To increase driver awareness in a fully autonomous vehicle, we developed several haptic interaction prototypes that signal what the car is planning to do next. The goal was to use haptic cues so that the driver could be situation aware but not distracted from the non-driving tasks they may be engaged in. This paper discusses the three prototypes tested and the guiding metaphor behind each concept. We also highlight the Wizard of Oz protocol adopted to test the haptic interaction prototypes and some key findings from the pilot study.

Background Approximately 60%-80% of the primary care visits have a psychological stress component, but only 3% of patients receive stress management advice during these visits. Given recent advances in natural language processing, there is renewed interest in mental health chatbots. Conversational agents that can understand a user’s problems and deliver advice that mitigates the effects of daily stress could be an effective public health tool. However, such systems are complex to build and costly to develop. Objective To address these challenges, our aim is to develop and evaluate a fully automated mobile suite of shallow chatbots—we call them Popbots—that may serve as a new species of chatbots and further complement human assistance in an ecosystem of stress management support. Methods After conducting an exploratory Wizard of Oz study (N=14) to evaluate the feasibility of a suite of multiple chatbots, we conducted a web-based study (N=47) to evaluate the implementation of our prototype. Each participant was randomly assigned to a different chatbot designed on the basis of a proven cognitive or behavioral intervention method. To measure the effectiveness of the chatbots, the participants’ stress levels were determined using self-reported psychometric evaluations (eg, web-based daily surveys and Patient Health Questionnaire-4). The participants in these studies were recruited through email and enrolled on the web, and some of them participated in follow-up interviews that were conducted in person or on the web (as necessary). Results Of the 47 participants, 31 (66%) completed the main study. The findings suggest that the users viewed the conversations with our chatbots as helpful or at least neutral and came away with increasingly positive sentiment toward the use of chatbots for proactive stress management. Moreover, those users who used the system more often (ie, they had more than or equal to the median number of conversations) noted a decrease in depression symptoms compared with those who used the system less often based on a Wilcoxon signed-rank test (W=91.50; Z=−2.54; P=.01; r=0.47). The follow-up interviews with a subset of the participants indicated that half of the common daily stressors could be discussed with chatbots, potentially reducing the burden on human coping resources. Conclusions Our work suggests that suites of shallow chatbots may offer benefits for both users and designers. As a result, this study’s contributions include the design and evaluation of a novel suite of shallow chatbots for daily stress management, a summary of benefits and challenges associated with random delivery of multiple conversational interventions, and design guidelines and directions for future research into similar systems, including authoring chatbot systems and artificial intelligence–enabled recommendation algorithms.

In-car passive stress sensing could enable the monitoring of stress biomarkers while driving and reach millions of commuters daily (i.e., 123 million daily commuters in the US alone). Here, we present a nonintrusive method to detect stress solely from steering angle data of a regular car. The method uses inverse filtering to convert angular movement data into a biomechanical Mass Spring Damper model of the arm and extracts its damped natural frequency as an approximation of muscle stiffness, which in turn reflects stress. We ran a within-subject study ( N = 22), in which commuters drove a vehicle around a closed circuit in both stress and calm conditions. As hypothesized, cohort analysis revealed a significantly higher damped natural frequency for the stress condition ( P = .023, d = 0.723). Subsequent automation of the method achieved rapid (i.e., within 8 turns) stress detection in the individual with a detection accuracy of 77%.

Human error has been implicated as a causal factor in a large proportion of road accidents. Automated driving systems purport to mitigate this risk, but self-driving systems that allow a driver to entirely disengage from the driving task also require the driver to monitor the environment and take control when necessary. Given that sleep loss impairs monitoring performance and there is a high prevalence of sleep deficiency in modern society, we hypothesized that supervising a self-driving vehicle would unmask latent sleepiness compared to manually controlled driving among individuals following their typical sleep schedules. We found that participants felt sleepier, had more involuntary transitions to sleep, had slower reaction times and more attentional failures, and showed substantial modifications in brain synchronization during and following an autonomous drive compared to a manually controlled drive. Our findings suggest that the introduction of partial self-driving capabilities in vehicles has the potential to paradoxically increase accident risk.

