MAIN PROCEEDINGS
AutomotiveUI '18- Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Full Citation in the ACM Digital LibrarySESSION: Haptics and Gestures
May the Force Be with You: Ultrasound Haptic Feedback for Mid-Air Gesture Interaction in Cars
The use of ultrasound haptic feedback for mid-air gestures in cars has been proposed to provide a sense of control over the user's intended actions and to add touch to a touchless interaction. However, the impact of ultrasound feedback to the gesturing hand regarding lane deviation, eyes-off-the-road time (EORT) and perceived mental demand has not yet been measured. This paper investigates the impact of uni- and multimodal presentation of ultrasound feedback on the primary driving task and the secondary gesturing task in a simulated driving environment. The multimodal combinations of ultrasound included visual, auditory, and peripheral lights. We found that ultrasound feedback presented unimodally and bimodally resulted in significantly less EORT compared to visual feedback. Our results suggest that multimodal ultrasound feedback for mid-air interaction decreases EORT whilst not compromising driving performance nor mental demand and thus can increase safety while driving.
Exploring the Use of Mid-Air Ultrasonic Feedback to Enhance Automotive User Interfaces
Employing a 2x2 within-subjects design, forty-eight experienced drivers (28 male, 20 female) undertook repeated button selection and 'slider-bar' manipulation tasks, to compare a traditional touchscreen with a virtual mid-air gesture interface in a driving simulator. Both interfaces were tested with and without haptic feedback generated using ultrasound. Results show that combining gestures with mid-air haptic feedback was particularly promising, reducing the number of long glances and mean off-road glance time associated with the in-vehicle tasks. For slider-bar tasks in particular, gestures-with-haptics was also associated with the shortest interaction times, highest number of correct responses and least 'overshoots', and was favoured by participants. In contrast, for button-selection tasks, the touchscreen was most popular, enabling the highest accuracy and quickest responses, particularly when combined with haptic feedback to guide interactions, although this also increased visual demand. The study shows clear potential for gestures with mid-air ultrasonic haptic feedback in the automotive domain.
Selection Facilitation Schemes for Predictive Touch with Mid-air Pointing Gestures in Automotive Displays
Predictive touch is an HMI technology that relies on inferring, early in the pointing gesture, the interface item a driver or passenger intends to select on an in-vehicle display [1, 2]. It simplifies and expedites the selection task, thereby reducing the associated interaction effort. This paper presents two studies on drivers using predictive touch and focuses on evaluating the best means to facilitate selecting the intended on-display item. This includes immediate midair selection with the system autonomously auto-selecting the predicted interface component, hover/dwell and drivers pressing a button on the steering wheel to execute the selection action. These were arrived at in an expert workshop study with twelve participants. The results of the subsequent evaluation study with twenty four participants demonstrate, using quantitative and qualitative measures, that immediate mid-air selection is a promising assistive scheme, where drivers need not touch a physical surface to select interface components, thus touch-free control.
Reducing the Attentional Demands of In-Vehicle Touchscreens with Stencil Overlays
Vehicle manufacturers are increasingly using touchscreens to support driver access to controls. However, input mechanisms displayed on touchscreens lack the tactile sensations of physical controls, creating risks of greater demand for visual attention. These risks can potentially be mitigated by restoring some degree of tactile feedback to touchscreen interaction. This paper describes a study that examines whether touchscreen target selection during simulated driving is improved by overlaying the touchscreen with a see-through 3D printed stencil that allows underlying touchscreen controls to be located or guided by feel. Results showed that touchscreen targets were selected more quickly and with shorter periods of visual attention towards the touchscreen when the stencil was present than when it was absent. Subjective preferences also favoured the stencil condition. The work demonstrates the value of adding tactile feedback to touchscreen interaction, and shows that stencils are a simple and effective way to reduce attentional demands.
