Designing an Adaptive Interface: Using Eye Tracking to Classify How Information Usage Changes Over Time in Partially Automated Vehicles

While partially automated vehicles can provide a range of benefits, they also bring about new Human Machine Interface (HMI) challenges around ensuring the driver remains alert and is able to take control of the vehicle when required. While humans are poor monitors of automated processes, specifically during ‘steady state’ operation, presenting the appropriate information to the driver can help. But to date, interfaces of partially automated vehicles have shown evidence of causing cognitive overload. Adaptive HMIs that automatically change the information presented (for example, based on workload, time or physiologically), have been previously proposed as a solution, but little is known about how information should adapt during steady-state driving. This study aimed to classify information usage based on driver experience to inform the design of a future adaptive HMI in partially automated vehicles. The unique feature of this study over existing literature is that each participant attended for five consecutive days; enabling a first look at how information usage changes with increasing familiarity and providing a methodological contribution to future HMI user trial study design. Seventeen participants experienced a steady-state automated driving simulation for twenty-six minutes per day in a driving simulator, replicating a regularly driven route, such as a work commute. Nine information icons, representative of future partially automated vehicle HMIs, were displayed on a tablet and eye tracking was used to record the information that the participants fixated on. The results found that information usage did change with increased exposure, with significant differences in what information participants looked at between the first and last trial days. With increasing experience, participants tended to view information as confirming technical competence rather than the future state of the vehicle. On this basis, interface design recommendations are made, particularly around the design of adaptive interfaces for future partially automated vehicles

The safety and efficacy of tolterodine extended release in the treatment of overactive bladder in the elderly

After lifestyle and behavioral measures to control overactive bladder, the mainstay of pharmacological treatment is the use of antimuscarinic therapy. Overactive bladder predominantly affects older people, who experience the most severe disease, and are also at a greater risk of side effects from antimuscarinic therapy. Thus it is imperative that data are available on the efficacy and tolerability of this group of drugs when used in older people. This article reviews the pathophysiology of the condition, its effect on the elderly and the evidence for the use of extended release tolterodine in the elderly using data from placebo and active drug controlled studies

The Extended Pillar Integration Process (ePIP): A Data Integration Method Allowing the Systematic Synthesis of Findings From Three Different Sources

Mixed methods research requires data integration from multiple sources. Existing techniques are restricted to integrating a maximum of two data sources, do not provide step-by-step guidance or can be cumbersome where many data need to be integrated. We have solved these limitations through the development of the extended Pillar Integration Process (ePIP), a method which contributes to the field of mixed methods by being the first data integration method providing explicit steps on how to integrate data from three data sources. The ePIP provides greater transparency, validity and consistency compared to existing methods. We provide two worked examples from health sciences and automotive human factors, highlighting its value as a mixed methods integration tool

User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle

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 copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Synthesis and sintering of translucent yttrium aluminium garnet

The prime objective of this investigation is the optimization of powder synthesis process and evaluation of their sintering behavior for futuristic laser host material. Selection of precursor and process variables for the large scale synthesis of yttrium aluminum garnet (YAG) nanopowder emphasizes in the first part of the dissertation. Co-precipitation of aluminum nitrate, yttrium nitrate and ammonium bicarbonate precursors below room temperature and their subsequent rapid calcination at 900oC develops high crystalline, 40nm pure YAG nanoparticles in comparison with other six powder synthesis processes. Particles have near spherical morphology with consist of high surface area of 34m2/gm. Batch size upto 250gm powder synthesis process has a high degree of reproducibility. Second part illustrates five different consolidation and sintering techniques of optimized YAG nanopowders known as uniaxial pressing – atmospheric sintering (UAS), cold isostatic pressing – atmospheric sintering (CAS), hot pressing (HP), uniaxial pressing – atmospheric sintering – hot isostatic pressing (UAS-HIP) and spark plasma sintering (SPS). Low degree of heating rate during conventional atmospheric sintering provides uniform equiaxed microstructure and better densification in presence of the optimum amount of silica content. Combination of UAS – HIP exhibits highest densification which is just above the spark plasma sintering process. Hot isostatic pressed disc (Ø ~15mm and thickness ~1.5mm) shows 99.6% relative density with average grain size of 2.5 micron. The transmittance of disc is around 60% in the visible region as well as near infra-red region of 550nm and 1100nm, respectively. This translucency behaviour is further needed to improve for laser host transparent ceramics

