Driver vision from vehicles remains a challenge, particularly with the increasing prevalence of larger vehicles and larger A-pillars. Following work to explore these issues in Heavy Goods Vehicles (HGVs), attention has now turned to Sports Utility Vehicles (SUVs). Concerns have been raised by bodies such as Transport Canda and Transport and Environment about the driver vision challenges posed by these vehicles. Recent years has seen a growth in SUVs both in terms of the number of vehicles on the road, but also in their size. The trend towards larger vehicles has resulted in a higher eye point, and potentially greater blind spots adjacent to the vehicle. The size of these vehicles moves them into the territory of the smaller HGVs, and potentially similar vision issues. This paper describes the preliminary work in exploring driver vision from SUVs. 3D scan data was captured from 2 representative SUVs, one of North American origin and one of European origin. Work was then done in the DHM system SAMMIE, following the procedures in SAE standards J3099 and J941 amongst others, to reverse engineer key design reference points. This allowed representative eye points to be derived on which to base driver vision analyses of frontal blind spots, and interesting variation in suggested eye point when comparing standardized approaches to manikin posturing in DHM. In performing this work, a simple tool has also been created that allows the key reference points above to be established to support driver packaging analyses.

Heavy Goods Vehicles (HGVs) present a significant risk to Vulnerable Road Users (VRUs), including cyclists and pedestrians, due to extensive blind spots. While overall road fatalities in the EU and UK have declined by 22

The increase of the population that exhibits a Body Mass Index above 30 kg/m2 (bariatric populations) pose design challenges. In health care these challenges are particularly acute as they limit access for patients to key diagnostic tools such as MRI. There are also increasing numbers of bariatric staff in these settings which causes accessibility problems for staff interacting with medical equipment and patients whilst performing medical procedures and other care tasks. This paper presents initial work in the modelling of bariatric populations in a DHM system, utilizing data gathered in previous work from a sample of 100 bariatric people. The data associated with this sample included 13 anthropometric measures and a classification of overall form. The paper describes the process by which these data were used to model specific individuals in the SAMMIE DHM system. The challenges associated with defining a representative body form using these measures are presented along the subsequent application of the Bariatric digital personas in the design process for medical equipment.

Digital human modelling (DHM) is a tool that allows humans to be modelled in three-dimensional CAD. An almost infinite variety of humans can be modelled and families of so-called manikins can be created to act as virtual user groups, evaluating the interactions between humans and products, workplaces and environments. This chapter introduces the concept of DHM, its use of, and reliance on, anthropometric data from national populations and showcases two exemplar tools in SAMMIE and IPS IMMA. Case studies are presented that highlight the advantages DHM can bring to understanding the requirements of designing for the ageing population; covering designing for the ageing workforce, the exploration of transport accessibility and how users can generate representative manikin families to properly represent the diversity of people. DHM is demonstrated to be a powerful tool for practitioners aiming to understand and design for people, including older people within society.

Accidents between vulnerable road users and trucks have been linked to the inability of drivers to directly see the areas in close proximity to the front and sides of the vehicle cab. The lack of direct vision is mitigated through the use of mirrors. The coverage requirements of mirrors are standardized in a UNECE standard. Direct Vision for trucks is not currently standardized in any way. Research by the authors identified key requirements for a Direct Vision Standard (DVS) which was subsequently designed. The method used to quantify direct vison measures the volume of space that is visible, of an assessment volume around the vehicle cab, from a driver's eye point. The result is a volumetric score in m 3 . This standard is now being applied in London, England, and a UNECE version is in development. This paper describes how DHM was used to provide a measure of real-world performance which correlates to a high level with the volumetric score, and an automated version of this process that is being used in the UNECE version.

Numerous surveys have been conducted worldwide to capture 3D anthropometric data of individuals scanned in tight underwear. However, such semi-nude data are inadequate for designing workspaces for specialised user populations who wear protective clothing and equipment. Determining the offset between semi-nude and clothed configurations requires the same individual to be repeatedly scanned in exactly the same posture. Specifically, for the use in a high-resolution 3D body scanner, positioning aids for the standing and seated posture were developed to stabilise the posture during the scanning process without compromising data integrity. The mean absolute variability (MAV) index was introduced to determine the efficacy of the positioning aids. It was shown that the positioning aids efficiently reduce the variability in fore-and-aft and side-to-side directions. This way the precondition was created for the precise superimposition of scans permitting the offset between diverse clothing configurations to be determined.