Investigation of Thermal Stimuli for Lane Changes
Haptic feedback has been widely studied for in-car interactions. However, most of this research has used vibrotactile cues. This paper presents two studies that examine novel thermal feedback for navigation during simulated driving for a lane change task. In the first, we compare the distraction and time differences of audio and thermal feedback. The results show that the presentation of thermal stimuli does not increase lane deviation, but the time needed to complete a lane change increased by 1.82 seconds. In the second study, the influence of variable changes of thermal stimuli on the lane change task performance was tested. We found that the same stimulus design for warm and cold temperatures does not always elicit the same results. Furthermore, variable alterations can have different effects on specified tasks. This suggests that the design of thermal stimuli is highly dependent on what task result should be maximized.
SESSION: Attentive User Interfaces
Let Me Finish before I Take Over: Towards Attention Aware Device Integration in Highly Automated Vehicles
A major promise of automated vehicles is to render it possible for drivers to engage in nondriving related tasks, a setting where the execution pattern will switch from concurrent to sequential multitasking. To allow drivers to safely and efficiently switch between multiple activities (including vehicle control in case of Take-Over situations), we postulate that future vehicles should incorporate capabilities of attentive user interfaces, that precisely plan the timing of interruptions based on driver availability. We propose an attention aware system that issues Take-Over Requests (1) at emerging task boundaries and (2) directly on consumer devices such as smartphones or tablets. Results of a driving simulator study (N=18), where we evaluated objective, physiological, and subjective measurements, confirm our assumption: attention aware Take-Over Requests have the potential to reduce stress, increase Take-Over performance, and can further raise user acceptance/trust. Consequently, we emphasize to implement attentive user interfaces in future vehicles.
Predicting Environmental Demand and Secondary Task Engagement using Vehicle Kinematics from Naturalistic Driving Data
In this paper, we focus on exploiting the vehicle kinematic signals from a naturalistic driving dataset to estimate motor control difficulty associated with the driving environment (i.e., curvature and poor surface condition), and detect whether the driver was engaged in a secondary task or not. Advanced driver assistance systems can exploit such driver behavior models to better support the driving task and improve safety. Hidden Markov models were built from sequential data; lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher), whereas GPS speed and steering wheel position were used to classify secondary task engagement (yes vs. no). The resulting accuracy for lower motor control difficulty classification was 72.03%, whereas 71.27% was achieved for higher motor control difficulty. Cases of engagement in secondary task vs. not were classified with 84.4% and 74.0% accuracy, respectively.
Using Smartwatch Inertial Sensors to Recognize and Distinguish Between Car Drivers and Passengers
People increasingly interact with social media or other apps on their smartphones while driving car. This is naturally a major safety concern, and it remains unclear how to avoid or limit such interaction. We investigate this problem through human activity recognition (HAR) where we developed a system called IRIS, which collects smartwatch accelerometer data and analyses the data through machine learning and predicts if the data origins from a driver or a passenger. We report from a field experiment with 24 participants acting as drivers or passengers where we achieved an overall prediction accuracy of 87%. We further found that various road segments had less effect on the accuracy than anticipated, but we also found that passenger tasks had a negative effect on recognition accuracy. We discuss several implications from findings.
The Effect of Road Bumps on Touch Interaction in Cars
Touchscreens are a common fixture in current vehicles. With autonomous driving, we can expect touch interaction with such in-vehicle media systems to exponentially increase. In spite of vehicle suspension systems, road perturbations will continue to exert forces that can render in-vehicle touch interaction challenging. Using a motion simulator, we investigate how different vehicle speeds interact with road features (i.e., speed bumps) to influence touch interaction. We determine their effect on pointing accuracy and task completion time. We show that road bumps have a significant effect on touch input and can decrease accuracy by 19%. In light of this, we developed a Random Forest (RF) model that improves touch accuracy by 32.0% on our test set and by 22.5% on our validation set. As the lightweight model uses only features that can easily be determined through inertial measurement units, this model could be easily deployed in current automobiles.
