Enhancing the Collection and Analysis of General Aviation Insurance Data in Australasia Through the Use of HFACS

This paper provides an overview of a 5-stage program of work conducted over a three-year period that aims to develop a General Aviation (GA) safety database in Australia using data collected by aviation insurers. The need to standardize and enhance insurance data was recognized to support meaningful safety-based countermeasure development. Incorporating the Human Factors Analysis and Classification Scheme into the insurance assessment process is a major feature of the data enhancement process. Data collection by GA insurance assessors in Australia, using standardized and enhanced data collection methods, will begin in late 2007. Establishment of this database will allow for meaningful safety-based analyses of GA insurance incident data in the future

Variability in decision-making and critical cue use by different road users at rail level crossings

Collisions at rail level crossings (RLXs) are typically high-severity and high-cost, often involving serious injuries, fatalities and major disruptions to the transport network. Most research examining behaviour at RLXs has focused exclusively on drivers and consequently there is little knowledge on how other road users make decisions at RLXs. We collected drivers’, motorcyclists’, bicyclists’ and pedestrians’ self-reported daily experiences at RLXs for two weeks, focusing on behaviour, decision-making and information use in the presence of a train and/or activated RLX signals. Both information use and behaviour differed between road users. Visual information (e.g., flashing lights) was more influential for motorists, whereas pedestrians and cyclists relied more on auditory information (e.g., bells). Pedestrians were also more likely to violate active RLX warnings and/or cross before an approaching train. These results emphasise the importance of adopting holistic RLX design approaches that support cognition and behaviour across for all road users.Practitioner summary: This study explores how information use and decision-making at rail level crossings (RLXs) differs between road user groups, using a two-week self-report study. Most users make safe decisions, but pedestrians are most likely to violate RLX warnings. Information use (visual vs. auditory) also differs substantially between road user groups

Using pupillometry and gaze-based metrics for understanding drivers’ mental workload during automated driving

This Horizon2020-funded driving simulator-based study on automated driving investigated the effect of different car-following scenarios, and takeover situations, on drivers’ mental workload, as measured by eye tracking-based metrics of pupil diameter and self-reported workload ratings. This study incorporated a mixed design format, with 16 drivers recruited for the SAE Level 2 (L2; SAE International, 2021) automation group, who were asked to monitor the driving and road environment during automation, and 16 drivers in the Level 3 (L3) automation group, who engaged in a non-driving related task (NDRT; Arrows task) during automation. Drivers in each group undertook two experimental drives, lasting about 18 min each. To manipulate perceived workload, difficulty of the driving task was controlled by incorporating a lead vehicle which maintained either a Short (0.5 s) or Long (1.5 s) Time Headway (THW) condition during automated car-following (ACF). Each ACF session was followed by a subsequent request to takeover, which happened either in the presence or absence of a lead vehicle. Results from standard deviation of pupil diameter values indicated that drivers’ mental workload levels fluctuated significantly more when monitoring the drive during L2 ACF, compared to manual car-following (MCF). Additionally, we found that drivers’ mental workload, as indicated by their mean pupil diameter, increased steeply around takeovers, and was further exacerbated by the presence of a lead vehicle during the takeovers, especially in the Short THW condition, for both groups. Pupil diameter was found to be sensitive to subtle variations in mental workload, and closely resembled the trend seen in self-reported workload ratings. Further research is warranted to assess the feasibility of using eye-tracking-based metrics along with other physiological sensors, especially in real-world settings, to understand whether they can be used as real-time indicators of drivers’ mental workload, in future driver state monitoring systems

Engaging in NDRTs affects drivers’ responses and glance patterns after silent automation failures

The aim of this study was to understand driver responses to “silent” failures in automated driving, where automation failed during a simulator drive, without a take-over warning. The effect of a visual non-driving related task (NDRT) and a road-based vigilance task presented drivers’ take-over response and visual attention was also investigated. Currently, automated driving systems face a number of limitations that require control to be handed back to the driver. Much of the research to date has focused on explicit take-over requests (ToRs) and shows that drivers struggle to resume control safely, exacerbated by disengagement from the driving task, for instance, due to the presence of NDRTs. However, little is known about whether, and how, drivers will respond to more subtle automation failures that come without a warning, and how this is affected by NDRT engagement. Thirty participants drove a simulated automated drive in two conditions, which had 6 silent automation failures each (3 on a Curve, 3 in a Straight), with no ToRs. In one condition, drivers were required to constantly monitor the road, which was enforced by a road-based vigilance task (VMS Only). In the other, drivers performed an additional visual NDRT, requiring them to divide their attention (VMS + Arrows). Results showed that, in both conditions, all drivers eventually detected and responded to all silent automation failures. However, engaging in an additional NDRT during automation resulted in significantly more lane excursions and longer take-over times. Adding a visual NDRT not only changed the distribution of drivers’ visual attention before and after the failure but also how they divided their attention between information on the road environment and the human–machine interface, which provided information on automation status. These results provide support for how driver monitoring systems may be used to detect drivers’ visual attention to the driving task and surroundings, and used as a tool for encouraging driver intervention, when required

Enhancing the Collection and Analysis of General Aviation Insurance Data in Australasia Through the Use of HFACS

This paper provides an overview of a 5-stage program of work conducted over a three-year period that aims to develop a General Aviation (GA) safety database in Australia using data collected by aviation insurers. The need to standardize and enhance insurance data was recognized to support meaningful safety-based countermeasure development. Incorporating the Human Factors Analysis and Classification Scheme into the insurance assessment process is a major feature of the data enhancement process. Data collection by GA insurance assessors in Australia, using standardized and enhanced data collection methods, will begin in late 2007. Establishment of this database will allow for meaningful safety-based analyses of GA insurance incident data in the future

Executive Summary

This report reviews literature relevant to team training in complex environments. While technological developments allow for the training of higher-order cognitive skills in complex simulated environments, in the absence of sound learning methodologies, training systems may not fully achieve their desired objectives. There are relatively few attempts in the literature that focus on how best to use technology to support effective training, and little research effort has involved the use of technology in the development of effective training programs for teams rather than individuals. The effectiveness of team training systems, and specifically, the measures of team outcomes and team processes that could be used to measure team performance in distributed training, are also reviewed. Some areas for future research relevant to distributed team training are identified

Simulators for transportation human factors: research and practice

Simulation continues to be a growth area in transportation human factors. From empirical studies in the laboratory to the latest training techniques in the field, simulators offer myriad benefits for the experimenter and the practitioner. This book draws together current trends in research and training simulators for the road, rail, air and sea sectors to inform the reader how to maximize both validity and cost-effectiveness in each case. Simulators for Transportation Human Factors provides a valuable resource for both researchers and practitioners in transportation human factors on the use of simulators, giving readers concrete examples and case studies of how simulators have been developed and used in empirical research as well as training applications. It offers useful and usable information on the functional requirements of simulators without the need for any background knowledge on the technical aspects, focusing on the state of the art of research and applications in transport simulators rather than the state of the art of simulation technology. The book covers simulators in operational terms instead of task simulation/modelling and provides a useful balance between a bottom-up, academic approach and a top-down, practical perspective