Driver vision from vehicles is a long standing issue. One highly topical scenario includes accidents to vulnerable road users and in particular cyclists, from collisions with large goods vehicles (LGVs). In many of these cases driver vision is a potential causal factor in the occurrence of the accident. This paper presents research performed into the evaluation of driver vision, funded by the UK Department for Transport. To support the research, a 3D volumetric assessment technique was developed in the SAMMIE digital human modelling system. This highly visual technique provides an indication of the visible volumes of space around a vehicle and any blind spots. Vision was evaluated for a range of vehicle types from cars through to LGVs. To investigate the potential casual effects of vision in accidents and specifically those involving cyclists, scenarios were identified from UK Police accident data. These scenarios were then modelled and evaluated digitally. The results highlight that blind spots exist on many vehicles and for all driver sizes. Many of these blind spots can be countered by a change in posture of the driver. However, the most significant blind spot was found on Category N3 LGVs to the near-side of the vehicle. The research was also instrumental in a change to the EU Regulation 46 to remove the blind spot from future LGVs.

This paper presents research performed on behalf of Transport for London in the UK addressing the over representation of trucks involved in accidents with vulnerable road users where issues with driver vision are often cited as the main casual factors. A Direct Vision Standard for London and potentially for Europe has been developed that utilizes a volumetric assessment of field of view performance. This paper presents research into how to contextualize the somewhat abstract volumetric performance scores into real world metrics using digital human models. The research modelled 27 trucks currently available from major manufacturers and analyzed their volumetric performance. It also explored a supplementary process using digital human models define the minimum threshold of field of view performance. The current proposal utilizes thirteen human models, representing 5th %ile Italian females, positioned to front, left and right of the cab. The minimum standard was developed to ensure that no blind spot exists between the regulations for mirror coverage and the new Direct Vision Standard. The research is ongoing in line with the finalization of the standard at a European level.

A computer-based inclusive design tool (HADRIAN), developed under the EPSRC ‘EQUAL’ initiative, is being expanded through the EPSRC Sustainable Urban Environments programme. This development will result in the tool including data on transport usage and related issues, providing a database of physical, emotional and cognitive information for 100 individuals, including those who are older and/or physically disabled. The collection of anthropometry by use of body scanning technology, as well as issues concerning the collection of physical capability data, whether by field observation, questionnaire response, or laboratory trials, are discussed. The work detailed is ongoing, and presented here are the methodological and ethical issues arising from consideration of the needs of those wishing to make journeys, and the collection of data to facilitate better design and policy to ease that process. This paper should be read in conjunction with Porter et al. (2006) also presented at the conference.

Building exceptional user experiences means designing for users of all digital skill level. An increased emphasis on personalization and, with it, adaptive interfaces exacerbates the necessity for digital inclusivity. However, how can designers ensure that they are meeting the needs of those with high and low skillsets? The research reported here employed semi-structured interviews to explore whether the Digital Native Assessment Scale (DNAS) can be used as a tool to classify users and act as a surrogate for predicting their digital profiles. Sixteen participants answered questions about their everyday technology behaviours, as well as their attitudes towards technology. Nine themes emerged through thematic analysis, however only one of these themes was associated with an even, dichotomous split between high scorers on the DNAS and low scorers on the DNAS. Therefore, the DNAS only clearly indicated digital behaviour in a limited number of issues and cannot be relied upon as a proxy for the participant characteristics to be supported in interface design.

