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• Multimodal and auditory warnings had faster reaction times to tactile only warnings. • Multimodal warning resulted in fewer missed and false responses in all task conditions. • Multimodal warning rated as more noticeable and motivating to respond over unimodal. • Three set paced attention capturing tasks of similar workload were developed and used. In an automated car, users can fully engage in a distractor task, making it a primary task. Compared to manual driving, drivers can engage in tasks that are difficult to interrupt and of higher demand, the consequences can be a reduced perception of, and an impaired reaction to, warnings. In this study we compared three in-vehicle warnings (auditory, tactile, and auditory-tactile) which were presented during three highly attention capturing tasks (visual, auditory, and tactile) while the user was engaged in a self-driving car scenario, culminating in an emergency brake event where the warning was presented. The novel addition for this paper was that three set paced, attention capturing tasks, as well the three warnings were all designed in a pilot study to have comparable workload and noticeability. This enabled a direct comparison of human performance to be made between each of the attention capturing tasks, which are designed to occupy only one specific modality (auditory, visual or haptic), but remain similar in overall task demand. Results from the study showed reaction times to the tactile warning (for the emergency braking event) were significantly slower compared to the auditory and auditory-tactile (aka multimodal or multisensory) warning. Despite the similar reaction times between the in-vehicle auditory warning and the multimodal warning, the multimodal warning led to a reduced number of missed warnings and fewer false responses. However, the auditory and auditory-tactile warnings were rated significantly more startling than the tactile alone. Our results extend the literature regarding the performance benefits of multimodal warnings by comparing them with in-vehicle auditory warnings in an autonomous driving context. The set-pace attention capturing tasks in this study would be of interest to other researchers to evaluate the interaction in an automated driving context, particularly with hard to interrupt and attention capturing tasks. [ABSTRACT FROM AUTHOR]

• Introduction of a methodology for the investigation of emotions during driving. • Emotional experiences during driving depend on driving condition and type of road. • Causes of the emotional responses can be successfully assigned. The growing sophistication of technologies and sociological advances are major causes for the dramatic change the automotive sector is currently undergoing. To address changes from a human-centered design perspective an improved understanding of the occupants' emotional experience and behavior is required. Facial-Expression Analysis (FEA) is an emerging tool in support of such an approach, suitable for automotive research due to its non-contact application and low intrusiveness. The research described here investigated the dependency of the occupants' emotional experience on road types and driving conditions by investigating emotional responses and their causes through FEA and observational analysis. Twenty-one university students and staff were recruited for the real-time test on a planned road circuit covering different road types and conditions. Facial-expression data and video information from two in-car cameras were collected during an average driving time of 40 min per participant. A multi-method approach was applied for the data analysis, including both quantitative statistical analysis and qualitative observational analysis, as well as an inter-observer reliability test. Emotion frequencies were compared between the different road types, resulting in a percentage difference from the total average of emotion frequency of −6.09% below average for urban roads, +11.15% above average for major roads and +4.88% above average for rural roads. The causes most frequently assigned to the emotional responses in this dataset were poor road conditions and causes related to the navigation device. The research supported the dependency of emotional experiences on the driving condition and type of road. The study presents the first step of a human-centered design approach towards modern automotive design. The results have wide application in automotive design, applicable to the development of, for instance, an affective human-machine interaction or a personalized autonomous driving experience. [ABSTRACT FROM AUTHOR]

Touchscreen Human-Machine Interfaces (HMIs) are a well-established and popular choice to provide the primary control interface between driver and vehicle, yet inherently demand some visual attention. Employing a secondary device with the touchscreen may reduce the demand but there is some debate about which device is most suitable, with current manufacturers favouring different solutions and applying these internationally. We present an empirical driving simulator study, conducted in the UK and China, in which 48 participants undertook typical in-vehicle tasks utilising either a touchscreen, rotary-controller, steering-wheel-controls or touchpad. In both the UK and China, the touchscreen was the most preferred/least demanding to use, and the touchpad least preferred/most demanding, whereas the rotary-controller was generally favoured by UK drivers and steering-wheel-controls were more popular in China. Chinese drivers were more excited by the novelty of the technology, and spent more time attending to the devices while driving, leading to an increase in off-road glance time and a corresponding detriment to vehicle control. Even so, Chinese drivers rated devices as easier-to-use while driving, and felt that they interfered less with their driving performance, compared to their UK counterparts. Results suggest that the most effective solution (to maximise performance/acceptance, while minimising visual demand) is to maintain the touchscreen as the primary control interface (e.g. for top-level tasks), and supplement this with a secondary device that is only enabled for certain actions; moreover, different devices may be employed in different cultural markets. Further work is required to explore these recommendations in greater depth (e.g. during extended or real-world testing), and to validate the findings and approach in other cultural contexts. [ABSTRACT FROM AUTHOR]