Driving automation systems (DAS) purport to reduce the number of motor vehicle collisions and enhance driving safety by reducing driver workload, providing stable lane-keeping and automated braking when a hazard is detected. Current regulations require drivers to maintain situation awareness when supervising an autonomous vehicle in order to take over driving when necessary. As DAS become standard for motor vehicles, the driver’s role will shift from one of active engagement to passive monitoring. Prior studies have demonstrated that performance on monitoring tasks is reduced following sleep loss. Given the high prevalence of sleep deficiency in the US, we hypothesized that supervision of an autonomous vehicle would unmask underlying sleepiness compared to manual driving among individuals following their typical sleep schedules. During one laboratory visit, participants completed a simulated 42-minute manual and autonomous drive in randomized order. Electroencephalography and electrooculography were recorded continuously during both drives (BrainVision Recorder version 1.21). Slow rolling eye movements (SREMs) were marked by a blinded scorer and were defined as rolling eye movements lasting at least two seconds. The Karolinska Sleepiness Scale (KSS) was collected following each drive. Sleep diaries and actigraphy were collected for two weeks prior to the laboratory visit to measure self-selected sleep habits. SREMs and KSS scores were analyzed using mixed-effects models adjusted for drive order (SAS version 9.4). Seventeen participants (8 females, agemean: 33.7 ± 10.6 years) completed the study. Participants rated themselves sleepier (KSS manualmean: 5.4 ± 2.4; autonomousmean: 6.9 ± 2.1; p = 0.03) and had more SREMs during the autonomous (mean: 19.9 ± 19.5) versus the manual drive (mean: 9.7 ± 11.3; p = 0.04). Our findings demonstrate that people feel sleepier and experience more SREMs during autonomous versus manual driving. This suggests that DAS may reduce driver situation awareness. Further research is needed to determine whether these attentional failures reduce an individual’s ability to take control of a vehicle when necessary. Further analysis is needed to evaluate how sleep history may mediate these findings. SJSU Research Foundation

Automated driving systems that share control with human drivers by using haptic feedback through the steering wheel have been shown to have advantages over fully automated systems and manual driving. Here, we describe an experiment to elicit tacit expectations of behavior from such a system. A gaming steering wheel electronically coupled to the steering wheel in a full-car driving simulator allows two participants to share control of the vehicle. One participant was asked to use the gaming wheel to act as the automated driving agent while another participant acted as the car driver. The course provided different information and visuals to the driving agent and the driver to simulate possible automation failures and conflict situations between automation and the driver. The driving agent was also given prompts that specified a communicative goal at various points along the course. Both participants were interviewed before and after the drive, and vehicle data and drive video were collected. Our results suggest that drivers were able to interpret simple trajectory intentions, such as a lane change, conveyed by the driving agent. However, the driving agent was not able to effectively communicate more nuanced, higher level ideas such as availability, primarily due to the steering wheel being the control mechanism. Torque on the steering wheel without warning was seen most often as a failure of automation. Gentle and steady steering movements were viewed more favorably.

We have developed a generative, improvisational and experimental approach to the design of expressive everyday objects, such as mechanical ottomans, emotive dresser drawers and roving trash barrels. We have found that the embodied design improvisation methodology—which includes storyboarding, improvisation, video prototyping, Wizard-of-Oz lab studies and field experiments—has also been effective in designing the behaviors and interfaces of another kind of robot: the autonomous vehicle. This chapter describes our application of this design approach in developing and deploying three studies of autonomous vehicle interfaces and behaviors. The first, WoZ, focuses on the conceptual phase of the design process, using a talk-aloud protocol, improvisation with experts, and rapid prototyping to develop an interface that drivers can trust and hold in esteem. The second, the Real Road Autonomous Driving Simulator, explores people’s naturalistic reactions to prototypes, through an autonomous driving interface that communicates impending action through haptic precues. The third, Ghost Driver, follows the public deployment of a prototype built upon frugal materials and stagecraft, in a field study of how pedestrians negotiate intersections with autonomous vehicles where no driver is visible. Each study suggests design principles to guide further development.

Autonomous vehicles present Human Machine Interaction (HMI) designers and researchers with new challenges. While design decisions are typically based on financial, technical, political and personal reasons, central to novel interfaces for cars of the future is the user. Users are motivated by needs that are rooted in that which gives them meaning. Hence good design results from a process that centers on understanding users and their concerns. Over the course of a tutorial, our team will guide participants on a four-hour crash-course in human-centered design, focusing on HMI for future autonomous vehicles. To encourage interaction, the tutorial is structured around a design thinking lens, where attendees interact through a human-centered design exercise.