SESSION: User Experience and Acceptance
Who is Generation A?: Investigating the Experience of Automated Driving for Different Age Groups
The prevalence of Automated Driving Systems (ADS) is expected to open up many possibilities for different user groups with individual needs and challenges. Former secondary/tertiary tasks can become primary tasks, and driving with all its interactions and responsibilities steps back or disappears at all. At higher levels of AD it is expected that the elderly could maintain or regain individual mobility, thus, play a major role for future markets. To understand individual mindsets concerning technology acceptance and user needs we conducted an explorative interview study (N=27). In a simulated automated driving environment, driving experience over time was compared across three age groups (elderly people >65, younger adults <30, younger adults <30 with age simulation suite), utilizing the STAM model for content analysis. Results of the age-comparison indicate no major differences in the general technology acceptance, however, fine-grained analysis revealed interesting differences in participants' perceptions concerning UX design requirements.
How to Design Valid Simulator Studies for Investigating User Experience in Automated Driving: Review and Hands-On Considerations
Simulator studies have been conducted in the automotive domain since the 1960s. Recently, automated driving studies have become more popular as real-world automated cars start to emerge but at this time not all levels of automation can be realized. A simulation does not entail all details of real driving, creating a realistic simulation experience - both on a psychological and physical level - proposes recurring challenges. These are among others: sample acquisition, simulator sickness, simulator training, interface design, take-over requests and secondary tasks in automated driving simulator studies. In this paper, we review existing literature and summarize important lessons from simulations in the domain of driving automation to provide considerations for studies investigating driver behavior in the age of highly automated driving.
Calibration of Trust Expectancies in Conditionally Automated Driving by Brand, Reliability Information and Introductionary Videos: An Online Study
The design of a priori information about a conditionally automated driving (CAD) function influences the extent of effective usage of this function. The present online study investigated the effects of preliminary reliability and brand information on trust and acceptance for CAD. N = 519 participants were randomly assigned to (1) a reliability condition (high or low) and (2) an original equipment manufacturer (OEM) reputation condition (i.e., above average, average, below average, baseline). To measure the effect of CAD experience, participants were additionally exposed to four short videos of a driver interacting with a CAD function. Study results provide first evidence for an influence of OEM branding and reliability on CAD evaluation. We observed a trend towards more favorable attitudes for high compared to low reliability. This effect depends on the respective OEM reputation. The findings hold implications for the design of communication on automated vehicles to calibrate a priori assessment.
Acceptance Factors for Future Workplaces in Highly Automated Trucks
Once highly automated vehicles become available, drivers will be freed to perform activities other than driving when automated driving mode is activated. Such activities could include relaxing, reading, exercising, or working. The work of professional drivers such as truck drivers can be expected to be especially affected by this technology and to change accordingly as highly automated trucks enable the completion of other working related tasks during automated driving. But will such mobile working places be accepted by truck drivers? In this paper, we report on a survey (N=23) assessing technology acceptance towards Future Workplaces in highly Automated Trucks (FWAT). Results show that a majority of drivers is rather skeptical about FWAT, but that acceptance can be expected to strongly vary. Our paper provides some guidance on how to explain this variance and presents relevant acceptance factors for future FWAT usage.
SESSION: Augmented Reality
Establishing the Role of a Virtual Lead Vehicle as a Novel Augmented Reality Navigational Aid
This paper reports on two studies investigating how following a lead vehicle could act as a metaphor for an Augmented Reality (AR) system to support navigation tasks. For the first formative study, 34 participants completed a video-based evaluation of the role of a real lead vehicle when navigating a coherent journey. Verbal protocols indicated that a lead vehicle may be a valuable navigation aid at a range of different junction types, but not where drivers may desire a preview of upcoming steps or their overall orientation. A subsequent driving simulator study with 22 participants examined whether an AR lead vehicle may support drivers when navigating at complex junctions, specifically large multi-exit roundabouts. The virtual car led to good navigation and driving performance, which was comparable to a more traditional screen-fixed interface. Overall, this work demonstrates that a virtual lead vehicle may be beneficial within AR navigation devices.
