Therapy for Therapists: Design Opportunities to Support the Psychological Well-being of Mental Health WorkersOn-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, and text-based communication. Although such services have proven effective at reducing hopelessness, psychological pain, and suicidality, they put mental health professionals at high risk of burnout, secondary traumatic stress, and compassion fatigue. Our interviews with mental health workers across professions from four mental health organizations revealed that while mental health workers have a strong motivation to help individuals struggling to meet their mental health needs, they face various challenges, including heavy caseloads, having to navigate dealing with crisis clients and managing the impact of abuse and harassment. Although organizations spend a significant time training workers prior to their involvement with clients, the training lacks components of self-compassion and self-care. To overcome their challenges, participants identify the need to be self-reliant and engage in care practices ranging from socializing with coworkers to yoga and meditation. Although technology is an integral part of their work routine, participants, irrespective of their age, had misapprehensions regarding technology use in the mental health care space and for managing one’s own 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 psychological well-being.2024ACAishwarya Chandrasekaran et al.Session 1c: Care for the CaregiversCSCW
Designing Visual Signals to Support Situation Awareness Recovery in Conditional Automated DrivingConditionally 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.2024OLOkkeun Lee et al.Automated Driving Interface & Takeover DesignHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)AutoUI
Driven to Distraction: Exploring Mind Wandering During a Virtual Reality City DriveResearch 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.2024RCRebecca Currano et al.Automated Driving Interface & Takeover DesignEye Tracking & Gaze InteractionAutoUI
‘Talking with your Car’: Design of Human-Centered Conversational AI in Autonomous VehiclesThe 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.2024ARAkshay Rege et al.Conversational ChatbotsAI-Assisted Decision-Making & AutomationAutoUI
Little Road Driving HUD: Heads-Up Display Complexity Influences Drivers’ Perceptions of Automated VehiclesModern 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.2021RCRebecca Currano et al.Stanford UniversityHead-Up Display (HUD) & Advanced Driver Assistance Systems (ADAS)CHI
On-Road and Online Studies to Investigate Beliefs and Behaviors of Netherlands, US and Mexico Pedestrians Encountering Hidden-Driver VehiclesA 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.2020JLJamy Li et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsTeleoperated DrivingV2X (Vehicle-to-Everything) Communication DesignHRI
Defense Against the Dark Cars: Design Principles for Griefing of Autonomous VehiclesAs 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 onroad 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 griefing2020DMDylan James Moore et al.Automated Driving Interface & Takeover DesignOnline Harassment & Counter-ToolsSocial Platform Design & User BehaviorHRI
Is Too Much System Caution Counterproductive? Effects of Varying Sensitivity and Automation Levels in Vehicle Collision Avoidance SystemsAutonomous vehicle system performance is limited by uncertainties inherent in the driving environment and challenges in processing sensor data. Engineers thus face the design decision of biasing systems toward lower sensitivity to potential threats (more misses) or higher sensitivity (more false alarms). We explored this problem for Automatic Emergency Braking systems in Level 3 autonomous vehicles, where the driver is required to monitor the system for failures. Participants (N=48) drove through a simulated suburban environment and experienced detection misses, perfect performance, or false alarms. We found that driver vigilance was greater for less-sensitive braking systems, resulting in improved performance during a potentially fatal failure. In addition, regardless of system bias, greater levels of autonomy resulted in significantly worse driver performance. Our results demonstrate that accounting for the effects of system bias on driver vigilance and performance will be critical design considerations as vehicle autonomy levels increase.2020EFErnestine Fu et al.Stanford UniversityAutomated Driving Interface & Takeover DesignCHI
What a Driver Wants: User Preferences in Semi-Autonomous Vehicle Decision-MakingAutonomous 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.2020SPSo Yeon Park et al.Stanford UniversityAutomated Driving Interface & Takeover DesignCHI
The Case for Implicit External Human-Machine Interfaces for Autonomous VehiclesAutonomous 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.2019DMDylan James Moore et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsAutoUI
Visualizing Implicit eHMI for Autonomous VehiclesAutonomous vehicles' (AVs) interactions with pedestrians remain an ongoing uncertainty. Studies claim the need for explicit external human-machine interfaces (eHMI) such as lights to replace the lack of eye contact with and explicit gestures from drivers. To further explore this area, we conducted a naturalistic field study using the Ghostdriver protocol to explore how pedestrians react to a simulated driverless vehicle stopping at a crosswalk in real traffic on real roads. All pedestrians crossed in front of the vehicle with little hesitation, even though we did not signal anything beyond the vehicle's stopping motion. A few were surprised at the vehicle's novelty, however most paid little attention to its autonomous appearance. The video includes demonstrative examples of the kinds of reactions we observed, which we hope will further a dialogue on the role of eHMI in AV-pedestrian interactions.2019DMDylan James Moore et al.External HMI (eHMI) — Communication with Pedestrians & CyclistsAutoUI
Make This! Introduction to Electronics Prototyping Using ArduinoThis course is a hands-on introduction to interactive electronics prototyping for people with a variety of backgrounds, including those with no prior experience in electronics. Familiarity with programming is helpful, but not required. Participants learn basic electronics, microcontroller programming and physical prototyping using the Arduino platform, then use digital and analog sensors, LED lights and motors to build, program and customize a small paper robot.2018DSDavid Sirkin et al.Stanford UniversityAging-Friendly Technology DesignCircuit Making & Hardware PrototypingCHI
Make This! Introduction to Electronics Prototyping Using ArduinoThis course is a hands-on introduction to interactive electronics prototyping for people with a variety of backgrounds, including those with no prior experience in electronics. Familiarity with programming is helpful, but not required. Participants learn basic electronics, microcontroller programming and physical prototyping using the Arduino platform, then use digital and analog sensors, LED lights and motors to build, program and customize a small paper robot.2018DSDavid Sirkin et al.Stanford UniversityDesktop 3D Printing & Personal FabricationCircuit Making & Hardware PrototypingCHI