Given the proliferation of 'intelligent' and 'socially-aware' digital assistants embodying everyday mobile technology - and the undeniable logic that utilising voice-activated controls and interfaces in cars reduces the visual and manual distraction of interacting with in-vehicle devices - it appears inevitable that next generation vehicles will be embodied by digital assistants and utilise spoken language as a method of interaction. From a design perspective, defining the language and interaction style that a digital driving assistant should adopt is contingent on the role that they play within the social fabric and context in which they are situated. We therefore conducted a qualitative, Wizard-of-Oz study to explore how drivers might interact linguistically with a natural language digital driving assistant. Twenty-five participants drove for 10 min in a medium-fidelity driving simulator while interacting with a state-of-the-art, high-functioning, conversational digital driving assistant. All exchanges were transcribed and analysed using recognised linguistic techniques, such as discourse and conversation analysis, normally reserved for interpersonal investigation. Language usage patterns demonstrate that interactions with the digital assistant were fundamentally social in nature, with participants affording the assistant equal social status and high-level cognitive processing capability. For example, participants were polite, actively controlled turn-taking during the conversation, and used back-channelling, fillers and hesitation, as they might in human communication. Furthermore, participants expected the digital assistant to understand and process complex requests mitigated with hedging words and expressions, and peppered with vague language and deictic references requiring shared contextual information and mutual understanding. Findings are presented in six themes which emerged during the analysis - formulating responses; turn-taking; back-channelling, fillers and hesitation; vague language; mitigating requests and politeness and praise. The results can be used to inform the design of future in-vehicle natural language systems, in particular to help manage the tension between designing for an engaging dialogue (important for technology acceptance) and designing for an effective dialogue (important to minimise distraction in a driving context). [ABSTRACT FROM AUTHOR]

While researchers have explored benefits of adding augmented reality graphics to vehicle displays, the impact of graphic characteristics have not been well researched. In this paper, we consider the impact of augmented reality graphic spatial location and motion, as well as turn direction, traffic presence, and gender, on participant driving and glance behavior and preferences. Twenty-two participants navigated through a simulated environment while using four different graphics. We employed a novel glance allocation analysis to differentiate information likely gathered with each glace with more granularity. Fixed graphics generally resulted in less visual attention and more time scanning for hazards than animated graphics. Finally, the screen-fixed graphic was preferred by participants over all world-relative graphics, suggesting that graphic spatially integration into the world may not always be necessary in visually complex urban environments like those considered in this study. [ABSTRACT FROM AUTHOR]

• A head-up display with a local dynamic depth-variable viewing effect for attentions is developed and demonstrated. • An optical design utilising an electrically tuneable liquid lens is proposed to vary the image depth and magnification of the selected display area. • The eye-box of the head-up display is enlarged to accommodate driver's head movement, and the brightness of the displayed image is maintained to be uniform across the eye-box. Head-Up Displays (HUDs) can reduce duration and frequency of drivers looking away from traffic scenes, but information contents of different importance are usually displayed at the same time in contemporary HUD models. Such configurations increase the time that a driver searches for critical information and it is essential that the said information can quickly attract driver's attention without affecting his focus on the road. We introduce an alternative approach of displaying critical information with a variable depth in a designated local area of a HUD image. The variations are engineered to create a dynamic pop-up effect for hazard warnings, such as a car exceeding the speed-limit or approaching certain road signs. The image depth of the corresponding area is designed to vary by about half a metre and the image size by 1.4 times for a natural viewing experience, using an off-the-shelf liquid lens with electrically tuneable focus depths. The HUD optics are adjusted to have an extended eye-box to accommodate driver's head movement and a uniform image brightness across the eye-box. [ABSTRACT FROM AUTHOR]

Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone. [ABSTRACT FROM AUTHOR]