Camera-View Augmented Reality: Overlaying Navigation Instructions on a Real-Time View of the Road
Augmented reality navigation aids have been investigated in a number of studies, and results are encouraging, especially for large, head-up displays. However, such displays are not commercially available -- in fact they are rare in laboratories as well. In this paper we ask: would drivers be well-served with a navigation aid that overlays AR content on a live feed from a camera that shows the forward road? To answer this question we conducted a simulator-based study and compared the use of such a navigation aid to the use of a head-up AR aid, as well as to the use of 2D map shown on a head-down display. Our results confirm prior results that a head-up AR navigation aid can keep drivers' visual attention on the road, and that drivers like such a navigation aid. Our results also indicate that a camera-view AR navigation aid might not be well-received by drivers.
Effect of Volumetric Displays on Depth Perception in Augmented Reality
Augmented reality (AR) head-up displays (HUD) have previously been explored as a potential information delivery system for drivers. In driving scenarios, correct perception of virtual object distance assists with effective use of AR HUDs in safety-critical applications (e.g., collision warnings). AR volumetric displays purportedly offer increased accuracy of distance perception through consistent presentation of oculomotor cues such as vergence and accommodation over traditional displays. For this paper, we investigated volumetric AR displays as a mean of enhancing perception of virtual objects registered to the real world, specifically in terms of distance perception as a result of binocular cues. We designed and ran an experiment where participants controlled and placed virtual objects next to real-world counterparts at ranges between 7-12 meters. We found that the volumetric AR display outperformed a traditional fixed focal plane AR HUD when considering distances 5 meters greater than the traditional display's focal depth.
Augmented Reality Displays for Communicating Uncertainty Information in Automated Driving
Safe manual driving performance following takeovers in conditionally automated driving systems is impeded by a lack in situation awareness, partly due to an inappropriate trust in the system's capabilities. Previous work has indicated that the communication of system uncertainties can aid the trust calibration process. However, it has yet to be investigated how the information is best conveyed to the human operator. The study outlined in this publication presents an interface layout to visualise function-specific uncertainty information in an augmented reality display and explores the suitability of 11 visual variables. 46 participants completed a sorting task and indicated their preference for each of these variables. The results demonstrate that particularly colour-based and animation-based variables, above all hue, convey a clear order in terms of urgency and are well-received by participants. The presented findings have implications for all augmented reality displays that are intended to show content varying in urgency.
SESSION: Coordination with Other Road Users
Follow Me: Exploring Strategies and Challenges for Collaborative Driving
Current research on Vehicle-to-Vehicle (V2V) communication aims at improving interaction between different vehicles by communication technologies and is mainly focused on driver-to-driver interaction. But how do drivers and passengers of two vehicles that have the same destination communicate with each other? In such a collaborative driving scenario, several factors such as the environmental context or the behavior of the vehicle occupants may influence the communication. In order to explore how information is exchanged in collaborative driving, we conducted an exploratory in-situ study with seven groups of two driver/co-driver pairs each, located in two separate vehicles. During the ride, the participants had to drive collaboratively on a predefined route solving different subtasks. We found that different social (e.g., driving habits, unpredicted intentions) and contextual factors (e.g., night/rain conditions, size or color of the vehicle) influenced collaboration. Our findings provide a deeper understanding of collaborative driving and inform future V2V communication designs.
To Cross or Not to Cross: Urgency-Based External Warning Displays on Autonomous Vehicles to Improve Pedestrian Crossing Safety
Autonomous vehicles (AV) may be able to show visual displays on their external surface to support pedestrian communication with the AV. Pedestrian crossing at uncontrolled locations is safety-critical and clear communication between the pedestrian and the AV is important in this situation. However, research to date has not been clear on how the AV should communicate with pedestrians. We designed two sets of warnings on AVs based on the perception of warning urgency. Each set consisted of three warnings that differed in color and flashing pattern and indicated distinct safety-related information. A survey was conducted to investigate how people make decisions, warnings within and outside of the driving context, and perceived warning compliance. Results showed that people were risk averse in crossing and cars with warning displays were perceived as more urgent. This paper contributes uniquely in exploring research-based approaches on designing warnings to improve pedestrian crossing safety.
