Measured effects of workload and auditory feedback on remote pilot task performance.

Absent or reduced sensory cueing can deprive pilots operating remotely piloted aircraft beyond visual line of sight (BVLOS) of vital information necessary for safe flight. The present study tested the effects of real-time auditory feedback on remote pilot perception and decision-making task performance in an automated BVLOS flight, under three levels of workload (Low, Moderate and High). Results from 36 participants revealed workload and auditory feedback influenced perception task performance in terms of error type count, with misses more frequent than wrong identifications. In terms of performance in the decision-making task, under low and moderate levels of workload, auditory feedback was found to improve performance. Conversely, under high workloads, an inflexion or tipping point occurred whereby auditory feedback became detrimental to task performance. These results correspond with the expected behavioural responses to external stressors as predicted by the Arousal and Maximal Adaptability theory, and build upon previous findings related to workload, auditory feedback and remote pilot task performance. Practitioner summary: This study tested the effect of real-time auditory feedback and dynamic workloads on remote pilots' task performance. Auditory feedback and workload each influenced the perception tasks in terms of error types committed. Auditory feedback improved decision-making task performance under low and moderate workloads, and reduced performance under high workloads. These results may benefit practitioners by considering the nuanced effects of auditory feedback on human task performance within sensory deprived working environments, including those utilising teleoperated systems.

Effects of auditory and visual feedback on remote pilot manual flying performance.

Remotely Piloted Aircraft Systems (RPAS) have facilitated new growth in civil aviation. Unlike manned aircraft, however, they are operated without auditory feedback and normally flown under two visual conditions: in direct visual-line-of-sight to the remote pilot (VLOS) and beyond VLOS with first-person-view imagery transmitted via onboard cameras (BVLOS). The present research examined the effectiveness of audiovisual cueing on remote pilot manual flying performance. Eighteen pilots (three female) completed six navigation and 12 spotting tasks. Their flying performance (horizontal accuracy, vertical accuracy and timeliness) was examined under three different visual display types (VLOS (Control), BVLOS-Monitor & BVLOS-Goggles), with and without real-time auditory feedback, and two wind component (no wind and wind) conditions. Horizontal deviation and timeliness improved in the BVLOS-Monitor condition navigation task, while auditory feedback produced nuanced examples of improved and degraded pilot performance. These results indicate how the specificity of the task, combined with different levels of audiovisual feedback influences remote pilot performance. These findings support the rationalisation for the provision of multimodal dynamic sensory cueing in future RPAS. Practitioner summary: Accuracy and timeliness of remote pilot manual flying performance was measured under a combination of audiovisual feedback in calm and wind shear conditions. The inclusion of real-time auditory feedback as an additional sensory cue is uncommon; this study demonstrated nuanced examples of improved and degraded manual flying performance. The provision of dynamic sensory cueing made available to remote pilots in future RPAS should be considered.

Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue.

INTRODUCTION: Fatigue is one of the most crucial factors that contribute to a decrease of the operating performance of aircraft pilots and car drivers and, as such, plays a dangerous role in transport safety. To reduce fatigue-related tragedies and to increase the quality of a healthy life, many studies have focused on exploring effective methods and psychophysiological indicators for detecting and monitoring fatigue. However, those fatigue indicators rose many discrepancies among simulator and field studies, due to the vague conceptualism of fatigue, per se, which hinders the development of fatigue monitoring devices. METHOD: This paper aims to give psychological insight of the existing non-invasive measures for driver and pilot fatigue by differentiating sleepiness and mental fatigue. Such a study helps to improve research results for a wide range of researchers whose interests lie in the development of in-vehicle fatigue detection devices. First, the nature of fatigue for drivers/pilots is elucidated regarding fatigue types and fatigue responses, which reshapes our understanding of the fatigue issue in the transport industry. Secondly, the widely used objective neurophysiological methods, including electroencephalography (EEG), electrooculography (EOG), and electrocardiography (ECG), physical movement-based methods, vehicle-based methods, fitness-for-duty test as well as subjective methods (self-rating scales) are introduced. On the one hand, considering the difference between mental fatigue and sleepiness effects, the links between the objective and subjective indicators and fatigue are thoroughly investigated and reviewed. On the other hand, to better determine fatigue occurrence, a new combination of measures is recommended, as a single measure is not sufficient to yield a convincing benchmark of fatigue. Finally, since video-based techniques of measuring eye metrics offer a promising and practical method for monitoring operator fatigue, the relationship between fatigue and these eye metrics, that include blink-based, pupil-based, and saccade-based features, are also discussed. To realize a pragmatic fatigue detector for operators in the future, this paper concludes with a discussion on the future directions in terms of methodology of conducting operator fatigue research and fatigue analysis by using eye-related parameters.

Reconstruction of an informative railway wheel defect signal from wheel-rail contact signals measured by multiple wayside sensors.

Wheel impact load detectors are widespread railway systems used for measuring the wheel-rail contact force. They usually measure the rail strain and convert it to force in order to detect high impact forces and corresponding detrimental wheels. The measured strain signal can also be used to identify the defect type and its severity. The strain sensors have a limited effective zone that leads to partial observation from the wheels. Therefore, wheel impact load detectors exploit multiple sensors to collect samples from different portions of the wheels. The discrete measurement by multiple sensors provides the magnitude of the force; however, it does not provide the much richer variation pattern of the contact force signal. Therefore, this paper proposes a fusion method to associate the collected samples to their positions over the wheel circumferential coordinate. This process reconstructs an informative signal from the discrete samples collected by multiple sensors. To validate the proposed method, the multiple sensors have been simulated by an ad hoc multibody dynamic software (VI-Rail), and the outputs have been fed to the fusion model. The reconstructed signal represents the contact force and consequently the wheel defect. The obtained results demonstrate considerable similarity between the contact force and the reconstructed defect signal that can be used for further defect identification.

Robust Distributed Predictive Control of Waterborne AGVs-A Cooperative and Cost-Effective Approach.

Waterborne autonomous guided vessels (waterborne AGVs) moving over open waters experience environmental uncertainties. This paper proposes a novel cost-effective robust distributed control approach for waterborne AGVs. The overall system is uncertain and has independent subsystem dynamics but coupling objectives and state constraints. Waterborne AGVs determine their actions in a parallel way, while still minimizing an overall cost function and respecting coupling constraints robustly by communicating within a neighborhood. Our first contribution is the proposal of the system robustness level for the cost-effective robust distributed model predictive control (RDMPC) for waterborne AGVs. Cost-effective RDMPC models the price of robustness by explicitly considering uncertainty and system characteristics in a tube-based robust control framework. The second contribution is an efficient integrated branch & bound (B&B) and the alternating direction method of multipliers (ADMMs) algorithm for solving the cost-effective RDMPC problem. The algorithm exploits special ordered variable sets and combining branching criteria with intermediate ADMM results conducting smart search in B&B. Simulation results demonstrate the effectiveness of the proposed approach for cooperative distributed waterborne AGVs with cost-effective robustness.

Optimizing preventive maintenance policy: A data-driven application for a light rail braking system.

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.