a b s t r a c tThis study investigated user acceptance, concerns, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. Respondents scoring higher on neuroticism were slightly less comfortable about data transmitting, whereas respondents scoring higher on agreeableness were slightly more comfortable with this. Respondents from more developed countries (in terms of lower accident statistics, higher education, and higher income) were less comfortable with their vehicle transmitting data, with cross-national correlations between q = À0.80 and q = À0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.

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.

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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 Pearson product–moment correlation coefficient () and the Spearman rank correlation coefficient () are widely used in psychological research. We compare and on 3 criteria: variability, bias with respect to the population value, and robustness to an outlier. Using simulations across low (N = 5) to high (N = 1,000) sample sizes we show that, for normally distributed variables, and have similar expected values but is more variable, especially when the correlation is strong. However, when the variables have high kurtosis, is more variable than . Next, we conducted a sampling study of a psychometric dataset featuring symmetrically distributed data with light tails, and of 2 Likert-type survey datasets, 1 with light-tailed and the other with heavy-tailed distributions. Consistent with the simulations, had lower variability than in the psychometric dataset. In the survey datasets with heavy-tailed variables in particular, had lower variability than , and often corresponded more accurately to the population Pearson correlation coefficient () than did. The simulations and the sampling studies showed that variability in terms of standard deviations can be reduced by about 20% by choosing instead of . In comparison, increasing the sample size by a factor of 2 results in a 41% reduction of the standard deviations of and . In conclusion, is suitable for light-tailed distributions, whereas is preferable when variables feature heavy-tailed distributions or when outliers are present, as is often the case in psychological research.

An important question in automated driving research is how quickly drivers take over control of the vehicle in response to a critical event or a takeover request. Although a large number of studies have been performed, results vary strongly. In this study, we investigated mean takeover times from 129 studies with SAE level 2 automation or higher. We used three complementary approaches: (1) a within-study analysis, in which differences in mean takeover time were assessed for pairs of experimental conditions, (2) a between-study analysis, in which correlations between experimental conditions and mean takeover times were assessed, and (3) a linear mixed-effects model combining betweenstudy and within-study effects. The three methods showed that a shorter mean takeover time is associated with a higher urgency of the situation, not using a handheld device, not performing a visual non-driving task, having experienced another takeover scenario before in the experiment, and receiving an auditory or vibrotactile takeover request as compared to a visual-only or no takeover request. A consistent effect of age was not observed. We also found the mean and standard deviation of the takeover time were highly correlated, indicating that the mean is predictive of variability. Our findings point to directions for new research, in particular concerning the distinction between drivers' ability and motivation to take over, and the roles of urgency and prior experience.

Objective:In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians’ crossing intentions.Background:Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians’ interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI.Methods:Twenty-eight participants experienced an urban road environment from a pedestrian’s perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and “feel-safe” percentages were calculated.Results:For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs.Conclusion:An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous.Application:This research supports the development of automated vehicles that communicate with other road users.

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