Should older people be considered a homogeneous group when interacting with level 3 automated vehicles?

Exploring the future mobility of older people is imperative for maintaining wellbeing and quality of life in an ageing society. The forthcoming level 3 automated vehicle may potentially benefit older people. In a level 3 automated vehicle, the driver can be completely disengaged from driving while, under some circumstances, being expected to take over the control occasionally. Existing research into older people and level 3 automated vehicles considers older people to be a homogeneous group, but it is not clear if different sub-groups of old people have different performance and perceptions when interacting with automated vehicles. To fill this research gap, a driving simulator investigation was conducted. We adopted a between-subjects experimental design with subgroup of old age as the independent variable. The differences in performance, behaviour, and perception towards level 3 automated vehicles between the younger old group (60–69 years old) and older old group (70 years old and over) was investigated. 15 subjects from the younger old group (mean age = 64.87 years, SD = 3.46 years) and 24 from the older old group (mean age = 75.13 years, SD = 3.35 years) participated in the study. The findings indicate that older people should not be regarded as a homogeneous group when interacting with automated vehicle. Compared to the younger old people, the older old people took over the control of the vehicle more slowly, and their takeover was less stable and more critical. However, both groups exhibited positive perceptions towards level 3 automation, and the of older old people’s perceptions were significantly more positive. This study demonstrated the importance of recognising older people as a heterogeneous group in terms of their performance, capabilities, needs and requirements when interacting with automated vehicles. This may have implications in the design of such systems and also understanding the market for autonomous mobility.     

Human machine interface design for continuous support of mode awareness during automated driving: An online simulation

In the transition towards higher levels of vehicle automation, one of the key concerns with regards to human factors is to avoid mode confusion, when drivers misinterpret the driving mode and therewith misjudge their own tasks and responsibility. To enhance mode awareness, a clear human centered Human Machine Interface (HMI) is essential. The HMI should support the driver tasks of both supervising the driving environment when needed and self-regulating their non-driving related activities (NDRAs). Such support may be provided by either presenting continuous information on automation reliability, from which the driver needs to infer what task is required, or by presenting continuous information on the currently required driving task and allowed NDRA directly. Additionally, it can be valuable to provide continuous information to support anticipation of upcoming changes in the automation mode and its associated reliability or required and allowed driver task(s). Information that could support anticipation includes the available time until a change in mode (i.e. time budget), information on the upcoming mode, and reasons for changing to the upcoming mode. The current work investigates the effects of communicating this potentially valuable information through HMI design. Participants received information from an HMI during simulated drives in a simulated car presented online (using Microsoft Teams) with an experimenter virtually accompanying and guiding each session. The HMI either communicated on automation reliability or on the driver task, and either included information supporting anticipation or did not include such information. Participants were thinking aloud during the simulated drives and reported on their experience and preferences afterwards. Anticipatory information supported understanding about upcoming changes without causing information overload or overreliance. Moreover, anticipatory information and information on automation reliability, and especially a combination of the two, best supported understandability and usability. Recommendations are provided for future work on facilitating supervision and NDRA self-regulation during automated driving through HMI design.     

Do cyclists need HMIs in future automated traffic? An interview study

Cyclists are expected to interact with automated vehicles (AVs) in future traffic, yet we know little about the nature of this interaction and the safety implications of AVs on cyclists. On-bike human–machine interfaces (HMIs) and connecting cyclists to AVs and the road infrastructure may have the potential to enhance the safety of cyclists. This study aimed to identify cyclists’ needs in today’s and future traffic, and explore on-bike HMI functionality and the implications of equipping cyclists with devices to communicate with AVs. Semi-structured interviews were conducted with 15 cyclists in Norway and 15 cyclists in the Netherlands. Thematic analysis was used to identify and contextualise the factors of cyclist-AV interaction and on-bike HMIs. From the analysis, seven themes were identified: Interaction, Bicycles, Culture, Infrastructure, Legislation, AVs, and HMI. These themes are diverse and overlap with factors grouped in sub-themes. The results indicated that the cyclists prefer segregated future infrastructure, and in mixed urban traffic, they need confirmation of detection by AVs. External on-vehicle or on-bike HMIs might be solutions to fulfil the cyclists’ need for recognition. However, the analysis suggested that cyclists are hesitant about being equipped with devices to communicate with AVs: Responsibility for safety should lie with AV technology rather than with cyclists. A device requirement might become a barrier to cycling, as bicycles are traditionally cheap and simple, and additional costs might deter people from choosing cycling as a transport mode. Future studies should investigate user acceptance of on-bike HMIs among cyclists on a larger scale to test the findings’ generalisability, and explore other, perhaps more viable solutions than on-bike HMIs for enhancing AV-cyclist interaction.     

Public acceptance of connected vehicles: An extension of the technology acceptance model

To examine the public acceptance of connected vehicles (CVs), this study developed a novel connected vehicle acceptance model (CVAM) extending the technology acceptance model (TAM). The model was built based on a questionnaire survey of 2400 US adults. Perceived data privacy and security associated with the technology was found to shape the trust, attitude, and behavioral intention to use CVs, in addition to the predictors of original TAM: perceived usefulness and ease of use. Results revealed that trust mediates the effect of perceived data privacy and security on CV acceptance, though its directional relationship with perceived usefulness and perceived ease of use is unclear. Socio-demographic and other characteristics of respondents associated with CV acceptance and its predictors were explored. A number of theoretical and practical implications of the study findings are discussed.     

