To achieve an efficient and stable operation of blast furnaces in the steel industry while retaining the proficient human operators’ skills, a human–machine interface based on an ecological interface design (EID) was developed. EID is an interface design framework that reduces the cognitive workload of human operators by providing essential information on the controlled system in an intuitive way. The developed interface allows the operators to explore the possible control actions, by presenting the future predictions of the controlled variables when hypothetical control actions are taken, using a transient model. In addition, a graphical representation of the mass and energy balance that links the manipulated variables and controlled variables is provided to raise the situation awareness of the blast furnace operation. The developed interface is beneficial to determine appropriate control actions that maintain hot metal temperature and production rate near the target values and keep pressure drop below the upper bound while reducing carbon intensity and production costs.

This cohort study investigates the long-term effects of simulator-based hazard awareness training (HAT) on learner and novice drivers in the Netherlands, using a dataset of 2,372 participants over a 15-year period. Most prior studies on HAT have measured only immediate post-training outcomes; no longitudinal cohort study with a control group has previously examined both supervised and unsupervised driving outcomes over a multi-year horizon. Although the HAT and control groups showed small but statistically significant differences in gender composition, education level, and fear of driving at the start of training, the effect sizes were negligible (d ≤ 0.09), and these characteristics are addressed as covariates in the analyses.HAT improved performance during simulator training and supervised driving: HAT students’ viewing skills were better during the intersection test, required fewer on-road training hours, passed the driving exam in fewer attempts, and achieved a higher first-attempt pass rate than the control group. These benefits did not persist into unsupervised post-licensing driving. Violations, errors, and accident involvement were comparable between HAT and control group drivers in the first and last year after licensing. Personal characteristics — including gender, licensing age, self-assessed driving competence, and subjective driving difficulty — were stronger and more lasting predictors of post-licensing behaviour than training type. These findings suggest that hazard awareness is a trainable skill, but that training effects on risk-taking behaviour are moderated by individual characteristics that emerge most clearly once drivers operate independently, aligning with findings of a previous study on the same dataset. Teaching higher safety margins during supervised driving may offer a more durable route to reducing accident risk for novice drivers than higher-order skill training alone.

This erratum applies to the following published paper [1]. In Fig. 10(e) and (f) of the published version of the paper, the measured values for the t _f and T _l,f (both having values around 1 s for the considered dataset) were interchanged. This erratum includes both the published and corrected versions of Fig. 10 and the related paragraph in the paper's Results section. PUBLISHED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average look-ahead time t _f decreases slightly with bandwidth from around 1.05 to 0.9 s, Fig. 10(e) but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower t _f may not reflect a systematic adaptation to the bandwidth, but a more subtle adaptation to minimize the errors due to the additional high-amplitude sinusoids at 2.5 and 4 rad/s, see also Fig. 8c. The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain (K _f ≈ 0.95, Fig. 10(d)) and lag time-constant (T _{l, f} ≈ 1.15 s, Fig. 10(f)) are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ _f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure Presneted) CORRECTED VERSION In preview tasks, bandwidth changes yield only minor adaptations in the model parameters, see Fig. 10. Only the average target smoothing time-constant T _{l, f} decreases slightly with bandwidth [from around 1.05 to 0.9 s, Fig. 10(f)], but with substantial between-participant variability, as indicated by the overlapping confidence intervals. The lower T _{l, f} indicates that slightly more smoothing is applied to reduce tracking of the more high-amplitude high-frequency sinusoids in the 2.5 and 4 rad/s bandwidth signals through the feedforward response, see also Fig. 8(c). The general way in which participants use the available preview for control is, however, not affected by the target signal bandwidth: the target response gain [K _f ≈ 0.95, Fig. 10(d)] and look-ahead time [τ _f ≈ 1.15 s, Fig. 10(e)] are approximately invariant. The estimated control dynamics in Fig. 12 show that the target trajectory is tracked almost perfectly at all frequencies below 4 rad/s, mostly because the phase lead due to τ _f allows for synchronizing the CE output with the target signal (as opposed to pursuit tasks, see Fig. 11, bottom right). Therefore, different-bandwidth target signals provide no incentive for HCs to strongly adapt their control behavior in preview tasks. (Figure presented).

On final approach, an approach controller is responsible for separating aircraft lining up on the instrument landing system. In an attempt to increase traffic throughput, especially in strong headwind conditions, European regulation advises all European airports to move from distance-based to time-based separation. This effectively changes the controller’s task from a distance-based to a time-based problem. Further complications arise because of the European recategorization of aircraft types initiative, and experts fear that the gains foreseen with time-based separation will not be realized. This paper presents a visual tool integrated into the radar screen to assist controllers in performing time-based separation, the ideal turn-in point (ITIP) display. To assist controllers in selecting optimal approach strategies, starting from the moment aircraft enter the terminal control area, the display shows the possibilities and restrictions in the system rather than giving (restricting) advisories. A proof-of-concept experiment was performed with people knowledgeable in air traffic control (N = 8) and compared the ITIP to a current industry state-of-the-art display designed by U.K.’s National Air Traffic Services in scenarios of varying difficulty. Results show that with the ITIP tool, efficiency improved with similar or higher levels of safety and similar or lower workload. These promising results justify testing the interface with professional air traffic controllers. Future work aims at reducing clutter, increasing simulation fidelity, and increasing the level of support in complex traffic situations.

This paper investigates the effects of motion mismatches on simulator sickness and subjective ratings of the motion. In an open-loop driving simulator experiment, participants were driven through a recorded urban drive twelve times, in which mismatches were induced by manipulating the following three aspects in motion cueing: (i) mismatches in specific vehicle axes, (ii) mismatch types (scaling, missing, and false cues), and (iii) inconsistent scaling between different motion axes. Subjects (N=52) reported simulator sickness post-hoc (after each drive), as well as continuously during each drive, a first in simulator sickness research. Furthermore, subjective post-hoc motion incongruence ratings on the quality of the motion were extracted. Results show that longitudinal motion mismatches lead to the most simulator sickness and the highest ratings, followed by mismatches in lateral motion, then yaw rate. False cues induce the most sickness, followed by missing and then scaled motion. Inconsistent scaling between the axes has no significant effect. The continuous sickness ratings support that the occurrence and severity of simulator sickness are indeed related to mismatches in simulator motion of specific maneuvers. This paper contributes to an improved understanding of the relationship between simulator motion and sickness, allowing for more targeted motion cueing strategies to prevent and reduce sickness in driving simulators. These strategies may include the appropriate selection of the simulator, the motion cueing, and the sample of participants, following the presented results.