A Field Study of Pedestrians and Autonomous Vehicles
Autonomous vehicles have been in development for nearly thirty years and recently have begun to operate in real-world, uncontrolled settings. With such advances, more widespread research and evaluation of human interaction with autonomous vehicles (AV) is necessary. Here, we present an interview study of 32 pedestrians who have interacted with Uber AVs. Our findings are focused on understanding and trust of AVs, perceptions of AVs and artificial intelligence, and how the perception of a brand affects these constructs. We found an inherent relationship between favorable perceptions of technology and feelings of trust toward AVs. Trust in AVs was also influenced by a favorable interpretation of the company's brand and facilitated by knowledge about what AV technology is and how it might fit into everyday life. To our knowledge, this paper is the first to surface AV-related interview data from pedestrians in a natural, real-world setting.
¡Vamos!: Observations of Pedestrian Interactions with Driverless Cars in Mexico
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.
SESSION: Driving in Context
Where to Look: Exploring Peripheral Cues for Shifting Attention to Spatially Distributed Out-of-View Objects
Knowing the locations of spatially distributed objects is important in many different scenarios (e.g., driving a car and being aware of other road users). In particular, it is critical for preventing accidents with objects that come too close (e.g., cyclists or pedestrians). In this paper, we explore how peripheral cues can shift a user's attention towards spatially distributed out-of-view objects. We identify a suitable technique for visualization of these out-of-view objects and explore different cue designs to advance this technique to shift the user's attention. In a controlled lab study, we investigate non-animated peripheral cues with audio stimuli and animated peripheral cues without audio stimuli. Further, we looked into how user's identify out-of-view objects. Our results show that shifting the user's attention only takes about 0.86 seconds on average when animated stimuli are used, while shifting the attention with non-animated stimuli takes an average of 1.10 seconds.
Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation
Drowsiness is a main cause of serious traffic accidents, and problematic within the ongoing automation of the driving task. Several approaches for drowsiness detection have been published and are in operation in production cars for manual driving. To assess differences in the development of drowsiness between manual and automated driving, and to further investigate the potential of subjective ratings, we conducted a driving simulator study (N=30). The self-assessment was based on the Karolinska Sleepiness Scale (KSS), during and after driving. Furthermore, we examined the impact of travel time and driver age (20-25, 65-70 years). Results confirm that driving mode and travel time have a significant effect on the development of drowsiness. In both age groups, self-ratings were higher for automated driving and particularly by younger subjects. All subjects estimated themselves drowsier during driving. The gained knowledge can be helpful for the development of future driver-vehicle interfaces in driver drowsiness detection.
Don't Be Alarmed: Sonifying Autonomous Vehicle Perception to Increase Situation Awareness
Lack of trust can arise when people do not know what autonomous vehicles perceive in the environment. To convey this information without causing alarm or compelling people to act, we designed and evaluated a way to sonify an autonomous vehicle's perception of salient driving events using abstract auditory icons, or "earcons." These are localized in space using an in-car quadraphonic speaker array to correspond with the direction of events. We describe the interaction design for these awareness cues and a validation experiment (N=28) examining the effects of sonified events on drivers' sense of situation awareness, comfort, and trust. Overall, this work suggests that our designed earcons do improve people's awareness of in-simulation events. The effect of the increased situational awareness on trust and comfort is inconclusive. However, post-study design feedback suggests that sounds should have low levels of intensity and dissonance, and a sense of belonging to a common family.
Why Disable the Autopilot?
The number of systems in commercially available vehicles that assist or automate driving tasks is rapidly increasing. At least for the next decade, using such systems remains up to the discretion of the user. In this paper, different reasons why drivers may disengage the autopilot are investigated. This was done through a simulator study in which the system could drive fully automated, but where participants could also disengage the system. Qualitative data were collected about why participants disengaged the autopilot. The analysis of the data revealed six themes covering the reasons why participants disabled the autopilot: The speed maintained by the autopilot, the behavior of the autopilot in relation to overtaking other vehicles, onset of boredom, onset of sleepiness, lack of trust in the autopilot, and enjoyment of manual driving. On the basis of the results, design opportunities are proposed to counteract the tendency to not use automated driving systems.
