound. These findings suggest that synthetic engine sound will not only be effective at helping pedestrians to hear EVs, but also may help AV developers implicitly signal to pedestrians when the vehicle will yield.

Autonomous vehicle (AV) systems are developing at a rapid pace, not only in technological capabilities, but also in human-centered directions. Despite this development, we lack a nuanced understanding of driver preference in decision scenarios that semi-AVs will face, and of possible misalignment between semi-AV decisions and user preference. Using an online survey, we explore how participants would like semi-AVs to act and alert them of the vehicles' decisions in various scenarios. Participants reported varying levels of comfort with autonomy, desire to takeover control, and desire for AV informing. Individual differences, including level of experience with autonomy and situation awareness, affected perceptions of the vehicle. Our results highlight the importance of considering driver preference in AV decision-making, and we present an influence diagram that situates this factor among others. We also derive five design principles, including that a previous positive AV experience can lead to more harmful consequences for AVs when not aligned with driver preference.

As autonomous vehicles (AVs) become a reality on public roads, researchers and designers are beginning to see unexpected reactions from the public ranging from curiosity to vandalism. These behaviors are concerning, as AV platforms will need to know how to deal with people behaving unexpectedly or aggressively. We call this griefing of AVs, adopting the term from harassment in online gaming. We discuss several examples of griefing observed in on-road field studies using a Wizard-of-Oz driverless car. While Uber and Waymo have anecdotally mentioned vandalism towards AVs, we believe this to be the first public video available of AV griefing ranging from playful to aggressive. To stimulate discussion, we propose speculative design principles to address griefing.

Autonomous vehicles' (AVs) interactions with pedestrians remain an ongoing uncertainty. Several studies have claimed the need for explicit external human-machine interfaces (eHMI) such as lights or displays to replace the lack of eye contact with and explicit gestures from drivers, however this need is not thoroughly understood. We review literature on explicit and implicit eHMI, and discuss results from a field study with a Wizard-of-Oz driverless vehicle that tested pedestrians' reactions in everyday traffic without explicit eHMI. While some pedestrians were surprised by the vehicle, others did not notice its autonomous nature, and all crossed in front without explicit signaling, suggesting that pedestrians may not need explicit eHMI in routine interactions---the car's implicit eHMI (its motion) may suffice.

Recent research suggests that a robot's motors make sounds that can influence users' perception of the robot's characteristics. To more deeply understand users' associations with specific sonic characteristics, we adapted methods from sensory science including Check All That Apply (CATA) questions and Polarized Sensory Positioning (PSP) to tease out small differences in motor sounds in an online survey. These methods are straightforward for untrained people to do in an online setting, mathematically rigorous, and can explore a variety of subtle auditory and perceptual stimuli. We describe how to use these methods, interpret the results with several intuitive visual representations, and show that the results align with a previous study of the same dataset. We close by discussing benefits and limitations of applying these methods to study subtle phenomena in the HCI community.

How do sounds shape interaction with robots? The present study explores aural impressions associated with servo motors commonly used to prototype robotic motion. This exploratory analysis constructs a framework to objectively and subjectively characterize sound using acoustic analyses and novice evaluators on Amazon Mechanical Turk. Participants evaluated unfamiliar sounds through pairwise comparison, resulting in subjective ratings of servo motor sounds. In this study, subjective measures of sound correlated well internally, but correlated weakly with objective measures. Moreover, qualitative commentary offered by participants suggests both anthropomorphic associations with sounds as well as negative impressions of the sounds overall. We conclude with a roadmap for exploration into the field of consequential sonic interaction design.

How does a robot's sound shape our perception of it? We overlaid sound from high-end and low-end robot arms on videos of the high-end KUKA youBot desktop robotic arm moving a small block in functional (working in isolation) and social (interacting with a human) contexts. The low-end audio was sourced from an inexpensive OWI arm. Crowdsourced participants watched one video each and rated the robot along dimensions of competence, trust, aesthetic, and human-likeness. We found that the presence and quality of sound shapes subjective perception of the KUKA arm. The presence of any sound reduced human-likeness and aesthetic ratings, however the high-end sound rated better in the competence evaluation in the social context measures when compared to no sound. Overall, the social context increased the perceived competence, trust, aesthetic and human-likeness of the robot. Based on motor sound's significant mixed impact on visual perception of robots, we discuss implications for sound design of interactive systems.