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.

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.

We tested whether pilots would detect low-salient controllability problems more quickly during manual compared to automated flight. Using a moving-base simulator and a Piper Seneca aerodynamic model, airline pilots (n = 20) performed scenarios in which either a gradually ensuing single-engine failure or an icing accumulation occurred. Both scenarios were performed once during manual flight and once during automated flight, and were alternated with distraction scenarios. The icing accumulation was detected marginally significantly more quickly during manual flight, while there was no significant difference for the engine failure. Problems in manual flight were, as expected, most likely discovered from aircraft motions or control forces. Interestingly, there were several late detections during manual flight which appeared to be caused by subconscious manual corrections. In automated flight, the engine failure was discovered most often from the engine manifold pressure indication, while the icing accumulation was most often discovered from control column movement. The results therefore underline the importance of using back-driven controls, and further indicate that manual flight does not necessarily improve detection of problems that occur without display indications.

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).

The introduction of more advanced automation in air traffic control seems inevitable. Air traffic controllers will then take the role of automation supervisors, a role which is generally unsuitable for humans. Gamification, the use of game elements in non-gaming contexts, shows promising results in mitigating the effects of boredom in highly automated domains requiring human supervision. An example is luggage screening, where dangerous items are rarely found, through projecting fictional threats on top of x-ray scans. This paper presents and experimentally tests a proposed implementation of gamification within highly automated en-route air traffic control. Fictional flights were superimposed among automatically controlled real traffic, thus creating fictional conflicts that needed resolving. System supervisors were tasked to supervise the behaviour of a fully automated conflict detection and resolution system, while manually routing fictional flights safely and efficiently through the sector, avoiding conflicts with both real and fictional flights. Automation anomalies were simulated, as well as an automation failure event, after which the system supervisor needed to assume manual control over all traffic. The presence of fictional flights increased self-reported concentration levels and reduced boredom. However, some participants reported that fictional flights were distracting. Thus, while the use of fictional flights increases engagement, it might negatively affect other cognitive functions, and with that, compromise safety. Thus, while the implementation of such a tool might provide benefits in terms of skill retention and engagement, further research is recommended involving professional air traffic controllers, improved measurement tools and a longitudinal study that better excites boredom, complacency, and skill erosion in order to understand and mitigate its negative effects.

Previous studies have indicated that the attitude director indicator (ADI) used in commercial aviation is suboptimal in representing the bank angle direction, which can lead to confusion, roll reversal errors and increased workload. Confusion about the bank angle direction has been implied in several cases of loss of control in-flight (LOC-I). In the current study, we therefore tested whether bank angle representation can be improved by adding non-disruptive visual depth cues to the ADI. An enhanced ADI was created, in which three monocular cues were added: atmospheric haze (i.e. a gradient in color towards the horizon), a shadow line under the aircraft symbol, and perspective lines on the ground. Airline pilots (n = 25) were tasked with rolling back to level 96 times from unforeseen (30 or -30 degrees) bank angles after experiencing either matching or mismatching (disorienting) roll motion cues in a motion-base simulator. There was no outside visibility and pilots responded using the ADI only. Roll reversal errors and reaction times were compared within-subject between the enhanced and baseline ADI, which were both based on the B747. Pilots were tasked to respond immediately upon presentation of the display, so that their initial interpretation of bank angle direction could be measured. There was no significant difference in roll reversal errors, and a significant increase in reaction times, when using the enhanced ADI compared to the baseline ADI. This suggests that pilots had slightly more difficulty with reading the bank angle with the enhanced ADI. Of the pilots, 56% preferred the enhanced ADI over the baseline display as it is, 8% had no preference and 36% preferred the baseline ADI. The most valued addition was the perspective lines on the ground, which pilots remarked would also be helpful in recovering extreme attitudes. The most-heard concerns were about potential clutter caused by the added cues, and difficulty with accurate reading of the pitch angle due to the shadow lines. In conclusion, according to the pilots' feedback, the addition of depth cues to the ADI appears promising, but it should be tested using more challenging tasks. Further design changes also appear needed to prevent clutter and facilitate quick reading of the aircraft attitude.

This paper outlines the three-phase construction of the Startle and Surprise Inventories (Startle-I; Surprise-I) and Visual Analogue Scales for Startle and Surprise (Startle-VAS; Surprise-VAS). In Phase 1, seven experts in the field assessed the content validity of 14 items for surprise, 7 items for startle derived from fundamental and applied literature. Elimination of items was based on a 50% agreement of relevance. In Phase 2, 81 participants completed the retained 19 items nine times, each time immediately after watching a video clip. A multilevel exploratory factor analysis was applied to assess the construct validity of items. In Phase 3, concurrent validity of the Startle-VAS and Surprise-VAS was tested by comparing with the Startle-I and Surprise-I scores, respectively. The first two phases yielded a 11-item two-factor solution, corresponding to the constructs of startle and surprise. These results supported Startle-I and Surprise-I as measures of self-report startle and surprise, with Startle-VAS and Surprise-VAS as efficient alternatives.

This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment's first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based (ρ =0.94) and overall (ρ =0.69) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.