Accidents between vulnerable road users and trucks have been linked to the inability of drivers to directly see the areas in close proximity to the front and sides of the vehicle cab. The lack of direct vision is mitigated through the use of mirrors. The coverage requirements of mirrors are standardized in Europe. Direct Vision for trucks is not currently standardized in any way. Research by the authors identified key requirements for a Direct Vision Standard (DVS). Transport for London funded this work. This standard is now being applied in London, and a European version is in development. A key element of the definition of this standard was the application of DHM software to define a standardized eye point. This is used to create simulations of the volume of space to the exterior of the cab that a driver can see. Eye point definitions exist in standards for trucks, but the standards are defined in a manner which allows variability in the eye point location. This variability allowed some truck designs to gain an advantage over their competitors, leading to the requirement for a new definition for a common eye point. The paper describes the process that has been followed to define this eye point.

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.

Anthropometric data are data on human body size and shape and are the basis upon which all digital human models are constructed. All aspects of the utility of a human model are therefore governed by its anthropometric characteristics. The physical size, the postures that can be adopted, and the tasks that can be performed are all influenced by some degree by anthropometry. This chapter explores anthropometry from how anthropometric data are presented, how they can be used to define an individual or a population, and how they can be strategically used to inform ergonomics evaluations using human models. The chapter also discusses the caveats often associated with anthropometric data sources and the traditional approaches to their use. The chapter also highlights some of the key methods in the field of anthropometry and how they can be used to create human model families to support practitioners in their use of digital human modeling.

This paper presents the analysis of UK road accident data to inform the development of a Direct Vision Standard (DVS) for trucks in the UK. The research forms part of a project funded by Transport for London. The DVS allows any truck to be rated in terms of its performance in the field of view afforded the driver. The standard will be used to limit the movement of poorly rated vehicles within central London from 2020. The standard will also foster improved truck designs for direct vision in the future. The analysis used accident data from the UK STATS 19 database between 2010 and 2015. Data were categorized on causation data and a series of accident characteristics to identify scenarios of accidents between trucks and vulnerable road users. These scenarios then informed the design of the DVS, in particular the definition of the areas of greatest risk around the cab.

This paper presents research performed on behalf of Transport for London which has defined a direct vision standard (DVS) for trucks. The research has been conducted against a background of over representation of trucks being involved in accidents with vulnerable road users (VRUs), and the premise that the reliance upon indirect vision contributes to accidents. 52 vehicle configurations have been modelled using CAD data from manufacturers, and 3D scan data. The methodology utilizes volumetric projection of the field of view of the virtual driver via the windows. This projection is intersected with an assessment volume (AV). The comparison metric is the volume of the AV that can be seen by the virtual driver. This is correlated with VRU visibility simulations. The technique has been validated by manufacturers and will come into force in London in 2020. Vehicles that don’t meet a minimum threshold will be required to fit extra safety equipment.

This report presents research performed by Loughborough Design School (LDS) on behalf of Transport for London. The research has been conducted against a background of over representation of heavy goods vehicles (HGVs) being involved in road traffic accidents with vulnerable road users (VRUs) where ‘failed to look properly’ and ‘vehicle blind-spot’ are often reported as the main casual factors in the accident data. Previous work by LDS on driver’s vision from HGVs has identified the need to reduce reliance on indirect vision via mirrors through the specification of a direct vision standard (DVS) for HGVs. Recent work commissioned by TfL and performed by the Transport Research Laboratory (TRL) resulted in a draft DVS. This draft DVS has been evaluated and reworked by the LDS team to produce a viable and robust method to quantify direct vision performance of an HGV together with a means to rate that vision performance against a star rating standard. Throughout this process significant stakeholder consultation has been used to support the development of the DVS. A total of 27 vehicles representing the majority of the current Euro 6 N3 HGV fleet have been modelled in CAD. Where data were available these have been mounted at the highest, lowest and most sold heights to produce a sample of 54 test vehicles. A methodology has been developed that utilises volumetric projection of the field of view of the driver via the windows in the cab. This projection is then intersected with an assessment volume. The result is a volumetric representation of the space around a HGV cab that the driver can see to the front, driver and passenger sides. The volume of this space can be calculated to provide a rating of direct vision performance. An iterative design process was followed that explored different specifications of the assessment zone around the cab, factoring in the collision data with VRUs and the use of weightings to prioritise what needs to be seen. Two weighting schemes were evaluated one prioritising the volumes vertically, recognising the importance of being able to see closer to the ground, and a second prioritising the volumes directionally to address the prevalence of accidents being greater to the front and passenger side when compared to the driver’s side. The final specification of the volumetric assessment consists of a single, unweighted zone around the cab, informed by the current coverage of mirrors specified in UNECE regulation 46. This was done to foster direct vision that aims to remove the reliance on mirrors and thus should focus on providing direct vision of the areas currently covered by mirrors. The vehicle sample was then evaluated for its performance using this assessment, providing a volumetric score for each vehicle. These volumetric scores were then quantified by correlating them with a VRU simulation. Thirteen 5th %ile Italian female VRUs were placed around the vehicle and moved laterally to a point at which their head and shoulders could be seen. This served to provide context for the volumetric results such that a particular volume could be equated to an average distance at which the small adult could be seen. Furthermore, the VRU simulations provided a means to translate the volumetric performance into star ratings. Four star rating specifications were produced following an absolute (based on risk/safety) and a relative (based on the performance of the current fleet) approach. For both absolute and relative two iterations were proposed: 1. the VRU simulation distances were used to establish a threshold value, 2. the median volumetric result was used to establish a threshold value. The final option taken forwards used the VRU simulation distances for a 5th %ile Italian female to define the 1 star boundary. Vehicles able to provide direct vision of the VRUs at an average of