SESSION: Special Approaches
Design Guidelines for Reliability Communication in Autonomous Vehicles
Currently offered autonomous vehicles still require the human intervention. For instance, when the system fails to perform as expected or adapts to unanticipated situations. Given that reliability of autonomous systems can fluctuate across conditions, this work is a first step towards understanding how this information ought to be communicated to users. We conducted a user study to investigate the effect of communicating the system's reliability through a feedback bar. Subjective feedback was solicited from participants with questionnaires and semi-structured interviews. Based on the qualitative results, we derived guidelines that serve as a foundation for the design of how autonomous systems could provide continuous feedback on their reliability.
gAR-age: A Feedback-Enabled Blended Ecosystem for Vehicle Health Monitoring
Standard vehicle maintenance activities can be challenging, time-consuming, error-prone, and expensive. While there is a lot of innovative work that has incorporated latest technologies to provide newer forms of interaction between users and vehicles, there has been less inclination towards utilizing these technologies to enhance activities like vehicle maintenance. The ability to draw parallels simultaneously from physical interaction with vehicles and analysis of recorded data is vital to support prompt and effective decision-making. To blur the disparity between these real and virtual worlds, we present "gAR-age"- an ecosystem that enables maintenance personnel to interact with both worlds in a common setting. By learning from historical changes in vehicular components, user behavior, and feedback, this blended ecosystem allows multi-channel communication among users, featuring personalized contextual insights, thereby enabling users to make data-driven decision on the fly.
How Usability Can Save the Day - Methodological Considerations for Making Automated Driving a Success Story
It will not be long until Level 3 Automated Driving Systems (L3 ADS) enter the consumer market. An important role corresponds to methodology development. The present paper gives impetus to the process of developing robust methods for evaluating Human-Machine Interfaces (HMI) for L3 ADS. First, a literature review on automotive interfaces concerning methodology application is outlined showing that studies often lack to provide both self-report and observational data. To derive a comprehensive image of HMI quality, we recommend multi-method approach in user research. Subsequently, we provide an overview of state-of-the-art self-report and observational measures. From the availability of measures and the necessity to include both in user studies, the issue of the performance-preference dissociation arises. It threatens study designs and interpretation of results. Following methodological recommendations from the present work supports researchers and practitioners in the area of automated driving for proper study design and interpretation of study results.
Where Autonomous Buses Might and Might Not Bridge the Gaps in the 4 A's of Public Transport Passenger Needs: a Review
In the domain of public transport, automation provides several advantages which can lead to better services for the customers. For tapping the full potential of future automated public transport services, passenger requirements have to be thoroughly considered in order to avoid shortcomings discouraging future users to remain or become customers and to support the role of public transit as sustainable backbone of transport.
This paper assesses the potential of autonomous bus services with respect to four categories of passenger requirements described as the "4 A's of Public Transport Passenger Needs": availability, affordability, accessibility, and acceptability. Based on a review of currently discussed scenarios of automation, benefits and risks regarding the specific needs of different passenger groups are explored for these four categories. Finally, open issues which require special attention or further research are identified.
Eliciting Driver Stress Using Naturalistic Driving Scenarios on Real Roads
We propose a novel method for reliably inducing stress in drivers for the purpose of generating real-world participant data for machine learning, using both scripted in-vehicle stressor events and unscripted on-road stressors such as pedestrians and construction zones. On-road drives took place in a vehicle outfitted with an experimental display that lead drivers to believe they had prematurely ran out of charge on an isolated road. We describe the elicitation method, course design, instrumentation, data collection procedure and the post-hoc labeling of unplanned road events to illustrate how rich data about a variety of stress-related events can be elicited from study participants on-road. We validate this method with data including psychophysiological measurements, video, voice, and GPS data from (N=20) participants. Results from algorithmic psychophysiological stress analysis were validated using participant self-reports. Results of stress elicitation analysis show that our method elicited a stress-state in 89% of participants.
