A human factors perspective on automated driving Kyriakidis, M.; de Winter, J. C. F.; Stanton, N. ...
Theoretical issues in ergonomics science,
05/2019, Letnik:
20, Številka:
3
Journal Article
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Automated driving can fundamentally change road transportation and improve quality of life. However, at present, the role of humans in automated vehicles (AVs) is not clearly established. Interviews ...were conducted in April and May 2015 with 12 expert researchers in the field of human factors (HFs) of automated driving to identify commonalities and distinctive perspectives regarding HF challenges in the development of AVs. The experts indicated that an AV up to SAE Level 4 should inform its driver about the AV's capabilities and operational status, and ensure safety while changing between automated and manual modes. HF research should particularly address interactions between AVs, human drivers and vulnerable road users. Additionally, driver-training programmes may have to be modified to ensure that humans are capable of using AVs. Finally, a reflection on the interviews is provided, showing discordance between the interviewees' statements - which appear to be in line with a long history of HFs research - and the rapid development of automation technology. We expect our perspective to be instrumental for stakeholders involved in AV development and instructive to other parties.
•This meta-analysis compares social desirability between paper and computer surveys.•Social desirability in computer (Internet/offline) and paper surveys is the same.•Some of the large effects found ...in the past may have been due to sampling error.
The aim of this meta-analysis was to compare social desirability scores between paper and computer surveys. Subgroup analyses were conducted with Internet connectivity, level of anonymity, individual or group test setting, possibility of skipping items, possibility of backtracking previous items, inclusion of questions of sensitive nature, and social desirability scale type as moderators. Subgroup analyses were also conducted for study characteristics, namely the randomisation of participants, sample type (students vs. other), and study design (between- vs. within-subjects). Social desirability scores between the two administration modes were compared for 51 studies that included 62 independent samples and 16,700 unique participants. The overall effect of administration mode was close to zero (Cohen’s d=0.00 for fixed-effect and d=−0.01 for random-effects meta-analysis). The majority of the effect sizes in the subgroup analyses were not significantly different from zero either. The effect sizes were close to zero for both Internet and offline surveys. In conclusion, the totality of evidence indicates that there is no difference in social desirability between paper-and-pencil surveys and computer surveys. Publication year and sample size were positively correlated (ρ=.64), which suggests that certain of the large effects that have been found in the past may have been due to sampling error.
•Vibrotactile stimuli can effectively convey a take-over request in highly automated driving.•Participants could not reliably recognize directional cues presented via a vibrotactile seat.•Static ...vibrotactile patterns seemed to evoke faster reaction times than dynamic ones.
Vibrotactile stimuli can be effective as warning signals, but their effectiveness as directional take-over requests in automated driving is yet unknown. This study aimed to investigate the correct response rate, reaction times, and eye and head orientation for static versus dynamic directional take-over requests presented via vibrating motors in the driver seat. In a driving simulator, eighteen participants performed three sessions: 1) a session involving no driving (Baseline), 2) driving a highly automated car without additional task (HAD), and 3) driving a highly automated car while performing a mentally demanding task (N-Back). Per session, participants received four directional static (in the left or right part of the seat) and four dynamic (moving from one side towards the opposite left or right of the seat) take-over requests via two 6×4 motor matrices embedded in the seat back and bottom. In the Baseline condition, participants reported whether the cue was left or right, and in the HAD and N-Back conditions participants had to change lanes to the left or to the right according to the directional cue. The correct response rate was operationalized as the accuracy of the self-reported direction (Baseline session) and the accuracy of the lane change direction (HAD & N-Back sessions). The results showed that the correct response rate ranged between 94% for static patterns in the Baseline session and 74% for dynamic patterns in the N-Back session, although these effects were not statistically significant. Steering wheel touch and steering input reaction times were approximately 200ms faster for static patterns than for dynamic ones. Eye tracking results revealed a correspondence between head/eye-gaze direction and lane change direction, and showed that head and eye-gaze movements where initiated faster for static vibrations than for dynamic ones. In conclusion, vibrotactile stimuli presented via the driver seat are effective as warnings, but their effectiveness as directional take-over requests may be limited. The present study may encourage further investigation into how to get drivers safely back into the loop.
An analysis of article-level metrics of 27,856 PLOS ONE articles reveals that the number of tweets was weakly associated with the number of citations (
β
= 0.10), and weakly negatively associated ...with citations when the number of article views was held constant (
β
= −0.06). The number of tweets was predictive of other social media activity (
β
= 0.34 for Mendeley and
β
= 0.41 for Facebook), but not of the number of article views on PubMed Central (
β
= 0.01). It is concluded that the scientific citation process acts relatively independently of the social dynamics on Twitter.
Through a meta-analysis, this study investigated the relation of errors and violations from the Driver Behaviour Questionnaire (DBQ) to accident involvement.
We identified 174 studies using the DBQ, ...and a correlation of self-reported accidents with errors could be established in 32 samples and with violations in 42 samples.
The results showed that violations predicted accidents with an overall correlation of .13 when based on zero-order effects reported in tabular form, and with an overall correlation of .07 for effects reported in multivariate analysis, in tables reporting only significant effects, or in the text of a study. Errors predicted accidents with overall correlations of .10 and .06, respectively. The meta-analysis also showed that errors and violations correlated negatively with age and positively with exposure, and that males reported fewer errors and more violations than females. Supplementary analyses were conducted focusing on the moderating role of age, and on predicting accidents prospectively and retrospectively. Potential sources of bias are discussed, such as publication bias, measurement error, and consistency motif.
