As AI advances, and can draw upon previously disparate information sources, automated vehicles are gaining the ability to proactively collaborate with humans to accomplish everyday tasks. This collaboration will be based on increased sensing and information and will feature greater proactive behavior by the vehicle. However, there is little guidance on how vehicles should behave proactively, or even a shared understanding of what that means. Using prior work, we develop a framework of proactive interaction comprising three dimensions: initiation of action, alignment with users’ goals, and communication of context awareness. We apply the framework in a video-based online study (N=351) of a vehicle navigation system that exhibits proactive behavior. The system advises or acts, supports or counters users’ goals in two scenarios (to reach a work meeting quickly or carry groceries without walking too far), and provides a more or less detailed rationale. We find that when goals are misaligned, communicating contextual awareness decreases user experience and acceptance. As a result, if alignment of (immediate) goals is not possible—such as when drivers want to reduce walking distance but the closest parking lot is full—systems should exercise care in communicating their situation awareness, as this can reinforce users’ negative perceptions. Dispositional traits such as propensity to trust and locus of control do predict users’ trust, distrust, and experience in our scenarios. We also find that participants’ primary rationales for accepting the system’s decision are largely similar (saving time, trusting, and making sense) but rationales overall, especially for rejecting the system’s decision, are more varied than expected and often based on experience. Therefore, systems that look to personalize decision-making—such as whether to proactively execute versus merely suggest an action—could take into account users’ dispositional traits and other preferences, such as those as exemplified by these rationales.

Research has characterized mind-wandering as humans’ natural mental state, with moments of task-focused attention being the exception. With this framing, mind-wandering while driving likely occurs more than generally acknowledged, and seems poised to increase with higher levels of automation. This in turn may have adverse effects on drivers’ abilities to regain situation awareness or resume control when needed. Of the prior work on detecting mind-wandering while driving, none focuses on automation or complex urban environments. We ran an exploratory study (N = 14) of an automated drive through New York City in a two-dimensional virtual reality context, focusing on physiological measures such as gaze distribution, pupillometry, and heart rate. We also explored how drivers missing critical events may be a potential new measure. Results varied between focused and mind-wandering mental states and between moving and stopped driving contexts. These observations are an initial step toward understanding mind-wandering across diverse driving scenarios.

Conditionally automated driving systems face two main safety challenges: the inability to autonomously handle all situations the vehicle encounters, and the allowed inattention of drivers during these critical moments. Our study focuses on enhancing drivers’ situation awareness at such times by embedding information about system status and the road environment in the visual signals displayed when control is transferred from the automated driving system. Six visual signals, each including different levels of situation awareness information, were compared to examine how they influence drivers’ levels of situation awareness in a simulated environment. The results show that signals incorporating higher levels of situation awareness information about the environment significantly facilitate the recovery of situation awareness after engaging in non-driving related tasks. This research provides insights into how visual cues can be optimized to facilitate quicker recovery of situation awareness for drivers transitioning from non-driving tasks in conditionally automated vehicles.

The Development of Fully Autonomous Vehicles (AVs) would fundamentally change the nature of in-vehicle user interactions, behaviors, needs, and activities. Passengers free from driving would expect to undertake diverse Non-Driving-Related Tasks to keep themselves occupied. Introducing Conversational Artificial Intelligence (CAI) in Level 5 AVs could improve the in-vehicle user experience (UX). To explore this, firstly, we identify what roles and relationships can CAI play towards end-users of AVs through end-user interviews and thematic analysis. Secondly, we examine how end-users qualitatively assess the embodied UX of the CAI roles and relationships through guided brainstorming, post simulator interaction experiments employing Wizard of Oz setup and Participant Enactment methods. Results show that Tour Guide, Mentor, and Storyteller were the most preferred CAI roles, and that Human-CAI relationships are maintained if the CAI mediates in-vehicle user activities, interactions, sharing of vehicle control, and deep conversations. We discuss the research implications and propose design guidelines.

On-demand mental health services-including counseling, crisis hotlines, and peer support programs-are vital to the healthcare system, providing acute and ongoing support through telephone, online chats, and text messaging. Although such services have proven effective at reducing hopelessness, psychological pain, and suicidality, they put the providers of these services at high risk of burnout, secondary traumatic stress, and compassion fatigue. Our interviews with professionals from four mental health organizations revealed that while these workers have a strong motivation to help clients with mental health care needs, they face various challenges themselves, particularly regarding heavy caseloads, difficult crisis clients, and coping with repeated exposure to abuse and harassment. To overcome challenges, participants identify the need to be self-reliant and engage in self-care practices ranging from socializing with coworkers to yoga and meditation. Although organizations spend significant time training workers prior to their involvement with clients, the training typically lacks components on self-compassion and self-care. Designers might see technology as an opportunity to promote such practices; however, while technology is an integral part of their work routine, participants, irrespective of age, had misapprehensions regarding technology use in the mental health care space, including for managing their psychological well-being. We recommend design guidelines for HCI researchers, including developing contextualized just-in-time adaptive interventions to promote self-compassion and educating workers regarding the use of various technologies to manage their well-being.

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.

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.

A growing number of studies use a "ghost-driver" vehicle driven by a person in a car seat costume to simulate an autonomous vehicle. Using a hidden-driver vehicle in a field study in the Netherlands, Study 1 (N = 130) confirmed that the ghostdriver methodology is valid in Europe and confirmed that European pedestrians change their behavior when encountering a hidden-driver vehicle. As an important extension to past research, we find pedestrian group size is associated with their behavior: groups look longer than singletons when encountering an autonomous vehicle, but look for less time than singletons when encountering a normal vehicle. Study 2 (N = 101) adapted and extended the hidden-driver method to test whether it is believable as online video stimuli and whether car characteristics and participant feelings are related to the beliefs and behavior of pedestrians who see hidden-driver vehicles. As expected, belief rates were lower for hidden-driver vehicles seen in videos compared to in a field study. Importantly, we found noticing no driver was the only significant predictor of belief in car autonomy, which reinforces prior justification for the use of the ghostdriver method. Our contributions are a replication of the hidden-driver method in Europe and comparisons with past US and Mexico data; an extension and evaluation of the ghostdriver method in video form; evidence of the necessity of the hidden driver in creating the illusion of vehicle autonomy; and an extended analysis of how pedestrian group size and feelings relate to pedestrian behavior when encountering a hidden-driver vehicle.

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.

How will pedestrians from different regions interact with an approaching autonomous vehicle? Understanding differences in pedestrian culture and responses can help inform autonomous cars how to behave appropriately in different regional contexts. We conducted a field study comparing the behavioral response of pedestrians between metropolitan Mexico City (N=113) and Colima, a smaller coastal city (N=81). We hid a driver in a car seat costume as a Wizard-of-Oz prototype to evoke pedestrian interaction behavior at a crosswalk or street. Pedestrian interactions were coded for crossing decision, crossing pathway, pacing, and observational behavior. Most distinctly, pedestrians in Mexico City kept their pace and more often crossed in front of the vehicle, while those in Colima stopped in front of the car more often.

To explore cultural differences in driver behavior for the purposes of vehicle automation, we used eye tracking to measure fixation patterns of Japanese and US participants (N = 98) viewing video simulations of automated driving through San Francisco and Osaka. After each drive, we asked participants questions about objects and events from the video.