Multitasking with a touch screen user-interface while driving is known to impact negatively driving performance and safety. Literature shows that list scrolling interfaces generate more visual-manual distraction than structured menus and sequential navigation. Depth and breadth trade-offs for structured navigation have been studied. However, little is known on how secondary task characteristics interact with those trade-offs. In this study, we make the hypothesis that both menu's depth and task complexity interact in generating visual-manual distraction. Using a driving simulation setup, we collected telemetry and eye-tracking data to evaluate driving performance. Participants were multitasking with a mobile app, presenting a range of eight depth and breadth trade-offs under three types of secondary tasks, involving different cognitive operations (Systematic reading, Search for an item, Memorize items' state). The results confirm our hypothesis. Systematic interaction with menu items generated a visual demand that increased with menu's depth, while visual demand reach an optimum for Search and Memory tasks. We discuss implications for design: In a multitasking context, display design effectiveness must be assessed while considering menu's layout but also cognitive processes involved.

In light of recent incidents, it has become increasingly relevant to determine who is responsible in case of accidents involving automated vehicles. In this paper, we investigate the question of liability in automated vehicles of SAE levels 3 and above. We claim that there is a mismatch between current liability practices, where a designated driver is usually held responsible, and future perspectives, where the human assumes more and more a passive passenger -like role. Our claims are supported by the results from an interview study with insurance companies from two European countries. We show that insurers lack sufficient data to make informed decisions on how to apportion liability in SAE level 3+ scenarios. We discuss how these considerations have to be reflected in interfaces for the driver in order to make the legal status transparent for the driver.

This paper proposes a taxonomy of autonomous vehicle handover situations with a particular emphasis on situational awareness. It focuses on a number of research challenges such as: legal responsibility, the situational awareness level of the driver and the vehicle, the knowledge the vehicle must have of the driver's driving skills as well as the in-vehicle context. The taxonomy acts as a starting point for researchers and practitioners to frame the discussion on this complex problem.

This paper presents an overview of a requirements capture process and identified design challenges for a Mixed Reality (MR) platform which is designed to be used by security critical agents at operational command level. The platform aims to support a wide range of scenarios ranging from firearms training to chemical and nuclear incidents. The paper identifies a number of key design issues which are required to provide effective training across the range of scenarios discussed.

This study is assessing the sensitivity of an affordable BCI device in the context of driver distraction in both low-fidelity simulator and real-world driving environments. Twenty-three participants performed a car following task while using a smartphone application involving a range of generic smartphone widgets. On the first hand, the results demonstrated that secondary task completion time is a fairly robust metric as it is sensitive to user-interfaces style while being consistent between the two driving environments. On the second hand, while the BCI attention level metric was not sensitive to the different user-interfaces, we found it to be significantly higher in the real-driving environment than in the simulated one, which reproduces findings obtained with medical-grade sensors.

In aviation, pilots interact with autopilots almost on a daily basis. With semi-autonomous vehicles, this is not yet the case. In our work, we aimed at finding out what we can learn from pilots' current experiences for the domain of autonomous driving and what implications can be derived. We conducted three in-depth interviews with pilots to investigate how pilots currently handle handover situations to and from the autopilot, which information is relevant for this transition to be successful, how pilots react in critical situations, how handovers are trained, and how flying and handover skills are maintained. We compare the gained insights with the domain of autonomous driving and reflect on implications for handovers and (de) skilling. Our findings suggest that the AUI community can learn from aviation in areas such as situation awareness, transparency of system status, the need for a primary drive display, calibrated (dis) trust, and driver training.

Designing safe and effective systems for control transitions between human and vehicle is a difficult task, due to increased reaction times and potentially inattentive drivers. In order to respond to these difficulties, this paper presents an overview of interaction solutions for control transitions between manual and autonomous driving modes. The paper examines technology patents, as well as academic publications. The paper's first contribution is an examination of the current state of the art of control transition interfaces in automated vehicles. The paper's second contribution is the reusable categorization framework developed for this overview. The results are used to identify holes and potentials regarding control transition design, including strong focus on the system over the human, lacking fallback performance, and the potentials of effective driving mode communication. These aspects point the way towards the challenges to be solved - together with how they might be solved - for safe and effective control transitions.

The rise of autonomous and fully autonomous vehicles requires a closer examination of how people will interact with them. In this workshop, we especially address two areas: (1) What can we learn from mobile HCI for the interaction design between human drivers and the autonomous vehicle? (2) What opportunities for mobile HCI arise due to new freedoms for drivers, when they also take on the role of passengers in autonomous vehicles? The workshop will seek to further develop the challenges involved and/or to suggest early solutions through the use of a highly participative format.

This paper describes a collaborative digital story telling environment which uses a tablet, an augmented reality visor and an advanced data mining back-end system. This paper primarily focuses on collaboration as a method of designing new stories (from new or existing contents), sharing the experiences and improving sense of presence, flow and place. It further enhances the experience through the use of gamification to encourage collaboration and interaction between users. It also examines issues relating to visualization of stories.