WHITHER INTERNALISM? HOW INTERNALISTS SHOULD RESPOND TO THE EXTENDED MIND HYPOTHESIS

Abstract: A new position in the philosophy of mind has recently appeared: the extended mind hypothesis (EMH). Some of its proponents think the EMH, which says that a subject's mental states can extend into the local environment, shows that internalism is false. I argue that this is wrong. The EMH does not refute internalism; in fact, it necessarily does not do so. The popular assumption that the EMH spells trouble for internalists is premised on a bad characterization of the internalist thesis—albeit one that most internalists have adhered to. I show that internalism is entirely compatible with the EMH. This view should prompt us to reconsider the characterization of internalism, and in conclusion I make some brief remarks about how that project might proceed.     

PERSONS AND THE EXTENDED-MIND THESIS

The extended-mind thesis (EM) is the claim that mentality need not be situated just in the brain, or even within the boundaries of the skin. Some versions take "extended selves" be to relatively transitory couplings of biological organisms and external resources. First, I show how EM can be seen as an extension of traditional views of mind. Then, after voicing a couple of qualms about EM, I reject EM in favor of a more modest hypothesis that recognizes enduring subjects of experience and agents with integrated bodies. Nonetheless, my modest hypothesis allows subpersonal states to have nonbiological parts that play essential roles in cognitive processing. I present empirical warrant for this modest hypothesis and show how it leaves room for science and religion to coexist.     

How consumer drivers construe vehicle performance: Implications for electric vehicles

Electric vehicles (EVs) have emerged as potentially important contenders as low carbon vehicles. However from the perspectives of consumer (non-commercial) drivers, all types of EVs have limitations such as short range and higher cost that are significant barriers to widespread uptake. To displace a significant fraction of conventional vehicles, they may need to offer consumer drivers specific advantages that offset these limitations. Better performance might be such an advantage, since electric powertrains can offer performance benefits such as quieter operation and higher torque at low speeds. This qualitative study explored how vehicle performance is construed by consumer drivers, using a repertory grid approach to elicit drivers’ personal constructs. Drivers were found to construe performance in terms of two main dimensions, both situationally specific: dynamic performance (involving acceleration, power, and responsiveness during pulling away, overtaking and hill climbs) and cruising performance (involving smoothness and low noise, during high speed cruising on highways). Users of gasoline fuelled cars emphasised dynamic performance more than did users of diesel fuelled cars, but the opposite was the case for cruising performance. A conceptual model based on the findings could help focus design efforts on those aspects of performance that are most directly salient to drivers.     

Insights into the effectiveness of messaging promoting intentions to use connected vehicle technology

Connected vehicles offer considerable promise for reducing congestion, pollution, and crashes. Nonetheless, less than a third of road users are aware of the potential for connected vehicles to transform transport systems. This study examined the effectiveness of messaging aimed to increase the public’s knowledge of connected vehicle technologies using a short, animated video and its effects as assessed via a survey. Participants were assigned to either a control group, who were not exposed to the messaging, or an intervention group, who were exposed to the messaging, within a pre-post design. Participants (in the intervention group) answered questions about their knowledge of and experience with connected vehicle technologies prior to seeing the messaging and again following being exposed to such messaging together with their intentions to use these technologies in the future. As a theoretically informed investigation, the Theory of Planned Behaviour (TPB) constructs of attitudes, subjective norms, and perceived behavioural control were also measured. The results showed that the messaging increased knowledge about and intention to use connected vehicle technologies. Furthermore, changes in how the TPB constructs predicted intentions were found between the control and intervention groups which may help to explain how the messaging influenced participants’ intentions to use such technology in the future.     

Assessing the effect of long-automated driving operation,repeated take-over requests,and driver’s characteristics on commercial motor vehicle drivers’ driving behavior and reaction time in highly automated vehicles

Automated Commercial Motor Vehicles (CMVs) have the potential to reduce the occurrence of crashes, enhance traffic flow, and reduce the stress of driving to a larger extent. Since fully automated driving (SAE Level 5) is not yet available, automated driving systems cannot perform all driving tasks under all road conditions. Drivers need to regain the vehicle’s control when the system reaches its maximum operational capabilities. This transition from automated to manual is referred to as Take-Over Request (TOR). Evaluating driver’s performance after TORs and assessing effective parameters have gained much attention in recent years. However, few studies have addressed CMV drivers’ driving behavior after TOR and the effect of long-automated driving and repeated TORs. This paper aims to address this gap and gain behavioral insights into CMV drivers’ driving behavior after TOR and assess the effect of the duration of automated operation before TOR, repeated TORs, and driver characteristics (e.g., age, gender, education, and driving history). To accomplish this, we designed a 40-minutes experiment on a driving simulator and assessed the responses of certified CMV drivers to TORs. Drivers’ reaction time and driving behavior indices (e.g., acceleration, velocity, and headway) are compared to continuous manual driving to measure driving behavior differences. Results showed that CMV drivers’ driving behavior changes significantly after the transition to manual regardless of the number of TORs and the duration of automated driving. Findings suggest that 30 min of automated operation intensifies the effect of TOR on driving behaviors. In addition, repeated TOR improves reaction times to TOR and reduces drivers' maximum and minimum speed after TORs. Driver’s age and driving history showed significant effects on reaction time and some driving behavior indices. The findings of this paper provide valuable information to automotive companies and transportation planners on the nature of driver behavior changes due to the carryover effects of manual driving right after automated driving episodes in highly automated vehicles.