Looming Auditory Collision Warnings for Semi-Automated Driving: An ERP Study
Looming sounds can be an ideal warning notification for emergency braking. This agrees with studies that have consistently demonstrated preferential brain processing for looming stimuli. This study investigates and demonstrates that looming sounds can similarly benefit emergency braking in managing a vehicle with adaptive cruise control (ACC). Specifically, looming auditory notifications induced the faster emergency braking times relative to a static auditory notification. Next, we compare the event-related potential (ERP) evoked by a looming notification, relative to its static equivalent. Looming notifications evoke a smaller fronto-central N2 amplitude than their static equivalents. Thus, we infer that looming sounds are consistent with the visual experience of an approaching collision and, hence, induced a corresponding performance benefit. Subjective ratings indicate no significant differences in the perceived workload across the notification conditions. Overall, this work suggests that auditory warnings should have congruent physical properties with the visual events that they warn for.
Evaluating How Interfaces Influence the User Interaction with Fully Autonomous Vehicles
With increasing automation, occupants of fully autonomous vehicles are likely to be completely disengaged from the driving task. However, even with no driving involved, there are still activities that will require interfaces between the vehicle and passengers. This study evaluated different configurations of screens providing operational-related information to occupants for tracking the progress of journeys. Surveys and interviews were used to measure trust, usability, workload and experience after users were driven by an autonomous low speed pod. Results showed that participants want to monitor the state of the vehicle and see details about the ride, including a map of the route and related information. There was a preference for this information to be displayed via an onboard touchscreen device combined with an overhead letterbox display versus a smartphone-based interface. This paper provides recommendations for the design of devices with the potential to improve the user interaction with future autonomous vehicles.
An Investigation into Glance-free Operation of a Touchscreen With and Without Haptic Support in the Driving Simulator
In an empirical study conducted with 25 participants, touch interaction with and without haptic feedback on an 8-inch touchscreen with glance-free operation have been compared with each other. For this purpose, a main menu consisting of four control elements, each with a size of 86 mm x 51 mm, was selected from the touchscreen's existing menu structure. The comparison of with and without haptic support shows that the error rate without haptic feedback is significantly higher than with haptic feedback. This effect is shown in the driving task performed in the driving simulator. Three causes for the significantly higher error rate in conventional operation have been discovered. Furthermore, it was also discovered that the subjective operational stress with haptic support is significantly lower than without haptic support. There were no significant difference in driving lane deviation and efficiency.
Designing a Guardian Angel: Giving an Automated Vehicle the Possibility to Override its Driver
A function of an automated driving vehicle that can override a human driver while driving manually could work as a guardian angel in the car. It can take over control if it detects an imminent accident and has a possibility to avoid it. Because of the urgency of the intervention, there is not enough time to warn the driver in advance. In a study, feedback was collected from users how they perceived such an action while driving in a simulator. Additional feedback was collected about the general design and user interface of such a system. From an ethical point of view, we discovered discrepancies in the views of our participants regarding automated driving functions that need to be addressed in future development.
I See Your Point: Integrating Gaze to Enhance Pointing Gesture Accuracy While Driving
Mid-air pointing gestures enable drivers to interact with a wide range of vehicle functions, without requiring drivers to learn a specific set of gestures. A sufficient pointing accuracy is needed, so that targeted elements can be correctly identified. However, people make relatively large pointing errors, especially in demanding situations such as driving a car. Eye-gaze provides additional information about the drivers' focus of attention that can be used to compensate imprecise pointing. We present a practical implementation of an algorithm that integrates gaze data, in order to increase the accuracy of pointing gestures. A user experiment with 91 participants showed that our approach led to an overall increase of pointing accuracy. However, the benefits depended on the participants' initial gesture performance and on the position of the target elements. The results indicate a great potential to support gesture accuracy, but also the need for a more sophisticated fusion algorithm.