As a consequence of insufficient situation awareness and inappropriate trust, operators of highly automated driving systems may be unable to safely perform takeovers following system failures. The communication of system uncertainties has been shown to alleviate these issues by supporting trust calibration. However, the existing approaches rely on information presented in the instrument cluster and therefore require users to regularly shift their attention between road, uncertainty display, and non-driving related tasks. As a result, these displays have the potential to increase workload and the likelihood of missed signals. A driving simulator study was conducted to compare a digital uncertainty display located in the instrument cluster with a peripheral awareness display consisting of a light strip and vibro-tactile seat feedback. The results indicate that the latter display affords users flexibility to direct more attention towards the road prior to critical situations and leads to lower workload scores while improving takeover performance.

Developing conditionally automated driving systems is on the rise. Vehicles with full longitudinal and latitudinal control will allow drivers to engage in secondary tasks without monitoring the roadway, but users may be required to resume vehicle control to handle critical hazards. The loss of driver's situational awareness increases the potential for accidents. Thus, the automated systems need to estimate the driver's ability to resume control of the driving task. The aim of this study was to assess the physiological behaviour (heart rate and pupil diameter) of drivers. The assessment was performed during two naturalistic secondary tasks. The tasks were the email and the twenty questions task in addition to a control group that did not perform any tasks. The study aimed at finding possible correlations between the driver's physiological data and their responses to a takeover request. A driving simulator study was used to collect data from a total of 33 participants in a repeated measures design to examine the physiological changes during driving and to measure their takeover quality and response time. Secondary tasks induced changes on physiological measures and a small influence on response time. However, there was a strong observed correlation between the physiological measures and response time. Takeover quality in this study was assessed using two new performance measures called PerSpeed and PerAngle. They are identified as the mean percentage change of vehicle's speed and heading angle starting from a take-over request time. Using linear mixed models, there was a strong interaction between task, heart rate and pupil diameter and PerSpeed, PerAngle and response time. This, in turn, provided a measurable understanding of a driver's future responses to the automated system based on the driver's physiological changes to allow better decision making. The present findings of this study emphasised the possibility of building a driver mental state model and prediction system to determine the quality of the driver's responses in a highly automated vehicle. Such results will reduce accidents and enhance the driver's experience in highly automated vehicles.

The 3D digitisation of precious or delicate cultural heritage artefacts via photogrammetry is highly important for historical preservation purposes. Doing so can help mitigate against events such as natural disasters, war, and tourism damage, whilst enabling access to 3D data for researchers around the globe. While the digitisation of such artefacts offer many significant societal and academic benefits, the process in which data is captured is resource intensive and often results in inaccurate outcomes. This paper presents a novel small object scanner which automates the photogrammetry image acquisition process for the highly detailed and efficient 3D digitisation of cultural heritage artefacts across large museum collections.

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.