This workshop will focus on the problem of occupant and vehicle situational awareness with respect to automated vehicles when the driver must take over control. It will explore the future of fully automated and mixed traffic situations where vehicles are assumed to be operating at level 3 or above. In this case, all critical driving functions will be handled by the vehicle with the possibility of transitions between manual and automated driving modes at any time. This creates a driver environment where, unlike manual driving, there is no direct intrinsic motivation for the driver to be aware of the traffic situation at all times. Therefore, it is highly likely that when such a transition occurs, the driver will not be able to transition either safely or within an appropriate period of time. This workshop will address this challenge by inviting experts and practitioners from the automotive and related domains to explore concepts and solutions to increase, maintain and transfer situational awareness in semi-automated vehicles.

In the scope of autonomous driving, the question arises if the increased use of automated systems will have an impact on driver's skills in handling the car in the long term. In order to gain more insights on the issue of driver deskilling and how it relates to driving experience and time intervals of non-driving, we conducted an online survey (n=703) considering three driver groups. We found that initial skilling is more of an issue than deskilling after long periods of driving inactivity, i.e., while once learned driving skills seem to remain stable after longer periods of non-driving, they are much more influenced by driving experience in terms of annual mileage and frequency of use. Applied to the autonomous context, this means that drivers must be trained to a high enough skill level or require sufficient manual driving experience, in order to be able to react properly when driving themselves.

Innovative in-car applications provided on smartphones can deliver real-time alternative mobility choices and subsequently generate visual-manual demand. Prior studies have found that multi-touch gestures such as kinetic scrolling are problematic in this respect. In this study we evaluate three prototype tasks which can be found in common mobile interaction use-cases. In a repeated-measures design, 29 participants interacted with the prototypes in a car-following task within a driving simulator environment. Task completion, driving performance and eye gaze have been analysed. We found that the slider widget used in the filtering task was too demanding and led to poor performance, while kinetic scrolling generated a comparable amount of visual distraction despite it requiring a lower degree of finger pointing accuracy. We discuss how to improve continuous list browsing in a dual-task context.

In the future autonomous vehicles will drive on our roads. It is unlikely that we will immediately move from manual to fully autonomous vehicles, instead the mix will change over time and include a large number of semi-autonomous vehicles. As a result human drivers will need to take over in specific situations (e.g., when sensors fail) and there will be an interplay between autonomous systems and human agents. However, human drivers will not be able to practice driving so regularly. Our assumption is, that the reliance on semi-autonomous systems will lead to a deterioration in driving skills. In this paper, we present a three year project called MaDSAV (Maintaining Driving Skills in semi-Autonomous Vehicles), which tackles this problem.

This chapter describes an approach to the development of virtual representations of real places. The work was funded under the European Union’s €20 m Future and Emerging Technologies theme of the 5th Framework Programme, “Presence”. The aim of the project, called BENOGO, was to develop a novel technology based on real-time image-based rendering (IBR) for representing places in virtual environments. The specific focus of the work presented here concerned how to capture the essential features of real places, and how to represent that knowledge, so that the team developing the IBR-based virtual environments could produce an environment that was as realistic as possible. This involved the development and evaluation of a number of virtual environments and the evolution of two complementary techniques; the Place Probe and Patterns of place.

Early research on head-worn computers (HWCs) has focused on hardware and specific applications. However, there is little research about the everyday usage of head-worn computers in particular aspects such as: context of use, social acceptance across different activities, audiences and interaction techniques. This paper provides insights into the use of head-worn computers by capturing the opinions of novice and expert users through a survey, a three-week diary study, and interviews. The overarching finding is that the context of use is critical, either due to the need to support micro-interactions, or because the interaction paradigm itself should depend on the context of use.

There is currently a distinct lack of design consideration associated with autonomous vehicles and their impact on human factors. Research has yet to consider fully the impact felt by the driver when he/she is no longer in control of the vehicle [12]. We propose that spatialised auditory feedback could be used to enhance driver awareness to the intended actions of autonomous vehicles. We hypothesise that this feedback will provide drivers with an enhanced sense of control. This paper presents a driving simulator study where 5 separate auditory feedback methods are compared during both autonomous and manual driving scenarios. We found that our spatialised auditory presentation method alerted drivers to the intended actions of autonomous vehicles much more than all other methods and they felt significantly more in control during scenarios containing sound vs. no sound. Finally, that overall workload in autonomous vehicle scenarios was lower compared to manual vehicle scenarios.

This paper presents the findings from an observational field study conducted with 8 car drivers. The study attempted to create a taxonomy of sounds that present information to people whilst driving. We also aimed to determine whether participants noticed these sounds as they occurred and whether they paid attention to them. Furthermore, we asked the participants subjective questions regarding particular sonic attributes and their ability to catch driver’s attention. It was concluded that although certain sounds occur regularly, differing levels of attention are given to each depending on the information they present. Our study also revealed that while all sonic attributes play an impact in catching driver’s attention, some aspects are more noticeable than others. We conclude with a discussion of our future directions with regards to the findings obtained from our observational field study and outline the plan for our next study.