The power and sensitivity of four core driver workload measures for benchmarking the distraction potential of new driver vehicle interfaces

This study evaluated the power and sensitivity of several core driver workload measures in order to better understand their use as a component of future driver distraction potential evaluation procedures of the in-vehicle human machine interface (HMI). Driving is a task that requires visual, manual and cognitive resources to perform. Secondary tasks, such as mobile phone use and interaction with in-built navigation, which load onto any of these three processing resources increase driver workload and can lead to impaired driving. Because workload and distraction potential are interrelated, a comprehensive method to assess driver workload that produces valid and predictive results is needed to advance the science of distraction potential evaluation. It is also needed to incorporate into New Car Assessment Program (NCAP) testing regimes. Workload measures of cognitive (DRT [Detection Response Task] Reaction Time), visual (DRT Miss Rate), subjective (NASA-TLX [driver workload questionnaire]), and temporal demand (Task Interaction Time) were collected as participants drove one of 40 vehicles while completing a variety of secondary tasks with varying interaction requirements. Of the evaluated measures, variance and power analyses demonstrated that Task Interaction Time is the most sensitive in detecting differences in driver workload between different in-vehicle HMIs, followed by DRT Miss Rate, NASA-TLX and finally DRT Reaction Time. There were relatively weak correlations between each of the four measures. These results suggest that Task Interaction Time, coupled with a reliable visual demand metric such as DRT Miss Rate, eye glance coding, or visual occlusion, more efficiently detect differences in driver workload between different HMIs compared to DRT Reaction Time and the NASA-TLX questionnaire. These results can be used to improve the understanding of the utility of each of these core driver workload measures in assessing driver distraction potential.     

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.     

The Power of a “Maverick” in Collaborative Problem Solving: An Experimental Investigation of Individual Perspective‐Taking Within a Group

Integrating different perspectives is a sophisticated strategy for developing constructive interactions in collaborative problem solving. However, cognitive aspects such as individuals’ knowledge and bias often obscure group consensus and produce conflict. This study investigated collaborative problem solving, focusing on a group member interacting with another member having a different perspective (a “maverick”). It was predicted that mavericks might mitigate disadvantages and facilitate perspective taking during problem solving. Thus, 344 university students participated in two laboratory‐based experiments by engaging in a simple rule‐discovery task that raised conflicts among perspectives. They interacted with virtual partners whose conversations were controlled by multiple conversational agents. Results show that when participants interacted with a maverick during the task, they were able to take others’ perspectives and integrate different perspectives to solve the problem. Moreover, when participants interacted in groups with a positive mood, groups with a maverick outperformed groups having several perspectives.     

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.     

Impact of the driver’s visual engagement on situation awareness and takeover quality

During automated driving (SAE Level 3), drivers can delegate control of the vehicle and monitoring of the road to an automated system. They may then devote themselves to tasks other than driving and gradually lose situational awareness (SA). This could result in difficulty in regaining control of the vehicle when the automated system requires it. In this simulator study, the level of SA was manipulated through the time spent performing a non-driving task (NDRT), which alternated with phases where the driver could monitor the driving scene, prior to a critical takeover request (TOR). The SA at the time of TOR, the visual behaviour after TOR, and the takeover quality were analysed. The results showed that monitoring the road just before the TOR allowed the development of limited perception of the driving situation, which only partially compensated for the lack of a consolidated mental model of the situation. The quality of the recovery, assessed through the number of collisions, was consistent with the level of development of SA. The analysis of visual behaviour showed that engagement in the non-driving task at the time of TOR induced a form of perseverance in consulting the interface where the task was displayed, to the detriment of checking the mirrors. These results underline the importance of helping the driver to restore good SA well in advance of a TOR.     

Modeling the effects of perceived intuitiveness and urgency of various auditory warnings on driver takeover performance in automated vehicles

Existing driver models mainly account for drivers’ responses to visual cues in manually controlled vehicles. The present study is one of the few attempts to model drivers’ responses to auditory cues in automated vehicles. It developed a mathematical model to quantify the effects of characteristics of auditory cues on drivers’ response to takeover requests in automated vehicles. The current study enhanced queuing network-model human processor (QN-MHP) by modeling the effects of different auditory warnings, including speech, spearcon, and earcon. Different levels of intuitiveness and urgency of each sound were used to estimate the psychological parameters, such as perceived trust and urgency. The model predictions of takeover time were validated via an experimental study using driving simulation with resultant R squares of 0.925 and root-mean-square-error of 73 ms. The developed mathematical model can contribute to modeling the effects of auditory cues and providing design guidelines for standard takeover request warnings for automated vehicles.     

Extending affective capabilities for medical assistive robots

In this work, we discuss methodologies and implementation choices to enable a humanoid robot to estimate patients’ mood and emotions during postoperative home rehabilitation. The approach is modular and it has been implemented into a SoftBank Pepper robotic architecture; however, the approach is general and it can be easily adapted to other robotic platforms. A sample of an interactive session for the detection of the patient’s affective state is also reported.     

Evaluating the impact of human rights education on the adjustment of Greek primary school students

Human rights education has an encouraging effect on children's school routine. The aim of the present study was to investigate the effectiveness of a 12-session transformative human rights education intervention in improving children's school adjustment. Participants were 340 Greek primary school students assigned to intervention group (n = 187) and control group (n = 153). All members completed a written questionnaire 1 week before the implementation of the intervention, measuring their knowledge of human rights, school engagement, perceptions of the school environment, interpersonal relationships, empathy and perceptions, attitudes and feelings towards school. The completion process of the same questionnaire was repeated 1 week after the termination of the intervention and 4 months later. The results showed that the intervention was particularly beneficial as the intervention group members demonstrated a significant increase in their knowledge of human rights, school engagement, perceptions of the school environment, empathy and school liking, while experiencing a significant decrease in school avoidance and loneliness. Members of the control group did not report any significant improvement over time. The study's implications for future research on school-based human rights interventions are discussed.     

Explaining Own and Others' Behavior in a Controversial Issue: Animal Experimentation

Abstract

The authors investigated how 2 groups with different attitudes toward animal experimentation–researchers who conducted animal experiments and members of animal welfare organizations who protested against animal experiments–made attributions for the behavior of the opposing group. The 2 groups showed an actor-observer effect, mentioning more internal causes for the opponents' behavior and more external causes for their own behavior. Both groups were able to take the other's perspective, resulting in a reversed actor-observer effect. The less involved participants followed the pattern of ratings of the group whose attitudes corresponded to their own. In particular, the participants with a negative attitude toward animal experimentation rated researchers' behavior as more internally caused than did those with a positive attitude. The results illustrated how the participants formed and defended attitudes in a social context.     

The measurement of Human Life History strategy

This retrospective essay appraises J. Philippe Rushton’s application of life history to understanding the covariation among human traits in light of subsequent developments in the measurement and latent structure of Human Life History, covitality, and personality. We conclude that Rushton should be recognized for having initiated a theoretically and empirically highly fertile paradigm in human individual differences research.