Stating that one trusts a system is markedly different from demonstrating that trust. To investigate trust in automation, we introduce the trust fall: a two-stage behavioral test of trust. In the trust fall paradigm, first the one learns the capabilities of the system, and in the second phase, the ‘fall,’ one’s choices demonstrate trust or distrust. Our first studies using this method suggest the value of measuring behaviors that demonstrate trust, compared with self-reports of one’s trust. Designing interfaces that encourage appropriate trust in automation will be critical for the safe and successful deployment of partially automated vehicles, and this will rely on a solid understanding of whether these interfaces actually inspire trust and encourage supervision.

While automated driving systems will become increasingly capable and common in the future, there will still be instances when human drivers want or need to make corrections to the car's automated driving behavior. We conducted two studies exploring how driving interfaces could be designed to better execute the drivers' intentions. In our first study, adult participants (N=40) experienced a simulated driving scenario that varied the behavior of the car's automation (perfect driving and imperfect driving) and the intervention modalities (takeover and takeover+influence). At certain segments, the car's automation would drive perfectly or weave within the lane. During those times, participants could intervene using the available modalities. When experiencing instances of imperfect driving, drivers who had the ability to takeover+influence intervened more often than drivers who were only given the option to takeover. As intervening would require them to resume full control, drivers in the takeover condition were more tolerant of the imperfect driving. Also, most drivers tried to intervene initially by influencing the car, even those drivers who were only given the ability to takeover. In our second study, we examined how participants (N=40) of different demographics (high school students and seniors) would respond when they were subjected to the imperfect driving scenarios. High school drivers intervened just as much as the adult drivers. However, senior drivers intervened far less. These two studies suggest that when intervention is necessary, human drivers have a desire for shared control, which allows them to act as supervisors rather than operators of automated vehicles.

A driver’s awareness while on the road is a critical factor in his or her ability to make decisions to avoid hazards, plan routes and maintain safe travel. Situational awareness is gleaned not only from visual observation of the environment, but also the audible cues the environment provides police sirens, honking cars, and crosswalk beeps, for instance, alert the driver to events around them. In our ongoing project on “investigating the influence of audible cues on driver situational awareness”, we implemented a custom audio engine that synthesizes in real time the soundscape of our driving simulator and renders it in 3D. This paper describes the implementation of this system, evaluates it and suggests future improvements. We believe that it provides a good example of use of a technology developed by the computer music community outside of this field and that it demonstrates the potential of the use of driving simulators as a music performance venue.

This video introduces a methodology for simulating an autonomous vehicle on open public roads. The video showcases participant reaction footage collected in the RRADS (Real Road Autonomous Driving Simulator). Although our study using this simulator did not use overt deception--the consent form clearly states that a licensed driver is operating the vehicle--the protocol was designed to support suspension of disbelief. Several participants who did not read the consent form clearly strongly believed that the vehicle was autonomous; this provides a lens onto the attitudes and concerns that people in real-world autonomous vehicles might have, and also points to ways that a protocol that deliberately used misdirection could gain ecologically valid reactions from study participants.

This platform paper introduces a methodology for simulating an autonomous vehicle on open public roads. The paper outlines the technology and protocol needed for running these simulations, and describes an instance where the Real Road Autonomous Driving Simulator (RRADS) was used to evaluate 3 prototypes in a between-participant study design. 35 participants were interviewed at length before and after entering the RRADS. Although our study did not use overt deception---the consent form clearly states that a licensed driver is operating the vehicle---the protocol was designed to support suspension of disbelief. Several participants who did not read the consent form clearly strongly believed that they were interacting with a fully autonomous vehicle. The RRADS platform provides a lens onto the attitudes and concerns that people in real-world autonomous vehicles might have, and also points to ways that a protocol deliberately using misdirection can gain ecologically valid reactions from study participants.

In a fully autonomous car - steering, deceleration and acceleration are completely controlled by the intelligence built into it. This leads to a major change in user experience as the driver needs different information and is less involved in the driving itself. We are working on redesigning the user experience of the autonomous car from a holistic user experience perspective of the driver. Our design was driven by a more anthropomorphic view of the car. The driving metaphor being that an autonomous car and the driver are travelling companions. Our work currently is geared towards emulating the key characteristics of this friendship and formulating a design scheme to design each device (and modality within). In this paper we introduce this idea and exemplify some design decisions by referring to a new dashboard for the autonomous car simulator at Stanford. This paper will underline our position that we strongly believe user experience design for autonomous cars needs a tremendous shift towards a more elaborated understanding of user interactions as well as new approaches in order to address the challenges given by the changed experience of the autonomous driving.