The DBQ is a prominent measurement scale to examine drivers’ self-reported aberrant behaviors. The present study provides information about the validity of the DBQ and therefore has strong relevance for researchers and road safety practitioners who seek to obtain insight into driving behaviors of a population of interest.
►Meta-analysis shows that Driver Behaviour Questionnaire (DBQ) predicts self-reported accidents. ►Prediction is significant for both errors and violations. ►Prediction is prospective as well as retrospective. ►violations-accident correlation is strongest amongst young drivers.
A major question in human-automation interaction is whether tasks should be traded or shared between human and automation. This work presents reflections-which have evolved through classroom debates ...between the authors over the past 10 years-on these two forms of human-automation interaction, with a focus on the automated driving domain. As in the lectures, we start with a historically informed survey of six pitfalls of automation: (1) Loss of situation and mode awareness, (2) Deskilling, (3) Unbalanced mental workload, (4) Behavioural adaptation, (5) Misuse, and (6) Disuse. Next, one of the authors explains why he believes that haptic shared control may remedy the pitfalls. Next, another author rebuts these arguments, arguing that traded control is the most promising way to improve road safety. This article ends with a common ground, explaining that shared and traded control outperform each other at medium and low environmental complexity, respectively.
Practitioner summary: Designers of automation systems will have to consider whether humans and automation should perform tasks alternately or simultaneously. The present article provides an in-depth reflection on this dilemma, which may prove insightful and help guide design.
Abbreviations: ACC: Adaptive Cruise Control: A system that can automatically maintain a safe distance from the vehicle in front; AEB: Advanced Emergency Braking (also known as Autonomous Emergency Braking): A system that automatically brakes to a full stop in an emergency situation; AES: Automated Evasive Steering: A system that automatically steers the car back into safety in an emergency situation; ISA: Intelligent Speed Adaptation: A system that can limit engine power automatically so that the driving speed does not exceed a safe or allowed speed.
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the ...conditions in which EFA can yield good quality results for N below 50. Simulations were carried out to estimate the minimum required N for different levels of loadings (λ), number of factors (f), and number of variables (p) and to examine the extent to which a small N solution can sustain the presence of small distortions such as interfactor correlations, model error, secondary loadings, unequal loadings, and unequal p/f. Factor recovery was assessed in terms of pattern congruence coefficients, factor score correlations, Heywood cases, and the gap size between eigenvalues. A subsampling study was also conducted on a psychological dataset of individuals who filled in a Big Five Inventory via the Internet. Results showed that when data are well conditioned (i.e., high λ, low f, high p), EFA can yield reliable results for N well below 50, even in the presence of small distortions. Such conditions may be uncommon but should certainly not be ruled out in behavioral research data.
* These authors contributed equally to this work
We review the theoretical foundation for the need for human factors science. Over the past 2.8 million years, humans and tools have co-evolved. However, in the last century, technology is introduced ...at a rate that exceeds human evolution. The proliferation of computers and, more recently, robots, introduces new cognitive demands, as the human is required to be a monitor rather than a direct controller. The usage of robots and artificial intelligence is only expected to increase, and the present COVID-19 pandemic may prove to be catalytic in this regard. One way to improve overall system performance is to 'adapt the human to the machine' via task procedures, operator training, operator selection, a Procrustean mandate. Using classic research examples, we demonstrate that Procrustean methods can improve performance only to a limited extent. For a viable future, therefore, technology must adapt to the human, which underwrites the necessity of human factors science.
Practitioner Summary: Various research articles have reported that the science of Human Factors is of vital importance in improving human-machine systems. However, what is lacking is a fundamental historical outline of why Human Factors is important. This article provides such a foundation, using arguments ranging from pre-history to post-COVID.
•Highly automated driving (HAD) reduces workload considerably.•Adaptive cruise control (ACC) yields only a small reduction of workload.•ACC and HAD can improve but also degrade situation ...awareness.•Feedback can alleviate much of the Human Factors issues of ACC and HAD.
Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0%=minimum, 100=maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.
The revised prose version of the Babad Tanah Jawi was originally prepared by C.F. Winter Sr. (1799-1859), with the twofold aim of providing Javanese-language teaching material and of setting a ...standard for formal Javanese prose writing. At that time, Javanese was almost exclusively written in verse, which was not a medium suitable for the modern world that was dawning on Java. Although Winter achieved his aims in other ways and publications, the present text was mostly forgotten, or was just passed over as another copy of the Meinsma text (Pigeaud, Literature of Java). This was unfortunate, because it deprived linguists of one of the first attempts to create a standard Javanese prose language, and historians of a readable text that presented a Javanese view of Javanese history from the beginning until 1742. To belatedly set the record straight and to honour Winter’s contributions to the development of Javanese, I decided to publish this text in Javanese script and provide an English translation for the general public. Although historians of Java have endeavoured to incorporate Javanese sources in their research, it remains invaluable to view that history directly through the eyes of 17th and 18th century Javanese contemporaries.
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