Information requirements for future HMI in partially automated vehicles

Partially automated vehicles are increasing in prevalence and enable drivers to hand over physical control of the vehicle’s longitudinal and latitudinal control to the automated system. However, at this partial level of automation, drivers will still be required to continuously monitor the vehicle’s operation and take back control at any time from the system when required. The Society of Automotive Engineers (SAE) defines this as Level 2 automation and consequently a number of design implications arise. To support the driver in the monitoring task, Level 2 vehicles today present a variety of information about sensor readings and operational issues to keep the driver informed; so appropriate action can be taken when required. However, existing research has shown that current Level 2 HMIs increase the cognitive workload, leading to driver cognitive disengagement and hence increasing the risk to safety. However, despite this knowledge, these Level 2 systems are available on the road today and little is known about what information should be presented to drivers inside these systems. Hence, this doctorate aimed to deliver design recommendations on how HMIs can more appropriately support the driver in the use of a partially automated Level 2 (or higher) vehicle system. Four studies were designed and executed for this doctorate. Study 1 aimed to understand the information preferences for drivers in a Level 2 vehicle using semi-structured interviews. Participants were exposed to a 10 minute, Level 2 driving simulation. A total of 25 interviews were conducted for first study. Using thematic analysis, two categories of drivers: ‘High Information Preference’ (HIP) and ‘Low Information Preference’ (LIP) were developed. It was evident that the drivers' expectations of the partial automation capability differed, affecting their information preferences and highlighting the challenge of what information should be presented inside these vehicles. Importantly, by defining these differing preferences, HMI designers can be more informed to design effective HMI, regardless of the driver’s predisposition. Building on this, an Ideas Café public engagement event was designed for Study 2; implementing a novel methodology to understand factors of trust in automated vehicles. Qualitative data gathered from the 35 event attendees was analysed using thematic analysis. The results reaffirmed the importance of the information presented in automated vehicles. Based on these first two studies, it was evident that there was an opportunity to develop a more robust understanding of what information is required in a Level 2 vehicle. Information requirements were quantitatively investigated through two eye-tracking studies (Studies 3 and 4). Both used a novel three- or five-day longitudinal study design. A shortlist of nine types of information was developed based on the results from the first two studies, regulatory standards and collaborations with Jaguar Land Rover experts. This was the first shortlist of its kind for automated vehicles. These 9 information types were presented to participants and eye tracking was used to record their information usage during Level 2 driving. Study 3 involved 17 participants and displayed only steady state scenarios. Study 4 involved 27 participants and introduced handover and warning events. Across both studies, information usage changed significantly, highlighting the methodological importance of longitudinal testing over multiple exposures. Participants increased their usage of information confirming the vehicle’s current state technical competence. In comparison, usage decreased of future state information that could help predict the future actions of the vehicle. By characterising the change in information usage, HMI designers can now ensure important information is designed appropriately. Notably, the ‘Action Explanation’ information, that described what the vehicle was doing and why, was found to be consistently the most used information. To date, this type of information has not been observed on any existing Level 2 HMI. Results from all four studies was synthesised to develop novel design recommendations for the information required inside Level 2 vehicles, and how this should be adapted over time depending on the driver’s familiarity with the system and driving events. This doctorate has contributed novel design recommendations for Level 2 vehicles through an innovative methodological approach across four studies. These design recommendations can now be taken forward to design and test new HMIs that can create a better, safer experience for future automated vehicles